On 2014 Oct 01, Arturo Casadevall commented:

We thank the writer for his comments. The writer is critical of several of the statements in our editorial. We respond to each individually:

Joshua L Cherry states that: ‘The arguments are framed in philosophical terms, but there is no philosophical content, and the underlying question is one of scientific judgment.’

We found this comment puzzling. Our paper includes discussion on two of the branches of philosophy: epistemology and ethics (by the writer’s own admission). Framing the GOF experiments within the normative standards of the field microbiology explores how we seek knowledge in the area. This together with discussion of ethical issues constitutes philosophical content.

Joshua L Cherry states that: “[W]hen one does a risk-benefit analysis of this issue, the epistemic gain from GOF experiments should be included in the bookkeeping: if one does that, the benefits of GOF experiments are potentially so great as to warrant our risking more than we otherwise might.” This is a puzzling assertion. All of the benefits that have been considered are benefits of the resulting knowledge (“epistemic gain”). Perhaps the authors mean to refer to an intrinsic good of having knowledge, independent of any practical applications. If so, it is possible, and helpful, to state this more plainly, as I have just done. If there is a philosophical issue here, it is one of ethics, not epistemology. Perhaps the authors are referring to the practical value of the knowledge beyond its direct applicability to the case at hand. In either case the argument is weak. These are common properties of experiments, not special properties of potentially dangerous GOF experiments. The resources used for such experiments could be reallocated to other experiments that lack the serious safety concerns yet also have epistemic value (on any meaning), and quite possibly more of it.’

The writer states that ‘all the benefits that have been considered are benefits of the resulting knowledge’. This is not the case. A reading of our paragraph describing the knowledge gained from GOF experiments considers the information that highly pathogenic avian influenza viruses can become mammalian transmissible useful because it provides a warning that these viruses have the biological potential to acquire this property. Prior to these experiments there was debate as to whether these viruses could become transmissible. Now we know that they can and that is a clear epistemic gain. But that epistemic gain goes beyond the utilitarian gain from any immediate application of this new knowledge, in part because of the many other possible future uses of that knowledge, some of them practical, but some of them purely theoretical. We have no disagreement with the notion that the subject of ethics is interwoven into considerations for GOF experiments. However, the idea that we should reallocate resources away from this type of work would accomplish nothing in helping us prepare against an influenza pandemic. As the currently developing Ebola epidemic in West Africa shows us we must continue research into highly dangerous pathogens for it is knowledge in medicine and science that provide the defenses against new threats from nature.

Joshua L. Cherry states that: ‘The authors make much of the claim that the GOF experiments meet the “normative standards of the fields of microbiology and infectious diseases”. This is faint praise for experiments that, many fear, might cause pandemics. We might question the merits of these standards, despite their being normative, and might ask whether they are sufficient conditions for value, or merely necessary. Even putting those questions aside, at best this point establishes that the experiments have nonzero value, not that they are exceptionally powerful, as must be the case to justify exceptional danger. Again, there are plenty of other experiments that also satisfy these standards without posing the same threat.’

Meeting the normative standards of the fields of infectious diseases and microbiology is high praise for GOF experiments for these fields have delivered some of the greatest successes in medicine. The writer appears to be mixing two issues that are very different: the fear and the power of GOF experiments. Fear can be minimized with strict laboratory safeguards that greatly reduce the likelihood of accidents. With regards to the power of GOF experiments, these have already taught us that highly pathogenic avian influenza virus has the capacity for mammalian transmission, as noted above. Since mammalian transmission is necessary for a pandemic this information is incredibly important. Humanity now stands warned that with a few sequence changes H5N1 influenza virus can acquire the property of mammalian transmissibility. The writer states that ‘there are plenty of other experiments that also satisfy these demands without posing the same threat’ but provides no examples. In fact, we counter that there is no other technology or mechanism short of waiting for epidemic to occur that could have provided this information. If there were, it is likely that a scientist would have pursued it by now. Hence, we stand by the words that these experiments are exceptionally powerful and that we lack alternatives if we are to seek certain types of knowledge.

Joshua L. Cherry states that: The editorial seems to suggest such fallacious reasoning as this: The discovery that HIV causes AIDS established a cause-and-effect relationship about a pathogen and was very powerful; GOF experiments have established cause-and-effect relationships concerning pathogens; therefore, GOF experiments are very powerful.

The key word here is “seems”: it is unclear how the writer infers this from what we wrote. GOF experiments trace their ancestry to the powerful scientific techniques that that have been responsible for some of the greatest successes in medicine including establishing disease causation and intervening against such diseases with vaccines and drugs. GOF experiments are fully within the normative standards of microbiology and molecular biology and as such they are incredibly powerful investigative tools that humanity can use to learn about microbial threats. As noted above GOF experiments have already produced highly valuable information that is not available from any other source.

On 2014 Sep 23, Joshua L Cherry commented:

This editorial purports to offer new considerations that favor the continuation of potentially dangerous “gain of function” (GOF) experiments with pathogens with pandemic potential. The arguments are framed in philosophical terms, but there is no philosophical content, and the underlying question is one of scientific judgment. The philosophical language serves only to obscure and to create the illusion that a new consideration is being introduced.

The authors state that

“[W]hen one does a risk-benefit analysis of this issue, the epistemic gain from GOF experiments should be included in the bookkeeping: if one does that, the benefits of GOF experiments are potentially so great as to warrant our risking more than we otherwise might.”

This is a puzzling assertion. All of the benefits that have been considered are benefits of the resulting knowledge (“epistemic gain”). Perhaps the authors mean to refer to an intrinsic good of having knowledge, independent of any practical applications. If so, it is possible, and helpful, to state this more plainly, as I have just done. If there is a philosophical issue here, it is one of ethics, not epistemology. Perhaps the authors are referring to the practical value of the knowledge beyond its direct applicability to the case at hand. In either case the argument is weak. These are common properties of experiments, not special properties of potentially dangerous GOF experiments. The resources used for such experiments could be reallocated to other experiments that lack the serious safety concerns yet also have epistemic value (on any meaning), and quite possibly more of it.

The authors make much of the claim that the GOF experiments meet the “normative standards of the fields of microbiology and infectious diseases”. This is faint praise for experiments that, many fear, might cause pandemics. We might question the merits of these standards, despite their being normative, and might ask whether they are sufficient conditions for value, or merely necessary. Even putting those question aside, at best this point establishes that the experiments have nonzero value, not that they are exceptionally powerful, as must be the case to justify exceptional danger. Again, there are plenty of other experiments that also satisfy these standards without posing the same threat.

Through reference to “normative standards”, the authors make an unjustified connection between GOF experiments and such important findings as the discovery that HIV causes AIDS. They tell us that “GOF experiments are very powerful because such experiments can give direct information on cause-and-effect relationships” about infectious agents. In reality, not all experiments that provide such information are “very powerful”. The editorial seems to suggest such fallacious reasoning as this: The discovery that HIV causes AIDS established a cause-and-effect relationship about a pathogen and was very powerful; GOF experiments have established cause-and-effect relationships concerning pathogens; therefore, GOF experiments are very powerful.

Debate about GOF experiments has always been about whether the value of the knowledge they might yield is sufficient to justify their risks. Many claims for particular benefits have been abandoned, and some have been undermined by arguments put forth to downplay safety concerns. Referring to knowledge as “epistemic gain” adds nothing to the debate.

On 2014 Sep 17, Attila Csordas commented:

associated dataset available via PRIDE/ProteomeXchange: http://www.ebi.ac.uk/pride/archive/projects/PXD000661

On 2016 Aug 17, Mohammed AlJasser commented:

I have noticed that the language in which this article was written was labelled as Hebrew. However, the text is written in Arabic.

Thank you.

On 2017 Jan 11, Vinay Pasupuleti commented:

The abstract was erroneously deleted by journal editors and was published as an erratum at a later date. The full text clearly mentions that 6 articles were included (one article had results from a retrospective cohort study as well as a prospective cohort study)....therefore, a total of 7 observational studies. Total N in Table 1 is 102,767 as mentioned in the abstract (351+58+625+27+1848+246+170+160+96806+322+1077+1077 = 102,767).

On 2016 Jul 04, Swapnil Hiremath commented:

Looks like the abstract was erroneously deleted, see the erratum.

On the other hand, the point about number of studies is valid (6 in full text, 7 in abstract) - also the total N in full text (from table 1) adds up to > 200,000, compared to 102, 767 above in abstract. Does need to be clarified and addressed by the authors, in my opinion.

On 2016 Jun 30, Gabriel Rada commented:

This abstract contains several mistakes, most of them minor formatting issues. However, it states there are 7 included studies, when the actual number reported in the full text is 6. On the other hand, there is no abstract in the journal website, so it seems it was created for indexing purposes. I don't see any indication of this abstract being 'in process' in Pubmed/MEDLINE. My main issue is who is responsible of the information displayed here? The publisher or Pubmed?

On 2014 Sep 19, Ian Dworkin commented:

In addition to all of the data and scripts for analysis being available at Dryad, we have also made it available on github as a repository. https://github.com/DworkinLab/PitchersJEB2014_cricket_wings

On 2014 Sep 16, Mohamed Soliman commented:

I think this can be a subject of a prospective study to clarify this issue.

On 2014 Dec 17, Sean Ekins commented:

Some slides on earlier work are here http://www.slideshare.net/ekinssean/applying-computational-models-for-transporters-to-predict-toxicity

On 2014 Dec 02, Ross Mounce commented:

If you're not blind or visually-impaired you can read the full-text of this article here: http://www.readcube.com/articles/10.1038/nature13704?shared_access_token=yHbnKF1lQFfG6Yqg_0gr2tRgN0jAjWel9jnR3ZoTv0Npz8zRHm1DgGX6oHtfBFPcgnPUjlPawFWnxeOTbLrOjGH7l3QlaEbnesc2IXtHTVeOuMZDQmqTT1RwKvWt4cAzxfDdjGfUP69A9Nf_tsDhVyBIs8FPRuBKNpKDNqaeyuJ5LXzTuwk2lutlAMO8YfELzhepvyjx-hWRLbdqmtGvS4XaEtKELfoAAphhAFk0RmY=

On 2014 Oct 11, Igor Zwir commented:

Pre-selected SNPs The list of 2,891 pre-selected SNPs from the MGS study (1) utilized in (2) (see supplemental material) is available at http://m4m.ugr.es/resources/2891-SNP-PreSelection.txt. This list contains 99% of the SNPs reported in the supplemental material of (1).

(1) Shi J, Levinson DF, Duan J, Sanders AR, Zheng Y, Pe'er I, Dudbridge F, Holmans PA, Whittemore AS, Mowry BJ, Olincy A, Amin F, Cloninger CR, Silverman JM, Buccola NG, Byerley WF, Black DW, Crowe RR, Oksenberg JR, Mirel DB, Kendler KS, Freedman R, Gejman PV. Common variants on chromosome 6p22.1 are associated with schizophrenia. Nature. 2009;460I:753-7.

(2) Arnedo J, Svrakic DM, del Val C, Romero‑Zaliz R, Hernández-Cuervo H, Molecular Genetics of Schizophrenia Consortium, et al. Uncovering the Hidden Risk Architecture of the Schizophrenias: Confirmation in Three Independent Genome-Wide Association Studies. American J of Psychiatry 2014 (in press);172:1–15. Epub 9/15/2014.

On 2014 Oct 07, Igor Zwir commented:

Interdisciplinary work not so fast … Geneticist with a statistic background vs. engineers/computer scientists with a genetic background

Unfortunately, interdisciplinary works have been concurrently stimulated and frozen at the same time. Much of this contradiction is due to the lack of universal reviewers that know about everything. Science is specialized, in terms of education and consequently in terms of results. For example, most studies of complex disorders usually focus on single sources of data (genetics, neuroimages), eliminating the possibility of having complementary perspectives of the patients. In addition, there is a sort of disrupted communication between the biomedical researchers and math or computer science investigators. Biologist and physicians use to search for articles in pubmed (http://www.ncbi.nlm.nih.gov/pubmed), however, most of the engineers look for the Institute of Electrical and Electronics Engineers (IEEE) communications (https://www.ieee.org) and many of them ignore Pubmed. In contrast, many molecular biologists ignore what IEEE exists and what it represents. There are few IEEE publications that are available in pubmed and most of them are old. However, many of those that are not in Pubmed are much more rigorous than any method proposed in the best biomedical journals. This comment encourages both Pubmed and IEEE sites to solve their differences, which in turn, will help to have more and more informed reviewers for novel techniques in the post genomic era.

On 2014 Oct 07, Igor Zwir commented:

Rationale of the method applied in Arnedo et. al.

Clustering methods have been applied for long time in pattern recognition problems and in a variety of fields and actually are embedded in most of our devices, from a simple refrigerator to a Boing engine. In biomedical sciences there is a trend of considering that few classes of clustering exist (e.g., hierarchical cluster of K-Means). However, datamining, knowledge discovering, and machine leaning are fields of computer sciences with very rich and different either theoretical or empirical methods. Much of the power of these methods is based on the fusion of them, taking advantages of each one.

The methodology and framework proposed in this Arnedo et. al encodes a generalized factorization method (GFM) that was designed to identify structural patterns or clusters (substructures) that characterize complex objects (1-8) embedded in databases (6, 7, 9, 10). Unfortunately, solving these kinds of complex problems cannot be performed by a single clustering method but utilizing and combining the advantages of many of them, as were implemented in the GFM and summarized below.

First, we utilized the notion of Model-based clusters (5, 11, 12), where the centroid/prototype of a cluster is not a point but a model (e.g., line segment, ellipsoids, (5, 13, 14) or see C-varieties algorithm (12)) -or a family of models (8) - defined as a relation between cohesive subset of points that meet certain constraints (e.g., Relational clustering (12), undirected graph (12)). When the subjacent equations of the models are unknown, they can be estimated from the data by identifying relations between subsets of observations that show similar patterns under a specific subset of relevant descriptive features (attributes) (4, 5, 15). Here we proposed that in a pure data driven analysis these unknown models (grey boxes) can be learned as local relationships between subsets of features and observations, which are termed biclusters. The biclusters are often represented as sub-matrices (4, 5, 15). Other models proposed in this work (black boxes) consist of those that are encoded by a particular method devoted to recognize certain classes of patterns.

Second, to identify a family of models (8) or biclusters we planned the use of the NMF (16) factorization algorithms (tensor analysis), which can uncover cohesive data represented as sub-matrices (factors). When sorted and thresholded, each sub-matrix represents a bicluster. Notably, biclustering produces several local models of the data (17), each one capturing intrinsic relationships between subsets of observations sharing subsets –instead of all- features. Diverse biclusters –instead of uniform data explanations- provide a better understanding of the described object that is diluted when together, all features are used in a single global model of data typically performed by clustering methods (17).

Third, we proposed in this work that biclusters can be fuzzy (overlapping, see Fuzzy clustering (18)), where voxels and/or subjects can belong to more than one bicluster), as well as possibilistic (non-exhaustive partitions, see Possibilistic clustering (12)), where certain voxels and/or subjects may not be assigned to any bicluster, and thus, outliers can be detected.

Fourth, we demonstrated that biclusters could be identified without choosing a priori a particular number of clusters (11, 13, 14, 19). Setting this parameter as a small number may eventually generate few but large data partitions here defined as general biclusters that may underfit the data. In contrast, using a big number of clusters will generate many clusters with partitions of small size (i.e., more granular partitions) here defined as specific biclusters that may overfit the data (1, 5, 11, 13, 14, 19-21). Although there are many different validity indices (12, 18) that suggest the best number of clusters for a given dataset, they often produce contradictory results (11, 13, 14, 19). This problem is exacerbated when Centroid-based (e.g., k-means, NMF) or Distribution-based clustering (e.g., EM) is utilized because two successive numbers of clusters may generate completely different cluster arrangements (see (12, 18) for a review). Instead of fighting over the lost battle of uncovering a single optimal number of clusters, biclusters were calculated from partitions generated by all number of clusters between 2 and √n (12), where n is the number of observations (subjects). We postponed the selection of the best clusters until all partitions were examined to select a set of clusters that together provide an optimal description of the sample. These clusters could be chosen from different partitions that were generated by different number of clusters (see Consensus clustering (1, 5, 11, 13, 14, 19-21)).

Fifth, formulation of the bicluster identification problem from different partitions may produce redundant, excessively specific and/or excessively general biclusters (4, 5, 15, 22, 23). To select optimal biclusters, we proposed a GFM that performs the following Consensus clustering strategy: (i) eliminates all redundant biclusters by comparing the similarity between their subjacent sub-matrices using Hypergeometric statistics (see Methods and Material in Supplement 1), and selecting only one representative bicluster from each set of equivalent sub-matrices. Biclusters harboring <10% of the total number of subjects were not considered to avoid a trend to obtain singleton biclusters. (ii) From the non-redundant biclusters, GMF selects all biclusters that are optimal in terms of specificity, generality and diversity. To do so, it selects all non-dominated biclusters where one bicluster is not worse (i.e. dominated) than another in both objectives: specificity and generality (4, 5, 15, 22, 23). Moreover, to ensure diversity, GMF applies the non-dominance metric only among biclusters within a neighborhood (22, 23) (i.e., biclusters with a relatively high overlapping of subjects and voxels with). This Multiobjective and Multimodal optimization process is analogous to Minimum Description-Length methods (24) and was carried out as described in (1-8). (iii) The remaining optimal biclusters are hierarchically organized by inclusion of their subjects into connected or disjoint hierarchies (i.e., subgraphs or subnetworks).

Sixth, and after the first step of identifying interesting patterns describing objects embedded in the data, we went one step further by also finding characteristic descriptions for each group, where each group represents a concept or class using Conceptual clustering. In this work we incorporated factors such as the generality and simplicity of the derived bicluster descriptions by (1) relational clustering validation against data driven independent descriptions obtained in other domains of knowledge, and (2), incorporate the previous structured knowledge into predictive multiclassifiers mixing machine learning and multiobjetive optimization techniques.

