On 2020-05-28 16:15:27, user Dimitris Lat wrote:

Hi, I was wondering if the authors had any information on the N-glycan array that was used and the nature of the glycans it contained?

On 2020-05-28 14:30:05, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-05-28 00:39:15, user Elizabeth Molnar wrote:

A novel approach to fundamentals of neurogenesis, one must work for smell and taste satisfaction.

On 2020-05-27 23:47:48, user Michael Toney wrote:

It would be great to know the source of the RBD protein used in this study.

On 2020-05-27 22:07:38, user mcsu wrote:

In Fig 1 it is striking in that:<br /> 1) The vast majority of both clinical and Ag samples in the study do not display either DMI or strobi resistance at all and display significant genomic variation. <br /> 2) The clinical and Ag samples that display both DMI and strobi resistance appear to be in the same clade despite originating from geographically diverse locations.<br /> Could suggests that they may have a similar parental source?<br /> Is there a reasonable explanation for this? <br /> Could it suggest something other than a general environmental source of these highly related isolates?

On 2020-05-27 20:08:58, user Andrés Morales wrote:

Nice work and very useful protocol.

There is one point that you might want to revise. In your manuscript, you mention that "other reports from in situ analysis that reported 27.5% binucleation". However, in our studies of liver tissue, we found that around 75% of hepatocytes are binucleated - in total agreement with your study. You might be interested in having a look at our manuscripts to have some quantitative comparison of the morphological parameters of different cellular and sub-cellular components of (mouse and human) liver tissue. We reconstructed liver tissue sections ~100 μm thick:

https://elifesciences.org/a... (Morales-Navarrete H., el at..A versatile pipeline for<br /> the multi-scale digital reconstruction and quantitative analysis of 3D tissue architecture.Elife, 2015)

https://elifesciences.org/a... (Morales-Navarrete H. el at. Liquid-crystal organization of liver tissue. eLIFE. 8:e44860, 2019)

https://www.nature.com/arti... (Segovia-Miranda F., et al. Three-dimensional spatially resolved geometrical and functional models of human liver tissue reveal new aspects of NAFLD progression. Nature Medicine. 25 (12), 1885- 1893, 2019)

On 2020-05-27 16:03:02, user delagoya wrote:

Can you please update the supplementary material with the specific versions of DRAGEN, Sentieon, and Strelka?

On 2020-05-27 15:53:21, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-05-24 15:31:59, user Sinai Immunol Review Project wrote:

Main findings<br /> In addition to its primary target, the ACE2 receptor on host cells, the SARS-CoV-2 spike protein and its glycosylated residues may interact with other carbohydrate-binding receptors on innate immune cells. The results of those interactions have not been well described; activation of myeloid cells via these pathways may contribute to hyper-inflammation and thrombogenesis. In order to describe potential interactions between the glycosylated spike protein and conventional carbohydrate-binding proteins on myeloid cells, Chiodo et al. used solid-phase immunoassays to determine the binding specificity of the spike protein to C-type lectins and Siglecs found on immune cells.

The authors surveyed spike protein binding to dendritic cell-specific intercellular adhesion molecule-3-grabbing non-integrin (DC-SIGN), Langerin, macrophage galactose binding lectin (MGL), the mannose receptor (MR), dectin-1 (used as a negative control), macrophage inducible calcium-dependent lectin receptor (MBL), and the sialic acid-binding immunoglobulin-type lectins (Siglecs-3, 5, 7, 9, 10). The SARS-CoV-2 spike protein showed strong binding to DC-SIGN and MGL, but not to Langerin, MR, and MBL. Of note, DC-SIGN bears an immune-receptor tyrosine-based activation motif (ITAM), suggesting that it is capable of direct signaling. The spike protein also demonstrated selective binding to Siglec-3, 9, and 10, which bear immune-receptor tyrosine-based inhibition motif (ITIM) and have direct immuno-suppressive effects.

The authors extended this method and concept to assess potential interactions between SARS-CoV-2 spike protein and structural components of pathogens that have been cited to contribute to infections secondary to COVID-19. Additional assays using capsular polysaccharides (CPS) of Streptococcus pneumoniae and lipopolysaccharides (LPS) from Pseudomonas aeruginosa, Salmonella typhimurium, and Shigella flexneri revealed that the spike protein can strongly recognize CPS from S. pneumoniae serotypes Sp19F and Sp23F and LPS from P. aeruginosa but not that of the two enteric pathogens.

