1. Last 7 days
    1. On 2024-03-04 20:57:10, user Jeffrey Ruberti wrote:

      This is a nice piece of work showing collagen dynamics including the fate of endogenous collagen added to the a cell culture system. See the following paper that already demonstrated exogenous collagen incorporation into cell synthesized matrix and for methods to produce an exogeneous labelled collagen that is minimally disruptive to fibril assembly. Siadat, S.M., Silverman, A.A., Susilo, M.E., Paten, J.A., Dimarzio, C.A., Ruberti, J.W., 2022. Development of Fluorescently Labeled, Functional Type I Collagen Molecules. Macromolecular Bioscience 22, 2100144.. https://doi.org/10.1002/mab...

    1. On 2024-03-04 18:23:27, user Bilikere Dwarakanath wrote:

      It is increasingly becoming clear that resistance to Immune Checkpoint Blockade (ICB) can compromise the efficacy of cancer therapy at large. This has compelled the need to understand mechanisms underlying ICB resistance so that effective therapy can be designed to overcome ICB resistance. In this direction, the pioneering work of Dr. Khleif’s lab showing the role of CD38+PD1+CD8 T cells in ICB resistance was a landmark contribution (Ref #17 of this communication). Using elegantly designed pre-clinical studies (with mouse TC-1 and melanoma tumor models) as well as clinical samples (melanoma patients; pre- and post-therapy) they convincingly established the role of CD38 (high)PD1+CD8 T cells in ICB. This submission by Or-Yam Revach et al., (Dr. Jenkin’s Lab at Harvard Medical School) reinforces the importance of CD38+PD1+CD8 T cells in ICB resistance with CD38 as a marker of exhaustion. These two findings are expected to stimulate further efforts on developing approaches to overcome ICB resistance.

    2. On 2024-02-25 15:12:20, user Vivek Verma wrote:

      It is a pleasure to see this publication being submitted where the authors report the role of CD38 in promoting T cell exhaustion. It is so heartwarming to see that the authors were able to recapitulate all our findings reported at (https://pubmed.ncbi.nlm.nih.... In this research, we showed that CD38 expression on PD1+CD8 T cells is a marker of T cell dysfunction, and these CD38+PD1+CD8 T cells are directly associated with ICB resistance. Using various genetic and cell depletion experiments, we demonstrated the causality of these cells in the failure of anti-PD1 therapy in mouse melanoma models. In addition to mouse models, using melanoma patient cohorts treated with anti-PD1 ICB, we have shown that therapy-resistant patients had higher numbers of these cells in their tumors pre- and post-treatment compared to sensitive patients. <br /> This study is also in line with several other reported observations where the role of the CD38-NAD+ axis in T cell exhaustion has been extensively reported (https://pubmed.ncbi.nlm.nih..., https://pubmed.ncbi.nlm.nih.... This study, in conjunction with the prior reports, adds to the growing body of literature highlighting the importance of CD38 as a marker of exhaustion rather than activation.

    3. On 2024-02-24 04:04:41, user Michael B. Atkins wrote:

      Great to see this important work building on my colleague Samir Khleif's discoveries published in Nature Immunology (Ref 17).

    1. On 2024-03-04 17:13:18, user alexander_zlobin wrote:

      Hi!<br /> Please correct Fig.1. These proteases have HID, not HIE. This is a very serious and meaningful distinction. The incorrect tautomer on the scheme undermines the soundness of the study, since it questions the understanding of the enzymology of these enzymes.

    1. On 2024-03-03 18:08:21, user Lenzen Sigurd wrote:

      Dear authors and readers of this bioRxiv preprint,

      The approval of the anti-CD3 antibody by the FDA in November 2022 now enables a “disease modifying” therapy for the first time, which can delay the manifestation of T1DM by two to three years [1].

      And in combination with an anti-TNFα antibody, such a therapy even opens up the prospect of a long-term therapeutic effect with curative potential in the foreseeable future. The successful implementation of such a therapy is based on numerous studies over the last decades, which have shown (not cited in this manuscript) that the proinflammatory cytokine TNFα plays a central role in the destruction of pancreatic beta cells in the pathogenesis of Type 1 Diabetes Mellitus (T1DM) [2, 3].

      The authors Alexandra Coomans de Brachène et al. of the current MS now present results that lead them to hypothesize that the interferons IFNα and especially IFNγ play a prominent role in the destruction of beta cells in T1DM. This is in contrast to the situation in the endocrine pancreas in vivo, both in patients with T1DM and in reliable spontaneous animal models of human T1DM [4]. The gene expression and protein expression of IFNγ is only low in infiltrated pancreatic islets of the human T1DM pancreas [4] and in the infiltrated pancreas of the IDDM rat model of T1DM, which is closest to the human situation [5], while the highly expressed TNFalpha is the pro-inflammatory cytokine that is centrally responsible for the destruction of pancreatic beta cells in the infiltrated T1DM pancreas [2].

      Combination therapy with anti-TCR in rats (ie, anti-CD3 in mice and humans) and anti-TNFα eliminates the infiltration with proinflammatory cytokines [3, 6], which cannot be achieved with a combination therapy with anti-IFNγ [3]. An exclusive reference to an antidiabetic effect in the NOD mouse model of T1DM is inadequate, as the authors do in the current manuscript. A large number of studies in the NOD mouse showed therapeutic success in this mouse model using a wide variety of preventive therapies, but none of these therapies could be successfully transferred to the situation in patients with T1DM [7]. A successful transfer of such a therapeutic concept into an effective translational therapy for patients with T1DM, which enables a return to a normal metabolic state, is therefore not recognizable based on the facts presented, at least not in the foreseeable future. Therefore, IFNγ does not play a central role in the T1DM pathogenesis. Such a concept lacks the necessary experimental basis.

      REFERENCES

      [1]          Herold KC, Gitelman SE, Gottlieb PA, Knecht LA, Raymond R, Ramos EL (2023) Teplizumab: a disease-modifying therapy for type 1 diabetes that preserves beta-cell function. Diabetes Care

      [2]          Jörns A, Arndt T, Meyer zu Vilsendorf A, et al. (2014) Islet infiltration, cytokine expression and beta cell death in the NOD mouse, BB rat, Komeda rat, LEW.1AR1-iddm rat and humans with type 1 diabetes. Diabetologia 57: 512-521

      [3]          Jörns A, Arndt T, Yamada S, et al. (2020) Translation of curative therapy concepts with T cell and cytokine antibody combinations for type 1 diabetes reversal in the IDDM rat. J Mol Med (Berl) 98: 1125-1137

      [4]          Jörns A, Wedekind D, Jähne J, Lenzen S (2020) Pancreas pathology of latent autoimmune diabetes in adults (LADA) in patients and in a LADA rat model compared with type 1 diabetes. Diabetes 69: 624-633

      [5]          Lenzen S, Arndt T, Elsner M, Wedekind D, Jörns A (2020) Rat models of human type 1 diabetes. Methods Mol Biol 2128: 69-85

      [6]          Jörns A, Akin M, Arndt T, et al. (2014) Anti-TCR therapy combined with fingolimod for reversal of diabetic hyperglycemia by beta cell regeneration in the LEW.1AR1-iddm rat model of type 1 diabetes. J Mol Med (Berl) 92: 743-755

      [7]          Lenzen S (2017) Animal models of human type 1 diabetes for evaluating combination therapies and successful translation to the patient with type 1 diabetes. Diabetes Metab Res Rev 33

      Sigurd Lenzen, MD<br /> Professor of Experimental Diabetes Resarch <br /> Institute of Experimental Diabetes Research<br /> Hannover Medical School

    1. On 2024-03-01 15:08:07, user Tianyu Liu wrote:

      Hi, it seems that I cannot reply the problem in the community part, so I will write my response here:

      Hi, thanks for your checking. scGPT v1 should be trained based on 10M cells rather than the 33 M cells (scGPT). We did observe a better performance of scGPT v1, thus we are doubting the contribution of increasing the number of cells for pre-training. In the discussion part, we share our ideas about doing data ablation via online learning for further improvement.

    1. On 2024-03-01 10:20:33, user Carla Perpiñá-Clérigues wrote:

      Published:

      Perpiñá-Clérigues, C., Mellado, S., Galiana-Roselló, C. et al. <br /> Novel insight into the lipid network of plasma extracellular vesicles reveal sex-based differences in the lipidomic profile of alcohol use disorder patients.<br /> Biol Sex Differ 15, 10 (2024). https://doi.org/10.1186/s13...

    1. On 2024-03-01 02:07:13, user Jeff Ellis wrote:

      I think there are several problems with section 1of the results and the legend of Fig 1.In the results section there are two experiments being described. <br /> In the first differentially tagged Pwl2 and Bas1( presumably an effector known from previous work to localise to the BIC) expressed in the same strain is inoculated onto rice rice and Pwl2 and Bas1 are shown to co-localise, which demonstrates Pwl2 is secreted into the BIC.

      In the second experiment two different strains, one carrying Pwl2 marked with GFP and the other carrying Pwl2 marked with RFP are used to co-infect rice.The claim that a BIC contains either RFP or GFP and not both only becomes meaningful if you were to state here that you specifically scanned for individual cells at the infection site that were simultaneously infected by both strains.How many such cells were observed?

      In the legend of Fig 1the statement “ confirming that the BIC does not contain Pwl2 transferred from rice cells” occurs. This is very cryptic and no mention of this is idea made in the results section. Presumably the unstated hypothesis is that transfer between BICs in the same rice cell could occur. Although the data support this the hypothesis should be included in an expanded results section. Perhaps this experiment is not necessary in this paper?

      In Fig 1 legend line 689 I think the verb should be was not were. The dashed lines in Fig1 A and B are not explained..Line 692. This should be B and D and not C and D?

    1. On 2024-02-27 08:48:43, user Herman van Eck wrote:

      This is an interesting manuscript! However, the manuscript typically describes parallelism. It is not about convergent evolution.

    1. On 2024-02-26 20:40:13, user marcinkortylewski wrote:

      The final version of this manuscript is published after peer-review at Molecular Therapy Nucleic Acids - doi: 10.1016/j.omtn.2024.102137.

    1. On 2024-02-26 19:29:08, user Chris Estes wrote:

      Am I going crazy, or have they misread Nemecek & Poore (2018)? Nemecek & Poore use a 100g of meat/kg CO2 equivalent, but in this paper they cite it as a kg/kg CO2.

    1. On 2024-02-26 16:34:20, user Claudiu Bandea wrote:

      The origin of viruses: from hypothesis to fact<br /> Claudiu Bandea (February 26, 2024)

      The origin of viruses is one of the greatest mysteries remaining in biology. In previous comments regarding the recent discovery and characterization of Borgs [1, 2], I proposed that Borgs are incipient viral lineages that originated from symbiotic or parasitic archaeal lineages, as predicted by the fusion model of the origin of viruses, by reductive evolution from cellular ancestors [3-6] .

      The reduction hypothesis regarding the origin of viruses was proposed in the mid-1930’s [7], during a period when knowledge of the structural and biochemical composition of viruses and of the diversity of cellular organisms was still emerging. However, by the middle of the last century, this growing body of knowledge led to the formalization of the modern concept of viruses [8].

      In 1957, Andre Lwoff, one of the founders of modern virology, defined viruses in his famous article “The Concept of Virus” as biological entities that: (i) have only one type of nucleic acid, DNA or RNA, (ii) multiply in the form of their genetic material, (iii) are unable to grow and to undergo binary fission, and (iv) lack energy metabolism [8]. The conceptual identification of viruses with virus particles, or virions - the transmissible, infectious forms in the viral life cycle - and the definition of viruses based on the physical, biochemical, and biological properties of these particles have both endured until recently in virtually all scientific literature and textbooks (discussed in [4, 9-16]).

      Not surprisingly, within the conceptual framework of viruses as virus particles, the historical hypotheses for the evolutionary origin of viruses focused on the structure and biochemical composition of virus particles: (i) the Pre-cellular or Virus-first Theory suggesting that viruses originated from precellular, self-replicating nucleic acids, or replicons, encoding for capsid proteins; (ii) the Endogenous or Escape Hypothesis proposing that viruses originated from cellular genomic sequences, or replicons, encoding for capsid proteins; (iii) and the historical Regression or Reduction Hypothesis proposing the reductive transition of parasitic cellular lineages, such as bacteria, into nucleocapsid-like structures.

      In context of the view of viruses as particles, the reduction hypothesis was questioned by Salvador Luria and James Darnell, the authors of one of the first textbooks of Virology [17], who wrote: “The strongest argument against the regressive origin of viruses from cellular parasites is the non-cellular organization of viruses. The viral capsids are morphogenetically analogous to cellular organelles made up of protein subunits, such as bacterial flagella, actin filaments, and the like, and not to cellular membranes.” (all quotes in italics) [17].

      Two decades later, in concert with a new perspective on the nature of viruses and a new definition based on their properties during the intracellular stage of the viral life cycle, I proposed a fusion hypothesis for the origin of viruses [3-5]. Briefly, according to the fusion hypothesis, viral lineages originated from cellular organisms that fused with their host cells through a process in which their cell membrane fused with the host membrane. By discarding their cell membranes, these novel organisms increased their access to resources present in their special environmental niche, the host cell, including the ribosomes and translation machinery. After synthesizing their specific molecules and replicating their genome using the resources found in the host cell, the parasites produced spore-like, transmissible forms, which started a new life cycle by fusing with other host cells. These incipient viral lineages diversified by reductive evolution into a myriad of viruses with smaller genomes and diverse life cycles. The origin and evolution of viruses ‘molecular organisms,’ overcomes the problems presented by the historical reduction theory.

      Nonetheless, unlike the new perspective on the nature of viruses, which, after decades in obscurity, is increasingly used to explain the biology of viruses and their role in shaping the metabolism and the evolution of their hosts [9, 11, 13, 14, 16, 18-27], the fusion model for the origin of incipient viral lineages has received little attention. Possibly, the main reason is the reminiscent scientific argument put forward by Luria and Darnell against the reduction theory, which has been recently re-articulated by Mart Krupovic, Valerian Dolja, and Eugene Koonin in their article “Origin of viruses: primordial replicators recruiting capsids from hosts” [28].

      They write: “Thus, the evolution of giant viruses, irrespective of the numerous interesting and puzzling aspects of their genome layout and biology, can be accommodated in the evolutionary scenario proposed here. Also, no evidence exists for the possible origin of viruses from intracellular parasitic bacteria. As intracellular parasitic or symbiotic bacteria have evolved numerous times and have independently given rise to extremely reduced forms, including organelles (119,120), the absence of bacteria-derived viruses suggests that the evolutionary path from a cell to a virus is impracticable.

      Patrick Forterre and Mart Krupovic emphasized the same problem with the historical reduction hypothesis: “virions were so different from any kind of cell (even the most reduced parasitic cells) that the regression hypothesis (the idea that parasitism triggered the reductive evolution from cells to viruses) was discarded as senseless by most biologists (for an exception, see Bandea 1983).” [16].

      Indeed, many symbiotic intracellular bacterial lineages evolved by regressive evolution into organelles, and several parasitic bacteria have reduced the number of their genes and proteins to a fraction of those found in their ancestors, or for that matter to a fraction of those found in some viruses. Yet, these organelles do not resemble virus particles. Nevertheless, like in the case of Luria and Darnell’s argument, the rationale used by these authors for questioning the historical reduction theory does not apply to the fusion hypothesis.

      Another scientifically sound rationale for dismissing the historical reduction hypothesis emerged from phylogenetic studies refuting the hypothesis that giant viruses originated from a fourth domain of cellular life by reductive evolution [29-31]. These studies support an evolutionary relationship of giant viruses with smaller viruses, which is consistent with the theory that they are polyphyletic and did not originate from a fourth domain. These results, however, are also consistent with the fusion hypothesis, which supports the evolutionary relationship of giant viruses with smaller viruses and contradicts the fourth domain hypothesis.

      Indeed, one of the fundamental predictions of the fusion hypothesis is that new giant virus lineages originated from diverse parasitic pre-cellular and cellular lineages throughout the history of life. Another prediction of the fusion hypothesis is that only cellular lineages that parasitize evolutionarily related hosts from the same cellular domain can transition to a viral type of biological organization. This explains the apparent homology of some of their genes with those of their hosts, which has been usually interpreted as evidence for the accretion model for the evolution of viruses towards complexity.