On 2014 Oct 07, Igor Zwir commented:

References

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On 2014 Oct 07, Igor Zwir commented:

None

On 2014 Oct 07, Igor Zwir commented:

None

On 2014 Oct 07, Igor Zwir commented:

None

On date unavailable, commented:

None

On 2014 Oct 06, C. Robert Cloninger commented:

References

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16. Zwir I, Huang H, Groisman EA. Analysis of Differentially-Regulated Genes within a Regulatory Network by GPS Genome Navigation. Bioinformatics. 2005;21(22):4073-83.
17. Romero-Zaliz R, Del Val C, Cobb JP, Zwir I. Onto-CC: a web server for identifying Gene Ontology conceptual clusters. Nucleic Acids Res. 2008;36(Web Server issue):W352-7.
18. Romero-Zaliz R, C. Rubio R, Cordón O, Cobb P, Herrera F, Zwir I. A multi-objective evolutionary conceptual clustering methodology for gene annotation within structural databases: a case of study on the gene ontology database. IEEE Transactions on Evolutionary Computation . 2008;12:6:679-701.
19. Harari O, Park SY, Huang H, Groisman EA, Zwir I. Defining the plasticity of transcription factor binding sites by Deconstructing DNA consensus sequences: the PhoP-binding sites among gamma/enterobacteria. PLoS Comput Biol. 2010;6(7):e1000862. PMCID: 2908699.
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On 2014 Oct 06, C. Robert Cloninger commented:

Conclusion

We appreciate the opportunity to clarify the fundamental differences between the assumptions and goals of traditional GWAS and our novel approach that addresses the complexity of common disorders with sophisticated and well-validated machine-learning and data-mining methods. We hope that the profound differences in the approaches with which Breen and colleagues are familiar and those developed by us should stimulate greater understanding of the challenges faced by the fields of psychiatric and medical genetics. We recognize that this new approach will cause a period of reexamination of standard methodology in this field, but every major advance in genetics, and in all of science for that matter, has always required flexibility and creative thinking. There are always things that we can improve upon in any method, and we recognize that many incremental improvements are essential for the advance of science.

We have put forth a new data-driven method that allows the uncovering of complex genotypic-phenotypic relations when they are present without imposing this as an a priori assumption. We uncovered relationships are in fact highly complex, which allowed us to identify individuals at high risk and to associate specific SNP clusters with specific clinical syndromes despite the presence of extensive pleiotropy and heterogeneity. This approach, like all those that have preceded it, is undoubtedly imperfect and will also require refinement and may ultimately give way to yet another approach that will explain more. Such methodological evolution is nothing more than the typical course of advancement in science. We hope that these exciting developments will lead to new ways to push the boundaries of accepted science, and help us to question a priori assumptions that restrict our understanding of all the information embedded in data.

If this discussion has shown us nothing else, it is that this process of questioning and reflection has already begun. Ultimately, beyond all of the technical issues, our main goal is to help those in need. With schizophrenia, we know the need is great from the tremendous outpouring of requests for guidance and help that we have received, and we know that there are many people with other diseases who may benefit from our new approach. We can all be comforted knowing that our debate can bring us closer to doing what we are really here to do – that is, helping those suffering from debilitating diseases and finding ways to promote their health and well-being. Whatever path leads us there is worth considering. So let us not permit our philosophical or scientific differences to prevent us from allowing for a sufficient diversity in our tactics, because we never know what path will lead us towards our common goals of improving health and reducing the burden of disease.

On 2014 Oct 06, C. Robert Cloninger commented:

The Facts about Replication and Significance Testing of SNP Selection

B-S expressed concern about our replication process. Of course in traditional GWAS, replication has always been a serious problem, which is the basis for the rationale of PGC to carry out meta-analysis of large collections of samples despite their heterogeneity and limited phenotypic description. It was most challenging for us to identify samples with adequate clinical description to apply our novel approach, but the reward was in identifying strong effects that replicated consistently across three samples, including the Portuguese Islands study that used the same diagnostic instrument in a specific ethnic sample. The samples were independently recruited and independently analyzed, as we stated clearly in the published report. SNP sets, phenotypic sets and associations were separately calculated for the three samples to avoid weighted or biased aggregations. Then, we used a well-known co-clustering test based on the hypergeometric distribution to establish the replicability of results from one sample in the other. This test has been used widely in molecular biology (15, 16, 25), and as a general strategy for validating clusters. For example, it has also been implemented into software packages such as TIBCO/Spotfire. The concerns expressed by B-S about replication have no reasonable justification. Thus we feel that the concerns expressed by B-S about replication are overstated and empirically unfounded. Again, the strength of this new approach is it allows us to avoid some of the major problems that plague traditional GWAS approaches.

B-S also expressed their concern about the use of a permutation test, claiming that "because SNP sets differ in allele frequency between cases and controls, this procedure does not generate a valid null distribution". The permutation test was used not to establish the significance of the SNP sets, which was evaluated by the SKAT method (14), but rather to test the validity (and approximate probability) of the association between SNP sets and symptom sets. Controls were not used in this test at all, as they have no symptoms of psychosis. Moreover, these symptoms were not even evaluated in the reported inventories. The misunderstanding of B-S is probably due to a lack of familiarity with this new statistical procedure, which highlights the previously discussed difficulties people have when first trying to understand a novel approach.

On 2014 Oct 06, C. Robert Cloninger commented:

The Facts about Linkage Disequilibrium

B-S also expressed concern that it is likely that our SNP sets may be merely artifacts of blocks of markers in linkage disequilibrium (LD). LD is strictly defined as the non-random association of alleles of neighboring polymorphisms derived from single ancestral chromosomes, but some broad measures of LD extend the concept to include covariation of polymorphisms that are not linked, including even associations among genetic variants on separate chromosomes (8). Many variables influence covariation of polymorphisms, including demographic variables (admixture, population size, migration, ancestral population bottlenecks), selection (including epistasis), and variation In recombination rates in different parts of the genome (9, 10). Consequently LD is a serious problem for the traditional group-wise statistical approach of traditional GWAS, so care is taken to analyze groups with distinct ancestries separately in order to help disentangle different causes of association. However, in our novel person-centered approach, the identification of subpopulations is an intrinsic aspect of identifying the genotypic-phenotypic architecture. We identify sets of variables that naturally cluster within individual subjects as measured by covariance of polymorphisms or phenotypic traits within particular subgroups of individuals in a population. We identify SNP sets and phenotypic sets independently of one another and then test how these independently identified sets fit together like a lock and a key. In a strictly data-driven manner the hidden structure of the overall population is decomposed into subpopulations of subjects to allow valid tests of genotypic-phenotypic association despite admixture in the total population from which SNP sets and phenotypic sets are extracted (11). We allow the possibility that some constituent SNPs of a particular set may be associated (in LD) as adventitious hitchhikers that are closely linked or may be epistatic sets that are functionally adaptive and maintained by selection pressure even though they are unlinked (12). However, being linked (co-localized) or in LD is neither a necessary nor a sufficient condition for being a constituent in a SNP set: set membership depends on co-variation of polymorphisms in particular subpopulations of individuals whether the genetic variants are in LD in the total population or not. LD is actually one way that epistatic sets of genetic variants can be maintained in functionally adaptive blocks if the epistatic selection is strong, but most interactive sets of genetic variants are not in LD. Accordingly, we uncover constituents of SNP sets regardless of their LD status as candidates for functionally adaptive epistatic sets. Then we measure their potential functional interaction by testing for their differential association with phenotypic variability. We also consider the known function of the genes and regulatory sites as part of our analysis of the complex pathway from genotypic networks to distinct clinical syndromes. Thus we jointly utilize genotypic, biological, and phenotypic information as part of an integrated systems analysis that allows for observed or hidden stratification in the total population.

In addition to this fundamental difference in conceptual and procedural approach to LD, the concerns of B-S about our findings are simply unfounded empirically. In total, approximately 2/3 of the SNPs in high-risk sets map to regions that are so far apart in genomic distance (greater than 100,000 base pairs) that they are highly unlikely to be in LD. We found that 9 of 42 high-risk SNP sets have some SNPs located on different chromosomes. These facts indicate that the identified SNP sets are not the result of particular genomic constraints such as LD or being within the region of the same gene. In any case, the presence of LD would not explain or invalidate the association of groups of SNPs within a particular SNP set with a particular phenotypic set. For example, one of our SNP sets maps exclusively to SNPs upstream of the NTRK3 gene, as was also found to be strongly associated with schizophrenia by standard GWAS techniques published by the authors of the commentary. In addition SNPs from another SNP set map inside the same gene. Each of these SNP sets involving different components of the NTRK3 gene are associated with different symptoms. Although LD is viewed as a statistical problem for traditional GWAS, in our person-centered approach it is viewed as the result of adaptive mechanisms that can conserve the functional connectivity of epistatic sets of genetic variants, thereby contributing to the differential development of individuals in subpopulations. The functional adaptation facilitated by gene-gene interactions is fundamentally important for healthy development of individuals and for the evolution of populations, as described in Sewall Wright’s classical work on complex adaptive systems and evolution (13). Our concurrent consideration of the functions of gene products and associations between different genotypic networks with specific phenotypic syndromes precludes any suggestion that the highly replicable effects we observed are artifacts.

B-S have also suggested matrix factorization algorithms similar to the methods employed by us have been used to identify regions with long-range LD. This is certainly true and is not a problem in itself. Long-range LD is an indicator of functional connectivity that is not adequately explained by physical proximity, so it is included in what we want to detect in order to account for gene-gene interactions thoroughly (14). Matrix factorization methods like ours have been used for most of the current software applications in data-mining, including a wide variety of biomedical problems (15-19), facial recognition (20), gene expression (21-23), and other complex problems (24). There is no reason to avoid the use of this powerful method for pattern recognition within fuzzy data sets for uncovering hidden order within the complex association of genotypic and phenotypic variables that characterize complex medical disorders.

On 2014 Oct 06, C. Robert Cloninger commented:

The Challenge of Understanding and Accepting a Change in Perspective

Is our approach to concurrent genotypic-phenotypic of possible complex relationships a fruitful new approach without the limiting assumptions of standard GWAS, or are our observations really artifactual in ways that have been overlooked by us and by multiple sets of peer reviewers with relevant expertise about these novel methods? The American Journal of Psychiatry gave us generous space for the published article, including clinical vignettes with associated genotypic information to help people see what we have done even if the technical details of the statistical procedures may seem obscure when you first start looking at complex genotype-phenotype relationships through the illuminating lens of sophisticated machine-learning and data mining procedures. We expected that there would be widespread interest and scrutiny of this new data-driven approach with less restrictive assumptions, so we prepared an extensive online supplement specifying procedures, all components of the sets of SNPs and clinical variables as well as a detailed analysis of the associated gene products, their functions, and disease associations.

The full list of SNPs used in our analysis is being made available for others to continue to test. We believe in transparency and collegiality as key ingredients in the advance of science because it is essential for the spirit of empiricism that our data driven method emphasizes. The precise procedures for reproducing the list of SNPs was detailed already in our supplemental information and should be reproducible by experienced investigators. We will continue to consider reasonable requests for assistance from qualified investigators.

B-S expressed their concern that the “methodology is opaque (even to experts), meaning that their results cannot be independently validated”. First of all, the complexity of a method does not invalidate the approach: complex methods may be necessary to deconstruct and understand complex processes. We cannot continue to look for hidden relationships with methods that do not shine light where it is needed. That said, the manuscript was exhaustively evaluated under strict peer review process, which included a separate report from an independent statistician. Because of their many insightful comments, there is no doubt that the referees understood the method and provided recommendations that we conscientiously addressed in the re-submission process. Moreover, the PGMRA method utilized in this work was also evaluated by expert reviewers in bioinformatics and genetics for the journal Nucleic Acid Research (2). The method is well-described but does require relevant expertise beyond what is required for traditional GWAS. Fortunately, we have made a web-server application of the method publicly available as a service to the field. PGMRA is applicable to a wide variety of analyses besides GWAS, including brain imaging and related methods for uncovering order in complex hidden relationships, which may help to further characterize the pathway from genotype to phenotype more objectively than can be done by categorical diagnoses or symptom inventories in samples so large that costs become prohibitive for thorough assessment. We know that many are increasingly criticizing overspecialization in the fields of science, but the neglect of strictly data-driven techniques from machine-learning and data-mining that do not require restrictive a priori assumptions may well be precisely what has prevented us from understanding the complex genotypic-phenotypic architecture of common disorders like the schizophrenias.

We understand that our new approach is challenging long-held assumptions and that there may be a desire by some to put the genie back in the bottle, but we feel that looking at the complexity of the schizophrenias is a necessary evolution for the field; it is an evolution whose time has come and is currently transforming other fields of science and genetics. There is overwhelming evidence across multiple disciplines that living systems and psychosocial behavior are simply too complex and interactive to ignore the real underlying complexity. Nonetheless, we were a bit surprised to see this discussion in a public forum that is not peer reviewed. We would rather have thoughtful constructive consideration of the scientific merits of alternative approaches, including their fundamental philosophical differences in perspective and goals, as well as scientific differences in assumptions and procedures. One of the major obstacles to evaluating GWAS is that it can be difficult or impossible for scientists in many fields to evaluate complex technical procedures with which they are unfamiliar. The challenge of changing one’s perspective can be great and feel counterintuitive, as physicists experienced more than a century ago when quantum mechanics called into question our more natural inclination to a Newtonian perspective. That is why we feel it would have been more constructive to have neutral review by people with relevant expertise in many aspects of methods that span bioinformatics, statistics, genomics, and phenomics, all of which are needed to adequately judge the strengths and weaknesses of a novel approach like ours. Even people with extensive experience in traditional approaches to GWAS may not be sufficiently knowledgeable about these well-tested, but relatively new, machine-learning and data-mining techniques that have allowed us to develop a new, and, we hope, more generative approach to GWAS.

Nevertheless, as scientists we are dedicated to identifying and learning how to move our fields of inquiry forward in order to better understand the underlying mechanisms of disease and to identify effective personalized treatments for complex disorders. We have found it is crucial to pay balanced attention to both phenotypic variability and genotypic variability if we are ever to describe the complex development of common and complex medical disorders like the schizophrenias. We do not feel that this public forum is the best place to have this discussion with Breen and colleagues, but here too we may have a philosophical difference. That said, because they have chosen this forum to voice their criticism, we feel it is important that we take the time to address the facts and give people a broader context so that they can understand the arguments and our responses to their concerns. Ultimately, we feel that this is more of a misunderstanding and a miscommunication due to a lack of a common scientific and philosophical approach, and that with time we hope to find more common ground. Ultimately, the data will settle any dispute.

On 2014 Oct 06, C. Robert Cloninger commented:

Reply to Breen et al (their Part 2 of 2) On behalf of: C. Robert Cloninger, M.D., Ph.D. (Departments of Psychiatry and Genetics, Washington University School of Medicine, St. Louis, MO, USA); Igor Zwir, Ph.D. (Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA; Department of Computer Science and Artificial Intelligence, University of Granada, Spain); Gabriel A. de Erausquin, M.D., Ph.D. (Roskamp Laboratory of Brain Development, Modulation and Repair, Department of Psychiatry and Behavioral Neurosciences, University of South Florida, Tampa, FL, USA); Dragan M. Svrakic, M.D., Ph.D. (Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA); Coral del Val, Ph.D. (Department of Computer Science and Artificial Intelligence, University of Granada, Spain); Javier Arnedo, M.S. (Department of Computer Science and Artificial Intelligence, University of Granada, Spain); Rocío Romero-Zaliz, Ph.D. (Department of Computer Science and Artificial Intelligence, University of Granada, Spain); Helena Hernández-Cuervo, M.D., B.Sc. (Roskamp Laboratory of Brain Development, Modulation and Repair, Department of Psychiatry and Behavioral Neurosciences, University of South Florida, Tampa, FL, USA);

The reply to the comments of Breen et al (their Part 2 of 2) by Cloninger on behalf of all of us responsible for analysis in the Arnedo et al article has the following five components which are posted separately as follows: (4) The Challenge of Understanding and Accepting a Change in Perspective (5) The Facts about Linkage Disequilibrium (6) The Facts about Replication and Significance Testing of SNP Selection (7) Conclusion (8) References for all sections of our postings

On 2014 Sep 28, Gerome Breen commented:

Part 2 of 2 (see below) On behalf of: Gerome Breen, PhD (Institute of Psychiatry, King’s College London, London, UK) * Brendan Bulik-Sullivan (Broad Institute, Cambridge, MA, USA) * Mark Daly, PhD (Broad Institute, Cambridge, MA, USA) * Sarah Medland, PhD (QIMR Berghofer, Brisbane, Australia) * Benjamin Neale, PhD (Broad Institute, Cambridge, MA, USA) * Michael O’Donovan, MD PhD (Cardiff University, Cardiff, UK) * Stephan Ripke, PhD (Broad Institute, Cambridge, MA, USA) * Patrick Sullivan, MD (Karolinska Institutet, Stockholm, Sweden) * Peter Visscher, PhD (University of Queensland, Brisbane, Australia) * Naomi Wray, PhD (University of Queensland, Brisbane, Australia) *

• Authors ordered alphabetically, all made equivalent contributions

C. Linkage disequilibrium (LD).

Pairs of SNPs that are physically close in the genome are often correlated due to LD. Furthermore, in samples containing individuals with different ancestry, SNPs on different chromosomes whose allele frequencies differ between populations will appear to be correlated. These are both well-known phenomenon from population genetics.

The typical size of blocks defined by high LD is on the order of 20,000 bases, but LD is far from uniform across the genome. Using a large European sample genotyped with Affymetrix 6.0 arrays, we had previously computed the locations of particularly large blocks of LD (defined using SNPs with r2 > 0.5). The first step in the statistical methodology described by Arnedo et al. is to identify so-called “SNP sets” – sets of SNPs that travel together – which the authors believe contain some information about clinical subtypes of schizophrenia: “we first identified sets of interacting … SNPs that cluster within subgroups of individuals … regardless of clinical status” (no LD limitations were imposed). Of the 237 SNPs in Table S3 from Arnedo et al., 153 (65%) mapped to exceptionally large LD blocks larger than 100,000 bases (median 275kb, interquartile range 165-653kb, maximum 1.2 mb).

Arnedo et al. claim repeatedly that sets of SNPs that travel together are informative about clinical subtypes of schizophrenia. A more parsimonious interpretation of the SNP clusters identified by Arnedo et al. is that these SNPs represent a combination of (1) SNPs in large LD blocks and (2) SNPs whose allele frequencies differ substantially between European and African sample subsets. Indeed, matrix factorization algorithms similar to the methods employed by Arnedo et al. have been used to identify regions with long-range LD (see Price et al., AJHG, 2008, link below).