In summary, these findings point to ACE2-independent recognition of carbohydrate-binding elements on myeloid cells by SARS-CoV-2 spike protein. While the downstream effects of these interactions remain unclear, this study demonstrates that SARS-CoV-2 viral particles have the potential to directly interact with myeloid cells, without using the ACE2 receptor or intracellular TLRs.

Limitations<br /> All experiments described in this report were in vitro experiments that utilized solid-phase immunoassays. No cellular responses were assessed. Moreover, it is unclear if these interactions are different, if the S protein were derived from infected cells in vivo. Therefore, it is difficult to conclude whether recognition of the SARS-CoV-2 spike protein and its carbohydrate residues by lectins or Siglecs typically found on macrophages and dendritic cells results in an immune response that significantly contributes to COVID-19 immuno-pathology. Therefore, the conclusions concerning the immuno-modulatory impact that these ACE2-independent, carbohydrate-based interactions may have are speculative.

Significance<br /> It is known that carbohydrate-binding proteins on myeloid cells facilitate the internalization of pathogens for lysosomal degradation. So, the experiments described here may provide some context for how monocytes and their myeloid derivatives, including monocyte-derived DCs and macrophages, could harbor SARS-CoV-2 particles or viral proteins, despite the lack of evidence of ACE2 receptor expression in these cells. Albeit, it has been hypothesized that ACE2 receptor expression can be induced by interferon in mononuclear phagocytes.

In addition to demonstrating that the spike protein is capable of recognizing certain C-type lectins and Siglecs found on monocyte-derived DCs and macrophages, the authors interrogated the ability for the spike protein to interact with polysaccharide components derived from S. pneumoniae, which has been documented as the most common cause of secondary pneumonia in COVID-19 patients. Additional mechanistic studies in cell culture and in in vivo models are required to verify whether these interactions, in fact, contribute to the immuno-pathology of COVID-19 and viral dissemination.

This review was undertaken by Matthew D. Park as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn school of medicine, Mount Sinai.

On 2020-05-27 15:45:40, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-05-13 21:27:26, user Bruce Conklin wrote:

Great study by @FaranakFattahi, it will be important to see if antiadrogenic drugs can alter the course of COVID-19. It also could explain why pubescent people (AKA kids) seem protected from many of the deadly effects of COVID-19!

On 2020-05-13 11:31:55, user Zaniar Ghazizadeh wrote:

Correction: troponin T cut-offs: normal (<0.01 ng/mL) and abnormal (>=0.01). The corrected version will be posted soon.

On 2020-05-13 10:37:11, user Zaniar Ghazizadeh wrote:

Correction: troponin T cut-offs: normal (<0.01 ng/mL) and abnormal (>=0.01). The corrected version will be posted soon.

On 2020-05-27 15:28:07, user Edward Marcotte wrote:

Is the xiFDR v2.0 software available? The Rappsilber lab link only seems to provide version 1.4.3.1: https://www.rappsilberlab.o...

On 2020-05-27 15:22:47, user Esmeralda R. wrote:

This is a great preprint. <br /> Can't wait to see the final version of it. But I'm already planning to use it to discuss with my labpeers in a virtual journal club<br /> Grama Esmeralda

On 2020-05-27 14:40:38, user Vaughn Cooper wrote:

Note: this was submitted on 20-05-11 but the journal delayed posting here.

On 2020-05-27 15:07:11, user David Curtis wrote:

I looked at the exome-sequenced subjects in UK Biobank and did not find an overall excess of variants in TMPRSS2. The preprint is here: https://www.medrxiv.org/con...<br /> In that small sample the MAF of rs12329760 was 0.16 in those who had tested positive (so were seriously unwell) but 0.23 in those not tested. This is consistent with your hypothesis. However I just took a look at the whole UK Biobank sample and it looks as though the MAF in the 636 subjects who tested positive is 0.21, which is about the same as the background frequency of 0.23:

CHR SNP A1 A2 MAF_A MAF_U NCHROBS_A NCHROBS_U<br /> 21 rs12329760 T C 0.2131 0.2274 1272 973644

So I think that if this variant does have any effect on susceptibility to COVID-19 infection then the effect size is probably fairly small.