      As mentioned above, the current phylogenetic analyses do not exclude the reductive evolutionary diversification of the giant viruses into smaller viruses. Indeed, as recently noted by Natalya Yutin, Yuri Wolf, and Eugene Koonin: “The only alternative, however non-parsimonious, to the massive gene gain scenario appears to be independent early emergence of multiple ancestral giant viruses followed by massive losses in the branches leading to the smaller extant viruses.” [29]. This alternative is exactly what the fusion hypothesis predicts, with the realization that this process has occurred throughout the history of life, which explains the extraordinary diversity of the extant viruses, including thousands of relatively small viruses. It is difficult to envision how these small viruses have evolutionary survived for several billions years since their presumed origin, as postulated in the accretion model [28].

      Interestingly, at the other end of the accretion model, we could envision the possibility that some complex viral lineages transition into cellular lineages. That would be, indeed, an extraordinary event, but I’m not aware of any evidence suggesting such a transition. However, as discussed in the following, given the general evolutionary trend of symbiotic and parasitic organisms, the accretion model is questionable.

      I find the logic of viral evolution, as recently articulate by Koonin, Dolja and Krupovic, to be the cornerstone for our thinking about the origin and evolution of viruses: “Overall, the logic of virus evolution is defined by the key biological feature of viruses, namely their obligate intracellular parasitism.” [32]. I also find this logic of evolution to be applicable to thousands of parasitic cellular lineages from all cellular domains. In this context, unlike the virus-first hypothesis and the escape hypothesis, or their hybrid formulations, which are based on the principle of viral evolution towards complexity, and which dominate the current scientific literature, the fusion hypothesis is consistent with the well-documented reductive evolution of thousands of intracellular parasitic microorganisms. This prompts the critical question: Why would viruses evolve in the opposite way?

      Nevertheless, the holy grail of the fusion model is that it can be addressed experimentally. Even better, because this model predicts that new incipient viruses originated from parasitic cellular lineages throughout the history of life, it is possible that this natural evolutionary process can be observed in real time. Hypothetically, Borgs are incipient viral lineages that originated relatively recently, through a fusion mechanism, from archaeal ancestors evolutionarily related to their hosts [1, 2]. More extraordinary though, as I previously discussed [2, 4] some extant parasitic cellular lineages, such as parasitic red algae, are currently at various stages in their evolutionary transition into viral lineages.

      Do the current data and observations regarding the biology and life cycle of parasitic red algae support a scientific transition of the fusion hypothesis into an established fact? The fusion of these parasites with their hosts cells is surely a fact. The use of host cell resources, including, in my assessment, the host cell translation machinery and ribosomes, to synthesize their specific proteins and other components is also a fact. Another fact is that after replicating their genome using host-cell resources, these parasites direct the morphogenesis of their reproductive, spore-like, transmissible forms, which initiate a new life cycle. So, are some parasitic red algae viruses, and has the fusion hypothesis transitioned into a fact?

      References:

      1. Bandea, C., Will Borgs Illuminate the Evolutionary Origin of Ancestral Viral Lineages? bioRxiv, 2021: p. https://www.biorxiv.org/con....

      2. Bandea, C., New evidence supports the hypothesis that Borgs are incipient viral lineages. bioRxiv, 2023: p. https://www.biorxiv.org/content/10.1101/2023.08.01.549754v1#comments.

      3. Bandea, C.I., A new theory on the origin and the nature of viruses. J Theor Biol, 1983. 105(4): p. 591-602.

      4. Bandea, C., The Origin and Evolution of Viruses as Molecular Organisms. Nature Precedings, 2009: p. https://www.nature.com/articles/npre.2009.3886.1.

      5. Bandea, C.I., A unifying scenario on the origin and evolution of cellular and viral domains. Nature Precedings, 2009: p. https://doi.org/10.1038/npre.2009.3888.1.

      6. Bandea, C.I., Are Antarctic Nanohaloarchaeota emerging viral lineages? Preprints, 2019: p. https://doi.org/10.20944/preprints201911.0308.v1.

      7. Green, R.G., ON THE NATURE OF FILTERABLE VIRUSES. Science, 1935. 82(2132): p. 443-5.

      8. Lwoff, A., The concept of virus. J Gen Microbiol, 1957. 17(2): p. 239-53.

      9. Forterre, P., Giant viruses: conflicts in revisiting the virus concept. Intervirology, 2010. 53(5): p. 362-78.

      10. Racaniello, V., The virus and the virion. Virology Blog. About Viruses and Viral Diseases, 2010: p. https://www.virology.ws/2010/07/22/the-virus-and-the-virion/.

      11. Forterre, P., Manipulation of cellular syntheses and the nature of viruses: The virocell concept. Comptes rendus. Chimie, 2011. 14(4): p. 392-399.

      12. Claverie, J.M. and C. Abergel, Giant viruses: The difficult breaking of multiple epistemological barriers. Stud Hist Philos Biol Biomed Sci, 2016. 59: p. 89-99.

      13. Nasir, A., E. Romero-Severson, and J.M. Claverie, Investigating the Concept and Origin of Viruses. Trends Microbiol, 2020. 28(12): p. 959-967.

      14. Enquist, L.W. and V. Racaniello, Virology: From Contagium Fluidum to Virome. In: Fields Virology, Vol. 4. Fundamentals, 7th Edition edited by P. M. Howley and D. M. Knipe, Wolters Kluwer. 2024.

      15. Kostyrka, G., La place des virus dans le monde vivant. PhD Thesis, Université Panthéon-Sorbonne-Paris I, 2018: p. https://tel.archives-ouvertes.fr/tel-02359424/document.

      16. Forterre, P. and M. Krupovic, The origin of virions and virocells: the escape hypothesis revisited, in Viruses: essential agents of life. 2012, Springer. p. 43-60.

      17. Luria, S. and J. Darnell, General Virology 1965, New-York: Wiley.

      18. Moniruzzaman, M., et al., Dynamic genome evolution and complex virocell metabolism of globally-distributed giant viruses. Nat Commun, 2020. 11(1): p. 1710.

      19. Correa, A.M.S., et al., Revisiting the rules of life for viruses of microorganisms. Nat Rev Microbiol, 2021. 19(8): p. 501-513.

      20. Rosenwasser, S., et al., Virocell Metabolism: Metabolic Innovations During Host-Virus Interactions in the Ocean. Trends Microbiol, 2016. 24(10): p. 821-832.

      21. Howard-Varona, C., et al., Phage-specific metabolic reprogramming of virocells. Isme j, 2020. 14(4): p. 881-895.

      22. Braga, L.P.P., et al., Novel virocell metabolic potential revealed in agricultural soils by virus-enriched soil metagenome analysis. Environ Microbiol Rep, 2021. 13(3): p. 348-354.

      23. DeLong, J.P., et al., Towards an integrative view of virus phenotypes. Nat Rev Microbiol, 2022. 20(2): p. 83-94.

      24. Depuydt, C.E., et al., Human Papillomavirus (HPV) virion induced cancer and subfertility, two sides of the same coin. Facts Views Vis Obgyn, 2016. 8(4): p. 211-222.

      25. Bandea, C.I., Endogenous viral etiology of prion diseases. Nature Precedings, 2009: p. https://www.nature.com/articles/npre.2009.3887.1.

      26. Bandea, C.I., The Prion Hypothesis at Forty: Enlightening or Deceptive? J Alzheimers Dis, 2022: p. https://www.j-alz.com/editors-blog/posts/prion-hypothesis-forty-enlightening-or-deceptive.

      27. Caetano-Anollés, G., J.M. Claverie, and A. Nasir, A critical analysis of the current state of virus taxonomy. Front Microbiol, 2023. 14: p. 1240993.

      28. Krupovic, M., V.V. Dolja, and E.V. Koonin, Origin of viruses: primordial replicators recruiting capsids from hosts. Nat Rev Microbiol, 2019. 17(7): p. 449-458.

      29. Yutin, N., Y.I. Wolf, and E.V. Koonin, Origin of giant viruses from smaller DNA viruses not from a fourth domain of cellular life. Virology, 2014. 466-467: p. 38-52.

      30. Williams, T.A., T.M. Embley, and E. Heinz, Informational gene phylogenies do not support a fourth domain of life for nucleocytoplasmic large DNA viruses. PLoS One, 2011. 6(6): p. e21080.

      31. Moreira, D. and P. López-García, Evolution of viruses and cells: do we need a fourth domain of life to explain the origin of eukaryotes? Philos Trans R Soc Lond B Biol Sci, 2015. 370(1678): p. 20140327.

      32. Koonin, E.V., V.V. Dolja, and M. Krupovic, The logic of virus evolution. Cell Host Microbe, 2022. 30(7): p. 917-929.

    2. On 2024-01-03 01:25:21, user Claudiu Bandea wrote:

      New evidence supports the hypothesis that Borgs are incipient viral lineages <br /> (Claudiu Bandea, Dec 28, 2023)

      The discovery of Borgs as giant extrachromosomal elements, presumably inhabiting Methanoperedens archaea, was first published in 2021, in bioRxiv [1]. More than a year later, the study was also published in Nature under a slightly different title and content [2]. The study, which reported the sequencing and analysis of more than a dozen Borg genomes (661,708 to 918,293 kb in length), including four genomes that were fully curated and analyzed, found no evidence of viral characteristics.

      On the basis of these results, the authors asserted the following: “We can neither prove that they are archaeal viruses or plasmids or minichromosomes, nor prove that they are not. Although they may ultimately be classified as megaplasmids, they are clearly different from anything that has been previously reported” (all quotes in Italics) [2]. This statement raises a critical question: what kind of evidence would warrant the classification of Borgs as viruses, megaplasmids, or minichromosomes? Surprisingly, the authors did not address this essential issue.

      Despite the Borgs’ apparent lack of viral characteristics, in a commentary entitled “Will Borgs Illuminate the Evolutionary Origin of Ancestral Viral Lineages?” [3], I suggested that Borgs are incipient viral lineages and, thus, illuminate one of the biggest mysteries in biology – the origin of viruses.

      Remarkably, in a new article published in bioRxiv by the same group [4], we learn that, after all, Borgs do encode numerous putative viral proteins, including several capsid proteins, as well as proteins implicated in the replication, recombination, and spread of Borgs to new host cells. The new study presents additional evidence, including a high ratio between the number of Borgs and their presumed Methanoperedens hosts and a distinct methylation pattern of their genomes, which point to an extracellular stage in the Borgs’ life cycle and to their viral nature.

      As I outlined in my previous commentary [3], the rationale for proposing that Borgs might be incipient viral lineages, even in the absence of the conventional physical, biochemical, and biological features historically used to define viruses (see below), was rooted in the Fusion Hypothesis regarding the evolutionary origin of viruses [5-7].

      According to this hypothesis, the ancestral or incipient viral lineages originated from ecto- or endo-symbiotic or parasitic cellular lineages that fused with their host cells. By fusing with their host cells and discarding their cellular membrane, these lineages transitioned to new type of biological organization and structure (see below), which gave them full access to the host cell resources, including the host’s ribosomes and other components of the translation machinery. After synthesizing their specific molecules and replicating their genome using the resources found in their special environmental niche (i.e., the host cell), this new type of organisms induced the assembly and morphogenesis of reproductive, transmissible forms, which started a new life cycle by fusing with other host cells.

      The absence of a cellular membrane within the host cell presented the incipient viral lineages with unique reductive evolutionary opportunities, not readily available for parasitic or symbiotic cellular lineages, which led to a myriad of new viruses with diverse lifestyles and biochemical composition. As outlined below, the fusion model completely changes the conventional views regarding the nature of viruses, their evolutionary origin, and their role in shaping the evolution of cellular lineages.

      The nature of viruses

      Ever since viruses were identified more than a century ago as infectious agents that passed through filters thought at that time to retain all microorganisms, they have been conceptually identified with the virus particles, or virions - the transmissible infectious forms in the viral life cycle. Accordingly, viruses have been defined based on the physical, biochemical, and biological properties of these particles, as illustrated in virtually all scientific literature and textbooks to date.

      For example, in his seminal book, The Molecular Biology of the Gene, James Watson, who was highly familiar with nucleic acids, as well as with viruses [8], wrote: “All viruses differ fundamentally from cells, which have both DNA and RNA, in that viruses contain only one type of nucleic acid, which may be either DNA or RNA” [9]. A decade later, in A Dictionary of Virology, viruses were defined as “Infectious units consisting of either RNA or DNA enclosed in a protective coat” [10], and in the 1990s, a classic microbiology textbook, Zinsser Microbiology, stated that viruses “consist of a genome, either RNA or DNA, that is surrounded by a protective protein shell” [11].

      Surely, the authors of these scientific publications were fully aware that, during the intracellular stage of their life cycle, many viruses, such as the “DNA viruses” and retroviruses, have both type of nuclei acids, DNA as well as RNA, and that many viruses are much more complex than a nucleic acid wrapped in a protein coat. Yet, all these renowned scientists fell victim to the concept of viruses as virus particles and used the physical, biochemical, and biological properties of these particles to define viruses. This is a strong example of the power of concepts in science. A concept that clearly misrepresents the experimental findings and observations can persist for decades, or, as in the case of viruses, for more than a century.

      Forty years ago, in 1983, I proposed that, like many parasitic cellular lineages, viruses pass in their life cycle through two phenotypically distinct stages: the extracellular, reproductive forms represented by the virus particles, and the intracellular forms in which the viral molecules and components are “free” or dispersed within their host cell [5].

      The viral particles are highly specialized structures that are used by some viruses for their transmission to new host cells. This role of viral particles in the viral life cycle explains their properties, including their apparent inert status and the presence of only one type of nucleic acid - DNA or RNA. Many viruses, however, do not produce viral particles, using instead alternative modes of transmission [12]. This fact alone indicates that identifying viruses with the virus particles misrepresents their nature. Nevertheless, the fundamental biological properties of viruses, whether they do or do not produce virions, are expressed during the intracellular stage of the viral life cycle, when viruses replicate their genome and synthesize their specific molecules, many of which are not components of the viral particles.

      To identify viruses phenotypically during the intracellular stage of their life cycle with the integrative sum of all their molecules, and to differentiate them conceptually from the parasitic lineages that maintain a cellular membrane within the host cell, I proposed the concept of molecular structure and labeled viruses as molecular organisms [5, 6].

      Although the concepts of molecular organisms and molecular structure (which, by analogy with the host cell’s cytoplasm, can be called viroplasm) are more suggestively envisioned within the framework of the fusion hypothesis, these concepts are also applicable in context of the other hypotheses regarding the origin and evolution of viruses (see below). Significantly, these concepts set the foundation for including other biological entities, such as plasmids, endogenous viruses, and viroids, within the same domain of biological organization - the viral domain.

      In a commentary entitled “What makes a virus a virus?” [13], Roland Wolkowicz and Moselio Schaechter wrote that the identity of viruses as historically conceptualized and defined (i.e., as virus particles) is missing “the most fundamental aspect of what makes a virus a virus: it breaks up and loses its bodily integrity, with its progeny becoming reconstituted after replication from newly synthesized parts” and that “We are surprised from our own experience that the world of virology has not fully embraced this outlook” .

      After the discovery of giant viruses, Jean-Michel Claverie asked, “What if we have totally missed the true nature of (at least some) viruses?” [14], and in a series of publications Patrick Forterre and his colleagues have discussed extensively the limitations of the concept of viruses as virus particles and suggested alternative ways to define viruses and to identify them during the intracellular stage of their life cycle [15-18].

      As I discussed in the original publication [5], referring to the intracellular stage of viruses as an “eclipse phase,” denoting the “disappearance” of viruses, was confusing. Likewise, identifying viruses with their genome, thereby ignoring the other viral molecules and components, misrepresents their nature. An alternative approach was to no longer refer to a virus as an individual biological entity, but as an integrated virus-host cell system (i.e., the infected cell0. Recently, Patrick Forterre labeled this integrated system with the term “virocell” [15, 17, 18].

      This approach was sharply criticized by Purificación López-García and David Moreira on both scientific and epistemological grounds [19, 20], and recently the virocell term was redefined by DeLong et al., [21], but Forterre rebutted the criticism [18].

      Nevertheless, these highly relevant discussions bring forward the acute problems with the dogma of viruses as virus particles and stress the need for a new scientific and academic perspective on viruses, which can productively integrate the extraordinary amount of knowledge about viruses and their role in shaping the life and evolution of their hosts and of the ecosystem in which their live [15, 22-30].

      The scientific limitations and academic confusion associated with the concept of viruses as virus particles in virology and related biomedical fields [31-33] remain to be fully addressed. However, questioning the validity of this dogma, which has guided several generations of researchers to extraordinary discoveries and progress in virology, is challenging.