D. SNP selection.

Arnedo et al. conducted genetic clustering analyses on 2,891 SNPs selected on the basis of in-sample P-values from analysis of association with case-control status and selected from a total of ~700,000 SNPs. It is therefore expected that linear or non-linear combinations of these SNPs will be associated with case-control status in the same sample (their risk statistic); this is true even if the selected SNPs are not truly associated. A permutation test is used to assess the significance of the observed phenotype/genotype clustering. In this permutation test, subjects are randomly allocated to “SNP sets” but, since the SNPs were selected because they differ in allele frequency between cases and controls, this procedure does not generate a valid null distribution. As a result, the reported P-values are incorrect.

The strategy used by Arnedo etl al. is an example of estimation and selection of effects in a dataset and then testing (or re-estimating) them in the same data, a common pitfall of prediction analyses (Wray et al. 2013, Nature Reviews Genetics, link below). To construct a valid permutation test, the authors should have randomized case-control status in the association analysis step, selected a new set of ~3,000 SNPs and generated a distribution of their coincident test index under a truly null distribution.

E. Replication.

Replication of results is a well-acknowledged strategy for generating confidence in reported findings. Arnedo et al. state that they replicated their findings in two samples but, upon closer examination, it is unclear precisely what replicated, exactly how this was done, and whether the degree of “replication” deviated from that expected by chance. It was also unclear whether the replication control samples were or were not independent from the discovery sample. Such non-independence is another common pitfall in prediction or validation analysis.

Conclusions.

Given the remarkable claims made by Arnedo et al., it is essential that alternative explanations be excluded. Unfortunately, the authors do not provide the necessary evidence. As presented, their methodology is opaque (even to experts), meaning that their results cannot be independently validated. Arnedo et al. do not consider alternative explanations for the phenomena that they observe, such as confounding from ancestry and LD, even though these are well-known issues for the statistical methods that they employ and have been studied extensively in the statistical and population genetics literature. In addition, their multistep analysis approach is subject to multiple issues as noted above.

We believe that it is highly likely that the results of Arnedo et al. are not relevant for schizophrenia. We urge great caution in the interpretation of the results of study.

Links: • Arnedo et al. in PubMed, http://www.ncbi.nlm.nih.gov/pubmed/25219520 • Press release from Washington University (St Louis), https://news.wustl.edu/news/Pages/27358.aspx • Media coverage via Google News, https://news.google.com/news?ncl=dwmwseq1Mco0xVMLXz5qVDa7uJqbM&q=cloninger&lr=English&hl=en&sa=X&ei=hgIjVO-0GoGOyASxtIGAAw&ved=0CD0QqgIwCQ • Nonnegative matrix factorization and ancestry inference: http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1001117 • Pitfalls of predicting complex traits from SNPs: http://www.ncbi.nlm.nih.gov/pubmed/23774735 • Using principal components analysis to identify regions with long-range LD: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2443852

On 2014 Sep 28, Gerome Breen commented:

None

On 2014 Oct 06, C. Robert Cloninger commented:

The Facts about Gender and the X-Chromosome

B-S express concern about gender effects in our results. Traditional GWAS focuses on average effects in heterogeneous groups, but our novel approach focuses on uncovering genotypic-phenotypic relationships in individuals regardless of their gender. B-S were concerned about the possible bias of results from 15 SNPs on the X-chromosome among 245 SNPs in high-risk SNP sets. However, a simple test based on the number of chromosomes shows that 15 SNPs cannot substantially confound the results. It is true that three of our 42 high-risk SNP sets have some SNPs on the X Chromosome, but when this is considered in context along with the remaining 39 SNP sets, the influence of gender was insignificant by a Kolmogorov test. All SNP sets have consistent associations with distinct phenotypic sets regardless of gender. The effects of gender and location of genetic variants on the X-chromosome have a negligible influence on our findings. In fact, the small number of SNPs on the X-chromosome in SNP sets at high risk for schizophrenia shows that our person-centered method does not select SNPs that are in LD indiscriminately; the X-chromosome has many highly conserved sets of epistatic genes in LD that influence gender and brain function (7), but these are not overrepresented in our SNP sets at high risk for schizophrenia. We thank B-S for calling attention to another finding that demonstrates that our method for identifying SNP sets is highly selective for particular phenotypes.

On 2014 Oct 06, C. Robert Cloninger commented:

The Facts about Ancestry and Population Stratification

B-S expressed concern that our findings may be an artifact. As scientists we are committed to trying to disconfirm findings we have made, no matter how strong the existing evidence may be. Findings about association need to be resolved experimentally at the molecular level not only for our findings but also for the findings of other published GWAS, which we plan to do in the near future. We have previously considered the variables that concern B-S carefully, but did not report these observations in detail for two reasons. First, their impact was empirically negligible for our approach, as we will describe, and we prioritized space to the variables that were most significant. Second, although the phenomena that concern B-S are often a serious problem for traditional group-wise approaches to GWAS, they are not problematic for our novel approach because it directly tests the association between genotypic variability and phenotypic variability within individuals after deconstruction of the observed or hidden structure of the population. We will describe the facts about each of these phenomena and explain why these criticisms fall short of explaining our findings.

B-S expressed concern that we did not take the necessary steps to correct for population stratification bias in the way they would have done using their traditional group-wise statistical approach. They suggest that the clusters we identified simply reflect “SNPs whose allele frequencies differ substantially between European and African sample subsets”, and go so far as to claim that the SNP sets we uncovered may not be relevant to schizophrenia at all. However, for that claim to be valid, ethnicity must have a strong influence on the risk and symptoms of schizophrenia, but that requirement is unlikely to be satisfied based on previous observations and was not found in our data. We did consider both sex and ancestry as co-variates in the pre-selection of SNPs with at least loose association with schizophrenia. This pre-selection was performed to reduce the large search space using the logistic association function included in the PLINK software suite (5). Our analysis was performed in this way to be compatible with the supplementary tables reported in (6) for African Americans (AA), European-Americans (EA), and individuals of mixed African and European ancestry (AA-EA). The most important fact about ethnic stratification is that there were multiple examples of SNP sets containing varying mixes of subjects from different subpopulations for each disease sub-type in each of our three independent samples of subjects. For example, in the Molecular Genetics of Schizophrenia (MGS) sample, the SNP set 22-11 was represented by 48% AA and 52% EA, SNP set 21-8 by 55% AA and 45% EA, SNP set 31-22 by 53% AA and 47% EA, SNP set 54-51 by 79% AA and 21% EA, and SNP set 71-55 by 52% AA and 48% EA.

In fact, all the SNP sets that appeared to be ethnically stratified (i.e., contained mostly AA or EA subjects in MGS sample, such as 56-30 or 42-37) replicated their association with specific phenotypic indicators of different classes of schizophrenia in subjects of another ethnicity in CATIE or in the Portuguese sample. Although concerns about ethnic stratification may be valid elsewhere, ethnic stratification had little impact on our results and cannot explain the robust association of specific SNP sets with specific phenotypic sets regardless of ethnicity or sample. These observations show the great utility of detailed consideration of phenotypic variability in individual people in our approach, compared to the sensitivity to confounding by population stratification in traditional GWAS when heterogeneous phenotypes are lumped together indiscriminately as cases. The concerns of B-S about ethnic stratification point out a limitation of traditional group-wise GWAS that is averted by our novel approach. We thank them for drawing attention to another strength of our approach, one that we did not have enough space to report previously.

We will discuss population stratification in more detail together with our reply about linkage disequilibrium (see posting 5), and discuss significance testing following our comments on replication in later sections of our reply to Part 2 of their comments (see posting 6).

On 2014 Oct 06, C. Robert Cloninger commented:

Two Distinct Perspectives and Methodological Approaches to GWAS

We expected our paper uncovering the hidden risk architecture of the schizophrenias to be controversial because it takes a fundamentally new approach to solve problems that have plagued the field of medical genetics for more than a decade without resolution (1). We went through rigorous peer review regarding the method with experts in bioinformatics and genetics (2) and then again regarding the application of our new approach to the schizophrenias (1). The critical comments of Breen and other colleagues of Sullivan (B-S) highlight the fact that we have a fundamentally new approach with a distinct perspective and properties from the traditional method they have used for several years. It is important to understand how our novel approach differs from the traditional one in order to appreciate the opportunities it provides for the advancement of science.

First, in our novel approach, common disorders are recognized to have a complex etiology in which multiple genetic and environmental variables interact in complex ways to influence the risk of disease in an individual person. B_S are experienced in approaches to genome-wide association studies (GWAS) that allow detection of only the average (additive) effects of individual genes in groups of people. We regard the traditional group-wise approach to GWAS as overly restrictive because it is well established that genes typically function in concert with one another, resulting in substantial epistasis in schizophrenia and many other common disorders (3). Fitness, health, and behavior are properties of persons, not genes. Nevertheless, the traditional approach can be useful when its a priori assumptions are satisfied. The traditional and novel approaches to GWAS should be viewed as being complementary perspectives and procedures.

Second, our novel approach allows for the possibility of complex relationships between multilocus genotypes and multifaceted phenotypes. In other words, different sets of genetic polymorphisms can be associated with the same phenotype (“equi-finality” or genetic heterogeneity), and the same set of genetic polymorphisms can be associated with multiple distinct phenotypes (“multifinality” or pleiotropy). They focus only on heterogeneous groups of cases, neglecting phenotypic variability among cases. It is important to note that our novel approach does not make any a priori assumption that complexity is present, but we do allow it to emerge from the data when present, as occurred clearly in our analysis of multiple independent samples of people with the schizophrenias.

Third, we carry out person-centered analyses that specify genotypic-phenotypic relationships within each individual by using clustering methods in which subjects are one matrix dimension and the other matrix dimension is either genotypic or phenotypic information. In other words, our analyses are informative about each individual, thereby providing a basis for identifying specific causes of illness in each person as a basis for tailoring treatment in a personalized way. In contrast, the traditional GWAS considers only average effects in groups of people, making it unjustified to say anything with confidence about a specific individual. Such traditional methods have failed to produce any reliable genetic test for the diagnosis of any psychiatric disorder in an individual person. In addition, phenomena such as population stratification and linkage disequilibrium may confound interpretation of the group-wise statistics of traditional GWAS, whereas these phenomena are easily evaluated in our person-centered approach.

Fourth, our approach is entirely data-driven using machine learning and data mining procedures that are unbiased and unsupervised (i.e., no a priori assumptions are made). Such data-driven methods have been used successfully in many fields of science but not for GWAS prior to our work. In contrast, the a priori assumptions made in the traditional GWAS approach, as used by B-S, have produced only weak associations between the average effects of individual genes and the diagnosis of schizophrenia. In fact, Sullivan and others proposed the formation of the Psychiatric Genetics Consortium (PGC) to address the problem of weak and inconsistent associations. Unfortunately, even large samples still produce only weak associations (4). In addition, even large collections of subjects have encountered what is called the “missing heritability problem” in medical genetics: most of the variability in risk for the schizophrenias has remained unexplained. For example, the resemblance of monozygotic cotwins of people with schizophrenia is much greater than can be explained by the average effects of individual genes, indicating that multiple genes act in concert to influence risk (3). Whereas the heritability of schizophrenia is estimated to be 81% from twin studies, only about 25% of the variability has been explained by traditional GWAS.

In contrast, by applying our novel approach to GWAS we observed that sets of single nucleotide polymorphisms (SNPs) allow identification of individuals at very high risk (70% or more) and replicated the findings consistently in three independent samples. Our results can explain much more about disease risk than traditional group-wise approaches, so we are not surprised that such strong findings may come as a shock to those who have become accustomed to the weak associations identified by traditional GWAS. Again, we expected that this would be very controversial, but we’re optimistic that this fundamentally new approach will open up many new opportunities for people interested in medical genetics. Our results were so unexpected that peer-reviewers demanded replication in independent samples before acceptance. Even we were delighted with the strong replication: 81% of 42 SNP sets associated with 70 to 100% risk of schizophrenia replicated almost exactly across three independent samples, and different SNP sets were associated with distinct phenotypic syndromes in which the gene products suggest possible pathways by which the functions and expression of the genes in the brain may explain the different clinical features of individual patients.

In summary, traditional approaches to GWAS focus on the average effects of individual genes in groups of people, whereas our novel approach focuses on the interactive effects of groups of genes in an individual person. Consequently the differences between these two approaches have profound consequences for the way they view and handle phenomena like linkage disequilibrium, population stratification, and X-linkage. Unfortunately, Breen and colleagues have not adequately appreciated the profound differences between the traditional methods of GWAS with which they are familiar and the novel approach we have developed. As a result, the criticisms they made reflect their concerns about problems that regularly occur when a traditional group-wise approach is implemented, but these concerns may have minimal pertinence to our person-centered approach and findings.

On 2014 Oct 06, C. Robert Cloninger commented:

None

On 2014 Oct 06, C. Robert Cloninger commented:

Reply to Breen et al (their Part 1 of 2)

On behalf of: C. Robert Cloninger, M.D., Ph.D. (Departments of Psychiatry and Genetics, Washington University School of Medicine, St. Louis, MO, USA); Igor Zwir, Ph.D. (Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA; Department of Computer Science and Artificial Intelligence, University of Granada, Spain); Gabriel A. de Erausquin, M.D., Ph.D. (Roskamp Laboratory of Brain Development, Modulation and Repair, Department of Psychiatry and Behavioral Neurosciences, University of South Florida, Tampa, FL, USA); Dragan M. Svrakic, M.D., Ph.D. (Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA); Coral del Val, Ph.D. (Department of Computer Science and Artificial Intelligence, University of Granada, Spain); Javier Arnedo, M.S. (Department of Computer Science and Artificial Intelligence, University of Granada, Spain); Rocío Romero-Zaliz, Ph.D. (Department of Computer Science and Artificial Intelligence, University of Granada, Spain); Helena Hernández-Cuervo, M.D., B.Sc. (Roskamp Laboratory of Brain Development, Modulation and Repair, Department of Psychiatry and Behavioral Neurosciences, University of South Florida, Tampa, FL, USA);

The reply to the comments of Breen et al (their Part 1 of 2) from all of us responsible for analysis in the Arnedo et al article has the following three components which are posted separately as comments as follows: (1) Two Distinct Perspectives and Methodological Approaches to GWAS (2) The Facts about Ancestry and Population Stratification (3) The Facts about Gender and the X-chromosome

The references for all the parts of our reply are included in the final postings about Part 2 of their comments (see posting 8 of our reply).

On 2014 Sep 28, Gerome Breen commented:

Part 1 of 2. On behalf of Gerome Breen, PhD (Institute of Psychiatry, King’s College London, London, UK) * Brendan Bulik-Sullivan (Broad Institute, Cambridge, MA, USA) * Mark Daly, PhD (Broad Institute, Cambridge, MA, USA) * Sarah Medland, PhD (QIMR Berghofer, Brisbane, Australia) * Benjamin Neale, PhD (Broad Institute, Cambridge, MA, USA) * Michael O’Donovan, MD PhD (Cardiff University, Cardiff, UK) * Stephan Ripke, PhD (Broad Institute, Cambridge, MA, USA) * Patrick Sullivan, MD (Karolinska Institutet, Stockholm, Sweden) * Peter Visscher, PhD (University of Queensland, Brisbane, Australia) * Naomi Wray, PhD (University of Queensland, Brisbane, Australia) *

• Authors ordered alphabetically, all made equivalent contributions

In this study published on September 15, Arnedo et al. asserted that schizophrenia is a heterogeneous group of disorders underpinned by different genetic networks mapping to differing sets of clinical symptoms. As a result of their analyses, Arnedo et al. have made remarkable and perhaps unprecedented claims regarding their capacity to subtype schizophrenia. This paper has received considerable media attention. One claim features in many media reports, that schizophrenia can be delineated into “8 types”. If these claims are replicable and consistent, then the work reported in this paper would constitute an important advance into our knowledge of the etiology of schizophrenia.

Unfortunately, these extraordinary claims are not justified by the data and analyses presented. Their claims are based upon complex (and we believe flawed) analyses that are said to reveal links between clusters of clinical data points and patterns of data generated by looking at millions of genetic data points. Instead of the complexities favored by Arnedo et al., there are far simpler alternative explanations for the patterns they observed. We believe that the authors have not excluded important alternative explanations – if we are correct, then the major conclusions of this paper are invalidated.

Analyses such as these rely on independence in many ways: among variables used in prediction, absence of artifactual relationships between genotypes and clinical variables, and between the methods of assessing significance and replication. Below we identify five specific areas of concern that are not adequately addressed in the manuscript, each of which calls into question the conclusions of this study.

A. Ancestry/population stratification.

Two of the three samples the authors studied (MGS and CATIE) have substantial proportions of subjects of European and African ancestry. The third sample is from southern Europe. Ancestry is an extremely well known confounder in genetic studies with a great capacity to yield false associations. Correct inference from genomic data in samples like these requires exceptional care. In the analyses they present, there is almost no mention of how this known bias was addressed or evaluation of its impact on their results. In the samples they used, their references to sets of SNPs that track together is essentially the definition of uncorrected population structure/stratification. Indeed, a central component of their statistical methodology – nonnegative matrix factorization – has been previously employed as a method for ancestry inference in the population genetics literature (Englehardt and Stephens, PLoS Genetics, 2010, link below).

We were unsuccessful in attempts to obtain the full list of SNPs that Arnedo et al. analyzed. Instead, we evaluated the SNPs listed in Table S3 (448 SNP entries, 245 unique SNPs as SNPs could be present more than once, and 237 SNPs with valid allele frequencies in HapMap3). We computed the absolute value of the difference in allele frequencies between the CEU (northwest European) and YRI (Yorubas from Nigeria) groups for all HapMap3 SNPs passing basic quality control (688K SNPs genotyped using Affymetrix 6.0 arrays to match the MGS sample). We then contrasted the SNPs used by Arnedo et al. with all other affy6 SNPs. The Table S3 SNPs had markedly larger differences between a European and an African group. The mean for the absolute difference in allele frequency was 0.27 for the Table S3 SNPs used by Arnedo et al. versus 0.19 for all other SNPs. These highly significant differences underscore our concerns about population stratification bias.