On 2020-05-27 11:13:47, user Shama Virani wrote:

"HPV16 variant lineage assignment was based on the maximum likelihood tree topology constructed in MEGA, including 16 HPV16 variant sublineage reference sequences" was cited with the Burk paper. However, the Burk paper only showed 10 HPV16 variant sublineage reference sequences. The Mirabello paper also said the same thing in its methods but in Table 1 of the Mirabello paper, showed the 10 sublineages from the Burk paper. Can you please clarify?

On 2020-05-27 09:46:09, user Gary Mirams wrote:

We're doing a journal club on this - is the supplementary info also available?

On 2020-05-27 01:35:17, user Mahdis Monajemi wrote:

Where can I find the supplementary tables for the paper? (They are not included in Supplementary Material)

On 2020-05-26 23:39:30, user Sam Wheeler wrote:

Number of covid-19 articles: 4257 Articles (3448 medRxiv, 809 bioRxiv).<br /> I wish I could filter out some subtopics I am not interested in. Takes too much time to read even abstracts of 4000+ articles.

Or filter out articles that were published >1week ago with zero comments.

On 2020-05-25 05:18:20, user Philip Cohen wrote:

This is great work, thank you. An amazing response from the research community. I tweeted a summary here: https://twitter.com/familyu...

On 2020-05-26 22:46:52, user Adi Natan wrote:

Thank you for mentioning my code, I hope it will continue to be beneficial for your research. I'd appreciate if you could please cite it in the future: A. Natan, “Fast 2d peak finder,” https://www.mathworks.com/m... (2013). http://scholar.google.com/s...

On 2020-05-26 20:53:40, user Sinai Immunol Review Project wrote:

Main Findings<br /> To study the cell tropism of SARS-CoV-2 and the changes in molecular program of infected cells, the authors infected organotypic human bronchial epithelial cells and analysed them by single-cell RNAseq and electron microscopy over a course of three days. In their organotypic model consisting of ciliated cells, basal cells, club cells, goblet cells, neuroendocrine cells, ionocytes, and tuft cells, they found that ciliated cells were the primary target although basal and club cells were also susceptible. All susceptible cells expressed the ACE2 receptor, but the expression level of ACE2 did not correlate with the increased susceptibility of ciliated cells. scRNAseq also revealed that while only infected cells produced type I and III IFN, this IFN induced ISG production in uninfected bystander cells as well. Pro-inflammatory IL-6 was specifically produced by infected cells supporting a recent study from Xu et al. (PNAS, 2020) showing the efficacy of tocilizumab in relieving COVID-19 symptoms. Lastly, analysis of the transcriptome revealed that infection of ciliated cells upregulated genes involved in apoptosis and viral gene expression while downregulating genes involved in cilia function and homeostasis.

Limitations <br /> The authors did not address how well the organotypic bronchial epithelial model reflects pathogenesis in vivo. The relevance of this model would be strengthened with a comparison of clinical samples even if the early time points are not captured. Additionally, cells were observed for up to 3 days post infection so further transcriptional changes that may occur at the height of infection would be missed.

Significance<br /> This study offers in-depth view into tropism of SARS-CoV-2 beyond the expression of ACE2. The identification of ciliated cells as primary targets has implications in the treatment of respiratory symptoms of Covid-19 patients. The organotypic model may be a promising tool for future studies as it recapitulates the findings from scRNAseq of primary human tissues showing the expression of ACE2 in various cells like nasal ciliated cells (Nat Med, 2020). Thus far, human blood vessel and kidney organoids have proven useful for demonstrating the potential of soluble recombinant ACE2 to inhibit SARS-CoV-2 infection (Monteil et al., Cell 2020).

Credit<br /> Reviewed by Dan Fu Ruan, Evan Cody and Venu Pothula as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai

On 2020-05-26 19:38:22, user Jared Roach wrote:

Some further comments.

1. In order for the statistics to work and reported probabilities to be of utility, I believe the sampling of sequences needs to be uniform and independent across the population. For example, all sequences gathered from within a long-term care facility or other isolated locale such as a prison should be considered a single node (with the model possibly tweaked to allow for increased mutation on the branches leading to/from that node). So for WA-2, all sequences associated with Life Center and its first responders should be considered a single node. Thus, the so-called "entire WA outbreak clade" might actually contribute only a few nodes to the graph, rather than the many that are portrayed in the Figures. This comment is related to my concern about superspreaders (and the consequent zero or minimal spreaders implied in order to get an average observed R).