      The origin and evolution of viruses

      As it would be expected, in the context of the dogma of viruses as virus particles, the hypotheses regarding their evolutionary origin focused on the virions and their structure: (i) thePre-cellular or Virus-first Theory suggested that viruses originated from precellular, self-replicating nucleic acids, or replicons, encoding for capsid proteins; (ii) the Endogenous or Escape Hypothesis suggested that viruses originated from cellular genomic sequences, or replicons, encoding for capsid proteins; (iii) and the historical Regressive or Reductive Hypothesis proposed a reductive transition of parasitic cellular lineages, such as bacteria, into nucleocapsid-like structures.

      Within the concept of viruses as virus particles, the validity of the regressive hypothesis was questionable as Salvador Luria and James Darnell pointed out more than half a century ago: “The strongest argument against the regressive origin of viruses from cellular parasites is the non-cellular organization of viruses. The viral capsids are morphogenetically analogous to cellular organelles made up of protein subunits, such as bacterial flagella, actin filaments, and the like, and not to cellular membranes.” [34].

      Indeed, many parasitic and symbiotic bacteria have a fraction of the genomic and proteomic repertoire of some viruses. For example, several endosymbionts, such as Carsonella, Hodgkinia, and Tremblaya, have a genome that is less than 200 kb and encode less than 200 proteins [35]. Yet, no symbiotic or parasitic bacteria with highly reduced genomes and metabolic capability resemble virus particles.

      As predicted by the fusion hypothesis, only symbiotic or parasitic lineages that have a genetic and metabolic system compatible with that of their host cells would be able to fuse with them and transition to a viral type of biological organization. Accordingly, only bacterial, archaeal, and eukaryotic lineages, hosted by bacterial, archaeal, and eukaryotic host cells, respectively, could evolve into viral lineages [6, 7, 36].

      Interestingly, numerous symbiotic and parasitic lineages that inhabit their kin and have reduced genomes and metabolic capabilities have been recently discovered, including highly diverse groups of DPANN archaea and CPR bacteria [37-42]. Hypothetically, some of these archaeal and bacterial lineages are in the process of transitioning into incipient viral lineages [6, 36], similar to the putative cellular ancestors of Borgs [3]. Nevertheless, one of the major appeals of the fusion hypothesis is that, unlike the other hypotheses, it can be addressed experimentally, as some members of these groups archaea and bacteria could be developed as fusion model organisms.

      Surprisingly, though, the strongest evidence for the fusion hypothesis is found among more complex organisms - the eukaryotes. According to the fusion model, the nucleomorphs, some of which have a very small genome (<1 Mb) [43], originated from algal endosymbionts that fused with their host cells. Although, currently conceptualized as organelle-like entities, the nucleomorphs are genuine molecular organisms that have maintained their nucleus.

      Even more surprising is the fact that numerous parasitic algal and fungal lineages have a life cycle and biological organization that, as I previously pointed out [6], represent overwhelming evidence for the fusion hypothesis. Indeed, several obligate parasitic species of red algae fuse with their host cells and use the host resources, including, in my assessment, the host ribosomes and other components of the translational machinery, to synthesize their molecules, replicate their genome, and induce the morphogenesis of spore-like progenies [44-50].

      I cannot overemphasize the significance of these discoveries which support the fusion hypothesis and should be considered breakthrough discoveries not only in the field of parasitology, but also in evolutionary science, and biology.

      Many viruses have been discovered serendipitously, including the recent finding in Chaetognaths, a small phylum of marine invertebrates, of two complex viruses, which have yet to be characterized at the molecular level [51, 52]. As more investigators become familiar with the fusion hypothesis and its predictions, it is likely that new types of viruses, as well as of new cellular lineages that are transitioning into incipient viral lineage, will be discovered.

      Although, similar to tens of thousands of symbiotic and parasitic cellular lineages, the viral lineages have evolved towards reduced genomes and proteomes, there is clear evidence of frequent exchanges of genetic material with their hosts and other coinfecting organisms [6, 7, 53]. Considering also their high mutational rates, the deep phylogenetic analysis of viruses is inherently difficult [54-58]. Therefore, trying to establish deep phylogenetic relationships among viruses, reaching the origin and early evolution of life, is likely to be a futile effort.

      The origin of incipient viral lineages from symbiotic or parasitic cellular lineages by a fusion mechanism is consistent with the current sequence-based phylogenetic analysis indicating orthologous relationships between the genes of some complex viruses and those of their hosts. The fusion hypothesis is also consistent with the complex biology and the life cycle of many viruses [59-62]. Also, unlike the virus-first, and the escape hypotheses, which dominate the current scientific literature [57, 63-65], the fusion hypothesis is consistent with the reductive evolution of thousands of endosymbiotic/parasitic microorganisms, which prompts the critical question: Why would viruses evolve in the opposite way?

      Unlike the other two hypotheses on the evolutionary origin of viral lineages, the fusion hypothesis also unambiguously addresses one of the most intriguing scientific and philosophical questions: Are viruses alive? If the viral lineages originated from cellular microorganisms as proposed in the fusion model, then, there are few remaining arguments, if any, against their living status and their rightful place on the Tree of Life [5-7, 66-68].

      Finally, it is relevant to mention that the fusion model on the origin of viral lineages is an integral part of a broader perspective - the fusion/anti-fusion theory - regarding the origin and evolution of pre-cellular and cellular lineages, including the archaeal, bacterial, and eukaryotic cellular domains and some of their defining characteristics [7]. Many aspects of this unifying theory, which addresses the major transitions in the history of life, including its origin, can be found as discrete published ideas and hypotheses [69-74].

      Luria’s Credo: There is an intrinsic simplicity of nature and the ultimate contribution of science resides in the discovery of unifying and simplifying generalization, rather than in the description of isolated situation - in the visualization of simple, overall patterns rather than in the analysis of patchworks [75].

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    1. On 2024-02-26 08:01:28, user rock dong wrote:

      Dear Chenhao zhang, etc, could you share the dataset you used to compare Highfold vs AfCycDesign, with a download link? I hope to self compare your new algorithm vs AfCycDesign on your curated dataset that's 4.1.1/4.1.2/4.1.3. It would be best you can share your data curation methon for 4.1.3. thanks!!

    1. On 2024-02-25 14:44:57, user Ian Myles wrote:

      This is a fascinating study. The senior author presented this at a conference and the work is excellent. It would be interesting to see a follow up study that included 32degC as a condition. 32degC would provide insights into how the cells respond when at skin (surface) temperatures. But as it stands, I hope this paper finds a place in a journal that will afford it the platform it deserves.

    1. On 2024-02-24 05:45:54, user BL Somani wrote:

      In Methods it has not been described how the L-GA was added to cell culture experiments. Was it added first followed by DMEM medium or it was mixed with the medium and added/ Please give your comments. Moreover , you mentioned that it inhibits Hexokinase but you have not described that it specifically inhibits HK-2 which is mitochondrial enzyme and you mentioned no upstream intermediates were increased, in case of HK-2 inhibition which upstream intermediates are expected to increase?

    2. On 2024-02-23 07:38:38, user BL Somani wrote:

      Well I have gone through the article but in, Methods, I dont see any description about addition of L-GA, at what stage this was added and how it was added. Was it added to the cells first and the medium later or it was mixed with the medium and added to the cell culture plate. Please give your comments. Thanks

    1. On 2024-02-23 23:44:15, user Doris Loh wrote:

      The preprint stated that "taurine (TCI Chemicals) was dissolved directly into complete cell culture media at a concentration of 40 mg/mL", which is 319.62 mM. However, throughout the entire study, the only concentration of taurine used in various experiments was either 160 mM or 100 μM. Was 40 mg/mL a typo or did the authors use 320 mM instead of 160 mM?

    1. On 2024-02-22 21:54:05, user Davidski wrote:

      Hello authors,

      It's extremely unlikely that there are any significant genetic differences between Sarazm_EN_1 (I4290) and Sarazm_EN_2 (I4210), and also unlikely that the former has significant South Asian ancestry while lacking Anatolian farmer ancestry.

      The only significant difference between them is that I4290 is lower coverage. I suspect that this, coupled with your use of the very low quality Iran_Mesolithic_BeltCave in the outgroups, might be the problem in your qpAdm analysis.

      I4290 and I4210 appear to be very similar in all of the PCA, qpAdm and ADMIXTURE analyses that I've done. Indeed, they're close to each other in all of my PCA, including across many different dimensions, except of course the PCA that reflect different levels of coverage in the samples being run.

      For instance, here's a PCA that looks specifically at differences in South Asian and Anatolian genetic affinities. As you can see, there's practically no difference between I4290 and I4210.

      https://blogger.googleuserc...

      It is possible that I4290 and I4210 both have some sort of minor South Asian-related ancestry, but if so, then this type of low level South Asian-related admixture was ubiquitous in Eneolithic/Chalcolithic Central Asia.

      For more details please refer to this blog post and comments in which I show that both I4290 and I4210 can be modeled in qpAdm as mixtures between Botai Eneolithic and a subset of Geoksyur Chalcolithic samples.

      https://eurogenes.blogspot....

    1. The Prompt API uses the Gemini Nano model in Chrome. While the API is built into Chrome, the model is downloaded separately the first time an origin uses the API.

      大多数人认为内置API应该包含所有必要组件,无需额外下载,但作者明确指出模型需要单独下载。这与人们对'内置'API应该即开即用的普遍认知相悖,暗示用户首次使用时可能会面临显著的下载时间和存储压力。

    2. The Prompt API for the web is still being developed. While we build this API, refer to our best practices on session management for optimal performance.

      大多数人认为浏览器AI功能应该是成熟且生产就绪的,但作者明确表示该API仍在开发中。这与人们对Chrome作为成熟浏览器应该提供稳定可靠功能的认知相悖,暗示AI功能可能还不够稳定,需要开发者额外注意性能优化。

    3. The network requirement is only for the initial download of the model. Subsequent use of the model does not require a network connection. No data is sent to Google or any third party when using the model.

      大多数人认为使用Google的AI模型必然会涉及数据传输和隐私问题,但作者强调模型完全在设备上运行且不向Google发送数据。这与人们对大型科技公司AI服务通常涉及数据收集的普遍认知相悖,暗示Chrome的AI功能可能比想象的更加注重隐私保护。

    4. The Prompt API isn't available in Web Workers for now, due to the complexity of establishing a responsible document for each worker in order to check the permissions policy status.

      大多数人认为现代浏览器API应该支持Web Workers以实现并行处理,但作者明确表示Prompt API不支持Web Workers。这与人们对浏览器API应该全面支持现代Web开发模式的认知相悖,限制了开发者在后台线程中使用AI的能力。

    1. On 2024-02-22 08:03:02, user Christoph Grunau wrote:

      This is an excellent tool. We have been using it for many years now and it still outperforms the other methods for detecting differentially modified chromatin regions. It allows for the generation of chromatin "colours" and it produces reliable mutagène profiles. A particular advantage is that it relies on observed/expected values for this and not on absolute RPKM or other enrichment values. Works with or without input. I recommend it.

    1. On 2024-02-21 20:04:00, user Daphne wrote:

      Summary:

      Fusidic acid (FA) and its derivative fusidic acid cyclopentane (FA-CP) are antibiotics administered to treat Staphylococcus aureus (SA) infections. Previous studies have established that FA acts by impeding release of elongation factor G (EF-G) from the ribosome, thereby inhibiting translation. However, because previous structures of FA bound to ribosome have been solved from Gram-negative bacteria that are resistant to FA, our understanding of precisely how FA acts on Gram-positive species like Staphylococcus aureus and how resistance arises is incomplete. In this paper, the authors solve cryo-EM structures of FA- and FA-CP bound ribosomes from Staphylococcus aureus to understand their interactions with the ribosome in a native Gram-positive environment and put previously observed resistance mutants in context.The major success of this paper is its development of a structural rationale for previously observed FA-resistant mutants, and development of a structural basis for improved FA derivatives. Additionally, the authors found novel posttranscriptional modifications within the SA ribosome. The major weakness of this paper is in validation of these novel rRNA modifications. In conjunction with FA-bound Gram-negative ribosomes, these structures are important for defining the core interactions that mediate FA resistance, which could also help to enumerate the space of possible future resistant mutants.

      Major points:

      Given the high level of sequence conservation in ribosomes across different bacterial species, it would be informative to explain the benefits of investigating ribosomes specifically from S. aureus. Are the FA binding site and EF-G in S. aureus distinct from the FA binding site and EF-G in bacteria used in previous structural studies of FA-bound ribosomes? Or is there something unique about S. aureus physiology and its development of antibiotic resistance? Providing a specific explanation, perhaps by citing what previous studies in the field have not captured or providing sequence conservation metrics, would emphasize the novelty of these structures and how they contribute to our understanding of FA resistance.

      “The CHI state is the most abundant in our sample, and only a minor population is in POST state. In the FA data, 60% of the ribosomes are bound to EF-G, and 93% of those are in CHI state. In the FA-CP data, 72% of the 70S particles contain EF-G, out of which 88% are in CHI state.” Would an S. aureus ribosome dataset with EF-G bound but no FA in the sample have no detectable POST population? A comparison to EF-G bound with no FA in the sample would also strengthen the claim “In our data, however, we do observe POST state, indicating that FA inhibition allows time for the SSU head to back-swivel in presence of EF-G.” Before making claims about how FA changes the landscape of EF-G bound-ribosome conformations, it would be helpful for readers to understand the initial landscape of EF-G bound-ribosome with no FA – perhaps explaining observations from previous studies would help with this.

      In Figure 4, any differences in hydrogen bonding or other FA interactions between the three different structures should be highlighted (or lack thereof should be mentioned). Although the text describes different rotamers and positions of switch II and residues like R464, it is unclear to us how that impacts FA binding interactions.

      In Figure 5, the explanation of resistance mutations would be clearer if there were a side-by-side diagram of what interactions are present in a FA-susceptible ribosome vs FA-resistant mutant, as opposed to simply highlighting all the affected residues. As is, Figure 5 is unclear about whether multiple mutations are required to confer resistance or if each residue individually confers resistance, and the relative increase in resistance that each mutation confers. Discussion of how these mutations rationalize previously observed resistance on a structural level, and whether this structure can also rationalize the degree to which they confer resistance, would be helpful. Furthermore, we are curious how the FA-CP analog corresponds to these mutations – does the structure rationalize FA-CP resistant mutants as well? An additional figure in the style of Figure 5 but with FA-CP, showing FA-CP resistant mutants from previous studies, would further show how this structure can provide a basis for understanding resistance mutants.

      For the rRNA modifications observed in Figure 6:

      We are curious whether these modifications could be related to EF-G, fusidic acid, fusidic acid cyclopentane interactions and relevant resistance mutations. Speculation in the discussion about possible consequences of these modifications, resistance-related or not, would be helpful for readers to understand why these modifications are important.

      The authors showed the single modifications in Figure 6, but perhaps a few supplemental figures showing how the modifications interact with functionally important parts (i.e. decoding center and peptidyl transferase center) of the ribosome would be helpful for reader comprehension.

      Since FA-CP was tested against S. aureus ATCC 29213 in Garcia Chavez et. al. 2021 (reference #28), but the ribosomes in this paper are from S. aureus NCTC 8325-4, we are also curious if these rRNA modifications are general to all S. aureus ribosomes or if there is some strain-specific modification.

      The authors made their conclusions about novelty based on their map in comparison to E. coli rRNA modifications. These modifications could also be compared to other S. aureus ribosomal structures instead of the E. coli structures. Additionally, for further investigation of the cryoEM density itself, there are programs such as qPTxM or Curiosity (both of which can be found at https://github.com/irisdyoung) that could help validate that the modification is present and clarify any heterogeneity in the density. Validation by mass spectrometry or other experimental techniques would also increase confidence in the model.

      Minor points:

      In Figure S4, it would be helpful to show the hydrogen bonds or other interactions that lock switch II in place.

      Regarding this sentence, “ The other is an intermediate so-called chimeric hybrid state (CHI) of translocation, in which the head of the small ribosomal subunit (SSU) remains swiveled and tRNAs make A- and P-site interactions with the mRNA codon and the SSU head, but P- and E-site interactions with the 30S body and the 50S (12, 18).” It was not clear as to what the authors meant after the word but.