B. X chromosome (chrX).

We noted that 15 of 237 of the SNPs in Table S3 were on chrX (again, Table S3 contains a fraction of the SNPs used in the modeling). Inclusion of chrX SNPs will partly reflect the sex of participants. Arnedo et al. say in their supplement that they include sex as a covariate in their regressions, but they do not describe how they account for sex in their matrix factorization. For example, since males have only one copy of chrX, genotypes for males will be either 0 or 1 whereas chrX genotypes for females will be either 0, 1 or 2. This difference will be salient to clustering algorithms such as those employed by the authors, so it seems likely that some component of the clusters of individuals identified by Arnedo et al. simply reflect genotype differences between sexes rather than clinical features of schizophrenia. It is well-known in statistical genetics that the sex chromosomes require special handling, but this issue is not addressed by Arnedo et al.

On 2014 Oct 12, GianCarlo Panzica commented:

Behavioral effects of the treatment are detailed in this paper: Lagunas N, 2010

On 2015 Apr 24, Melissa Raven commented:

Inappropriate generalisation of mortality in mental disorders

This paper is one of an increasing number that inappropriately and dramatically generalise mortality estimates from samples of patients in secondary and very often tertiary treatment for serious chronic mental disorders (in many cases psychotic disorders) to the much broader population of people with potentially diagnosable mental disorders, which are usually much less severe and are associated with much lower mortality.

In this case, Mehta et al. have claimed that people with common mental disorders die 15-20 years earlier than people without such disorders. However, there is evidence of such a mortality gap only for people with chronic serious mental disorders (particularly psychosis) treated in secondary/tertiary settings. They have thereby grossly inflated the premature mortality associated with (and in effect attributable to) common mental disorders.

This statement is the problem:

'Interventions based on robust evidence with this approach have the potential to address a public health challenge that results in a 15–20 year premature mortality gap, in which 75% of people with common mental disorders receive no treatment,24,25 and which is increasingly sidelined as wellbeing policy ahead of the evidence.' (p. 3)

Mehta et al.'s reference 24, Ormel et al. (2008, p. 370), supports the claim that 75% of people with common mental disorders receive no treatment (in high-income countries). So far so good.

The source of the 15-20 year premature mortality gap statistic is neither of the two references cited. It is most likely Wahlbeck et al. (2011), which reported 'men with mental disorders still live 20 years less, and women 15 years less, than the general population' (p. 453). However, the sample was of people admitted to hospital for mental disorders. Wahlbeck et al. acknowledged that the patients were not representative of all people with mental disorders: 'Less serious non-hospitalised cases of mental disorders, possibly with a better life expectancy outcome, are not included' (p. 457).

The Wahlbeck et al. study was cited by Thornicroft and Docherty (2014) in support of their claim of a 15-20 year mortality gap, in their chapter in the UK Chief Medical Officer's Annual Report (Davies 2014) (the focus of Mehta et al.'s paper):

'men with mental disorders on average live 20 years less, and women 15 years less, than the general population' (p. 198)

Davies prominently and uncritically repeated Thornicroft & Docherty's claim (without citing any references) in her chapter 1, in her rationale for focusing on population mental health:

'There is an unacceptably large 'premature mortality gap': people with mental illness die on average 15–20 years earlier than those without, often from avoidable causes.' (p. 12)

A box below that claim unequivocally states that 'Mental illness … includes common mental disorder (including anxiety and depression), which affects nearly 1 in 4 of the population, and severe mental illness, such as psychosis, which is less common, affecting 0.5–1% of the population', leaving no doubt that the mortality claim applies to common mental disorders.

Mehta et al.'s reference 25 (Chang et al. 2010) did not report a mortality gap. However, another paper by several of the same authors (Chang et al. 2011) reported a somewhat smaller premature mortality gap (8 to14.6 years for men, and 9.8 to 17.5 years for women). [Clearly Mehta et al. have accidentally cited the wrong paper by Chang et al., making it harder to check the validity of their claim.] Again the sample was unrepresentative and biased towards more severe and more chronic cases. Chang et al. appropriately emphasised:

'Potential limitations include the secondary healthcare setting; this should present no problem for high penetrance disorders, such as schizophrenia and bipolar disorder, where most cases will have received secondary care input. However, findings for substance use disorders and depressive disorders should be viewed with circumspection since those appearing on a secondary care register are likely to be an unrepresentative (and probably more severe) subset of community cases. Ultimately, the findings for life expectancy should be taken as referring to people with these mental disorders who had made contact with secondary mental health services within the given time period and are not necessarily applicable to all cases in a given community.' (p. 5)

Mehta et al. (and Thornicroft & Docherty, and Davies) have ignored these authors' cautions. Their claim that people with common mental disorders die 15-20 years earlier than people without such disorders seriously misrepresents the evidence. It is ironic that Mehta et al.'s misleading sentence begins and ends with a focus on evidence, and occurs in a paper subtitled 'evidence-based priorities'. Similarly, it is ironic that this misleading claim is repeatedly emphasised in a report subtitled 'Investing in the Evidence'.

See also my PubMed Commons comment about Walker et al.'s (2015) systematic review and meta-analysis of mortality in mental disorders http://www.ncbi.nlm.nih.gov/pubmed/25671328, which similarly inappropriately generalises mortality from treatment samples of severe cases to the broader population of people with potentially diagnosable disorders, and my more detailed comment about it in JAMA Psychiatry (Raven 2015).

References

Chang CK, Hayes RD, Broadbent M, et al. (2010). All-cause mortality among people with serious mental illness (SMI), substance use disorders, and depressive disorders in southeast London: a cohort study. BMC Psychiatry, 10, 77. http://www.biomedcentral.com/1471-244X/10/77

Chang CK, Hayes RD, Perera G, et al. (2011). Life expectancy at birth for people with serious mental illness and other major disorders from a secondary mental health care case register in London. PLoS One, 6(5), e19590. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0019590

Davies SC. (2014). Annual Report of the Chief Medical Officer 2013: Public Mental Health Priorities: Investing in the Evidence. London: Department of Health. https://www.gov.uk/government/publications/chief-medical-officer-cmo-annual-report-public-mental-health

Mehta N, Croudace T, & Davies SC. (2014). Public mental health: evidenced-based priorities. Lancet, 385(9976), 1472-1475. http://www.sciencedirect.com/science/article/pii/S0140673614614008

Ormel J, Petukhova M, Chatterji S, et al. (2008). Disability and treatment of specific mental and physical disorders across the world. British Journal of Psychiatry, 192(5), 368-375. http://bjp.rcpsych.org/content/192/5/368.long

Raven M. (2015). Inappropriate use of epidemiological data in analysis of mortality in mental disorders , JAMA Psychiatry. [comment re Walker et al. 2015) http://archpsyc.jamanetwork.com/article.aspx?articleid=2110027#tab10

Thornicroft G, & Docherty M. (2014). Chapter 12 Mind the gaps – treatment, funding, access and service provision. In S. C. Davies, Annual Report of the Chief Medical Officer 2013: Public Mental Health Priorities: Investing in the Evidence. London: Department of Health. 2014. https://www.gov.uk/government/publications/chief-medical-officer-cmo-annual-report-public-mental-health

Wahlbeck K, Westman J, Nordentoft M, Gissler M, Laursen TM. (2011). Outcomes of Nordic mental health systems: life expectancy of patients with mental disorders. British Journal of Psychiatry, 199(6), 453-458. http://bjp.rcpsych.org/content/199/6/453

Walker, ER, McGee R, & Druss BJ. (2015). Mortality in Mental Disorders and Global Disease Burden Implications: A Systematic Review and Meta-analysis. JAMA Psychiatry, 72(4), 334-341. http://archpsyc.jamanetwork.com/article.aspx?articleid=2110027

On 2017 Sep 30, Misha Koksharov commented:

When using firefly luciferases as an output signal in synthetic biology circuits in bacteria, it is really beneficial to use thermostable versions because they are dramatically brighter at 37°C (and other elevated temperatures) - as exemplified on the following slide:

http://forumbgz.ru/user/upload/file593017.jpg

Though, when growing bacteria at common room temperatures (or lower) this is not that important (as can be seen above). The reason for these effects is relatively low thermostability of WT firefly luciferases (P. pyralis, L. mingrelica, etc) combined with poor efficiency of bacterial chaperones in keeping luciferase properly folded at elevated temperatures.

On 2017 Jun 16, Jacob H. Hanna commented:

The first of three papers from Smith group describing human transgene free "Reset cells", have failed to describe ability to generate teratomas from "transgene free" cells. Mouse naive pluripotent cells have the intrinsic "self organizing capacity" to enter a formative/primed state after in vivo SC injection and make teratomas within 4-8 weeks. I find this stunning and scientifically disturbing and wonder whether the human "reset" cells being induced do not qualify to be annotated as pluripotent cells at all. I hope the authors can clearly point out and directly address this critical caveat.

On 2014 Sep 15, Samir Ounzain commented:

Very nice editorial discussing the interesting observations recently published from the Yvan Devaux laboratory (Vausort M, Wagner DR, Devaux Y. Long Noncoding RNAs in Patients With Acute Myocardial Infarction. Circ Res. 2014 Sep 12;115(7):668-77. doi:10.1161/CIRCRESAHA.115.303836. Epub 2014 Jul 17. PubMed PMID: 25035150).

The potential for lncRNAs to serve as biomarkers in cardiovascular disease is very exciting and warrants further investigation. In this editorial Skroblin and Mayr raise a couple of very interesting points that need further investigation and discussion.

They state ''All selected lncRNAs have been identified in noncardiac tissues. This lack of cardiac specificity questions their usefulness as predictors of cardiac dysfunction. At best, measuring lncRNA expression in full blood might reflect inflammation at the site of MI''.

We agree strongly with this statement and would like to emphasise that cardiac specific/restricted lncRNAs likely represent the most important candidates both as biomarkers but also as highly specific therapeutic targets for future manipulation. Indeed, we recently demonstrated that through utilising very deep RNA-sequencing coupled with ab initio transcript reconstructions it is possible to identify 1000s of novel heart specific lncRNAs. Importantly we feel sequencing to a high depth, in a very specific context (we sequenced to a depth of 400x106 PE-reads per sample in isolated border zones post myocardial infarction, as compated to 50x106 PE-reads used in most comparable studies)is critical for the idenfication of highly cell/tissue and context specific lncRNAs, that ultimately we believe will represent the most attractive biomarkers and therapeutic targets.

It is worth noting that when utilising this approach, we found that novel previously unknown lncRNAs were both a) much more cardiac specific than mRNAs and annotated lncRNAs (indeed many exhibit cell type specificity within the heart, b) were better correlated with cardiac physiological traits vs cannonical biomarker mRNAs and annotated lncRNAs, supporting the notion novel heart enriched lncRNAs could represent very attractive biomarkers, c) were highly associated with unique heart specific chromatin signatures linked to active -cis control enhancers, again has implications in therapeutic settings as enhancer reprogramming post stress underpins the fetal activation of gene program that is implicated in pathological remodelling and finally d) using a novel approach to infer lncRNA functions based on developmental chromatin state transitions, we found that novel lncRNAs were highly enriched in functional clusters linked to very specific cardiac functional processes including contractility etc.

Many of the novel lncRNAs we identified in this study were conserved in human, and we hope future studies will leverage our data for the identificaiton of novel heart specific biomarkers that may be circulating in the blood. Most studies to date have only assesed the annotated long non-coding transcriptome in this setting, and we believe our paper demonstrates that novel cardiac enriched lncRNAs are likely much more interesting candidates for analysis.

Ounzain S, Micheletti R, Beckmann T, Schroen B, Alexanian M, Pezzuto I, Crippa S, Nemir M, Sarre A, Johnson R, Dauvillier J, Burdet F, Ibberson M, Guigó R, Xenarios I, Heymans S, Pedrazzini T. Genome-wide profiling of the cardiac transcriptome after myocardial infarction identifies novel heart-specific long non-coding RNAs. Eur Heart J. 2014 Apr 30. [Epub ahead of print] PubMed PMID: 24786300.

On 2016 Nov 22, MICHAEL BALLARD commented:

This is the republication of an article that was previously retracted from the Journal of Neuroscience. According to the authors' retraction notice, they: “...discovered errors in the quantification of the expression and/or phosphorylation of a subset of signaling pathways, particularly related to Figures 4 and 5D. Despite these errors, the major conclusions of the paper remain substantiated." The inaccurate figures and results have been excluded from this republication. The original, retracted version can be found on PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23658180 and at the Journal of Neuroscience site: http://www.jneurosci.org/content/33/19/8423.long

On 2014 Sep 15, Amanda Capes-Davis commented:

The authors use the KB cell line to study autophagy in oral squamous cell carcinoma (OSCC). Please be aware that KB is cross-contaminated with the HeLa cell line and is cervical adenocarcinoma, not OSCC.

Cell lines should be tested for cross-contamination, using a consensus method such as short tandem repeat (STR) profiling. For a list of known cross-contaminated cell lines, see http://iclac.org/databases/cross-contaminations/.

Many journals now require authentication testing before publication. KB is extensively used in specialist oral journals such as J Oral Pathol Med. A requirement for authentication testing in these journals would make oral research more reliable and reproducible.

On 2014 Nov 24, Guillaume Filion commented:

This article is one of the "CISCOM meta-analyses", which are very similar papers written by different authors. For more information about the CISCOM meta-analyses, check the blog post "A flurry of copycast on PubMed" at the following link http://blog.thegrandlocus.com/2014/10/a-flurry-of-copycats-on-pubmed

On 2014 Dec 02, Ross Mounce commented:

If you're not blind or visually-impaired you can read the full-text of this article here: http://www.readcube.com/articles/10.1038/nature13698?shared_access_token=cLcTlh3EllK6O8l8caB6L9RgN0jAjWel9jnR3ZoTv0M7f3sQdjEvES3BdHl3GcoG1tGJSA21P681sN4HWLyCw-WJWwux96yV21jKV0mAUsG9OeU_oEeLSAzIInc3iaqbgdUnUX23n_V8ZesvBPtGPutkAzddWCupW7duuimcCMZ2m1smdXTouYqwgJ6eYD3fWuAVaFOWUnqr6aVOOlQ-XKlaogr1pEj-_jV4ckY9HiM=

On 2014 Dec 02, Ross Mounce commented:

If you're not blind or visually-impaired you can read the full-text of this article here: http://www.readcube.com/articles/10.1038/nature13737?shared_access_token=gDaLL4_VLMnHFJJDKwWhXdRgN0jAjWel9jnR3ZoTv0M44QzV2niutuQ_3EPBwGdiRZEyfDaEz0LUo_8qb7JzEbUL92YMxvAn6KAWxrj_NnJuIdtZ4-b_yZyW31fLNECpZNpTWyR0Vw3qLQjRTkMj1MICAHfBnwLwUEBGenfnYpNo_gM_uo9G4YD79nIDD2HAGd603-yQ1qEnM6oNMZsSpPJXPF80dApdpxHpaeW3kxU=

On 2014 Dec 30, MARK WEST commented:

Comment on “Striatal firing rate reflects head movement velocity” by Namsoo Kim, Joseph W. Barter, Tatyana Sukharnikova and Henry H. Yin

We read the experiments conducted by Kim et al. with enthusiasm. We feel that it is important to comment on previous studies that demonstrated a direct relationship between striatal activity and movement velocity, which Kim et al. support. Originally demonstrated in the nonhuman primate (DeLong, 1973), several studies in rodents have shown relationships between firing rates of dorsolateral striatal neurons and movement velocity. Kim et al. find that both putative dorsal striatal projection neurons and putative fast-spiking interneurons fire during head movements and exhibit a firing rate relationship with the velocity of head movement. Given our previous demonstration of firing rate correlations with head movement velocity in the subpopulation of dorsolateral striatal projection neurons selectively related to head movement (Pederson et al., 1997; Tang et al., 2007), it is interesting that firing of putative striatal interneurons also exhibits a relationship to movement velocity (although not subjected to sensorimotor examination of the entire body by Kim et al). We want to add information regarding dorsolateral striatal projection neurons, over half of which fire selectively in relation to activity of single body parts (Carelli and West, 1991; Cho and West, 1997), as revealed by sensorimotor examination of the entire body. These neurons exhibit movement properties which may have been outside the scope of the authors’ study. First, movement-related firing patterns are not limited to neurons phasically related to head movements, as they are found throughout the dorsolateral striatum containing single body part neurons. For instance, we have also demonstrated similar properties in neurons phasically related to vibrissae movement (Carelli and West, 1991), forelimb movement (Carelli et al, 1997) or tongue movement (Mittler et al., 1994; Tang et al., 2008). Second, each single body part neuron exhibits a preferred direction of movement. Third, though velocity of movement is a major predictor of firing rate change for many single body part neurons, not all these neurons are sensitive to velocity and some may be sensitive to one or a combination of several movement-related characteristics (e.g., movement length, movement position, etc). References: Carelli RM, West MO (1991) Representation of body by single neurons in the dorsolateral striatum of the awake, unrestrained rat. J Comp Neurol 309:231 249. Carelli RM, Wolske M, West MO (1997) Loss of lever press-related firing of rat striatal forelimb neurons after repeated sessions in a lever pressing task. J Neurosci 17: 1804-1814. Cho J, West MO (1997) Distributions of single neurons related to body parts in the lateral striatum of the rat. Brain Res 756:241-6. DeLong, MR (1973) Putamen: Activity of Single Units during Slow and Rapid Arm Movements, Science 179:1240-1242. Mittler T, Cho J, Peoples LL, West MO (1994) Representation of the body in the lateral striatum of the freely moving rat: Single neurons related to licking, Exp Brain Res 98:163-167. Pederson CL, Wolske M, Peoples LL, West MO (1997) Firing rate dependent effect of cocaine on single neurons of the rat lateral striatum. Brain Res 760:261-5. Tang C, Pawlak AP, Prokopenko V, West MO (2007) Changes in activity of the striatum during formation of a motor habit. Eur J Neurosci 25:1212-1227. Tang CC, Root DH, Duke DC, Zhu Y, Teixeria K, Ma S, Barker DJ, West MO (2009) Decreased firing of striatal neurons related to licking during acquisition and overtraining of a licking task. J Neurosci 29(44):13952-13961.

On 2015 Dec 11, Mark Johnston commented:

An interactive protocol from this article is freely available at protocols.io.

On 2014 Oct 15, Amanda Capes-Davis commented:

Kudos to the authors for their integrity in retracting this article. Cell lines ACCM and ACC2 are known to be misidentified; instead of arising from salivary gland, ACCM and ACC2 are actually HeLa, from cervical adenocarcinoma.

For a list of known misidentified cell lines, see http://iclac.org/databases/cross-contaminations/.