2. I'm not sure that WA State, Snohomish, & King County public health immediately (or ever) accepted Trevor Bedford's tweet thread as gospel. I don't think they "gave up"; I believe they ran out of personnel.

3. It is cool to see a Tweet thread as a major reference in a preprint article that is receiving press attention. Once I had reporters turn their nose up at one of my PNAS articles because "it wasn't published in Science, Nature, or NEJM." Changes in science communication are spreading as fast as the virus.

On 2020-05-26 08:45:02, user Jared Roach wrote:

The SIR model used to simulate molecular tree topology does not appear to include superspreader events. If not, the simulated topologies are likely to be radically (no tree pun intended) different than the real topologies. We know superspreader events occurred in Snohomish County, including the publicized choir practice event.

My second musing is the absence of geographic proximity modeling. If WA-1 and WA-2 are not related as closely as previously thought, how likely/unlikely is that these sequences were found a few miles from each other? Would it now be more probable that unrelated sequences would have been found with a bit more geographic separation?

On 2020-05-26 17:14:03, user Sinai Immunol Review Project wrote:

The main finding of the article: <br /> The pathophysiology of severe pneumonia and acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-induced is related with an overproduction of early response proinflammatory cytokines, leading to an increased vascular permeability and high risk of death. Chest radiation therapy using low doses (<1 Gy) was beneficial in the past to treat pneumonia, exerting anti-inflammatory effects in the lung, however, the involved mechanism remained uncertain. The authors proposed that macrophages could be involved in the counteracting lung inflammation after irradiation, and investigated this through in vitro studies with human and mouse lung macrophages, and in vivo pneumonia models in mice. <br /> Normal human lung cells were collected from 3 donors (n=2 to lung cancer, n=1 to benign disease). From these cells, macrophages were isolated and stimulated for 6 hours with Poly(I:C) (1ug/mL) and were irradiated using 0.5 or 1 Gy doses, or non-irradiated. In relation to the murine model, C57BL/6 mice received intratracheally two doses (100ug and 50ug – two consecutive days) of lipopolysaccharide (LPS) or toll-like receptor 3 ligand Poly(I:C). Six hours after the second intranasal dose, 0.5 or 1 Gy of irradiation was applied to the animal thorax. After this procedure, spectral computed tomography (CT) of the mice chest at different time points was performed. Flow cytometry was performed for human lung macrophages and for mouse lung cells. Cytokines concentrations in culture supernatants from in vitro experiments were profiled. <br /> The quantification of supernatants demonstrated that both doses of irradiation, 0.5 and 1Gy, significantly decreased IFNy and increased IL-10 secretion in human lung macrophages stimulated with Poly(I:C) in comparison to non-irradiated Poly (I:C)-stimulated cells. The flow cytometry analysis showed higher percentage of human lung macrophages producing IL-10 after 0.5 Gy irradiation dose when compared with the other conditions, and decrease IL-6 production after both doses of irradiation in comparison to non-irradiation cells. In the in vivo pneumonia mouse model, the authors showed that low dose thorax irradiation of mice treated with LPS or Poly(I:C), resulted in increased IL-10 production by a distinct group of macrophages dubbed nerve- and airway-associated macrophages (NAMs) compared to non-irradiated mice. In addition, the lung CT scans revealed that lungs irradiated at 1Gy presented less tissue density, indicating lower pulmonary inflammatory process in the lung.

Critical analysis of the study: <br /> The data presented in this manuscript is quite limited, its mostly restricted to production of IL-10, IFN-g and IL-6 (one experiment) by lung macrophages without any other functional analysis. The only data on lung inflammation are the CT scans, adding tissue histology would have improved the analysis. The description of experimental details is incomplete, and the number of human tissues analyzed in the in vitro culture is very small. Given its potential importance in the mechanisms of SARS-CoV-2 lung pathology, the role of alveolar macrophages and interstitial macrophages could have been explored in more depth. The authors could had analyzed others pro- and anti-inflammatory markers, like TNFa, IL-1b, IL-1Ra, IL-8, as well as mediators involved in lung inflammation resolution such as VEGF-A. The manuscript has a comprehensive discussion on the effects of chest irradiation on lung inflammation.