      Reviewed by Mohamad Dandan, Daphne Chen, and James Fraser

    1. On 2024-02-21 19:14:22, user Priyanka Bajaj wrote:

      In a previous paper, the authors expressed fragments of peptides to identify sequences that would act as “dominant negative” inhibitors of the parent protein. However, screening peptides for inhibitory effects in cells by bulk competition and deep sequencing has limitations. Due to their limited size, peptides can potentially have multiple non-specific or off-target consequences such as multiple target binding, cytotoxicity, or non-specific binding to the target protein of interest. Validating that a peptide is a true inhibitor is critical, however, determining the mechanism of inhibition across multiple fragment sequences can be very time consuming. Genetically this could be done testing fragments in overexpressed target protein backgrounds to help confirm that the interaction between a given fragment and the desired target is due to target inhibition but, for libraries of fragments, a more high throughput method would be desirable. While in some cases (such as with GroEL and GroES) the authors inferred that the inhibitory effect of fragments were specific interactions due to the concentration of the target protein (i.e. correlation between higher expression of target and inhibition), the mechanisms of inhibition for several proteins were not definitively proven to occur through native interactions which leaves an open question as to whether these fragments are true inhibitors. Here they develop a computational screen to increase the confidence that inhibitory peptides work via the desired on-target mechanism, the authors have now developed a computational tool that is built upon AlphaFold called FragFold. FragFold structurally predicts the fragment bound to the target protein, with inhibitory fragments identified experimentally having a predicted high number of contacts between fragment and target.

      The major strength of this paper is in developing a method that could be used to identify regions of proteins that are involved in PPI’s based on evolutionarily related sequences that recapitulate the native binding interfaces. The major weakness of this paper is that the underlying method of MSA concatenation is not clearly explained (see Major point 1). Why is the discontinuous unpaired strategy optimal relative to other AlphaFold-multimer-like strategies? Overall, the paper demonstrates the power of AlphaFold to closely recapitulate the structures of experimentally determined fragment binding interfaces by working exclusively in sequence space in a high-throughput manner. Further, in the absence of experimental structures, the authors present plausible AlphaFold predictions of fragment bound structures that are supported by biochemical and genetic data which could further contribute to the utility of this method in studying known PPI’s.

      There are a few points we would like to bring to the attention of the authors to strengthen the manuscript further.

      Major points:

      1) The authors state that they generated multiple sequence alignments for both the fragments and target protein prior to running AlphaFold2 to minimize computation time. Although it is not clearly explained, the authors claim they concatenated these two MSA’s into a single MSA. We interpret this to mean that the input MSA’s were not directly pairing the fragment sequences to the target sequences but leaving either side of the fragment sequence and the target sequence blank to force AlphaFold to co-predict the structure of the fragment with the target protein by treating it as a single discontinuous sequence. While we were surprised by the simplicity of this method and the ability to remain in sequence space based on evolutionary similarity of fragment and target protein sequences, there are several questions we have regarding this implementation.<br /> From the explanation provided with figure 1A, the fragment sequence appears to be directly paired to the target sequence, however in fig 1B the method appears to work by using the evolutionary information of many orthologs and related proteins of both fragment and target to co-predict their structures using a discontinuous input sequence. Is this correct? Could the authors provide a clearer description of how they are concatenating the MSA’s? We were also curious to know how different concatenation strategies affect the accuracy of predictions. For example, could the authors also try concatenating directly paired fragment-target sequences from the same species of origin (or even try this as a single continuous sequence)? If the sequences are continuous, does adding linker regions between the fragment and target alter the results? Does the order of concatenation affect the results (concatenating from the N or C-termini of the target?).

      2) In Figure 2A, the peaks indicating inhibitory activity correlate positively with the observed peaks in calculated weighted N_contacts, which forms the basis for all inferences. However, the data reveals an interesting observation in the initial segment of the protein fragment (0 to 100 aa) that while there is a peak indicating predicted binding by the AlphaFold model, there is no corresponding inhibitory activity observed for the protein in that region. Any comments on this discrepancy?

      3) The authors introduce (f_native,pairwise) and (f_native,binding) to quantify similarities between experimentally solved structures and AlphaFold2 models. We found the explanation of these metrics to be confusing, one refers to the fragment and the target site but the other refers to the native binding site bound by the fragment. Is one referring to the contacts made in the experimental structure and the other the contacts in the AlphaFold model? Further clarification of what these precisely correspond to would be helpful for discerning the similarities and differences between the two.

      4) In both figures 2 and 3 the authors show structures of the experimentally solved complexes and the predicted AlphaFold models side by side. We were curious to know whether the AlphaFold models were able to recapitulate the sidechain conformations. In addition we were also curious to know whether the AlphaFold model recapitulated any key contacts made between the binding site and the fragment (ex: salt bridges, electrostatic interactions between charged amino acids, pi-pi stacks, hydrogen bonds between sidechains).

      5) The authors explain their use of tiling to generate inhibitory fragments and that overlapping fragments generate greater predicted binding peaks. Have the authors attempted to use smaller fragments in their program (i.e. what is the smallest fragment size that AlphaFold can still predict to be correctly binding?). We are curious to know if effects vary by changing the fragment sizes. Further, can this method be expanded upon to study multiple different fragments that bind to different sites on the target protein simultaneously?

      Minor points:

      1) The orange and yellow lines used to show contacts between the fragment and target are difficult to distinguish from each other. Consider a different set of colors?

      2) Figure 4C+E. The use of black in the model makes it difficult to distinguish the sidechains and interactions with the fragment.

      • Reviewed by CJ San Felipe, Priyanka Bajaj and James Fraser (UCSF)
    1. On 2024-02-21 16:10:16, user Susanne Fuchs wrote:

      I really like your paper and currently working on vocalizations in an evolutionary games where people need to create new vocalisation. I had the feeling that the participants (adults) were less creative than I thought they could have been - and maybe this also links back to what you have done. Would be very curious to see some of your data. How long did you take to record all these babies? And why did you do a catgegorization and did not do a bottom up acoustic analysis? Is there any reason?

    1. On 2024-02-20 16:27:03, user Diego del Alamo wrote:

      (The comments below are my own thoughts and aren’t meant to serve as a substitute for peer review)

      This manuscript presents a much-needed quantitative examination of structures of LeuT fold transporters, which are helical membrane proteins that import and export a wide variety of substrates in and out of cells. In the context of protein dynamics, this superfamily is characterized by a diverse range of conformational changes amongst its members, with some helices staying fixed in some representatives but not others throughout their respective transport cycles. In this analysis, the authors break down these conformational changes between pairs of structures using a rotationally- and translationally-invariant method for tracking helical movements (distance difference matrices, or DDMs). From these movements, the authors conclude that bundle-hash rocking is the foundation defining all conformational changes in proteins in this superfamily.

      The results are compelling, but my enthusiasm is somewhat dampened by the use of a comparatively small dataset and relative absence of mathematical rigor. I think this can easily be addressed with a bit of additional analysis.

      The basis for the main finding, stated above, derives from a principal component analysis (PCA) of these DDMs. If my understanding is correct, the authors use distance differences in 22 pairs of structures across nine proteins and arrive at six distinct motions that can explain most of these changes. While the authors show the reconstruction error when different numbers of principal components (PCs) are used in Fig 3D, I did not see a mathematical justification for selecting six components specifically. It might help to compute a statistical criteria such as the Akaike or Bayesian Information Criterion to verify that six PCs is the appropriate number.

      By the same token, it would be beneficial to run some cross-validation on some intentionally left out structural pairs. A low reconstruction error on proteins left out during parametrization would go some way toward supporting the authors’ conclusion that these movements are shared. For example, can the conformational dynamics of NSSs like SERT and LeuT be explained entirely using PCs derived from structures in other families?

      Finally, I would strongly encourage the authors to expand their analysis to include new structures deposited after mid-2021. I understand that this would add a lot of work, as I suspect that segmentation and assignment of residues to helices is done manually. But given the rapid clip at which these LeuT-fold structures are being deposited in recent years, it could significantly increase the size of the dataset. Off the top of my head, this would add NKCC1, KimA, and SGLT2, and probably others.

      Beyond that, a few things here and there stood out:<br /> • It isn’t clear based on Fig 3F or the text if the PCA itself is segmented by specific steps in the conformational cycle, or if the structural pairs are unlabeled during analysis (I suspect the latter from the text).<br /> • On the use of pymol cealign to align pairs of structures, it is a little strange given that this is a sequence-independent method intended to align proteins with little to no sequence homology. However, given the low RMSD between their pairs of structures, and that the paper’s bulk focuses on alignment-independent analysis, this is unlikely to affect the conclusions much at all (I've also tested it on a few pairs and the results look more or less identical to other sequence- and structure-based alignment methods). With that in mind though, I wouldn't state RMSD values in the text if they were calculated this way, unless they are supplemented by other metrics, such as TM-score<br /> • The name MntH is used throughout the text, except at the very end where the name DraNramp is used. I assume these are the same protein?<br /> • I just want to say that including an analysis where the membrane serves as a reference plane for a structural analysis is a great idea and very much appreciated, and I hope others follow your example and do the same thing

    1. On 2024-02-20 09:47:16, user Nils Schuergers wrote:

      Nice work! Instead of reference 42 you probably wanted to cite "Nils Schuergers, Tchern Lenn, Ronald Kampmann, Markus V Meissner, Tiago Esteves, Maja Temerinac-Ott, Jan G Korvink, Alan R Lowe, Conrad W Mullineaux, Annegret Wilde (2016) Cyanobacteria use micro-optics to sense light direction eLife 5:e12620"

    1. On 2024-02-19 15:17:57, user Mathis Riehle wrote:

      super nice - congratulations conceptional very persuasive paper!

      Q: is there a link to the movies to have a look? Would love to use them in class in the future - nothing like seeing is believing.

      slight critique - the bar graph overlays on top of the data in figs 2B, D & F, 3D, 4C & E, 5C & D & 6C are OK - if you think that you need them - but consider toning their intensity down to allow one to 'see the data' - they are a bit 'in the way'/too strong imho.

      Not quite sure if the n numbers given in the figures for 'cells' are for independent experiments or neighbouring cells? If they are in the same dish it would quite difficult in dens(ish) cultures as shown to be assured of independence; in your methods you talk about 3-8 independent experiments - here exemplary analysis showing the data as visualised via e.g. SuperPlotsofData by J Goedhard (https://huygens.science.uva... & DOI: 10.1083/jcb.202001064) would give assurance that these datasets are good to be assembled/pooled together?

    1. On 2024-02-19 11:12:56, user Andrew Almonte wrote:

      An interesting paper. Regarding the sequences in Table 2, you state that the ompC protein in clade C3 has a 181_182insDPD in strains ST73 and ST12. It's written as N/DPD/F, but position 181 is a G in the reference genome. Is this a clerical mistake or am I misunderstanding the alignment?

    1. On 2024-02-18 12:34:22, user Michael Polymenis wrote:

      The paper has been published. Mol Biol Cell. 2023 Dec 1;34(13):br20. doi: 10.1091/mbc.E23-05-0166. Epub 2023 Oct 4.<br /> PMID: 37792491

    1. On 2024-02-17 21:21:04, user arva43 wrote:

      Hi,<br /> Please note that some of your figures are unreadable, as they were uploaded in mirror mode, and the quality of some of them is quite poor. <br /> Bests

    1. On 2024-02-16 19:24:51, user Joerg Deutzmann wrote:

      Exceptional work and a big step in improving productivity in MES systems! The authors did a great service to the field by highlighting that MES has the potential to compete with gas fermentations! Congratulations!

      In case my quick comment from earlier gets published, I apologize for the partial double post. However, after reading the paper more carefully, I wanted to amend the post and add the following:<br /> 1) One concern regards the presentation and perception of the data, which could provide an unjust hurdle for others in the field when publishing their MES improvements. While nothing is stated wrongly, the abstract already implies a >200-day production of carboxylic acids at an unprecedented rate, efficiency, and current density. However, the stated maximum values are derived from what seems to be single measurements (CP) or a series of two values (CA) in a time series and represent spikes in productivity. Thus, other studies that might achieve improved average KPIs, e.g., stable production rates between the average rate and the peak rate achieved in this study (which seem to differ by a factor of about two; ~20 kg/m3 vs. ~40 kg/m3 butyrate in Fig. S4A, for example), could be regarded as not improving the field, because the production rates presented in the abstract and highlighted in the main text give the impression that higher rates have been achieved already during long-term operation. <br /> This leads me to a quantification question: How confident are the authors regarding the accuracy of the values of individual measurements? The data is significantly scattered, which could be due to a scatter in the sampling and quantification methods or due to biological fluctuations. While it is certainly possible that the microbial system performs exceptionally well for a few days only to subsequently crash and almost halt production for the next few days, a high scatter in the quantification methods (i.e., high measurement or sampling uncertainty) could result in a similar data profile. Unfortunately, the temporal resolution of the data is not high enough to clearly show biological fluctuation (with the potential exception of the 2-4 point peak in Figure 4D). Without clear evidence that the exceptional production rates and increases in product concentration are indeed caused by true biological fluctuations, highlighting these occasional values throughout the manuscript seems misleading. Even if these production rates are the result of biological fluctuations, a more detailed discussion of the highly fluctuating performance and its impact might be warranted. If dense biofilms are prone to cause these enormous fluctuations that would open an exciting and important field of research for biofilm-based MES. Nevertheless, the described system seems unable to continuously produce the exceptional amounts of product frequently highlighted in the manuscript. Therefore, reporting averaged production rates (over at least 3-5 measurements) together with peak values in sections like Abstract, Summaries, Conclusions, and Highlights (which are most likely to be posted most visibly and therefore picked up by data mining and AI nowadays used to gather data) would be a more honest representation of the results and seems more appropriate to me. The rates and other performance indicators are extraordinary enough, even without the focus on a few exceptional values.

      2) Furthermore, a comparison to “state of the art” MES systems and the statement that “biofilm-based MES have so far outperformed MES driven by microorganisms in suspension by several orders of magnitude” should be backed by values cited from the appropriate sources. The rates and other KPIs for studies cited in the paragraph starting line 59 are not mentioned to allow verification of the order of magnitude statement. Further, I would like to add that we recently also achieved integrated electrosynthesis of acetate with suspended cells at 40-95% CE and at rates that approach glucose-fed chemostats at high acetate titers, which is in the order of the “state of the art” KPIs cited later in the paper (https://doi.org/10.1016/j.b... and supplements). A direct comparison to a variety of studies in multiple KPIs instead of hand-picking pair-wise comparisons would help to paint a more holistic picture of the improvements this study adds to the field.

      3) The microbial biomass reported is very high. Even to a degree that almost sounds impossible. Do the authors report wet weight or dry weight? I assumed dry weight because the cited reference states “Consequently, assuming νN,X = 0.2 molN molX−1 as coefficient of nitrogen in the elemental formula of dry biomass, (Popovic, 2019) the total amount of biomass in the reactor was obtained… “.<br /> 390 g/L dry weight would exceed the dry mass density of the cytoplasm of E. coli (https://doi.org/10.1371/jou....<br /> Further, water content is essential within biofilms and has been determined for several pure cultures and mixed biofilms and usually ranges from 70-98% (e.g., https://doi.org/10.1080/104..., whereas the maximum water content at 390 g/L cell mass would be less than 65% and exclude larger flow channels. Further, the cathode volume presumably also includes the carbon felt (assumed density 0.1g/cm3 with a carbon density of 2g/cm3 = ~5% of the cathode volume). Thus, less than 60% of the cathode compartment volume would be water. To push 2 cm/s of fluid through such a dense biofilm seems almost impossible to me! <br /> Could you provide a little more detail into how this incredible biomass density was calculated and whether this density is comparable to other biofilm systems? Further, do you assume EPS is in the biofilm, and would this contain N as well? Could ammonia pass the Cation Exchange membrane and be lost? Could ammonia (or other volatile N-compounds) be stripped in the bubble column and impact the biomass quantification in a long-term experiment?

      4) Why focus the comparison to other non-MES systems on syngas fermentation? H2/CO2 gas fermentation would be more similar to the MES process. For Example, Kantzow and colleagues produced 148g/L/d acetate or (>2.4 gc L-1 h-1) from H2/CO2 in a continuous system with suspended cells (10.1016/j.jbiotec.2015.07.020). Further, Figure 5 B is likely not the best visualization of the comparison to other studies, because the points of all comparison studies cannot be distinguished on the [X] axis. The SI table containing the values would be more informative than this figure in the main MS.

      I’m happy to discuss more and again congrats to a MES system that performs so well!

      P.S. Based on your experience with this system, do you think Clostridium luticellarii and Eubacterium limosum would be good candidates for pure culture MES?

    1. On 2024-02-13 04:16:02, user GN wrote:

      Great paper and fantastic use of iPOND to examine the DNMT1-DNA adduct proximal proteome.

      I would like to get clarification on something on this paper if possible.

      Lines 325-327 state that the data shows SUMO-dependent ubiquitylation (of DNMT1-DNA adducts) is promoted by RNF4 and TOPORS. Perhaps I missed it, but I could not see direct evidence for ubiquitylation in the data figures. I had interpreted that the authors were inferring ubiquitylation from the effects of TOPORS/RNF4 KO on DNMT1-DNA adducts and the known roles of these enzymes.