On 2014 Sep 30, Ryan Radecki commented:

Post-publication commentary:

"Alas, Poor (Literally) Detroit"

High-quality care can mean many things. In the ideal sense, it conveys cooperation and coordination between the many facets of healthcare delivery – great physician care, the best possible translation of medical evidence to an individual patient, outstanding nursing, electronic systems that double-check and triple-check for safety, and an army of staff to support patients in all settings to ensure best possible health.

Or, high-quality care can just be a number. A surrogate number, obliquely related to the ideals of quality care....

http://www.emlitofnote.com/2014/09/alas-poor-literally-detroit.html

On 2014 Dec 21, Massimiliano Tognolini commented:

Dear Colleagues, I do not want to discuss the connection between Alzheimer’s disease and EphA4 when demonstrated with biological tools (siRNA, mutant mice, peptides, antibodies…) in vitro or in vivo. However, major concerns raises when rhynchophylline was used. Its pharmacological characterization was very weak and flaws were present. More details are reported as comment of original paper: http://www.ncbi.nlm.nih.gov/pubmed/?term=epha4+ameliorates

On 2014 Sep 30, Ryan Radecki commented:

Post-publication commentary:

"Are a Third of Research Conclusions Wrong?"

As I covered last year, half of what you’ve been taught in medicine is wrong – we just don’t know which half.

And, it turns out, sometimes even the same authors taking a second look at the same data as before, can come up with new – and wildly different – conclusions.

This is a review of 37 randomized-controlled trials published after 1966 paired with 37 “re-analyses” of the same data. These trials span the entire medical domain, from mycophenolate therapy after cardiac transplantation to homeopathy for fibrosis. Of these 37 re-analyses, 32 of them involved authors from the original research group. These re-analyses differed by changing statistical techniques, outcome definitions, or other study interpretation methods....

http://www.emlitofnote.com/2014/09/are-third-of-research-conclusions-wrong.html

On 2015 Oct 07, Bill Cayley commented:

For evaluation of strep throat, perhaps "Less" is "more": https://lessismoreebm.wordpress.com/2015/05/11/rapid-diagnostic-tests-for-group-a-streptococcal-pharyngitis-a-meta-analysis/

On 2017 Aug 11, Kerin Tyrrell commented:

The genus Alistipes is characterized as an obligate anaerobe and asaccharolytic; however, this study describes the new species as microaerophilic and "asaccharolytic" although "Activities present are α-glucosidase, β-glucosidase, N-acetyl-β-glucos-aminidase, mannose and rafinnose (sp) fermentation". Can the authors please comment?

On 2017 Aug 07, Seán Turner commented:

Both Enorma timonensis Ramasamy et al. 2016 [PMID 25197477] and Collinsella massiliensis Padmanabhan et al. 2016 [PMID 25197489] are described as Gram-positive, non-motile, non-sporulating rods isolated from the feces of a 53 year-old woman in an intensive care unit. Their genomes are >99.9% similar, identical within the limits of sequencing/assembly error. They should be considered heterotypic synonyms.

Strain CSUR P902 is cited as the type strain of both "Corynebacterium ihumii" Padmanabhan et al. 2014 [PMID 25197488] and Collinsella massiliensis Padmanabhan et al. 2016 [PMID 25197489].

On 2017 Aug 07, Seán Turner commented:

Strain CSUR P902 is cited as the type strain of both "Corynebacterium ihumii" Padmanabhan et al. 2014 [PMID 25197488] and Collinsella massiliensis Padmanabhan et al. 2016 [PMID 25197489].

On 2017 Aug 07, Seán Turner commented:

Both Enorma timonensis Ramasamy et al. 2016 [PMID 25197477] and Collinsella massiliensis Padmanabhan et al. 2016 [PMID 25197489] are described as Gram-positive, non-motile, non-sporulating rods isolated from the feces of a 53 year-old woman in an intensive care unit. Their genomes are >99.9% similar, identical within the limits of sequencing/assembly error. They should be considered heterotypic synonyms.

On 2015 Sep 28, Donald Forsdyke commented:

General Principle of Juxtaposition- and Concentration-Dependent Activation of Complement

Seeking to understand "immune system … ability to discriminate self from nonself," the authors here describe an elegant “clustering-based mechanism” for complement activation by MBL that is “akin to the mechanism governing signaling through many cellular receptors,” and distinguishes the lectin and classical pathways of complement activation (1). Their demonstration of the general principle of “juxtaposition- and concentration-dependent activation,” is in keeping with studies with the MBL, concanavalin-A, that were reported in 1977 (2).

However, the latter studies were not mentioned (1), and a new review states that “the ‘lectin pathway’ was discovered in the early 1980s” (3). Thus, it is possible that those engaged in this fascinating field are unaware of the earlier work that began to be reported in 1970 (4). Subsequent studies in the 1970s (5-8) culminated in demonstration of “the importance of the sequence of binding events” (9). It is to be hoped that the full relationship of our earlier work to modern studies will be determined in future work.

(1) Degn SE, et al. (2014) Complement activation by ligand-driven juxtaposition of discrete pattern recognition complexes. Proc. Natl. Acad. Sci. USA 111:13445-13450.

(2) Forsdyke DR (1977) Role of receptor aggregation in complement-dependent inhibition of lymphocytes by high concentrations of concanavalin-A. Nature 267:358-360. Forsdyke DR, 1977

(3) Thielens NM, Gaborlaud C, Thiel S (2015) Complement: classical and lectin pathways. In: eLS John Wiley & Sons, Chichester.

(4) Milthorp P, Forsdyke DR (1970) Inhibition of lymphocyte activation at high ratios of concanavalin A to serum depends on complement. Nature 227:1351-1352. Milthorp P, 1970

(5) Milthorp P, Forsdyke DR (1973) Serum factors affecting the incorporation of (3H)uridine by lymphocytes stimulated by concanavalin A. Studies of the role of complement. _Biochem. J. 132:803-812.Milthorp P, 1973

(6) Milthorp P, Forsdyke DR (1973) A comparison of the activation of thymus and lymph-node cells by concanavalin A and phytohaemagglutinin. Effects of complement. J. Immun. Meth. 2:269-277.Milthorp P, 1973

(7) Forsdyke DR (1978) Role of complement in the toxicity of dietary legumes. Med. Hypoth. 4:97-100 Forsdyke DR, 1978

(8) Forsdyke DR, Milthorp P (1979) Early onset inhibition of lymphocytes in heterologous serum by high concentrations of concanavalin-A. Further studies of the role of complement with suramin and heated serum. Int. J. Immunopharmacol. 1:133-139.Forsdyke DR, 1979

(9) Forsdyke DR (1980) Lectin pulses as determinants of lymphocyte activation and inactivation during the first six hours of culture: sequential action of concanavalin-A and complement cause cell lysis. Can. J. Biochem. 58:1387-1396. Forsdyke DR, 1980

On 2014 Nov 22, IRWIN FEINBERG commented:

Giedd et al suggest that the changes they measured longitudinally in MRI-estimated cortical volume (and in cortical thickness in previous reports) might index synaptic proliferation and elimination. This seems implausible. As the authors of this study likely recognize, changes in dendritic spines are well-below the limits of current MRI resolution. In addition, the trajectories and magnitudes of the MRI changes deviate from those of the best available data for synaptic density (Huttenlocher 1979). Synaptic density is falling steeply while MRI cortical measures are changing only slightly across adolescence. Although Huttenlocher’s synaptic density data do not contain measurements during adolescence, they do show a 40 to 50% drop off from late childhood to early adulthood, much larger than the 10-15% decline in gray matter volume reported by Giedd et al. There are other brain measures (cortical metabolic rate and NREM delta amplitude) with cross-sectionally measured trajectories that visually and statistically more closely parallel the synaptic density data and might therefore better index this developmentally critical brain variable (Feinberg, Thode et al. 1990). The NREM delta trajectories have recently been confirmed in a longitudinal study which also identified ages 12-16.5 yrs as a period of accelerated brain maturation (Campbell and Feinberg 2009). In contrast to the sleep EEG, MRI measures do not reveal any period of accelerated change, nor would one expect this in post-natal measures of brain structure.

While I do not believe the MRI studies index synaptic elimination, I strongly agree with Giedd et al that it is critical to establish the cellular basis of the developmental MRI changes. (Our laboratory has also emphasized this point (Campbell and Feinberg 2009)). However, it is unlikely that animal studies of the sort proposed by Giedd et al could provide conclusive answers because of species differences. For example, cortical synaptic development in monkeys is isochronic rather than heterochronic as in humans ((Rakic, Bourgeois et al. 1986)). What is urgently needed is a large scale multisite replication of Huttenlocher’s heroic, post-mortem study of human brains. New investigations could include biochemical as well as ultramicroscopic measures of dendritic components and morphology. Such data could also fill in the age gaps in Huttenlocher’s dataset, providing more detailed maturational trajectories for synaptic connectivity. In addition, when ante-mortem MRI studies are available, they could be related to the post-mortem findings, so that the anatomic basis of developmental MRI changes could be determined. Replications lack research glamor. In this instance, however, a replication (and major extension) is urgently needed to understand existing findings and to provide a firm foundation for interpreting MRI and other studies of post-natal brain development.

Campbell, I. G. and I. Feinberg (2009). "Longitudinal trajectories of non-rapid eye movement delta and theta EEG as indicators of adolescent brain maturation." Proceedings of the National Academy of Sciences of the United States of America 106(13): 5177-5180, PMID 19307577.

Feinberg, I., et al. (1990). "Gamma distribution model describes maturational curves for delta wave amplitude, cortical metabolic rate and synaptic density." Journal of Theoretical Biology 142(2): 149-161, PMID 2161971.

Huttenlocher, P. R. (1979). "Synaptic density in human frontal cortex - developmental changes and effects of aging." Brain Research 163(2): 195-205, PMID 427544.

Rakic, P., et al. (1986). "Concurrent overproduction of synapses in diverse regions of the primate cerebral cortex." Science 232(4747): 232-235, 3952506.

On 2014 Sep 13, Hilda Bastian commented:

The authors make an important point: just because a systematic review has not assessed publication bias (PB), it does not mean that there is none.

However, in this study, there were only 36 reviews that did not assess publication bias, and nearly half of those were in the minority subset of reviews that didn't have a comprehensive search strategy. For many, a comprehensive search strategy is a defining characteristic of a systematic review (e.g. in DARE, the Database of Reviews of Effects). Those reviews may not be able to provide an adequate overview of published studies, either.

The authors point out that a limitation of their study is that there were many (planned) subgroup analysis - and it's on a small number of reviews. Especially as the number of adequately systematic reviews was small, the exclusion of the Cochrane Database of Systematic Reviews - a journal that publishes systematic reviews and was eligible for their study - is disappointing. The reason given for the exclusion was because the results of the (Moher D, 2007) study showed that publication bias "is regularly performed in articles published in this database." However the authors of that study concluded the assessment of publication bias was disappointing overall. For Cochrane reviews in that study, publication bias was assessed (or intended to be assessed) in only 32% of those reviews (and it was considered in another 39%).

(Disclosure: I work on projects related to systematic reviews at the NCBI (National Center for Biotechnology Information, U.S. National Library of Medicine.)

On 2014 Sep 29, Allan Frey commented:

I am a scientist and was an editor of a major biological journal for several years. A portion of my basic research involved the use of very low intensity microwave radiation as a tool to study the function of biological organisms. This latter resulted in numerous published journal articles, book chapters and a book.

The authors of the Koyama et al journal article (1) present a rather peculiar exercise that they did. It appears that they wanted to gather some data and present it in a cell phone context as though it has relevance to cell phones. They lead off their abstract and also lead off their introduction talking about cell phones. Their extensive interpretations of their data are not relevant to cell phones and thus the article is misleading from a Public Health perspective.

It appears that they used a microwave oven frequency signal generator to produce an unmodulated signal. Such a signal is suitable for a study on heating tissue, not for a study which starts off discussing cell phones. Since cell phones use modulated signals, a cell phone relevant study requires a modulated signal and also requires a relevant carrier frequency. There is an enormous amount of published biological data that shows that the biological effects are different when one uses an unmodulated oven type signal instead of a modulated signal with a relevant carrier frequency. This is well known; it was even brought out in my book that was published 20 years ago (2).

There appear to be other major faults in their exercise that make it irrelevant to cell phone research and possibly other research. But since their energy frequency and modulation were inappropriate and this, in itself, discredits their discussing it in the context of cell phones, there is no need to discuss other faults in their exercise.

1. Koyama S, Narita E, Suzuki Y, Taki M, Shinohara N, Miyakoshi J. Effect of a 2.45-GHz radiofrequency electromagnetic field on neutrophil chemotaxis and phagocytosis in differentiated human HL-60 cells.J Radiat Res. 2014 Sep 5. pii: rru075. PMID 25194051. [Epub ahead of print]

2. Frey AH. Ed. On the nature of electromagnetic field interactions with biological systems. R. G. Landes Co., Austin, 1994

On 2014 Sep 09, Attila Csordas commented:

associated dataset available via PRIDE/ProteomeXchange: http://www.ebi.ac.uk/pride/archive/projects/PXD000666

On 2016 Feb 21, Daniel Corcos commented:

It would be interesting to know the prevalence of mammography in recent vs earlier birth cohort. Epidemiological evidence suggest a strong effect of mammography in triggering breast cancer in these populations. http://www.ncbi.nlm.nih.gov/pubmed/25082516/#comments

On 2014 Sep 13, Gary Mirams commented:

The authors have made the code that was used for this study available to download from http://www.eheartsim.com/?page_id=90

On 2016 Mar 18, ZHONGMING ZHAO commented:

My lab recently moved to the University of Texas Health Science Center at Houston. The software is now available at https://bioinfo.uth.edu/DESnowball/.

On 2014 Nov 24, Guillaume Filion commented:

This article is one of the "CISCOM meta-analyses", which are very similar papers written by different authors. For more information about the CISCOM meta-analyses, check the blog post "A flurry of copycast on PubMed" at the following link http://blog.thegrandlocus.com/2014/10/a-flurry-of-copycats-on-pubmed

On 2015 Sep 16, F Morceau commented:

Despite HL-60 cells are classified in APL they do not express PML-RARa:

Cell. 1993 Aug 13;74(3):423-31. The acute promyelocytic leukemia-specific PML-RAR alpha fusion protein inhibits differentiation and promotes survival of myeloid precursor cells. Grignani F et al.,

On 2018 Jan 19, Natalie Clairoux commented:

Free version of this article available at http://hdl.handle.net/1866/19739

On 2017 Oct 10, A. Mesut Erzurumluoglu commented:

We seem to have found the same results (i.e. same nonsense variant in CCDC151 and same disease phenotype) round about the same time (check "Manuscript Received" dates of both papers, May/June 2014) but it seems our cilia ultrastructure photos do not match exactly. Maybe something to look into for future research...

Our paper's (Alsaadi and Erzurumluoglu et al, 2014) URL: http://onlinelibrary.wiley.com/doi/10.1002/humu.22698/abstract

On 2016 Feb 07, Fang-Cheng Yeh commented:

The tool and source code are available at http://ws-recognizer.labsolver.org/

On 2015 Jun 30, thomas samaras commented:

This an excellent report on the relation between nutrition, genes and socioeconomic factors on height. Unfortunately,a worldwide taller height has harmful ramifications and the assumption that reaching our genetic potential is beneficial has not been explored by most researchers. For example, we have the genetic potential to weigh 300 lb (136 kg) but we know this would create a lot of health and environmental problems. Therefore, we shouldn't assume that reaching our genetic potential for height is desirable.

The 2007 WCRF report stated that as a result of industrialization and urbanization, chronic diseases have increased along with growth in height and weight. The lack of Western chronic disease before industrialization also applied to the elderly. WHO also attributed our chronic diseases to the Western diet. Many studies have found pre-Western people following traditional diets have very few Western diseases even at older ages. Many of these populations were found to be short and free of coronary heart disease and stroke.

While demographers and epidemiologists have associated increased height with better health and longevity, many biologists, gerontologists and other researchers have found smaller body size is best for a healthy and long life. Many biological mechanisms support their conclusions.

Research on the ramifications of increasing human height covers over 40 years. The evidence is solid that slower growth, later sexual maturation and shorter height promote health and longevity. As Levine recently reported, animal protein is not best for good health. See below.

Note that Levine’s study finds higher protein is tied to a 4 times increase in type 2 diabetes for all ages over 50 years. Also animal protein after 65 years of age is good for us only if it doesn’t increase IGF-1 levels. The paradox is that animal protein increases IGF-1 per their own study, but other researchers have found plant protein reduces it somewhat.

A few papers for further reading are listed next.

Further reading

1 Levine, Longo, Fontana, et al. Low protein intake is associated with a major reduction in IGF-1, cancer, and overall mortality in the 65 and younger but not older population. March, 2014, Cell Metabolism,19, 407-417.

2 Samaras TT. Evidence from eight different types of studies showing that smaller body size is related to greater longevity. Journal of Scientific Research & Reports. 2014: 3 (16): 2150-2160, 2014; article no. JSRR.2014.16.003.

3 Samaras TT (ed): Human Body Size and the Laws of Scaling: Physiological, Performance, Growth, Longevity and Ecological Ramifications. New York: Nova Science Pub; 2007.

4 He Q, Morris BJ, Grove JS, Petrovitch H, Ross W, Masaki KH, et al. Shorter men live longer: Association of height with longevity and FOXO3 genotype in American men of Japanese ancestry. Plos ONE 9(5): e94385. doi:10.1371/journal.pone.0094385.

5 Salaris L, Poulain M, Samaras TT. Height and survival at older ages among men born in an inland village in Sardinia (Italy), 1866-2006. Biodemography and Social Biology, 58:1, 1-13.

6 Bartke A. Healthy Aging: Is Smaller better? A mini-review. Gerontology 2012; 58:337-43.

7 Thomas Samaras, Commentary: Human growth, height, size: Reasons of be small. WPHNA World Public Health Nutrition Association, vol. 2, No. 3, March 2011.

 8 Samaras,Thomas T. Why Smaller Humans Are in Our Future, Policy Innovations, 10/20/2014

On 2016 Oct 11, Gillian Parker commented:

It would be good to look at this paper alongside Julia Twigg's classification, similarly developed from qualitative research in the late 1980s (see https://www.cambridge.org/core/journals/journal-of-social-policy/article/models-of-carers-how-do-social-care-agencies-conceptualise-their-relationship-with-informal-carers/257776AD70ADD40F45683DF45086813A).

There are echoes here of her model of how service providers view carers: as resources; as co-workers; and or as co-clients (with the person they are caring for).

There are even stronger echoes of Lewis and Meredith's work (Daughters Who Care, 1988; London:Routledge ISBN 0-415-00682-1) where, again based on qualitative work, they classified three types of response to caring: balancing care; integrating care; and immersion in care.

It would be good to think about how all three models/typologies stack up with each other, across different types of carers in different circumstances, and across time!

On 2014 Nov 20, Bernard Carroll commented:

This report breaks some new ground in design – the between-site train-and test exercise and the cross-validation of case assignment to unipolar or bipolar groups. As the authors stated, the data are not sufficiently strong for clinical use. My comments are intended to improve the quality of reporting of this and similar studies.

1. There is selective highlighting of some results and a failure to present all the important findings clearly. In particular, the performance of the classification algorithms in distinguishing patients from normal control subjects was relegated to the Supplementary Material. It can be calculated from eTable 7 that between 27.5% and 38% of controls would be misclassified in the 1-way comparison with unipolar depressed cases. The corresponding Kappa coefficients of concordance would be fair at 0.48 for the SVM method and poor at 0.28 for the GPC method. Results for the bipolar contrast with control subjects were similarly weak. If the method cannot do better than this with normal subjects then clinical use is a very long way away. These sobering data properly belong in the main body of the paper.

2. The cross-site training - testing results for the algorithms were described as “highly significant” (page 1226). Actually, for the train (Munster) and test (Pittsburgh) exercise the Kappa values that can be calculated from Table 3 were weak at 0.28 for SVM and 0.24 for GPC methods. They were only slightly better for the Pittsburgh – Munster exercise (Kappas each 0.38).

3. P-values were given in Table 3 and in eTables 5,6,7 – but there is no statement of what statistical analyses generated these P-values. Were they Goodness of Fit Chi-squared tests? Standard tradecraft requires that such analyses be clearly described.

4. No correction of P-values was made for multiple comparisons. That is another aspect of standard tradecraft.

5. No data were shown for test-retest reliability of the algorithm-derived group assignments.

6. All the analyses were predicated on the untenable assumption that the clinical diagnoses were 100% accurate. As the DSM-5 field trials taught us, that is far from the case in the real world of clinical assessment – the Kappa value for major depressive disorder diagnoses averaged over 4 sites was poor at 0.28 (Regier et al 2013). The authors failed to consider whether this confound degraded the strength of their findings (see a discussion of this issue in Carroll BJ 1989). At the very least, a statement of diagnostic reliability for the cases in this study is needed.

References

Carroll BJ. Diagnostic validity and laboratory studies. Rules of the game.<br> In: The Validity of Psychiatric Diagnosis, eds., L.N. Robins and J.E. Barrett, Raven Press, New York, 1989, pp. 229-245.

Regier DA et al. DSM-5 Field Trials in the United States and Canada, Part II: Test-Retest Reliability of Selected Categorical Diagnoses. Amer J Psychiatry 2013; 170: 59-70.

On 2014 Sep 11, Samir Ounzain commented:

Very nice piece of work from the Visel and Pennacchio laboratories. Enhancer-templated ncRNA transcription is emerging as a common feature associated with active tissue specific enhancers. We have recently using the excellent ChIP-Seq data sets generatated by this laboratory made similiar observations specifically within the heart in various in vivo and in vitro models of cardiac differentiation and pathological remodelling. Interestingly many of these enhancers are not only templating eRNAs, as described in this study, but also PolyA+, unidirectional and multi-exonic lncRNAs. Furthermore loss of function demonstrates these enhancer templated lncRNAs are critical for the regulation of enhancer target coding genes.

I would be fascinated to see how many of the TSTRs identified in this study are producing spliced lncRNAs and furthermore if loss-of-function leads to repressed enhancer activity on target genes.

Great work and I look forward to future studies from this group exploring these possibilities.

For those more interested in heart specific enhancer templated lncRNAs, please refer to our recent publications.

Ounzain, S., Micheletti, R., Beckmann, T., Schroen, B., Alexanian, M., Pezzuto, I., Crippa, S., Nemir, M., Sarre, A., Johnson, R., et al. (2014a). Genome-wide profiling of the cardiac transcriptome after myocardial infarction identifies novel heart-specific long non-coding RNAs. European heart journal.

Ounzain, S., Pezzuto, I., Micheletti, R., Burdet, F., Sheta, R., Nemir, M., Gonzales, C., Sarre, A., Alexanian, M., Blow, M.J., et al. (2014b). Functional importance of cardiac enhancer-associated noncoding RNAs in heart development and disease. Journal of molecular and cellular cardiology 76C, 55-70.

On 2015 May 12, University of Lausanne Genomics, Ecology and Evolution Journal Club commented:

This paper presents very interesting example of adaptive radiation analysis. This is a summary of the discussion at our journal club. Authors aimed to understand the molecular mechanisms and genetic changes underlying phenotypic diversity in African cichlid fish. For this purpose, they sequenced the genomes of five lineages of African cichlids, including an ancestral lineage with low diversity, and analyzed six closely related species underwent recent radiation from Lake Victoria. According observed results, authors postulate that multiple evolutionary mechanisms, such as increased gene duplication, accelerated evolution of coding and regulatory elements, regulation of gene expression by novel microRNAs and transposons insertion, retention of ancient polymorphism, together with relaxed purifying selection can trigger rapid diversification in African cichlids. However, Figure 2 and some figures presented in supplementary information are unclear. It is not clear that application of three different analyses for investigation of gene duplicates was necessary due to evident constraints of methods and expected low level of overlap between results. In any case, this paper provides a great resource to extend our understanding of adaptive radiation.

On 2014 Sep 08, Raha Pazoki commented:

Inflammation in Arrhythmia

The role of inflammation in risk of arrhythmia is still unclear and the study by Lazzerini and colleagues Lazzerini PE, 2015 demonstrate important findings. Systemic inflammation as reflected by a rise in serum inflammatory biomarkers has been linked to arrhythmia and sudden cardiac death in the setting of other diseases such as myocardial infarction Elmas E, 2008 where the majority of fatal arrhythmia occurs. The question that is arisen by this study is that whether treatment of other types of systemic or local inflammation as is during myocardial infarction prevents arrhythmia and sudden cardiac death?

On 2014 Sep 16, Serge Ahmed commented:

In this new Shattuck Lecture, Eric Kandel and Denise Kandel (K2) announce that they will let science speaks for itself about the role of “nicotine as a gateway drug” to other drug addictions and will not distort “what science does and does not tell us” about this controversial matter, as many others would generally do.

However, this paper is more about cross-sensitization between nicotine and other drugs in mice than about “nicotine as a gateway drug” to other drug addictions in human populations. Basically, K2 present evidence showing that forced pre-exposure to nicotine can enhance the proportion of individual mice that become sensitive to the stimulant and rewarding effects of a low dose of cocaine. In fact, after nicotine pre-exposure, a large majority of mice become sensitized to cocaine (i.e., up to 78-98% of mice). This proportion contrasts with the small minority of cigarette smokers who go on to develop cocaine addiction in human populations (i.e., 20%). This large discrepancy complicates the extrapolation between mice and humans but is ignored by K2. Would it imply that nicotine-exposed mice are more vulnerable to cocaine addiction than cigarette smokers? Or, more likely, would it indicate that nicotine-induced sensitization to cocaine per se should not be confused with cocaine addiction?

Research on cross-sensitization between different drugs, including nicotine and cocaine, in rats and mice were very popular among addiction researchers in the 80s and 90s. However, because drug sensitization is clearly not drug addiction, these studies have since been replaced by other, more valid animal models of drug addiction. This research is entirely ignored by K2. Thus, it remains to be demonstrated whether and to what extent initial nicotine self-administration in mice or rats can increase the proportion of individuals that develop more relevant behavioral features of cocaine addiction. Thus, we are back to the initial “scientific question of the role of nicotine as a gateway drug” to other drug addictions.

It is perhaps the privilege of a Nobel laureate (Eric Kandel is one of the recipients of the 2000 Nobel Prize in Physiology or Medicine) to write on any subject matter by ignoring the relevant work of most other prize-free scientists. Ironically, however, a laureate is awarded this prize precisely because many other scientists have recognized the importance and relevance of his/her own previous work.

Reading this Shattuck lecture, one is reminded of the novel “Pierre Menard, author of The Quixote” by Jorge Luis Borges. This novel illustrates how knowing the author of a text can dramatically impact our reading and interpretation of it. So I propose the following test for the interested reader. Please try to read this article as if it had not been co-authored by Eric Kandel. I bet that you will be surprised by how little this article contributes to the scientific question that it seeks to address.

On 2014 Sep 07, Jonathan Eisen commented:

I wrote a blog post about this article for the microbiology of the Built Environment network blog. See Nice summary of the Earth Microbiome Project by Gilbert et al..

Also I then asked the 1st author pif the paper to discuss the history behind the paper which he did in this blog post: Behind the scenes at the Earth Microbiome Project.

On 2014 Nov 24, Guillaume Filion commented:

This article is one of the "CISCOM meta-analyses", which are very similar papers written by different authors. For more information about the CISCOM meta-analyses, check the blog post "A flurry of copycast on PubMed" at the following link http://blog.thegrandlocus.com/2014/10/a-flurry-of-copycats-on-pubmed

On 2015 Feb 27, Geriatric Medicine Journal Club commented:

This interesting randomized trial of melatonin to prevent incident delirium following hip fracture was discussed at the October 2014 Geriatric Medicine Journal Club (follow #GeriMedJC on Twitter). The full transcript of the discussion can be found here: http://gerimedjc.blogspot.com/2014/11/gerimedjc-october-31-2014.html?spref=tw Highlights of the discussion include the disappointment in the negative result but that this may not be the end of the melatonin and delirium story.

On 2014 Sep 20, Ryan Radecki commented:

Post-Publication Peer Review:

"Patient Satisfaction: It’s Door-to-Room Times (Duh)"

As customer satisfaction becomes rapidly enshrined as our reimbursement overlord, we are all eager to improve our satisfaction scores. And, by scores, I mean: Press Ganey.

So, as with all studies attempting to describe patient satisfaction, we unfortunately depend on the validity of the proprietary Press Ganey measurement instrument....

http://www.emlitofnote.com/2014/09/patient-satisfaction-its-door-to-room.html

On 2015 Nov 16, Raphael Stricker commented:

Response to the Study of Fallon et al.

Jyotsna S. Shah PhD, Nick S. Harris PhD

IGeneX Reference Laboratory, Palo Alto, CA

November 15, 2015

The analysis of Lyme disease serologic testing by Fallon et al. [1] reported that “Specialty Lab B” had an overall Borrelia burgdorferi test specificity of 42.5%. We therefore asked Dr. Fallon to send us the key for the study samples that were tested by “Specialty Lab B”, and we reviewed the data for all positive and negative samples.

In the study by Fallon et al., it is notable that two-tier testing endorsed by the Centers for Disease Control and Prevention (CDC) had a sensitivity of only 48.6% for patients with persistent symptoms following Lyme disease treatment, similar to the low sensitivity described in the medical literature [2]. In contrast, if both the IgM and IgG Western blot (WB) results are considered positive, 33 of 37 patients (89%) with persistent Lyme disease symptoms were positive by “Specialty Lab B” interpretation.

“Specialty Lab B” includes bands 31 kDa (Osp A) and 34 kDa (Osp B) in its interpretation of WB results [3]. Antibodies to these proteins are present later in the disease [4] as well as in patients vaccinated with the now-defunct Osp A vaccine. In addition, “Specialty Lab B” uses two strains of B. burgdorferi (B31 and 297) to make WB strips. In strain 297, the 39 kDa antigen is well expressed [5], whereas in strain B31 expression of the 39 kDa antigen can vary between 4-50% [4, 6]. These factors may explain the higher WB sensitivity of “Specialty Lab B”.

According to the revised interpretive criteria of “Specialty Lab B”, a WB is considered positive if two of the following six bands are reactive with patient serum: 23-25 kDa (Osp C), 31 kDa (Osp A), 34 kDa (Osp B), 39 kDa (BmpA), 41 kDa (Flagellin) and 83-93 kDa. However at position 31 kDa on the WB, a non-specific protein co-migrates with Osp A [3]. Therefore both the IgM and IgG WBs are reported as indeterminate if only bands 31 kDa and 41 kDa are present. Likewise, it is known that some viral antibodies cross-react with the 83-93 kDa B. burgorferi antigen. Therefore the IgM WB is reported as negative if only bands 41 kDa and 83-93 kDa are present, and an indeterminate IgM WB is reported if only bands 31 kDa and 83-93 kDa are present. Thus “Specialty Lab B” offers the option to retest using a recombinant Osp A antigen WB to clarify whether a positive band at 31kDa indeed represents reactivity to B. burgdorferi or not.

“Specialty Lab B” reported a significantly higher number of “healthy” controls (23/40) as positive compared to other labs. In contrast, only one “healthy” control was positive by two-tier test criteria. Of the 23 positive “healthy” controls, three had insufficient samples for retesting and therefore were excluded from further analysis. Three control samples with bands 41 kDa and 83-93 kDa on IgM WB were considered negative. The remaining 17 samples were tested with recombinant antigen WBs. Thirteen, including two positive by CDC two-tier criteria, had B. burgdorferi-specific antibodies. Two sera with a band at 31 kDa on WB were considered negative as they did not test positive on the Osp A recombinant antigen WB. Therefore 13/37 healthy controls had B. burgdorferi specific antibodies, and only two had a false-positive WB. Thus the performance of the WB by “Specialty Lab B”, when using revised interpretation criteria and recombinant antigen WB, demonstrates a specificity of 91.7%.

According to the study by Fallon et al. [1], the sensitivity of Lyme disease antibody testing was 48.6% in the best laboratories using CDC two-tier criteria. In an effort to improve that sensitivity, “Specialty Lab B” uses additional band criteria to raise its combined IgM and IgG WB sensitivity to 89% [3]. Considering the significant underdiagnosis of Lyme disease based on poor two-tier test sensitivity, it is important to improve this sensitivity so that clinical cases of Lyme disease will not be missed.

Lyme serology continues to be problematic and we continue to look for ways to improve sensitivity and specificity. In particular, the specificity of the tests is problematic because people living or traveling to endemic areas can have antibodies without disease [7], and antibodies of other diseases can cross-react with B. burgdorferi antigens. Therefore analysis of clinical history and symptoms is essential for the accurate diagnosis of Lyme disease. We agree with the conclusions drawn by Fallon et al. [1] that interlaboratory variability is considerable and remains a problem in Lyme disease testing.

References

1.Fallon BA, Pavlicova M, Coffino SW, Brenner C. A comparison of Lyme disease serologic test results from four laboratories in patients with persistent symptoms after antibiotic treatment. Clin Infect Dis. 2014; 59:1705-10.

2.Stricker RB, Johnson L. Lyme disease diagnosis and treatment: Lessons from the AIDS epidemic. Minerva Med 2010;101:419-25.

3.Shah JS, Du Cruz I, Narciso W, Lo W, Harris NS. Improved sensitivity of Lyme disease Western blots prepared with a mixture of Borrelia burgdorferi strains 297 and B31. Chronic Dis Int. 2014;1:7.

4.Ma B, Christen B, Leung D, Vigo-Pelfrey C. Serodiagnosis of Lyme borreliosis by Western immunoblot: reactivity of various significant antibodies against Borrelia burgdorferi. J Clin Microbiol.1992; 30: 370–376.

5.Engstrom, SM, Shoop E, Johnson RC. Immunoblot interpretation criteria for serodiagnosis of early Lyme disease. J Clin Microbiol. 1995; 33: 419–427.

6.Aguero-Rosenfeld ME, Nowakowski J, Bittker S, Cooper D, Nadelman RB, et al. Evolution of the serologic response to Borrelia burgdorferi in treated patients with culture-confirmed erythema migrans. J Clin Microbiol. 1996; 34: 1–9.

7.Steere AC, Sikand VK, Schoen RT, Nowakowski J. Asymptomatic infection with Borrelia burgdorferi. Clin Infect Dis. 2003; 37: 528–532.

Disclosure: RBS is a member of the International Lyme and Associated Diseases Society (ILADS) and a director of LymeDisease.org. He has no financial or other conflicts to declare. JSS and NSH are members of ILADS, and they have financial interests in IGeneX Reference Laboratory.

On 2015 Apr 24, Robert Knight commented:

The morpholino used is designed to the start ATG and has been described previously by Cornell and Eisen (2002) and by Andermann, Ungos and Raible (2002). Titration of this morpholino was shown to lead to a loss of RB neurons and progressively trigeminal neurons, lateral line nerve and dorsal root ganglia. Crucially, this also caused a reduction or loss of neuroD expression and did not affect development of primary motor neurons, revealing a specific affect on sensory neurons. We validated our use of this morpholino by quantifying the effects on RB neuron development relative to morpholino dose and showed that despite a near total loss of RB neurons in an embryos, there was no affect on the number of neurons in the mesencephalic trigeminal nucleus (MTN) or in the nucleus of the tract of the posterior commissure (nTPC). We further showed that we could not detect ngn1 expression in either MTN or nTPC neurons, supporting the results of the morpholino knockdowns. We did not examine trigeminal ganglia neurons, therefore cannot comment on whether these were also affected by loss of ngn1 function as previously described.

On 2015 Apr 19, Sandra Rieger commented:

Knocking down ngn1 with a morpholino does not always lead to 100% knockdown of both trigeminal and RB neurons. It largely depends on the morpholino concentration and the type of morpholino used. Therefore, the statement that ngn1 is not required for trigeminal neuron development is not convincing, especially since crucial controls for the functionality of the morpholino injections are missing.

On 2015 May 04, Dorothy V M Bishop commented:

This is a very useful study. Thanks. Will be sharing it with colleagues.

Here in the UK we have been concerned with similar issues. We did some surveys using focus groups for those with trisomy X and XYY, and similar topics cropped up.

We have not written this up formally, but we used the information as background when developing materials for parents and for children to help those deciding whether and how to disclose a diagnosis. These are freely available for download here:

http://figshare.com/articles/Booklets_for_parents_and_children_XYY_and_Trisomy_X/1203560

On 2014 Sep 20, Ryan Radecki commented:

Post-publication commentary:

"Appropriate Resource Utilization Can Be Taught!"