      Would appreciate if the authors or anyone else could help clarify this.

      Thanks<br /> GN

    1. On 2024-02-12 14:59:12, user Dianne Little wrote:

      Nice set of recommendations that will be useful for the field. Some of the citations in text don't match correct citations in the refence list.

    1. On 2024-02-09 14:44:59, user Christopher Ours wrote:

      The prior comment was written by Leslie Biesecker, Yosuke Mukoyama, Marjorie Lindhurst, Shaima Raji Abdul Rahiman Sirajuddeen, and Christopher Ours

    2. On 2024-02-09 13:32:31, user Christopher Ours wrote:

      It is good to see that an organoid model of the AKT1 c.49G>A p.Glu17Lys variant has been created. It may turn out to be useful for studying Proteus syndrome. Unfortunately, the preprint by He et al includes a number of errors and misconceptions about Proteus syndrome, previously published results, and general concepts about therapeutic research.

      It is odd that they expected to find the Proteus syndrome variant in a lymphoblast cell line (Coriell GM12209) as it has been shown that no living patient with Proteus syndrome has the variant detectable in a peripheral blood sample (PMID 21793738). In that publication, only two of 38 blood samples were positive and both of those were from deceased patients and could not be replicated. As stated in GeneReviews “It is strongly recommended to not use peripheral blood for molecular genetic testing in individuals with suspected PS.”<br /> The authors refer to our work (PMID 31194862) when they state: “Chimera animal models9 with germline transmission of the conditional AKT1 allele recapitulate the clinical manifestations of PS syndrome in the skin, bones, and vasculature…” It makes no sense to refer to chimeras and germline transmission. As well, these animals did not have an abnormal skeletal phenotype, nor did they have significant skin manifestations.<br /> The statement “Animal models are also unsuitable for identifying patient-specific treatment responses due to variations across species.” is perplexing. Animal models are widely used to model therapeutic effects. It is unclear what is meant by “patient-specific”.<br /> They claim their model is “consistent with the vascular malformation systematically seen in Proteus syndrome patients”. Indeed, there are no manifestations of the disorder seen “systematically”. The disorder is mosaic and thus not all patients have any particular manifestation.<br /> It makes no sense to refer to a “50% mosaic mutation”. The correct term to use here would be ‘variant allele fraction’. In a constitutional heterozygous genotype, VAF is 50%. If 50% of cells harbor a heterozygous variant, the VAF is 25%. It is nonsensical to say that a given mutation level is “mirroring clinical observations”. Since every patient has a different level of VAF and that level varies across tissues, it is not clear that this claim has any meaning.<br /> It is not correct that miransertib “…has shown efficacy in reducing facial bone overgrowth and the size of cerebriform connective tissue nevi in PS patients after one year of treatment, although a survey of 41 patients indicated no significant improvement in overall clinical outcomes as per the Clinical Gestalt Assessment (CGA) for PS10” (10 is the citation for PMID 35461279). In fact, that publication included no data on treatment.<br /> The authors seem to have misinterpreted our publication on vascular abnormalities in mouse models of Proteus syndrome (PMID 33030203). While the AKT1 activating variant caused an abnormal honeycomb-shaped capillary network with increased density of endothelial tubes, the key conclusion of that paper is that this variant caused a defect in the vascular remodeling of the capillary network, preventing the formation of the higher ordered vascular branching network. The He et al paper should discuss a technical limitation of their vascular organoid method in analyzing vascular remodeling in vitro.<br /> It is surprising that the methods section of the publication does not include any information on the sourcing of the therapeutics used in the study. It is important to include this in the methods as there are numerous sources of therapeutic compounds that are of dubious quality.

    1. On 2024-02-08 20:50:30, user Lin-xing Chen wrote:

      Dear authors, congrats!

      I found that it is really difficult for me to understand the figure caption of Figure 4a, could you please explain?

      And, could the metagenomes reported in this study be re-analyzed freely by other researchers?

    1. On 2024-02-07 17:53:03, user Thomas Munro wrote:

      This is very interesting. Other possible electron donors could be radicals generated by the crystallization conditions, e.g. HEPES and other buffers, residual nickel from affinity chromatography, and certain N-terminal sequences. I've argued that the ligands in 5c1m and 1jvn have undergone radical reactions for this reason: https://doi.org/kx34

    1. On 2024-02-07 14:06:01, user Antriksh Srivastava wrote:

      This is a great work, it directly relates to my recent publication in PCE (10.1111/pce.14821), we tried to quantify the effects of stomatal conductance reduction both positive and negative from a modelling perspective.

    1. On 2024-02-07 06:53:30, user Zia Mehrabi wrote:

      Note: While the percentage losses produced in this paper seem to check out, an error does exist in the last equation for the monetary impacts. Specifically, the multiplication of gross production value by percentage losses will not represent the total economic costs. To obtain these monetary impacts you need to subtract the observed value from the counterfactual production value. In practice the influence of this error is small in the study context, where the percentage cumulative losses are small (~5%). But any work building on this could correct it for more precise estimates. Fixing it will become important when trying to estimate economic impacts of individual events, where the percentage losses can be considerable. ZM.

    1. On 2024-02-06 12:27:16, user David Wilson wrote:

      Figs 1C and 4A: It is not possible to determine with high confidence the source of the GFP signalin the shared face between endodermis and pericycle. Therefore it is very risky to consider this signal as Endodermis inner lateral. In fact, in panel 1C magnified, the signal seems from the pericycle (based on the orientation of the GFP arches).

    1. On 2024-02-05 10:41:44, user Alessandro Pesaresi wrote:

      The apparent noncompetitive inhibition observed in the study can be attributed to the experimental conditions employed—namely, the use of a low inhibitor-to-enzyme concentrations ratio ([Inhib]=0-100 nM, [Enzyme]=150 nM). Under conditions where the [I]/[E] ratio is less than 10, the binding of substrate to enzyme leads to substrate depletion, resulting in the apparent emergence of an uncompetitive inhibition component. Consequently, it is reasonable to suggest that the inhibition of 3CLpro by Ensitrelvir is just competitive, and the apparent binding to the ES complex is likely an artifact.

      Despite these findings, the applied analytical methods in the paper show promise. It would be worthwhile to consider exploring the investigation using the entire protein substrate(s) rather than a short peptide. Such an approach has the potential to provide valuable insights into the mechanism of enzyme-substrate recognition, illuminating potential enzyme recognition sites that are distant and independent from the catalytic cleft.

    1. On 2024-02-05 10:31:17, user Christiane Dahl wrote:

      You may want to have a look at Löffler et al 2020 (Front Microbiol 11 578209) where the occurrence of two sets of dsr genes in Nitrospirae bacterium CG2_30_53_67 has already been described. It has also been mentioned that that Nitrospirae bacterium CG2_30_53_67 may contains one dsrABL set specifically adapted to sulfur oxidation and the other specialized for sulfite reduction. In addition, on the same page (7) several dsr gene sets in Gailellales (a member of the Actinomycetota) bacterium SURF19 are mentioned. The article by Löffler et al 2020 is not cited in the current preprint but should be referenced. <br /> In line 99, the following is stated: "It has been suggested these organisms have the potential to switch between reductive and oxidative sulfur cycling pathways." The statement comes without a reference. It would be good to insert one.

    1. On 2024-02-02 14:42:58, user Claus Loland wrote:

      This is a very interesting paper addressing an important long sought issue. The involvement of K+ counter transport in SERT has been known since the beginning of the 1980'ies, but its actual binding site has remained elusive. The binding sites for Na+ and Cl- have been solved by cryo-EM and since K+ binding seems to be competitive to Na+, it is logical to look for K+ in either of the two Na+ sites.

      I must post two comments for this paper.

      1. In the endeavor of elucidating the functional role of K+, we found that it also plays a role in the transport of dopamine by the dopamine transporter (Schmidt et al. 2022 Nat Commun). The authors comment on this finding by stating that we did find an interaction with K+, but "it has been more difficult to demonstrate that the K+ efflux is coupled directly to dopamine uptake ". In the paper we show that it binds; we show that it increases the transport rate and concentrative capacity of dopamine. This is like what is shown in this paper. We also show that the rate of K+ efflux is increased in the presence of dopamine. Collectively, I find that quite strong. Just as strong as the data herein. So did the reviewers.

      2. I do not understand the bell-shaped curve for the APP+ uptake into proteoliposomes. Why does the fluorescence decrease? Is is photobleaching? As APP+ is being transported, the gradients start to dissipate. SERT will eventually go in an exchange mode expelling as much APP+ as is transported. The transport is saturated if you wish. It should not decrease unless external APP+ has been removed. Any take on this?

    1. On 2024-02-01 18:30:26, user Plough Jogger wrote:

      Is this paper real, as in, has anyone replicated it and gotten it to work? I ask because it was posted in 2017 and this group usually follows up with a peer-reviewed version, yet, I don't think there is a peer-reviewed version and its been 6 years.

    1. On 2024-02-01 17:02:23, user Joe Burdo wrote:

      Regarding this comment in the in vivo VTA results section (Figure 4): "Following a 2-week incubation period, ....." does this mean that two weeks passed between MEND injection and MF stimulation? If a calcium influx was observed in this scenario, the interpretation is that MEND persisted in the VTA for >= 2 weeks, yes? That's a pretty important finding, one to be highlighted more if so.

    1. On 2024-02-01 02:33:33, user Tania Gonzalez wrote:

      This pre-print is now peer-reviewed, edited, and published at Biology of Reproduction. Main data stays the same so these pre-print supplemental spreadsheets are safe to use. For the final version, we added details on the specific genes used to identify decidua "contamination" during quality control, combined numbers of protein coding and long noncoding genes (the pre-print mostly focused on protein coding only), included a comparison to our single cell RNA-seq (Sun et al 2020), added immunofluorescence for select genes, and added more about the biological significance of our results. [PMID: 38271627] https://doi.org/10.1093/bio...

    1. On 2024-01-30 21:38:19, user Vitaly V. Ganusov wrote:

      [NOTE: This preprint was discussed in the Journal club of Texas Biomed in Jan 2024. The listed comments are the result of that discussion]

      Summary

      It has been implicitly assumed that all mosquitoes, carrying Plasmodium sporozoites (SPZs) in their salivary glands (SGs), are infectious to their mammalian hosts. One recent study (Aleshnick et al. 2020) challenged this assumption by showing that the probability of mouse infection per a single mosquito bite increases with the number of SPZs the mosquito carries in the SGs. Here, authors extend this previous work to investigate how the number of SPZs in the salivary glands relates to the number of oocysts in the mosquito's gut and to the number of SPZs that mosquitoes expelled during probing on artificial skin. The authors went through many experimental steps to rigorously quantify the SPZ number (in mosquito and expelled) and the number of intact and ruptured oocysts. Authors also used two different ways to generate SPZs in mosquitoes - by feeding the mosquitoes blood culture of gametocytes and by feeding mosquitoes on blood from Plasmodium-infected individuals. While previous work focused on murine Plasmodium species, this study looked at human parasite (Plasmodium falciparum) that is likely to be more relevant to human malaria. The results are in line with several previous findings: the number of SG SPZs correlates with the number of oocysts and with the number of SPZs expelled into the skin, and mosquitoes with more oocysts have SPZs in SG earlier.

      Positive feedback

      There are many things to like about this paper. Although the question is not new, the authors approached it with high rigor of experimental design and performed many troubleshooting analyses (many of the latter are shown in Supplement, e.g., Fig 8). The experimental procedures are well described and problems associated with the data are mentioned. The analyses performed are mostly straightforward. A combination of different techniques (dissection, microscopy, PCR) provides a more comprehensive understanding of system. Studying human malaria parasites is important, and measurements done for mosquitoes fed on cultured gametocytes and on blood from P. Falciparum-infected volunteers are very interesting.

      Major comments

      1. The authors performed some basic statistical analyses but, in some cases, the choices of the models used were not clearly justified. For example, for looking at relationships between oocyst density and infection, authors used logistic regression. In correlation analyses, spearman rank correlation was used. Why were those the right choices? Using linear regression (Pearson correlation) could be more sensitive to trends, Deming regression is more appropriate when there is an error in both variables, and using alternative models to look at relationships between oocyst density and SG SPZ number, or SPZ number and expelled SPZs could reveal useful information (e.g., for different alternative models see PMID: 32453765).

      2. Additional points regarding statistical rigor/analyses. What is exactly oocyst density and infection prevalence (Fig 1B)? How can oocyst density be <1? Is that an average per several mosquitoes? This is not well explained. At 100 oocyst density there are still not 100% infections - how is that possible? Is that because time of sampling is too early and SPZs did not yet develop? Perhaps structuring the data on time since feeding could reveal interesting patterns. Also, analysis of binned data (e.g., Fig 2A and others) is not a good way to analyze data because answer may depend on binning choices (e.g., 17960243). Finally, presenting results when excluding mosquitoes that did not deposit SPZ as main result is incorrect as this likely introduces bias.

      3. Generating calibration curve and determining threshold for estimating the number of SPZs in the sample is important. How did you generate the dilutions of the SPZ numbers and how did you make sure that you had 100k vs. 5 in other samples? Did you count the 5 SPZs or was that estimated? If estimated, did you take into account errors with the dilution? I wonder if plotting the data using a log-log plot and doing a linear regression analysis would be useful for Fig 1A. Also, for Suppl Fig 2A, how many samples are the curves based on? Should that be plotted for every sample analyzed?

      4. While I appreciate the generate a robust artificial skin model, I wonder how realistic is using artificial skin with blood. In real skin, mosquitoes must probe to find the blood vessel while with the artificial skin, mosquito may find blood every time of probing - could that bias the results in some ways?

      5. Previous work established the importance of probing time and unimportance of taking the blood meal for infection (32453765). What were these parameters in your experiments and could they explain difference in how SPZ numbers in SG relate to number of expelled parasites?

      6. The finding that there is not "threshold" in the number of expelled parasites with SG SPZ number is interesting. What if you assume that infection occurs only if the number of deposited SPZs is above some critical number - will then you able to "match" the threshold model found previously (32453765)?

      7. One interesting mosquito had 9 ruptured oocysts but only about 2k SPZs - how is this possible? Where are the other SPZs? Could it be that they are in the “missing” parts of the salivary glands? That seems unlikely.

      8. The authors have very rich dataset but did not make conclusions that could be also interesting. For example, how many SPZs are made per oocyst given these data? What is the summary of maturation process from these data? Having a cartoon with steps and quantifying them (e.g., what is the efficacy of transfer of SPZs from gut to SG?) could be very useful (e.g., as Figure 6 or as graphical abstract).

      9. In their sporozoite quantification assay, the authors used the COX1 mitochondrial gene target. This gene is typically present in multiple copies in eukariots. However, it is not mentioned anywhere in the manuscript how many copies of the gene are present in the P. falciparum mitochondria. It is also not mentioned nor tested if all the different populations of P. falciparum used in this study have the exact same COX1 copy number. If they share the same copy number, then the sporozoite quantification assay presented is indeed robust. However, if this number of copies varies between P. falciparum populations, and moreover, within a population, then the quantification assay will lack robustness. In such cases, it would have been more valuable to use a single-copy nuclear gene. Could the authors provide more information regarding the COX1 copy number in P. falciparum and check that this number of copies is consistent across all populations?

      10. When looking at the Methods section, I realized that the authors have used two different populations of P. falciparum to infect their An. stephensi: NF54 from West Africa and NF135 from Cambodia. When measuring the duration of the EIP, the authors stated that '…mosquitoes were fed with P. falciparum NF54 and NF135 gametocytes…'. Did the authors infect their An. stephensi mosquitoes with a mix of these NF54 and NF135 gametocytes, or did they infect each batch of mosquitoes with one parasite population (i.e. either NF54 or NF135)? <br /> If they infected each batch of mosquitoes with one parasite population, why is this not reflected in the text and on the figures? Did one population produce more oocysts and sporozoites than the other? <br /> If the authors mixed NF54 and NF135, what is the rationale for doing this? I suspect these two populations also vary in their ability to produce gametocytes. Did the authors observe recombination between the two populations? <br /> Also do An. stephensi mosquitoes exhibit the same host-parasite compatibility/co-adaptation with both populations of parasites? If not, this could be a possible explanation for the weak correlation found between the total sporozoite load and the sporozoite expelled. I suggest that the authors carefully clarify these points, as it is quite confusing at the moment in the present manuscript.

      Minor comments

      1. Because of very large difference in SPZ numbers, plotting the data on log (or log-log) scale would help to see the data scatter better. Also, perhaps in some cases highlighting ranges of oocysts and/or SPZ number in SG that are "realistic for the field" (e.g., by using gray boxes) could be useful.