At least, that is the implication of this paper – and even though it’s probably not the most reliable demonstration of such an effect, its observations are likely valid.

This is a very convoluted study design aiming to comment upon whether residents trained in “conservative” practice environments differed from residents trained in “aggressive” practice environments. However, “conservative” and “aggressive” were defined by utilizing a Medicare database to calculate the “End-of-Life Visit Index”. Residents trained in a region where elderly patients received greater frequency of inpatient and outpatient care at the end-of-life were judged to have trained in an “aggressive” environment....

http://www.emlitofnote.com/2014/09/appropriate-resource-utilization-can-be.html

On 2014 Sep 08, Jean-Jacques Letesson commented:

THE COMPLETE TITLE IS

Replication of Brucella abortus and Brucella melitensis in fibroblasts does not require Atg5-dependent macroautophagy

On 2014 Sep 03, David Keller commented:

Why physicians should intensify, not abandon, PSA screening

The recommendation to stop screening for prostate cancer is incorrect and will lead to a large increase in preventable cancer deaths. This monumental error was caused by the fallacy that the results of randomized, controlled trials are superior evidence to epidemiological observation, no matter how badly the trials were designed, no matter how contaminated the trial data, and without regard to the effects of intention-to-treat analysis on diminishing the measured benefits of screening. The 16 physicians and allied healthcare professionals on the USPSTF (United States Preventative Services Task Force) all hold positions of high esteem in primary-care specialties, and are expected to volunteer an average of about 4 hours of service per week. Incredibly, no urologists were enlisted to provide their crucial perspectives on PSA screening. The "D"-level recommendation against PSA screening was based on 2 poorly-designed and badly-executed clinical trials, which both generated highly corrupted data.

First of all, one must keep in mind the important epidemiological observation that, since the introduction of widespread PSA screening in the early 1990’s, there has been a 40% decrease in prostate cancer deaths. While even such powerful observational data cannot prove cause-and-effect, it also cannot be overturned by questionable results from flawed randomized trials. The two trials used by USPSTF do not withstand even casual scrutiny.

Both of the large randomized PSA screening trials were done in a manner which ignores the essential biological behavior of cancer cells, which is to grow and propagate faster than healthy cells. Both trials set a fixed threshold PSA level for biopsy (such as 4.0 ng/mL). Consider two men: the first man has a PSA of 3.9 in year 1 and a PSA of 4.1 in year 2. The second man has a PSA of 0.5 in year 1 and a PSA of 3.5 in year 2. Which man should have his prostate biopsied? Any rational physician would biopsy the second man, whose PSA rose by 700%, rather than the first man, whose PSA rose only about 5%. Yet, a study with a fixed biopsy threshold trigger of 4.0 would biopsy only the first man, who clearly is much less likely to have prostate cancer. By not including the rate of increase of PSA in the biopsy decision, both trials ignored information in the PSA which has the strongest correlation with the presence of prostate cancer.

In the U.S.-based PLCO trial, 52% of the men in the control group had off-protocol PSA screening by their private physicians, while only 86% of men in the screening group adhered to the full screening protocol. The men in the PLCO control group had an average of 2.7 PSA screening tests done off-protocol over the 6-year course of study (1). The purpose of PLCO was to compare outcomes in PSA-screened men versus non-screened men, which simply did not occur. The results of PLCO were distorted beyond recognition by mandatory intention-to-treat analysis, which assigned outcomes to each subject's original randomization group, regardless of what they did during the study. The majority of "control" subjects who actually received PSA screening by their private doctors were counted as unscreened, thus diminishing the apparent benefit of PSA screening.

The Europe-based ERSPC study, which had lower rates of protocol violations, produced data showing a significant decrease in prostate cancer mortality due to PSA screening. An ERSPC sub-study done in the city of Goteborg had a particularly low rate of off-protocol PSA testing, and showed correspondingly higher benefits in mortality reduction. The number needed to screen (NNS) to prevent one prostate cancer death at 14 years of follow-up was 293 in Göteborg, despite the fact that 24% of the men invited to be screened never showed up but were included in the intention-to-treat analysis, thus diminishing the measured benefit of screening (2).

Another flaw in the ERSPC study was the long interval between sequential PSA tests, which averaged about 4 years (3). Such a long interval of time between samples can allow the most aggressive, fast-growing cancers to evade detection while still confined to the prostate and curable. Men over 50 should have their PSA checked at least once a year for this reason, more often if fluctuations occur, which can be averaged to produce a better estimate of the baseline PSA, deviations from the baseline, and the rate of increase of PSA. The noisier a signal, the more data samples are required to characterize it accurately.

Prostate cancer kills more men every year than any other cancer except lung cancer. It is a serious threat to all men over age 50 with a life expectancy of greater than 10 years. We know that it is curable when confined to the prostate. More PSA testing, not less, is called for. Improved diagnostic techniques, such as high-resolution prostate MRI imaging, and numerical filtering of PSA values, are being developed. One final randomized trial, which includes all of these improvements, must be conducted to obtain a fair assessment of PSA screening. The problem will be to find enough men willing to forgo screening to fill the control group.

In general, a man whose PSA is 0.1 has little to fear from prostate cancer, and a man whose PSA is 100 has much to fear from prostate cancer. The problem is to clearly and accurately determine how to proceed within those 3 orders of magnitude of data. The art of medicine is to act in the best interests of the patient when the scientific facts are not perfectly understood. Clinical common sense and scientific intuition demand that we measure and carefully consider the PSA levels of men over the age of 50, pending the results of further trials. Watchful waiting is always an option, which at least provides prognostic awareness.

The results of poorly-designed and poorly-executed controlled clinical trials give the mere illusion of high-quality data. Their results should not be relied upon as a basis for treatment decisions when they contradict powerful epidemiological data and our understanding of the biological basis of the disease.

References

1: Gulati R, Tsodikov A, Wever EM, Mariotto AB, Heijnsdijk EA, Katcher J, de Koning HJ, Etzioni R. The impact of PLCO control arm contamination on perceived PSA screening efficacy. Cancer Causes Control. 2012 Jun;23(6):827-35. doi: 10.1007/s10552-012-9951-8. Epub 2012 Apr 10. PubMed PMID: 22488488; PubMed Central PMCID: PMC3556907.

2: Hugosson J, Carlsson S, Aus G, Bergdahl S, Khatami A, Lodding P, Pihl CG, Stranne J, Holmberg E, Lilja H. Mortality results from the Göteborg randomised population-based prostate-cancer screening trial. Lancet Oncol. 2010 Aug;11(8):725-32. doi: 10.1016/S1470-2045(10)70146-7. Epub 2010 Jul 2. PubMed PMID: 20598634; PubMed Central PMCID: PMC4089887.

3: Schröder FH, Hugosson J, Roobol MJ, Tammela TL, Ciatto S, Nelen V, Kwiatkowski M, Lujan M, Lilja H, Zappa M, Denis LJ, Recker F, Berenguer A, Määttänen L, Bangma CH, Aus G, Villers A, Rebillard X, van der Kwast T, Blijenberg BG, Moss SM, de Koning HJ, Auvinen A; ERSPC Investigators. Screening and prostate-cancer mortality in a randomized European study. N Engl J Med. 2009 Mar 26;360(13):1320-8. doi: 10.1056/NEJMoa0810084. Epub 2009 Mar 18. PubMed PMID: 19297566.

On 2016 Feb 18, Kristina Hanspers commented:

The pathway in the graphical abstract for this paper (at CellPress) is available in the "Open Access Publication" collection at WikiPathways: http://www.wikipathways.org/index.php/Pathway:WP2942. This pathway can be downloaded for use in network analysis tools such as Cytoscape and PathVisio.

On 2014 Oct 16, Gaetano Santulli commented:

This study strongly endorses the hypothesis that counteracting neurohormonal hyperactivation represents a cornerstone for the treatment of heart failure. Remarkably, the Authors did not provide any data on the smoking status of the patients nor on catecholamine levels, which I believe would contribute compelling pathophysiological insights about the modulation of autonomic nervous system by neprilysin/angiotensin receptor inhibition. Indeed, cigarette smoking has been shown to increase catecholamine release, generating important effects on cardiovascular regulation essentially via sympathetic nervous system activation (1). Supporting this view, when examining the diabetic population (one third of the enrolled patients) no significant difference was found in the specific outcome ‘death from cardiovascular causes’. This finding could be at least in part ascribable to the autonomic neuropathy and neurovascular dysfunction described in diabetes mellitus (2). In this sense, an analysis that takes into account the duration of diabetes and therapy [i.e. insulin vs oral hypoglycemics (3)] in this population would also be interesting to the Reader.

References 1. Grassi G, Seravalle G, Calhoun DA, et al. Mechanisms responsible for sympathetic activation by cigarette smoking in humans. Circulation 1994;90:248-53. 2. Vinik AI, Maser RE, Mitchell BD, Freeman R. Diabetic autonomic neuropathy. Diabetes care 2003;26:1553-79. 3. Sardu C, Marfella R, Santulli G. Impact of diabetes mellitus on the clinical response to cardiac resynchronization therapy in elderly people. J Cardiovasc Transl Res. 2014 Apr;7(3):362-8.

Gaetano Santulli, MD, PhD Columbia University Medical Center, New York, NY, USA

On 2014 Sep 06, Ryan Radecki commented:

Post-publication commentary:

"Early P2Y12 Antagonists Just Don't Seem Useful"

When undergoing an early invasive strategy for myocardial infarction, the guidelines and trials typically support dual platelet inhibition. Most commonly, this regimen consists of aspirin and clopidogrel. However, the P2Y12 receptor antagonists ticagrelor and prasugrel have been promoted as options due to incremental increased platelet inhibition over clopidogrel. The theoretical benefits of early dual platelet inhibition include spontaneous lysis and prevention of re-thrombosis, as well as decreased early in-stent thrombosis. Unfortunately, the ACCOAST trial demonstrated early prasugrel was associated only with increased bleeding and no associated cardiovascular endpoint benefits.

Now we have ATLANTIC, with a similar treatment strategy, utilizing ticagrelor.

Which is also negative.

http://www.emlitofnote.com/2014/09/early-p2y12-antagonists-just-dont-seem.html

On 2014 Dec 02, Ross Mounce commented:

If you're not blind or visually-impaired you can read the full-text of this article here: http://www.readcube.com/articles/10.1038/nature13662?shared_access_token=BQL9myivU-3cmO5axZ1579RgN0jAjWel9jnR3ZoTv0MCIVtOxq5EYdtRL2Syk4FOxGT86my4na8qJhVHBU_GknelpJrXCVTH-E1scqbFF0FHa1FEzOAeg-mpOrmRw-gGayncCxNzwedC-AZp2TK4n31GchuII7u1RFQnJkTm2LSie8cRP9zMg1J9pxyOY-hovvPkWzEgSiLCugb1WJ-PyFpPpDJNKWGeZh9_IhUZXAU=

On 2015 Jan 05, Peter Gøtzsche commented:

Gastrointestinal cancers in the The SOLID-TIMI 52 trial

The SOLID-TIMI 52 trial assessed whether darapladib had any effect on major coronary events compared with placebo (1). A small table of adverse events showed that people on active drug had diarrhea twice as often as those on placebo whereas there was no difference in “any adjudicated gastrointestinal cancer.” Readers may wonder why gastrointestinal cancers were listed in such a small table, as diarrhea doesn’t usually lead to cancer. The word cancer was only mentioned in the table, with no comment in the text as to why. I think the reason for listing it in the table is that studies in rats and mice have shown that they developed jejunal adenocarcinomas – a very rare cancer - after exposure to darapladib (2). This should have been mentioned in the paper. I also believe that the investigators and the sponsor, GlaxoSmithKline, have an ethical duty to follow-up the 13,026 participants in the trial in the coming years to find out whether they develop jejunal cancers. A patient who stopped his participation in the trial because of diarrhea felt he had been used as a human guinea pig, as these animal studies had not been carried out when he was asked to enroll in the trial (3).

Conflicts of interest: none.

1. O'Donoghue ML, Braunwald E, White HD, et al. Effect of darapladib on major coronary events after an acute coronary syndrome: the SOLID-TIMI 52 randomized clinical trial. JAMA;312:1006-15.

2. GlaxoSmithKline. Toxicology section from the Investigator's Brochure on darapladip (24 pages). RM2003/00513/06. Undated.

3. Gøtzsche PC. “Human guinea pig” asks for animal studies. BMJ 2014;349:g6714.

On 2014 Sep 02, Robert Badgett commented:

This trial is included in a living meta-analysis at https://github.com/openMetaAnalysis/Colchicine-for-Pericarditis.

On 2017 Jul 14, Randi Pechacek commented:

Marcus Leung, first author of this paper, gave some background on a microBEnet blog post.

On 2015 Apr 08, DOMINIQUE BERGMANN commented:

An anonymous peer noted that panels 6D-E are identical. This is an error on our part and we have identified the source of the error and the raw data that corresponds to the genotypes pictured. Table 2 contains quantified data related to the information in panel 6D-E and is correct. All conclusions in the paper are based on the data in Table 2. We are working to correct this error with the journal.

On 2014 Sep 02, Haoquan Wu commented:

Around 7 years ago, we have shown an extensive degree of heterogeneity at the end of mature miRNA, which can affect detection by real-time PCR employing stem-loop primer mediated RT-PCR.

http://www.plosone.org/article/info:doi/10.1371/journal.pone.0001020

On 2015 Feb 06, Pavel Baranov commented:

This paper provides an invaluable dataset of context dependent translation initiation start (TIS) efficiency. While the optimal (Kozak) TIS context has been determined a long time ago, estimating the power of individual TIS based on the contexts was usually qualitative, at best it was based on position weight matrices where the weight of each individual nucleotide was taken into account independently of other nucleotides. By systematically randomizing the context surrounding AUG codons Noderer et al measured TIS efficiencies of all 11-mers containing AUG codons. The dataset is available in Supplementary Table 2.

On 2014 Oct 03, Friedrich Thinnes commented:

To finalize three-dimensional VDAC structure: Focus on Native VDAC will be indispensable

This study, from my point of view, marks a great moment of VDAC research:

1) It points another time to the relevance of cell membrane-standing VDAC-1 for the pathogenesis of Alzheimer´s Disease via apoptosis.

2) It gives strong support to the cell membrane-expression, more precisely plasmalemmal lipid raft-integration of vertebrate VDAC-1. Furthermore, the data concerning the posphorylation of VDAC-1 in correlation to regulation of channel opening/closing argue in favor of its involvement in cell volume regulation and thus apoptosis. They are in line with much evidence indicating that plasmalemmal VDAC-1 forms the channel part of a volume regulated anion channel complex (VRAC/VSOAC).

3) From here, VDAC-1 in the plasmalemma must be “fully closed” = collapsed = N-terminus accessible outside the barrel = closed for anions and cations, and there is evidence that the N-terminal part of native VDAC-1 can be reached by antibodies even in detergent solutions (Benz et al., 1992; Thinnes and Burckhardt, 2012).

4) Applying canonical incorporation into black membranes, detergent-solubilized native phosphorylated mammalian VDAC-1 as well as recombinant channel preparations from E. coli inclusion bodies show only “open” = anion-selective = N-terminal stretch inside the barrel and “closed” = cation-selective = semi-collapsed = N-terminal stretch inside the barrel channel phenotypes (Teijido et al., 2012). “Fully closed” = collapsed = N-terminus accessible outside the barrel VDAC-1 states thus cannot be studied by this approach. The same holds true for more recent crystallization-based approaches. However, upcoming laser-based approaches may work just on native VDAC-1 in solutions; thus improvements to get detergent solubilized native VDAC preparation may pay to keep on the schedule.

--Benz R, Maier E, Thinnes FP, Götz H, Hilschmann N (1992) Studies on human porin. VII. The channel properties of the human B-lymphocyte membrane-derived “Porin 31HL” are similar to those of mitochondrial porins, Biol. Chem. Hoppe Seyler. 373: 295–303. --Thinnes FP, Burckhardt G (2012) On a fully closed state of native human type-1 VDAC enriched in Nonidet P40. Mol Genet Metab 107: 632-633. --Teijido O, Ujwal R, Hillerdal CO, Kullman L, Rostovtseva TK, Abramson J (2012) Affixing N-terminal α-helix to the wall of the voltage-dependent anion channel does not prevent its voltage gating. J Biol Chem 287: 11437-11445.

On 2015 Aug 14, Oliver T Mytton commented:

We note the comments for Kaiser et al. We are in the process of compiling a full response. When this is ready we will post it on the BMC Public Health website, linked directly to the article.

Oliver Mytton Honorary Specialty Registrar, Centre for Diet and Activity Research (CEDAR), MRC Epidemiology Unit, Cambridge.

On 2015 Jul 08, Kathryn Kaiser commented:

Having recently published a paper on the same topic [1], we read this paper (published August, 2014) by Mytton and colleagues (the authors) with great interest. We identified several important issues that vitiate confidence in the paper by the authors [2].

Errors of calculation, stated protocol violations, and documentation:

Our first concern pertains to differences in calculations in a study we both included, as well as the irreproducibility of the standard errors (SEs) reported in the Figure 2a forest plot of effects on body weight in [2]. First, we believe there is a calculation error in how the authors derived the effect size and variance in [2] for Whybrow, 2006 [3], which we both included. For clarity, we note the correct citation for the Whybrow paper is 2006, though it is sometimes cited as 2007 in [2]. Per [4], in order to reduce the potential for the over influence of studies where there are two experimental groups (300 grams per day and 600 grams per day in the case of Whybrow, 2006 [3]) compared to only a single control group, the control number (in this case, n=14) should be divided in half and rounded if necessary to the nearest integer for meta-analysis. We both calculated the same mean differences (ours reported as standardized mean differences), but substantially different confidence intervals. In the original report of outcomes [3], the effects on body weight for both treatment groups were not significant, whereas in Figure 2a [2], the confidence intervals are represented as showing a statistically significant effect for both treatment groups.

After further examination of standard errors for the other studies in included by the authors, and being unable to recreate several of the values reported in the paper [2], we recomputed the meta-analytic statistics from the source papers after making corrections to these values (see list below for all corrected estimates). For the overall analysis, this resulted in a smaller effect [-0.54 (-1.05,-0.03)] than reported in [2]. In a more appropriate analysis and using corrected standard errors, we removed an outlier [5] that the authors had identified in the “Comparison with other studies” section on page 9 of [2]. This study had a drop-out rate of over 52 percent, did not conduct an intent-to-treat analysis, and included only 9 subjects in the final analysis. When removing this paper as suggested in [2] (but not done in Figure 2), the overall effect is -0.31 (-0.70, 0.07), p=0.11, with a drop in heterogeneity from 72 to 53%.

Recalculated forest plot of included studies, excluding [5] are as follows (Mean Difference in Kg, 95% Confidence Interval, Standard Error): Smith-Warner 2000: (-0.09, -0.40 to 0.22, 0.16); Whybrow 2006 (600g group): (-0.62, -1.52 to 0.28, 0.46); Whybrow 2006 (300g group): (-0.77, -1.73 to 0.19, 0.49); Basu 2010: (-0.90, -1.88 to 0.08, 0.50); Peterson 2011: (0.40, -0.05 to 0.85, 0.23); Dow 2012: (-0.50, -1.30 to 0.30, 0.41); Christensen 2013: (-0.90, -2.17 to 0.37, 0.65).

Concerning study inclusion based on the stated protocol, the authors were not consistent in the application of their criteria. The authors opted to include one study of individuals with newly diagnosed diabetes [6], despite their exclusion criterion of studies with “subjects with a medical illness that was liable to lead to weight loss or weight gain.”[2] Excluding only this study also results in non-significant findings [excluding [6]: -0.51 (-1.06, 0.03); excluding both the outlier [5] and [6] results in: -0.27 (-0.67, 0.13)]. In other words, correcting the apparent errors of inclusion and effect size calculations changes the conclusions as stated in the paper [2].

Other points of note:

Although some differences between our papers are the result of simple methodological differences, such as our limitation to longer than eight week studies versus the authors limitation to four weeks, other differences are more substantial. We note that the authors [2] did not use the term “obesity” in their list of keywords, which is an important omission in a review looking at weight outcomes. In addition, the authors included three studies that investigated changes in only a single fruit or vegetable, while we opted to include studies where the intervention was a variety of fruits and vegetables, in order to evaluate the more general public health message that is common today. This difference in inclusion simply represents a nuanced difference between our analyses, but it is important that searches are designed to adequately answer the question at hand. For instance, in applying the stated criteria given by the authors, we found three examples [7-9] of other studies of a single fruit or vegetable that were not included by the authors but appear to meet criteria, calling into question the thoroughness of the literature search.

Lastly, in trying to reproduce the authors work, we noted that the list of included studies in Table 1 includes Singh, 1992 [10] while the final analysis does not. The reviewer comments and author replies posted on line at the BMC Public Health journal website indicate the authors followed the reviewer recommendation to remove the Singh paper from the analysis, yet it remains in the published table and appears to also be counted in the number in the PRISMA diagram, Figure 1. According to the forest plots in Figure 2, 7 studies were included for weight outcomes and 5 studies were included for energy intake outcomes. Figure 1 does not match the included studies in the search results reported.

Conclusion:

It is clear that the report presented by the authors has several concerning errors, is not consistent within itself, and does not support the conclusion the paper states that these interventions, “…result in either a small reduction in body weight or reduced weight gain relative to controls.”[2] We respectfully request that the authors reply to these concerns in order to correct the record.

Kathryn A. Kaiser, Ph.D. Andrew W. Brown, Ph.D. David B. Allison, Ph.D. Office of Energetics, Nutrition and Obesity Research Center, School of Public Health, University of Alabama at Birmingham

References

\1.\ Kaiser KA, 2014 \2.\ Mytton OT, 2014 \3.\ Whybrow S, 2006 \4.\ http://handbook.cochrane.org/ \5.\ Weerts SE, 2011 \6.\ Christensen AS, 2013 \7.\ Lehtonen HM, 2010 \8.\ Thies F, 2012 \9.\ Vafa MR, 2011 \10.\ Singh RB, 1992

On 2014 Oct 21, Paula Rahal commented:

Acknowledgements

We thank Dr. Daniel Seabra and Dr. Eliney Ferreira Faria for their collaboration in collecting the samples and Dr. Ligia Maria Kerr for pathological review. This work was supported by São Paulo State Research Foundation - FAPESP (2010/01661-2) and CNPq.

On 2015 Nov 13, Lydia Maniatis commented:

The theory and method of this study simply doesn't hold up to scrutiny. Very serious problems include:

Methods

1. The authors claim to be motivated by previous findings of “large individual differences in lightness constancy with respect to changes in surface slant...” Yet they have only two observers per condition, and one of the observers did not complete all of the replications. Large individual differences are an indication that stimuli are ambiguous to observers, who consequently have to make quasi-guesses. In this type of situation there tends to be both intra- and inter-individual variability. Here, the small number of observers does not make either form of variability detectable.

2. They describe having subjected observers to an “induction procedure (Allred & Brainard, 2009; Doerschner, Boyaci & Maloney, 2004) to familiarize them with the difference between surface reflectance...and the amount of light reflected from a surface [luminance]...” Surface reflectance is not perceived, but inferred (the perceptual correlate is lightness), and luminance has no perceptual correlate. The relative luminances of surfaces in context and their geometrical relationships conspire to produce local impressions of both lightness and apparent illumination. You cannot train observers to perceive luminance, which, again has no perceptual correlate (it does not correspond to white, gray, black; virtually any luminance can take on any of these perceptual values).

An example can help make clear why such a “induction procedure” is theoretically and methodologically hollow. This is the video version of the Adelson checkerboard illusion, in which we see a square being moved from apparent shadow to apparent plain view, and changing apparent color. We can watch the video over and over again without thereby learning to see the square as constant in both its luminance and its reflectance.

1. Asking subjects to rate the similarity in lightness of surfaces on a scale of 1 to 30 seems quite unrealistic.

2. The mathematical acrobatics necessitated by the general shoddiness and ambiguity of the methods are moot.

Theory

The authors state that “Changing the orientation of an object's surface with respect to a directional light source affects the luminance of the reflected light [they mean "the luminance of the surface"] even when the light emitted by the illumination sources is held constant.” That's true.

However, the next statement is incomplete and misleading: “The visual system can stabilize lightness with respect to such geometrical effects...” This statement needs to be highly qualified. The visual system can achieve such “lightness constancy” (requiring disambiguation of reflectance and illumination) only if the image conditions support it. An isolated surface (as that used by the authors), whether fronto-parallel or slanted, will not offer much or any such support. Thus, it should not be surprising that the authors found "essentially no lightness constancy with respect to changing the slant of a test surface..." and that "the dissimilarity data were well-accounted for by a one-dimensional perceptual representation..."

While, again, this outcome was highly predictable, they authors are mystified by it. Noting that their results differ from those of Logvinenko and Maloney (2006), they confess that "We do not know the reason for this difference." But as they note, in that study the illumination differences were not only actual, they were (more importantly) APPARENT (a fact that can be ascertained on the basis of observation), because the setting was less spare and more informative. This is something that should have been considered before, not after data collection.

But lightness facts and relevant literature are not of interest to these authors, who dispense with the matter early on, saying: “This form of lightness constancy [the slant surface form?] is not well-understood.” And on to data collection.

On 2014 Oct 06, Leonid Teytelman commented:

Dear Authors,

We have published an analysis in S. cerevisiae, showing expression-dependent artifactual ChIP enrichment at highly expressed loci (Teytelman L, 2013 "Highly expressed loci are vulnerable to misleading ChIP localization of multiple unrelated proteins"). As you know, our finding raises the question of whether HOT regions may also be influenced by the same artifact.

It is great that you have considered our work and have thoughtfully responded to our analysis. Below, I would like to continue this discussion in an effort to better understand the artifact, its causes, and whether it may be contributing to the enrichment at the HOT loci.

1. “we have demonstrated that there is no correlation between our non-specific binding controls (IgG) and our measured transcription factor occupancy;”

Considering our results with no-tag control experiments, an IgG may fail to control for the artifact. It would be great if you could instead perform a GFP ChIP-Seq, similarly to what we have done in yeast.

2. The regions determined in ref. 41 have very low enrichment (twofold or less) of non-specific immunoprecipation in anti-GFP antibody controls over input DNA evaluated using a non-standard sliding-window approach. Importantly, immunoprecipitation/input ratios at this level are typically not considered enriched for binding in modern peak-calling procedures. For example, the median immunoprecipitation/input ratio for our human RNA Pol II experiments is 20-fold, and only 0.033% of human RNA Pol II peaks contain an immunoprecipitation/input ratio ≤ twofold.

The mean is low, but in both anti-GFP experiments, there are loci with 3-5x enrichment (figure 4D). Most importantly, while the anti-GFP enrichment at the hyper-ChIPable loci is low, please note that the level of enrichment is variable from protein to protein (2-5X for Sir proteins, but often >10X for Cse4).

3. Thus, it is essential to note that the term ‘hyper-ChIPable’, coined by ref. 41, is quite misleading, as a correctly performed ChIP experiment will evaluate statistically enriched regions, with higher immunoprecipitation/input ratios. The so-called hyper-ChIPable regions in ref. 41 are not binding regions as determined under ChIP-seq best practices. Hence, when statistical peak-calling was performed in ref. 41 (using the established MACS peak-caller) to evaluate signals only at significantly enriched regions (Supplementary Table 1) only 17 (<7.5%) of the 238 claimed ‘hyper-ChIPable’ regions were called significant by all three Sir proteins. In fact, 68% of their 238 regions do not contain a binding site for any Sir protein as determined by MACS, despite even very liberal settings used (P < 10−5, no fold enrichment cut-off). Thus, the data of ref. 41 contradict its own major claim that all three Sir proteins showed enrichment at the 238 sites.

By reporting the 238 sites with >2fold enrichment of Sir2, Sir3, and Sir4, we are in fact being extra-demanding in terms of the threshold. We are stringently requiring all three proteins to be enriched above a threshold at the locus. So a target with 5x enrichment of Sir2 and 1.8X enrichment of Sir3 would not pass this cutoff. A typical ChIP study will focus on a single factor at a time. Had we done that, we would have many more artifactual targets for each silencing protein, with many at 5x or higher enrichment. Furthermore, the level of the artifactual signal varies from protein to protein or experiment to experiment. For example, the Cse4 signal at highly-expressed loci can give 10x or higher enrichment.

4. Furthermore, as indicated in Supplementary Table 3 of ref. 41, the Sir2, Sir3 and Sir4 ChIP-seq experiments were performed only once each, which raises the question as to whether enrichment of Sir proteins at the 238 sites is reproducible. More rigorously, even for the remaining 17 genomic loci, their status as hyper-ChIPable is questionable as each region would first have to be established as a reproducible binding site in replicate experiments for each individual Sir protein. If you consider that Sir2, Sir3 and Sir4 ChIP-seq constitutes three replicates of Sir proteins, their data show that most of their claimed sites were not reproducibly enriched.

Most of our artifact-cause analysis focuses on genome-wide data, not on the 238 sites. The 238 Sir-enriched euchromatic loci were a launching point for the analysis, but most of the paper looks comprehensively at the link between expression and ChIP levels. Figures 3, 4, and 5 are all on genome-wide correlations between Pol II/III and ChIP.

As for reproducibility, we see the same peaks, with often 10x enrichment, in Ste12, Cse4, two distinct GFP experiments, and each of the three Sir ChIP-Seq datasets. The same exact loci come up in the Sir3 paper from Oliver Rando’s group (Radman-Livaja M, 2011).

5. In addition to the analytical differences outlined above, other potential sources for the marked differences between our data and the Sir-enriched regions of ref. 41 are deviations from a typical ChIP protocol. In particular, ref. 41 employed a significantly longer cross-link time (1 h as opposed to the typical 10–20 min). This might contribute to formation of large non-specific protein–DNA complexes, which can in turn increase non-specific immunoprecipitation.

Though not discussed in the manuscript, we have in fact performed experiments to investigate if the crosslinking concentration contributed to the misleading signal. We performed ChIP with the 1 hour crosslinking at room temperature at the following formaldehyde concentrations: 0.0625%, .125%, .25%, .5% and 1%, but did not find a proportionate decrease in the hyper ChIPpable signal with the decreasing formaldehyde concentrations. Moreover, the presence of hyper-ChIPability in the Snyder datasets (Cse4, Ste12), ours (Sir2, 3, 4, GFP), and Rando (Sir3) make it clear that the problem is not in some unusual protocol steps in our hands.

We also note that we initially performed the Sir ChIP-Seq experiments because of our interest in the Sir protein biology. Because the Sir proteins do not directly interact with the DNA, we used longer crosslinking times. This is not unique to our work.

In summary, much more work is needed to pinpoint the cause of the artifact and to evaluate whether some or all of the signal at highly expressed genes in many other reported ChIP studies could be artifactual. Much more work is necessary to develop the best controls and corrections for the artifact. However, the artifact we report is not minor and is not a consequence of the methodological details of our manuscript.

Also, please note the following papers, published almost in parallel with ours, on this topic:

Park D, 2013 "Widespread Misinterpretable ChIP-seq Bias in Yeast" (Different analysis methods but the same conclusions in S. cerevisiae, analyzing an entirely different set of factors with ChIP-Seq experiments.)

Kasinathan S, 2014 "High-resolution mapping of transcription factor binding sites on native chromatin" (Questions specificity of standard ChIP in S. cerevisiae and at HOT regions of Drosophila. This work possibly provides a solution to the artifact with a modification of the ChIP technique.)

Also, the following discussion of our work on PubPeer may be useful.

On 2014 Oct 14, Hilda Bastian commented:

Thanks for drawing attention to this interesting article. Dancer SJ, 2009 argued that what's done now in an outbreak is "a veritable blunderbuss approach." Dancer's own cross-over study of enhanced cleaning addressed MRSA, although the study was too small to identify a definite impact on infection (Dancer SJ, 2009).

Environmental strategies were included in a systematic review of measures to reduce the spread of VRE (with a search for evidence up to June 2012)(De Angelis G, 2014). De Angelis found only two studies, concluding that no definite impact on infection had been identified (Hayden MK, 2006; Williams VR, 2009).

This new retrospective study (Everett BR, 2017) seems to be the second looking at the implementation of this particular set of strategies. The first was undertaken by the team that developed the method and run the associated consultancy service (Watson PA, 2012).

Both routinely and during outbreaks, Dancer SJ, 2009 concluded, "there is a lot of work still to do to establish cleaning as an evidence-based science." That still seems to be the case.

On 2014 Oct 10, Paul Watson commented:

This is an excellent article showing a simple way to dramatically lower all hospital acquired infections with single cleaning algorithm from Steiros. The final infection rates are extremely low with immediate cost savings that I have not seen with any other infection control process. As a surgeon and an infection control practitioner, I am especially excited about the zero infection rate for total knee arthroplasties in this paper. This is something that we all strive for and has been achieved by few. Thanks for the article.

On 2014 Sep 30, Ryan Radecki commented:

Post-publication commentary:

"Emergency PCI for STEMI is Dead?"

This somewhat befuddling study tries desperately to create a problem where there probably truly wasn’t – but as soon as the conflict-of-interest disclosures come up, it’s clear why.

This is the 1-year outcomes from STREAM, a prospective, open-label, parallel-group trial enrolling participants with acute STEMI, but unlikely to undergo primary PCI within 1 hour of diagnosis. Participants were then randomized to either still undergo emergency PCI, or to fibrinolysis followed by urgent or emergency rescue PCI. The initial 1-month composite outcome, despite an excess of deaths secondary to intracranial hemorrhage in the fibrinolysis group, did not demonstrate any disadvantage to fibrinolysis with delayed PCI. There was actually a 2% absolute decrease in the composite outcome favoring fibrinolysis – and thus the 1-year follow-up, hoping this small advantage in morbidity would translate into a measurable mortality advantage....

http://www.emlitofnote.com/2014/09/emergency-pci-for-stemi-is-dead.html

On 2014 Sep 10, Vojtech Huser commented:

Data on how often patients use infobutton (when they login to PHR) is a very valuable contribution.

On 2017 May 17, Mihai Paduraru commented:

In relation to this article I published a Critical observation, due to the too many similarities to a randomized controlled trial by Wang et al.: https://doi.org/10.1016/j.ijsu.2017.01.019 (http://www.sciencedirect.com/science/article/pii/S1743919117300201)

On 2017 Jul 14, Randi Pechacek commented:

Nicholas Osborne and Richard Sharpe, co-authors of this paper, wrote a joint guest blog post on microBEnet explaining the interesting background for this paper.

On 2014 Aug 27, Daniel Kripke commented:

Despite much useful material, the report of Owens and colleagues about insufficient sleep was marred by insufficient evidence-based definition of insufficient sleep. “Sufficient sleep” was “defined as ≥8 hours,” but why? Consider that in a Japanese nation-wide sample of adolescents grade 7 through 12, ≥7-<8 hours was associated with the best General Health in a number of grade groups of boys and girls and overall ≥8-<9 hours was not significantly superior in predicting mental health [1]. Consider that in a unique lifetime study of mortality risk, girls sleeping 2 hours less than the average for age 16 had the same mortality as those sleeping the average, whereas mortality was about the same for boys sleeping 1 hour less than the average [2]. More evidence concerning optimal sleep durations for adolescents is needed. Should we recognize that much of the evidence-absent advocacy for children to sleep more has been financed by hypnotics manufacturers, and has been accompanied by a sharp increase in hypnotic prescriptions for children? 1. Kaneita Y, et al. J Clin Psychiatry 2007;68:1426-1435. 2. Duggan KA, et al. Health Psychol 2014, in press.

On 2014 Aug 30, Michelle Lin commented:

This is the link where the multimodal discussion was held: http://www.aliem.com/multiple-mini-interviews-annals-em-resident-perspective-article/

On 2014 Sep 23, Samir Ounzain commented:

Very nice editorial describing this work by Patrick Hofmann and Reinier Boon can be found here;

Hofmann P, Boon RA. Non-coding RNA enhances cardiac development. J Mol Cell Cardiol. 2014 Sep 18. pii: S0022-2828(14)00286-7. doi: 10.1016/j.yjmcc.2014.09.005. [Epub ahead of print] PubMed PMID: 25240640.

On 2015 Mar 18, Airton Stein commented:

This article is not my publication - Airton Tetelbom Stein from Brazil

On 2016 Oct 24, Sean Ekins commented:

Some more recent slides on other social media tools and also using twitter for collaborations in science http://www.slideshare.net/ekinssean/pros-and-cons-of-social-networking-for-scientists