      2. The authors could calculate the total number of SPZs in the mosquito and the skin which is better than that done in previous study that calculated residual SPZ number in SGs (32453765). However, would any conclusions that look at correlating SG SPZ number with other parameters change if you consider the remaining SPZ number rather than total per mosquito (e.g., Fig 2A, 3A/C, 4B, etc)?

      3. In Fig. 1D: What is the rationale for comparing mercurochrome staining of oocysts at day 8 with anti-CSP immunostaining at day 18? Why not compare both stainings at both time points (i.e., mercurochrome at day 8 and immunostaining at day 8, as well as mercurochrome at day 18 and immunostaining at day 18)? Otherwise, it's challenging to compare different staining methods at different time points and draw robust conclusions about the number of oocysts present.

      4. In their discussion, the authors never mentioned that potential co-evolution between hosts and parasites could explain why they observed a tighter correlation between total sporozoite load and sporozoites expelled in the context of the infection conducted in Burkina Faso. Indeed, for this experiment, naturally circulating P. falciparum parasites were used to infect their sympatric host, An. coluzzii. However, this is not the case for the lab experiment, where two geographically and genetically distant parasites (i.e., NF54 from West Africa and NF135 from Cambodia) were used to infect An. stephensi, Nijmengen Sind-Kasur strain, which originates from Pakistan and has therefore not coevolved with either of these parasite strains.

      5. In Fig. 2B: When examining the EIP, authors chose to assess the number of sporozoites per mosquito at days 9, 10, and 11 post-feeding with gametocytes. They also looked at the number of sporozoites in a single oocyst and compared these numbers at days 9 and 10. Why not also investigate day 11, as they did for the EIP? Do the authors know the dynamics of sporozoite growth in an oocyst? Does it exponentially increase until the oocysts burst? Does it reach a plateau at some point and then burst? It would be very interesting to explore these aspects.

      6. I did not see that the data are made available. Per FAIR principles (https://direct.mit.edu/dint..., the data should be shared with the community so they can be further analyzed.

    1. On 2024-01-29 20:25:30, user Ekaterina Voronina wrote:

      After revisions, this manuscript was accepted to Genetics, with minor changes. The link will be provided when available.

    1. On 2024-01-28 16:58:08, user William Foley wrote:

      Interested to see your manipualtive experiment with PEG blocks and herbivore diet. I think that the use of PEG as an adjunct to herbivore diets has outstripped any evidence of what it really does. Which tannins are bound by PEG? All tannin groups or only some?. Why is the emphasis on condensed tannins and not on elagitannins? Were elagitannins absent from your savvanah site? I think its important to acknowledge that we don't really understand what PEG does! Windley et al (2016) made some useful comments on this point but data is sparse. The interaction between tannins and herbivore nutrition is not simple with both positive and negative effects and I think your article would be stronger if this was acknowledged.! Finally the studies by Foley and Hume and Marsh did not take place in penned domestic ruminants as you state nor did they focus on diet selection as claimed.

    1. On 2024-01-26 22:14:47, user Julia Trevorrow wrote:

      Summary: <br /> In a field where conservative treatment options are limited for chronic spinal pain, it is inspiring to see molecular research on another potential pharmacological option.

      Strengths: <br /> (1) As opposed to opioids, SSRIs and NSAIDs, the usage of targeted senolytics to clear senescent osteoclasts addresses chronic pain at the cellular level. This provides a new modality among the available treatment regimens.

      (2) This is one of those articles where we see the promise of molecular medicine. The RNA primer selection was highly relevant and specific for neuronal development, differentiation and metabolism. These served to accurately assess the relationship of spinal hypersensitivity and the mechanism of action of senolytics. Additionally, this research helped to further tease apart the signaling pathways associated with senescence and senolytics.

      Weaknesses: <br /> (1) With regards to the sham group, there is a possibility that detachment of the posterior paravertebral muscles from L3-L5 possibly induced a level of mechanical instability. To mimic the stress of surgery in the sham group, saline injections into the paravertebral muscles could mimic the muscular disruption of surgery without inducing spinal instability.

      (2) Inclusion of a non-surgical "sham" group, female mice, and surgically altered, aged mice, in the control set would be helpful in identifying the mechanism of drug action.

      (3) Although mice are not bipedal, it may be helpful in preparation for the use in humans to evaluate the L5 and S1 endplates as well since the L5/S1 endplates are more likely to be degenerative in adults than L4/5.

      (4) It was difficult to assess the behavioral effects since there was no baseline prior to surgery, after surgery and before the administration of navitoclax.

      (5) Additionally, in studies moving forward, the risks and benefits of the systemic side effects of navitoclax should be evaluated.

    1. On 2024-01-24 09:55:31, user Francois Paquet-Durand wrote:

      Dear Readers of this pre-print,

      Please note that the TRIP reviews posted alongside the pre-print manuscript relate to the first version of the pre-print. As of 24. January 2024 the revised version is online, which addresses many of the earlier reviewer comments.

      Nevertheless, there are always points and questions that are still open and as the authors of this manuscript we would very much like to hear your opinions.

      François Paquet-Durand

    1. On 2024-01-23 15:37:22, user Nick Bauer wrote:

      The approach described herein is quite nice and simple, but it is missing some key details and discussion to understand the benefits of the approach and its potential limitations.

      The color glass filter used is not specified, and its performance is only characterized within a small range of wavelengths, which limits the fluorophores that can be used and the total number that could be used in this system, unlike some of the previous methods.

      It can of course be a strength that fluorophores in a small spectral range can be used for 2(to-4?)-plex imaging instead of having to use well-separated fluorophores which have wildly different photophysics, so that potential limitation is not in any way fatal. The paper would benefit from more discussion of how the present work fits into the current landscape, both positives and negatives.

    1. On 2024-01-23 09:00:36, user pedro estralin wrote:

      Raman and Tensile Probe images indicate manipulation of repetitive images and writing in the photos and are not reliable.

    2. On 2024-01-20 09:46:36, user professor esterdo. mikail wrote:

      the structure with the hydrogel should have the hydrogel structure such as probe tensile, DSC, swelling behavior, and characterization for the hydrogel at first. and then for the microonedle. maybe it was composoite not hydrogel.

      (Maybe electrochemical mesaurment was done without the surface .

      on the hand the microonedle should be analyzed for MTT test as biodegradability

      the antimicrobial test also not confirmed in the figure . it should be repeat

    1. On 2024-01-22 20:56:44, user Anonymous wrote:

      Version 1 of this manuscript could be improved by extending the time axes in figure 8 (antigen 6 sensorgrams) to match the times shown in figures 3-7 (antigens 1-5) and figures 9-10 (antigens 7-8). Currently, the time is cut off at about 400 seconds, shortly after the beginning of the dissociation phase of the measurement, whereas the other figures all extend out to around 1300 seconds.

      Usually, the reason for including sensorgrams in a published article is to convince the reader that the regression fit curves (red) approximately overlay the data curves (blue-green). If the shapes of the curves match, then it's a visual confirmation that the model fits the data, and that the -log10(KD) values obtained from the regression are therefore trustworthy. In figure 8, with the latter ~900 seconds of data missing, it's more difficult for the reader to make that determination.

      This is important because it's later shown in figure 24 that antigen 6 apparently gets some of the best results of the entire study. Compared to figures 19-23 and 25-26, the antibodies designed against antigen 6 have both the largest total number of binding affinities extending beyond the reference (i.e. largest number of dots above the line), and the highest affinity -log10(kD) values appear to extend farthest past the reference as well (i.e., the dot values along the vertical axis go farthest past the line).

      A key conclusion stated in the abstract (that the IgDesign tool can produce "improved affinities over clinically validated reference antibodies") rests on the premise that the tails of the affinity distributions plotted in figures 19-26 can sometimes extend past the reference line. However, if it turns out the -log10(KD) values aren't reliable to begin with, due to a poor regression fit result, then this conclusion is weakened or possibly even invalidated.

    2. On 2024-01-03 18:29:59, user anonymous wrote:

      Section D.3 of version 1 of the manuscript states that SPR binding affinity measurements on individual DNA variants are repeated four times: two duplicate measurements per experimental run, multiplied by two technical replicates of each run.

      In section F.3, version 1, figures 19-26 show SPR binding affinity measurements for several dozen DNA variants per antibody. The values plotted for -log10(KD)(M) appear to represent the sample mean of each set of four SPR measurements. These figures could be improved by also including error bars representing the sample standard deviation of each set of four measurements.

      Including error bars is important because the precision and accuracy of SPR experiments varies depending upon experimental context. Although some authors report standard deviations of +/-10% or better (see, for example, Table 1 in Brown, M.E. et al., (2020) "Assessing the binding properties of the anti-PD-1 antibody landscape using label-free biosensors" PLoS ONE 15(3) doi: 10.1371/journal.pone.0229206), the repeatability of SPR experiments can be impaired by many experimental artefacts: baseline drift, bulk shift discontinuities, mass transport effects, non-specific binding, manufacturing batch variation of sensor chips or other consumables, fitting to an incorrect kinetic model, etc.

      If the measurement uncertainties are large enough, then the observations of stronger binding affinities than the reference antibodies may not be statistically significant. Affinity values supposedly above the references could be explained away as random statistical noise around a true binding affinity which is actually left unchanged relative to the reference (presumably because the sequence mutations for those DNA variants ultimately had a neutral impact on the final shape of the folded antibody).

      This is a critical point, because if IgDesign does not actually produce mutant sequences with binding affinities signficantly above the reference, then it would weaken a key conclusion of the paper: in such a scenario, IgDesign would not be useful for affinity maturation.

    1. On 2024-01-22 17:35:49, user Mohieddin Jafari wrote:

      After reviewing your paper, I found it to be a little bit confusing. In the first Figure, you reference Fargpipe and DIA-NN, but the case studies appear to be centered around Spectronut. Moreover, it's unclear in the "Mass Spectrophotometry Methods" section which specific case study you are addressing. Do you have any updated version of this manuscript?

    1. On 2024-01-22 05:41:51, user Fraser Lab wrote:

      https://www.biorxiv.org/con...<br /> The paper aims to advance structure-based drug design by using serial crystallography to study room-temperature ligand-bound structures of the soluble epoxide hydrolase protein and discussing the implications for drug discovery. The major success of the paper lies in its innovative approach to microcrystallization and the use of serial crystallography, which allows for detailed room-temperature structural analysis. This technique provides a more dynamic picture of protein-ligand interactions, as evidenced by the observed potential temperature-dependent differences in ligand-binding modes and the improved resolution of flexible loops. The primary limitation of the method is the extended data collection time (1-2 hours).The paper does not fully explore the potential for reducing data collection time for these highly redundant datasets.

      Major Points

      1 - Data Collection Time: The data collection time of 1-2 hours is unusually long. The discussion could focus more on the prospects for reducing this time. An example is in the high redundancy in the “Table 1”. It would be beneficial to see an analysis of data subsets or suggestions for online processing to determine the minimal data collection time needed for viable interpretation. The paper could elaborate on the potential for automation in this method, especially given the lack of "looping" in the process that would help trade off for some of this concern.

      2 - Indexing Rate Variability: The paper should address the variability in indexing rates between different compounds. Understanding and explaining this variability is crucial for the broader application of this method.

      3 - Comparative Literature: Other papers discussing RT ligand binding differences could be cited, including recent works: <br /> - https://pubmed.ncbi.nlm.nih...<br /> - https://elifesciences.org/a...

      4 - Visualization Improvements in Figure 7: A correlation plot or residue plot in Figure 7 would provide a clearer visualization of the differences and regions of increased flexibility at room temperature.

      5 - The improved resolution of certain regions is not entirely unexpected (Fraser 2011 has other examples). The authors may be interested in reading Halle's study (PNAS 2004) on cryocooling for reasoning why the loops may be more ordered.

      6 - Interpretation of Compound Five: The conclusion about the change in pose for Compound Five requires further clarification. Detail the different refinements that led to this conclusion, considering alternative explanations like reduced occupancy should be presented.

    1. On 2024-01-20 17:19:23, user Lihua Song wrote:

      A revised draft will be submitted. I extend my deepest gratitude to the reviewers and editors for their invaluable suggestions and insightful comments, which have significantly contributed to the improvement of this work.

    2. On 2024-01-18 07:57:40, user Lihua Song wrote:

      There have been some folks trying to misinterpret our work as gain-of-function research. Let me be clear – that is not the case. What we've done is simply tested a passaged virus mutant, nothing more. The ACE2 humanized mice used in our experiments are unique and do not exist in nature. The outcomes from these tests cannot be applicable to humans.

    3. On 2024-01-17 12:14:46, user Lihua Song wrote:

      This preprint paper is being misinterpreted on social media. I would like to state the following facts:

      1. The GX_P2V virus has been published in Nature in 2020 (doi: 10.1038/s41586-020-2169-0). It is not a brand-new virus.

      2. The GX_P2V(short_3UTR) mutant was published in Emerging Microbes & Infections in 2022 (doi: 10.1080/22221751.2022.2151383). This cell-adapted mutant is the actual isolate published in the Nature paper. So, the original GX_P2V virus was not isolated. Clearly the original GX_P2V virus in the pangolin sample has severe growth deficiency in Vero cells.

      3. The GX_P2V virus is not a human pathogen, although, based on molecular and animal infection experiments, it can infect a broad spectrum of host species, like human, cat, pig, golden hamster, mouse, rat et al. There is no evidence of the original GX_P2V virus circulating in these animals, not even consider this GX_P2V(short-3UTR) mutant. Please refer to publications: EMBO J, doi: 10.15252/embj.2021109962 and J Virol, doi: 10.1128/jvi.01719-22.

      4. The GX_P2V(short_3UTR) isolate is highly attenuated in in vitro and in vivo models. In Vero, BGM, and Calu-3 cell lines, the virus induced only mild cytopathic effects, notably failing to produce viral plaques even on the human lung cell line Calu-3. In golden hamster and BALB/c mouse models, the virus can infect the animals' respiratory tracts but did not result in any observable disease symptoms. The attenuated nature of GX_P2V(short_3UTR) was also validated in two distinct human ACE2-transgenic mouse models. Please refer to publications: Emerging Microbes & Infections, doi: 10.1080/22221751.2022.2151383 and J Virol, doi: 10.1128/jvi.01719-22.

      The attenuation of GX_P2V(short_3UTR) was also hinted in the Nature paper on the GX_P2V(short_3UTR) isolate (doi: 10.1038/s41586-020-2169-0). In Extended Data Figure 1, after infecting Vero cells for five days, GX_P2V caused noticeable cytopathic effects, but which were limited to cell rounding and mild cytolysis, which starkly contrasted with the severe cytopathic effects reported in SARS-CoV-2.

      1. The public has developed a high level of population immunity against GX_P2V due to SARS-CoV-2 immunizations and infections. Collectively, the biological safety risk posed by GX_P2V(short_3UTR) is extremely low. I don’t think there is any immediate risk of spillover into the human population. Please refer to publication: J Med Virol, doi: 10.1002/jmv.29031.

      2. Based on previous reports on ACE2 humanized mouse models with SARS-CoV-1 and SARS-CoV-2, there is significant variability in the outcomes of infection in these models, a topic extensively documented in the literature. A single ACE2 humanized mouse model does not constitute a reliable paradigm for evaluating viral pathogenicity. While GX_P2V(short_3UTR) proved lethal in our mouse model, it's important to consider that it did not cause disease upon infecting two other distinct ACE2 humanized mouse strains. The findings reported in this paper do not alter the fundamental nature of GX_P2V(short_3UTR) as being highly attenuated.

      3. Several other research groups have repeatedly reported the spillover risk of this virus based on its spike protein binding to human ACE2. Those reports have not caught much attention. In our study, using a unique lethal model, we inadvertently reinforced the perception that this virus has a strong tropism for human brains and causes 100% mortality. We need to revise this in the subsequent revision of the paper and provide additional clarification on the intrinsic attenuated nature of the virus.

      4. The GX_P2V(short_3UTR) mutant is a promising live attenuated vaccine against pan-SARS-CoV-2. Partial results can be found in this preprint paper: https://www.researchsquare.....

    4. On 2024-01-14 05:29:31, user Lihua Song wrote:

      We realize this manuscript misleads readers to believe that the attenuated pangolin coronavirus GX_P2V(short_3UTR) posed a spillover risk to human brains, resulting in a 100% mortality rate, which sparked panic among the public. This virus has no pathogenicity in normal animals. This manuscript necessitates revision to accurately state the abnormal nature of this mouse model, and the fact that these animal outcomes cannot be applicable to humans.

    1. On 2024-01-20 00:06:45, user Pamela Bjorkman wrote:

      This paper was published as: Cohen, AA, Gnanapragasam, PNP, Lee, YE, Hoffman, PR, Ou, S, Kakutani, LM, Keeffe, JR, Wu, H-J, Howarth, M, West, AP, Barnes, CO, Nussenzweig, MC, Bjorkman, PJ (2021) Mosaic nanoparticles elicit cross-reactive immune responses to zoonotic coronaviruses in mice. Science 371: 735-741. PMCID: PMC7928838 doi:10.1126/science.abf6840

    2. On 2024-01-19 03:55:33, user Pamela Bjorkman wrote:

      This paper was published as: Cohen, AA, Gnanapragasam, PNP, Lee, YE, Hoffman, PR, Ou, S, Kakutani, LM, Keeffe, JR, Wu, H-J, Howarth, M, West, AP, Barnes, CO, Nussenzweig, MC, Bjorkman, PJ (2021) Mosaic nanoparticles elicit cross-reactive immune responses to zoonotic coronaviruses in mice. Science 371: 735-741. PMCID: PMC7928838 doi:10.1126/science.abf6840

    1. On 2024-01-19 22:44:36, user Guest wrote:

      I think this is great work ! Have you ever checked this work, seems relevant: "Characterizing chromatin landscape from aggregate and single-cell genomic assays using flexible duration modeling" ?

    1. On 2024-01-19 12:36:25, user Rob wrote:

      Is there a link to the "A references file of 500kb of subtelomere sequences was<br /> assembled from each of the genomes" to reproduce the work?

    1. On 2024-01-19 04:01:11, user Pamela Bjorkman wrote:

      This paper was published as: Barnes, CO, Jette, CA, Abernathy, ME, Dam, K-M A, Esswein, SR, Gristick, HB, Malyutin, AG, Sharaf, NG, Huey-Tubman, KE, Lee, YE, Robbiani, DF, Nussenzweig, MC, West, AP, Bjorkman, PJ (2020) SARS-CoV-2 neutralizing antibody structures inform therapeutic strategies. Nature 588: 682-687. PMCID PMC8092461 doi:10.1038/s41586-020-2852-1

    1. On 2024-01-19 03:58:39, user Pamela Bjorkman wrote:

      This paper was published as: Barnes CO, Schoofs T, Gnanapragasam PNP, Golijanin J, Huey-Tubman KE, Gruell H, Schommers P, Suh-Toma N, Lee YE, Cetrulo Lorenzi JC, Piechocka-Trocha A, Scheid JF, West AP Jr, Walker BD, Seaman MS, Klein F, Nussenzweig MC, Bjorkman PJ. (2022) A naturally arising broad and potent CD4-binding site antibody with low somatic mutation. Sci Adv. 8(32): eabp8155.

    1. On 2024-01-19 03:57:14, user Pamela Bjorkman wrote:

      This paper was published as: Gristick, HB*, Hartweger, H*, Loewe, M, van Schooten, J, Ramos, V, Oliviera, TY, Nishimura, Y, Koranda, NS, Wall, A, Yao, K-H, Poston, D, Gazumyan, A, Wiatr, M, Horning, M, Keeffe, JR, Hoffmann, MAG, Yang, Z, Abernathy, ME, Dam, KA, Gao, H, Gnanapragasam, PNP, Kakutani, LM, Pavlovitch-Bedzyk, AJ, Seaman, MS, Howarth, M, McGuire, AT, Stamatatos, L, Martin, MA, West, AP, Nussenzweig, MC, Bjorkman, PJ (2023) CD4 binding site immunogens elicit heterologous anti-HIV-1 neutralizing antibodies in transgenic and wild-type animals. Sci Immunol doi:10.1126/sciimmunol.ade6364 *Co-first authors.

    1. Microsoft continues to participate directly in OpenAI's growth as a major shareholder.

      大多数人认为在修改了合作协议后,微软可能会减少其在OpenAI的股权投资,但作者认为微软仍然是OpenAI的主要股东,这表明尽管合作关系有所调整,但双方仍然保持着深度的利益绑定,这可能是一种非传统的长期战略伙伴关系模式。

    2. Revenue share payments from OpenAI to Microsoft continue through 2030, independent of OpenAI's technology progress, at the same percentage but subject to a total cap.

      大多数人认为随着OpenAI技术的发展,其对微软的支付可能会增加或调整,但作者认为OpenAI对微软的支付将保持固定比例且有上限,这表明OpenAI正在寻求更可预测的财务安排,不受技术进步的影响,这可能是一种反直觉的风险管理策略。

    3. Microsoft will continue to have a license to OpenAI IP for models and products through 2032. Microsoft's license will now be non-exclusive.

      大多数人认为微软会寻求对OpenAI技术的独家使用权,以保持其在AI领域的竞争优势,但作者认为微软的许可权变为非独家,这打破了传统科技合作中的排他性模式,暗示OpenAI正在向更开放的合作方式转变,可能为其他合作伙伴铺平道路。

    4. Microsoft will no longer pay a revenue share to OpenAI.

      大多数人认为微软作为OpenAI的主要投资者和合作伙伴,会继续通过收入分成来支持OpenAI的发展,但作者认为微软已经改变了这一模式,这可能表明微软认为OpenAI的技术已经足够成熟,不再需要这种财务激励,或者微软有其他方式从合作中获益。

    5. OpenAI can now serve all its products to customers across any cloud provider.

      大多数人认为OpenAI会完全依赖微软Azure云服务,因为微软是其主要投资者和合作伙伴,但作者认为OpenAI现在拥有了多云策略的灵活性,这打破了科技巨头间典型的排他性合作模式,暗示OpenAI正在寻求更大的自主权和市场机会。

    1. On 2024-01-17 14:11:41, user Boris Hedtke wrote:

      This paper was published in February 2023 in Plant Physiology with the modified title <br /> "Two isoforms of Arabidopsis protoporphyrinogen oxidase localize in different plastidal membranes" <br /> Plant Physiology, Volume 192, Issue 2, June 2023, Pages 871–885

      https://doi.org/10.1093/plp...

    1. On 2024-01-17 08:02:00, user Rasmus Kirkegaard wrote:

      Cool analysis. I would recommend that you consider upgrading your reference genomes from Unicycler to Trycycler https://github.com/rrwick/P...<br /> Unicycler was great for hybrid assembly when long reads were mostly for sorting the short read based contigs in the right order. But with newer data the quality of the long read assembly is much better and Ryan has made a nice guide for curating the remaining errors using illumina data to achieve a perfect genome.

    1. On 2024-01-16 21:38:49, user JongYoon Jeon wrote:

      Hi, just encountered this manuscript looking for a mutation rate and thanks to this I could find a source reference, Smeds et al 2016. However, you might want to double-check the paper since Smeds et al. reported 2.3 × 10^-9 as mutations per site per year, not generation, if I read correctly. Thanks,

    1. On 2024-01-16 17:21:01, user Letarov Andrey wrote:

      Important UPD to my previous comment.<br /> Today, 17 January 2024, I had a Zoom meeting with Mr. Berryhill, Ms. Gill, Dr. Smith and Prof. Levin. We discussed the issues regarding my negative reaction to this preprint and I was convinced that there was no intentional disregard of our work or of the work of the others. Although some of my criticisms remain, I no longer have any doubts about the integrity of the authors. I apologise for my hasty judgement in the original post.

    1. On 2024-01-16 14:43:15, user Reviewer1 wrote:

      This study investigates the distribution of food source partitioning, across major groups of the animal kingdom. The overarching aim is to create a global trophic pyramid of biomass, partitioned by food source. The authors collected a large dataset on diet composition from the literature and other sources, ensuring a broad taxonomic spread. They then estimate diet partitioning for major taxonomic groups (~class) by averaging species-level data, and further estimate partitioned food source biomass by multiplying with class-level biomass estimates. This is taken to be provide a representation of a trophic pyramid, and the findings are discussed in the light of this concept. The major claim of this study is that they find a middle-heavy trophic pyramid, with invertivory more prominent (by biomass) than herbivory.

      The study pursues a very interesting question in studying the trophic pyramid on a global level. The authors have invested a lot of effort in compiling a large dataset on species-level diet partitioning, and such a dataset would certainly be very valuable for species-level comparisons and analyses, such as the taxonomic distribution of feeding styles or the evolutionary history of feeding specialisations. However, such questions are not the focus of the present study. Rather, an attempt is made to convert this species-level dataset into a trophic pyramid of food source biomass. In the process, the authors make several sweeping assumptions and generalisations, resulting in analyses that are not at all well supported by the underlying data.

      First, the conversion of species-level data to class-level partitioning of food sources, by averaging the data from available species, assumes that the compiled species are representative of the group (class) as a whole, and that a simple species average would provide a meaningful group average. Both are highly doubtful and not supported by any data.

      Second, the assumption is made that the class-level partitioning of food sources can be transformed into a partitioning of diet biomass by a multiplication with that group’s estimated biomass value. However, this will yield the biomass of that specific partition (e.g., the combined bodymass of all vertebrate herbivores) and not the biomass of their diet.

      Third, species groups (and their biomass) are assigned to a trophic level by their food source type, which leads to the three categories “herbivores” (= primary consumers), “invertivores” (= secondary consumers) and “vertivores” (presumably considered as predators including apex predators as they are placed at the top of the pyramid in Fig. 2). This is a strong oversimplification and does not represent a trophic pyramid. Most worryingly, the category “invertivores” will lump many higher-level consumers (third-level, fourth-level…) into the secondary consumer category, which as a result has by far the highest proportion (= biomass in this analysis). Thus, one of the key claims of the study, that the global trophic pyramid is middle-heavy, is likely due to a methodological artifact.

      In summary, the study attempts a methodological shortcut for deriving a trophic biomass dataset from species-level data, without verifying the assumptions. At the current time, there appears to be no ready substitute for species-level abundance or biomass data. Until such data are available for the majority of organisms, analyses of trophic pyramids on a global level may be premature.

      Recommendations for the authors:

      As mentioned in my public review, I commend the authors on compiling such a large and potentially very valuable dataset on species-level diet partitioning. I believe such a dataset can be very informative for species-level analyses, or possible investigations into the evolution of such partitioning. However, such a dataset cannot be transformed into a trophic dataset without corresponding data on species abundances and/or biomass. Your attempts to perform this transformation without such data unfortunately fall short, as it requires a series of sweeping assumptions that are almost entirely unfounded by real-world data.

      I will attempt to explain my views in the sections below:

      Title<br /> The title is misleading: in the current form, the manuscript deals with many more analyses than the number of herbivore and predatory species in each class. Though as I mentioned, this species-level analysis is actually the most relevant (and valid) analysis in your study while the trophic pyramid aspect is not.

      Introduction<br /> You provide a very nice overview of the different concepts of trophic pyramids and their development over time. As you point out, all these variants of the pyramid include a measure of scale for each level, such as ‘abundance’, ‘biomass’, or ‘energy’. It is also implicit in this introduction that this concept considers multiple levels (L42: “…food chains…”, L45: “…and so on up to…”) and not just three as in your following analysis.

      Materials and Methods<br /> The success of the method hinges on the representativeness of selected species. This is highly unlikely, as data on diet composition will be much more readily available for large or well-studied organisms, which are not necessarily the ones that are the most important (by number or biomass) members of their class. The authors themselves acknowledge that for many groups, even with a minimum of ~500 species per group, still only ~0.3 to 1.3% of described species are covered for insecta, arachnida, mollusca and crustacea (L265-267). In addition, I would strongly argue that even with good taxonomic coverage, as is achieved for birds and mammals, calculation of the group average has to consider the highly differing abundance and/or biomass of separate species. To illustrate these points, I would like to highlight the study’s data on the arachnida (Figs. 1 and 4). About 20% of their diet is considered as “parasite vertebrate”, with a considerable biomass. Without knowing the details of the species that were considered, I would assume that the majority of these are ticks, as these feed on (mostly) vertebrate blood. Roughly speaking, we know of maybe 60 000 species of arachnida, of which perhaps 1000 are ticks. On the species level, ticks therefore seem to be highly overrepresented in the dataset, possibly because it is straightforward to infer their food source from their specialized morphology. On the other hand, the group arachnida does not seem to consider very many oribatid mites, of which there are around 12 000 known species that are almost exclusively detritivore. In addition, oribatid mites are known to be extremely abundant in soils, so their biomass is likely many times that of ticks. A similarly obvious over-representation in terms of diet and biomass occurs in the marine dataset with “vertivore crustacea”. Please note that I only picked some obvious examples here, but that the same issues will be prevalent in all animal groups.

      Indeed, I believe that your method “validation” using bird species data shows that your estimate can be very unreliable, even for a well-covered group such as birds. Your Results (L345-347) show that “the respective contribution of invertebrates and vertebrates switched from 56% and 8% in the estimate to 23% and 45% in the species-weighted partitioning”. These are very large differences.

      A further point I would like to raise: using an animal group’s biomass to gauge the biomass of the separate diet partitions seems to oversimplify matters. You are assuming that the body biomass equals the diet biomass. However, foods have very different nutritional content (e.g., carbohydrates/protein/fiber). A Panda and a Polar Bear may have fairly similar body weights, but the panda needs to eat much more plant matter biomass due to the poor nutritional content.

      Overall, the Methods section is a little disjointed, and is difficult to match to the Results section. Also, some of the chosen methods are not well justified or explained. E.g., <br /> - How were Wikipedia sources selected and “confirmed” (L130), or how was the literature searched (L132)? <br /> - How did you incorporate a diet category that only exists for a single class (“plant-derived, L150”)? <br /> - How did you deal with separate diet data for juveniles and adults (L157)?<br /> - L184ff: It remains unclear why you compare your global dataset to two location-specific datasets. What did you aim to achieve? A validation of the global dataset in this manner appears dubious, as local datasets may always remain location-specific.<br /> - What is your justification for collecting a further dataset on dinosaur diet? You mention that you aim “to test if herbivory is related to higher body mass and lower metabolic rate” (L206), but then compile only diet data for these dinosaurs (inferred from dental morphology, adding a further level of uncertainty), and no data on body mass or metabolic rate. In addition, I would think that your dataset on mammal diet composition would be much more suitable for this purpose, as it appears to be quite comprehensive and would include many species with “high” body mass. Also, in extant mammal the diet composition has presumably been directly quantified, and not just inferred from dental morphology.<br /> - L214ff: Why have a specific method for assessing human diet? We are just one more species in your dataset.<br /> - L223ff: The use of reptile biomass data for amphibians is not justified. Your assessment that the differences in average body mass and population density ‘cancel each other out’ cannot be verified. If you do not have a good biomass estimate for amphibians, you cannot include this group in the analysis.<br /> - L265ff: Your statistical “validation” of achieving representative data from poor species coverage is inappropriate. By sampling 0.3% of bird species 10 00 times and calculating an average, you merely verify that you can calculate a good average from ~300 000 (~30 species x 10 000), overall randomly sampled, data points. To “validate” your approach, you need to investigate the variance of your 10 000 repeat samples, which presumably is extremely large.<br /> - L265ff: The Methods appear to be incomplete here, as the Results section describes an analysis that was weighted by bird species biomass and abundance (L340).

      Results<br /> Throughout the manuscript, but particularly noticeable in the Results section, you are using misleading terms to refer to your data and results. I believe this stems from your multiple assumption to derive trophic pyramid data from a species-level dataset. E.g.<br /> - Fig.1: “species in most animal groups”; this figure shows the group average diet composition, not the species proportions.<br /> - L355: “partitioning of diets… expressed as biomass (Fig. 2)”; this figure actually shows the biomass of the trophic group, not their diet.<br /> - Etc.

      L333: “we assumed a homogenous distribution of biomass across trophic levels in each group” – a further example of an unfounded assumption that weakens your analyses and conclusions considerably.

      The data on dinosaur diet is missing from the Results.

      Discussion<br /> As outlined above, I believe that your main conclusion of a middle-heavy global trophic pyramid is not supported by your analyses, as are other conclusions on the trophic pyramid. Your study does not support the conclusion of a “paradigm shift” (cf. L407).

      Finally, some further minor comments:<br /> L173: what is the category “Food I”, and why is it relevant to mention these categories here?<br /> L311: Conservation areas might include some “important species” that are missing elsewhere, but that should not distract from the fact that species lists remain highly biased and incomplete there, as everywhere. Most obviously, Kruger NP is bound to have more than 13 species of insect (Fig. S4). And certainly such species list do not consider the microfauna to a meaningful degree.<br /> L402: It seems very unfair to disparage previous efforts as biased, when your own study is based on highly incomplete datasets and unfounded assumptions.<br /> L476f: I find the definition of a carnivore from Román-Palacios et al. in this context highly misleading. Heterotrophs include fungi, which does not make a fungivore a carnivore.<br /> L494: There might have been larger insects in the prehistoric past (at least we know of one large dragonfly), but that hardly makes them “megafauna”.<br /> L523: “a world without insect would potentially mark the end of complex life on Earth” – there is certainly complex life in marine environments, where insects are not prevalent and their potential decline might not have large impacts.<br /> L676: “more abundant” – you are not considering abundance here.<br /> Fig. S8: Here you are literally comparing a species group with a single species (humans). I presume that your reasoning is that the diet of humans has important impacts on the global food web. This is a nice case in point that you absolutely need species-level information on abundance/biomass to construct trophic pyramids and food webs.

    1. On 2024-01-14 22:54:39, user Keji Zhao wrote:

      Very interesting study --- providing insights into how MutSb and CNG cooperate to drive the expansion of trinucleotide repeats in Huntington's and other relevant diseases.

      Do the authors know how well these trinucleotide repeats form nucleosome structure in cells?

    1. On 2024-01-12 11:13:24, user Ines Hellmann wrote:

      Finally, we completed the story and are proud to share it with the world.<br /> We started this nearly 10 years ago. The counterintuitive observation that the sequence of CREs that are active in more tissues were less conserved than the sequence of tissue-specific ones needed a solid explanation. We started with an avalanche of sanity checks.<br /> Remapped everything, re-evaluated peak-calling, included dinucleotide aware divergence measures, re-checked alignments. After we were convinced that the signal would not go away, we needed to find an explanation.<br /> We started to look for an unbiased measure of TFBS binding potential and add RNA-seq and ATAC-seq data from comparing macaques and humans. Finally, the fog cleared up showing that pleiotropic CREs show a higer functional conservation.<br /> It is just that this functional conservation of these large pleiotropic CREs is achieved by redundancy, thus TFBS are moving around and different locations got fixed in different species: i.e. compensatory evolution within the same CRE.<br /> Compensatory evolution has been suggested before as a common mode of evolution for CREs, it is only that for less pleiotropic elements this usually happens not within the same element, but between different CREs, i.e. the entire CRE jumps around.

    1. On 2024-01-11 10:11:41, user Beth wrote:

      Fetal sex is not mentioned in this study. Could you look at sex differences? Was fetal sex controlled for when looking at the effect of gravidity? Was there an equal balance of male/female placentas in the primi and multigravida groups? If not, this could confound the identified differences between these two groups.

    1. On 2024-01-09 12:47:09, user Andrea Page-McCaw wrote:

      A revised version of this article has been accepted for publication in Matrix Biology and is available online from their website. The title has been revised: "Peroxidasin is required for full viability in development and for maintenance of tissue mechanics in adults".<br /> Matrix Biol. 2023 Nov 22:S0945-053X(23)00117-8. doi: 10.1016/j.matbio.2023.11.005. Online ahead of print.<br /> K Elkie Peebles, Kimberly S LaFever, Patrick S Page-McCaw, Selene Colon, Dan Wang, Aubrie M Stricker, Nicholas Ferrell, Gautam Bhave, Andrea Page-McCaw<br /> PMID: 38000777 DOI: 10.1016/j.matbio.2023.11.005

    1. On 2024-01-08 17:48:05, user Jonathan wrote:

      The role of Creb3l2 and XBP1 in professional secretory cell has already been established in 2019 by Khetchoumian et al. Any reason why this paper wasn't mentioned in reference?

    1. On 2024-01-04 16:19:04, user Manuel Théry wrote:

      This manuscript has not yet been published in a peer-reviewed journal yet because we noticed that our engineered epithelial cell line, expressing ZEB1 under the control of doxycyclin, was contaminated with mycoplasma. We currently don't have the human ressources to make a new cell line, and repeat the key experiments in order to validate (at least) the main conclusions.

    1. On 2024-01-04 14:03:40, user Caroline wrote:

      Congratulations on the paper. It's very important that we start adding pieces to this immunity evolutionary puzzle.

      I just need to highlight that the work developed at the Baker Lab on the evolution of Argonautes, bringing archaeal sequences into debate and positioning them in the evolutionary context, must be cited here.<br /> (https://www.biorxiv.org/con...

      It's very important that both papers are linked so we can have a more comprehensive understanding of such a complex topic.<br /> Your paper basically

    1. On 2024-01-02 19:52:05, user Adam Zeno wrote:

      Incredibly interesting work in elucidating the molecular pathology of SMDCF ! I hope to see this work used in future investigations and treatments of skeletal disorders

    1. On 2024-01-02 11:29:19, user Anita Bandrowski wrote:

      Hi I am trying to track down this mouse "APP/PS1 mice (B6;C3-Tg(APPswe,PSEN1dE9)85Dbo/Mmjax from Jackson Labs, MMRRC Stock No. 34928, maintained as C57BL/6 x C3H strain)"

      The MMRRC mouse stock #34928 is Pleiades Promoter Project ES cell line mEMS592; That is not the right thing. The full name also does not come up except at MGI.

      Can you please check your records and provide the RRID for this mouse?

    1. On 2024-01-02 10:08:48, user Anita Bandrowski wrote:

      I am looking for the mouse that you got from MMRRC, but you state that you got the PG00171_Y_4_H09–Nfkbia vector from them. I don't think that is possible because they don't sell vectors. Can you check your lab records? Usually Addgene sells vectors, MMRRC sells mice. This is really odd.

    1. On 2024-01-02 04:27:32, user Jadora Ambrosia wrote:

      This article contradicts itself, stating in one instance that testosterone doesn't affect female "alpha" status, and in another instance that it does.

    1. On 2024-01-01 14:00:13, user Robert Arlinghaus wrote:

      This is a very interesting paper. I would like to draw attention to previous evolutionary selection experiments in other model species, especially zebrafish, that the authors either do not cite or I think misrepresent. The authors for example refer to Uusi-Heikkilä et al. (2015) saying that the positive size selected zebrafish became shyer. But if you look carefully at the paper, the positive size selection treatment did not differ from the controls (no effect on personality), while it was the negative size selection that became bolder. A number of follow up studies on the behavioural response were completed, several papers (not cited by the authors) first authored by Valerio Sbragaglia and later by Tamal Roy. Imporantly, examining boldness effects was found to be strongly context-dependent for the positive size selection line. In some cases there were strong trends for it to become bolder, consistent with the fast life history, but in the presence of predation threat, either no differences to control or shyer behaviour was found (e.g., Sbragaglia et al. 2022, Am Nat). Very consistently, the negative size selection line was found to be always bolder. So, in short, there was an asymetric selection response and a strong context dependency of the behaviorual effects in the positive size selection line. Importantly, the results did not disagree with the expectation (e.g., fast life history should be bolder) as claimed in this manuscript, but ecological context in which the experiment was completed moderated the response. Very consistent was the finding that the negative size selected line was consistently bolder. Imporantly, theory as shown in Andersen et al. (2018) showed that positive size selection with our without additional behavioural selection may bring about either bolder or shyer behaviour, depending on the size at which selection acts and which traits are under selection. I raise this to perhaps more critically evaluate past research and to compare your outcomes with our experimental evolutionary experiments to provide the full picture.

    1. On 2023-12-29 20:00:57, user Matthew Berg wrote:

      This manuscript has now been published in RNA Biology. https://doi.org/10.1080/154...

      Ecaterina Cozma, Megha Rao, Madison Dusick, Julie Genereaux, Ricard A. Rodriguez-Mias, Judit Villén, Christopher J. Brandl & Matthew D. Berg (2023) Anticodon sequence determines the impact of mistranslating tRNAAla variants, RNA Biology, 20:1, 791-804, DOI: 10.1080/15476286.2023.2257471

    1. On 2023-12-27 20:54:07, user Yury Goltsev wrote:

      Wonderful study! One small detail was not quite clear. What is the difference between the values computed in (two replicates) and (three replicates) type measurements and why the Q-values are better in (two replicates). Did one of your replicate transformations have a technical issue?

    1. On 2023-12-23 16:13:07, user Quinn Sievers wrote:

      Hello Andreotti lab!

      Quinn Sievers here, postdoc in the Abdel-Wahab lab at MSKCC. I enjoyed reading your paper and found it very informative.

      One comment I had was regarding line 255 of the manuscript where you assess kinase activity of the recombinant T474I and L528W mutants by monitoring Y551 phosphorylation; my understanding was that this site is typically phosphorylated by upstream kinases and that Y223 is an autophosphorylation site and therefore a better measure of kinase activity. I suppose since it was an in vitro assay it was not confounded by the presence of other kinases but I wonder if the Y223 would have shown discordant activity with Y551, particularly for the T474I mutant.

      Best,

      Quinn

    1. On 2023-12-22 15:43:41, user Curious Biophysicist wrote:

      As the title indicates that is the structure-informed language model that enables unsupervised antibody evolution, I would be curious if the authors could add the model predicted log-likelihoods to figure 3. This would help distinguish the contribution of the model from that of the experimental filtering applied at the end of the first round and strengthen the claim that model has learned and it's not just randomly sampling. Additionally, I would be curious what fraction, if any, of the beneficial model-recommended mutations are germline reversions. If the model is enabling evolution, one would expect non-reverting mutations.

    1. On 2023-12-22 13:24:14, user Hao Lu wrote:

      Thanks for your excellent work. I recently got fastq files from a CITEseq experiment and I am now trying to process it. I am new to sc analysis and generally not very experienced with bioinformatic tools upstream of R, apologies if my question sounds trivial. What is "feature-indexing function of Cellranger" for CITEseq ADT reads counting?

    1. On 2023-12-22 08:27:19, user Juri Rappsilber wrote:

      Dear Boris, <br /> thank you for inviting us to share our thoughts on this manuscript. We think keeping target and decoy proteins as joined entities is an important aspect of sound error estimation in crosslinking MS. Consequently, our open-source software for error estimation, xiFDR is doing this since version 1.0 (openly released on GitHub Sep 9, 2016, https://github.com/Rappsilb.... It is great to see that your lab is also now coming to this conclusion. There are a few details regarding the implementation that make a difference, but that are not clear to us from reading your manuscript. You do not mention how you report protein-protein interactions in the fused approach. Additionally, the level of crosslinks (CSM, peptides, residues) is not specified. Crucially, although the manuscript demonstrates a clear effect, it lacks a mechanistic explanation for the observed outcomes - both the lack of decoys in the non-fused and the recovery of decoys in the fused case. Further questions include: How are N-terminal errors accounted for? Why did you change your previous FDR formula to the one employed by us and others (and will you revise previous work accordingly...)? Finally, you are not comparing your results to those obtained by FDR boosting. This would be valuable, as FDR boosting can offset at least the gains attributed to context-sensitive subgrouping by the (corrected version of) mi-filter. <br /> Best wishes, Juri

    1. On 2023-12-21 23:58:19, user Rosalyn Fey wrote:

      Now published!<br /> "Discovery and Visualization of Age-Dependent Patterns in the Diurnal Transcriptome of Drosophila"<br /> Sebastian B, Fey RM, Morar P, Lasher B, Giebultowicz JM, Hendrix DA. Discovery and Visualization of Age-Dependent Patterns in the Diurnal Transcriptome of Drosophila. J Circadian Rhythms. 2022 Dec 8;20:1. doi: 10.5334/jcr.218. <br /> PMID: 36561348; PMCID: PMC9733130.

      https://doi.org/10.5334/jcr... doi.org="" 10.5334="" jcr.218="">

    1. On 2023-12-21 07:08:05, user 聂嘉俊 wrote:

      This is really a beautiful work. The authours provided a powerful tool for analyzing plant RLKs, which could be a great help for researchers.

    1. On 2023-12-20 20:44:07, user Gregory Way wrote:

      Ong et al. 2023 present an image analysis pipeline for 3D cell culture data. They use this pipeline to segment single cells and extract both single-cell and 3D-based features. The authors also pursue three application experiments, in which they expose cells to osmotic stress, topology modifications, and different gravity forces.

      We read this paper as part of a journal club, and have decided to compile a collective review and publicly share it with the authors. This was inspired by the Arcadia Science Preprint Review Pizza Party Initiative, and this represents our third preprint review. Overall, our review focuses on the need for additional clarity, strengthening methodological justifications, and an increased discussion about potential implications of the findings and techniques used. We provide several specific comments below:

      • The authors discuss “database architecture” (line 99, and throughout) but they do not provide any additional details on the architecture nor the technology used. Without these details, it is difficult to understand the role and functionality of the database in relation to the pipeline.
      • The authors describe their pipeline effectively, but they do not disclose any potential limitations or challenges. How user-friendly is the pipeline? Is it reproducible and interpretable? Will the pipeline work with other spheroid or organoid types?
      • The pipeline tool describes the power of flexibility. Users can enrich the database with additional features of their choice, but what benefit does this flexibility provide? Does having more features improve results? Does the software provide guidance to a user on how to decide these parameters?
      • The authors discuss that previous tools do not disclose compute time, but then they use a publicly available tool (StarDist) and report compute time. It is unclear how the authors improve compute time independently of these other tools. The authors also do not benchmark the full run time and resource usage of their framework.
      • The authors extensively discuss quality control, but they do not describe any impact of uneven illumination on the cell images. Do the images suffer from uneven illumination? Would illumination correction improve segmentation and feature extraction?
      • It appears that the authors are actually using spheroids and not organoids, but the terms are used interchangeably, which could be misleading.
      • The authors should consider making OrganoProfiler open source to increase community impact, and the authors should properly cite other open source software like VTAE and KNIME. Many other software tools are properly cited, thank you!
      • In line 546, the authors state: “...realistic simulated 3D image data…” but details on how they created the images and indicators if the simulation worked are lacking. Similarly, because the model was trained on these simulated images, how do the authors understand and trust accuracy metrics?
      • In line 610 the authors state: “In each image, an expert counted the nuclei that were missing and those with a significant precision issue in their contours, which exceeded 30% of the total nucleus area.” It is unclear why the authors chose precision over other metrics (e.g., IoU). Also, what were the expected error rates of the expert annotator and how might this impact performance interpretation?
      • We had several comments about clarity and claims that the paper makes in the discussion. The paper states: “Overall, our approach demonstrates a powerful and efficient methodology for imaging, analyzing, and extracting biological information from 3D cellular microsystems.” These are a lot of different focus areas, and it is not clear in which area the tool specifically innovates. Furthermore, the authors state that “large amounts of data can be analyzed with their approach”, but there are no benchmarking or resource usage details. Additionally, the authors state that they have developed a “groundbreaking method” but it is not clear what the authors are doing differently than what is already known/standard practice. Is it the collection of tools in a framework? Lastly, the authors declare no conflict of interests, but it is unclear how QuantaCell might factor in. For example, is QuantaCell a private company selling the software presented here?
      • We have several specific comments on select figures:<br /> Figure 1: The authors segment cytoplasm with Actin, but this may not be the best stain for all cell types. How would a user determine which cytoplasm stain to use if Actin is not a good option or is unavailable?<br /> Figure 2d is not mentioned in the text, and there is a typo in the y axis “median z-plan”<br /> Figure 3: The authors need to state how many organoids they used for the roundness analysis in Figure 3a - if this shows a single organoid then this approach may not be generalizable. Figure 3b says “2 distinct organoids per condition”, but there are only two curves. Showing one curve per organoid would help determine generalizability. Figure 3c depicts 10 organoids but the authors do not specify how they were selected<br /> Figure 4: Could the changes in cellular topology and morphology be due to the changes in oxygen exchange in the molds?<br /> Figure 5: Methodological details on measurements would help us understand what was transformed via PCA? Additionally, more details on controls are needed. We also have concerns about extensive user modification of data. We suggest making these modifications transparent and public. Without access to software it will be difficult to determine how the software works given that the data are modified.Without access to software it will be difficult to determine how the software discourages this practice. Also, the authors show only PC1; what is the expected explained variance of PC1? What signals are left in the other components?<br /> We could not find Supplementary Video 1, which is referenced in the text

      Collective review performed by:

      Gregory P. Way, PhD, University of Colorado, Department of Biomedical Informatics <br /> Erik Serrano <br /> Jenna Tomkinson <br /> Dave Bunten <br /> Michael J. Lippincott<br /> Cameron Mattson

    1. On 2023-12-20 18:45:20, user George Chistol wrote:

      our lab works on DNA replication initiation and we looked into the "neighbor network graph" for several key proteins in this process and I am sad to say that those graphs do not capture the well known protein-protein interaction networks. I am curious if there is a way to "filter" the results for the interactions with the highest significance/confidence. I am happy to talk about it with the authors if they're interested.<br /> Gheorghe Chistol, Assistant Prof.<br /> Stanford Chemical and Systems Biology