The importance and implications for the current epidemics: <br /> Research indicates that “cytokine storm”, an uncontrolled over-production of inflammatory markers which, in turn, sustain a systemic inflammatory response, is mostly responsible for the occurrence of severe acute respiratory syndrome. This manuscript shows that low dose thorax radiation is able to direct the pro-inflammatory lung cell responses into an anti-inflammatory response. Therefore, this therapy could be useful to mitigate lung inflammatory process in Covid-19. A point to highlight is that side and adverse effects must be well evaluated before a possible irradiation treatment in an infectious condition such as COVID-19.

Reviewed by Bruna Gazzi de Lima Seolin.

On 2020-05-26 16:16:32, user Ephrem W. wrote:

Thank you for the well articulated and important work we have been provided with, in this article; I found it as a guide for what my next step and work shall be in regard to RVF. <br /> However, I want to direct the attention of the authors to the fact that RVF virus was reported in 1995, following positive serological tests with no clinical disease, suggesting that the virus has been circulating in the country (Bouna, 2015; Teshome, Kasye, Abiye, and Eshetu, 2016). <br /> Saying so, I would like to direct you to a previous attempt made to predict the outbreak of RVF in Ethiopia, in-fact after recognizing the potential and risk of RVF exposure in livestock in the country, and responding to the needs identified in the National RVF Contingency Plan for Ethiopia. @ <br /> DOI:10.5539/jsd.v11n5p102

On 2020-05-26 16:07:54, user Zachary Burton wrote:

The bioinformatics is a bit above my pay grade, but I like the paper and find it interesting. I have long thought LUCA was more similar to Archaea than Bacteria, as described here. My lab has been interested in ancient evolution of tRNAomes, aminoacyl-tRNA synthetases, mRNA and rRNA. I am also very interested in eukaryogenesis, and its impact on transcription systems and gene regulation. I find the description in this paper to seem reasonable. I really liked the Pittis and Gabaldon paper from a few years ago, because it appeared to provide a good model. I understand that paper has been criticized with regard to the timescales of appearances of different eukaryotic organelles, but, so far, I cannot find newer papers that provide clearer models.

On 2020-05-26 13:58:12, user Katherine Huang wrote:

I'd like to suggest that the authors take a look at iCoMut: http://firebrowse.org/iCoMu...

On 2020-05-26 13:02:35, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-05-26 12:59:17, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-05-26 12:47:38, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-05-26 12:44:57, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-05-23 09:37:34, user Rob Leeson wrote:

If the ph in infected cells is changed from acid to alkaline can a ph sensitive virus like Mers,Sars, H1N1, covid-19 etc still replicate.

On 2020-05-26 12:41:11, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-05-26 12:37:54, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-05-26 12:34:16, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-05-26 12:20:29, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-05-20 11:31:53, user Niamh wrote:

Thank you for posting on bioRxiv, is the extended data available for review please?

On 2020-05-26 10:34:06, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-05-21 22:35:56, user GG Anderson wrote:

excellent work. Is the inhibition mechanism known? It seems that ORF3b could either block transcription by binding to TF region that controls IFN-1, or impede translation by binding to mRNA.

On 2020-05-26 10:14:14, user Ben Berman wrote:

Are the Hi-C maps from CAST or BL6 strain? It seems like you should be able to compare CTCF sites specifically not present in the strain of the Hi-C, and they should be less associated with TAD boundaries (assuming there are strain-specific TAD boundaries)

On 2020-05-25 20:02:19, user Lambodhar Damodaran wrote:

Nice paper! Just wanted to share another paper where An. stephensi was detected in Ethiopia. Work is currently being done to characterize insecticide resistance observed in the population there. https://www.sciencedirect.c...

On 2020-05-25 18:00:31, user Mobi wrote:

Dear authors of the manuscript,

I enjoyed reading this paper and it made me think. So now I have a question.

From Fig. S4F, S4K and S4L I can see that you were able to quantify the individual subgenomic viral mRNAs also in the single-cell RNAseq analysis. I was wondering if you might have identified clusters with distinct proportions of subgenomic mRNAs and if yes, have you been able to correlate changes in subgenomic mRNA composition with the expression of host genes (e.g. ISGs)?

Thanks for sharing your knowledge with me.

On 2020-05-18 14:21:34, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-05-13 18:29:44, user Sinai Immunol Review Project wrote:

Title Bulk and single-cell gene expression profiling of SARS-CoV-2 infected human cell lines identifies molecular targets for therapeutic intervention<br /> Wyler et al. biorXiv [@doi: 10.1101/2020.05.05.079194]

Keywords<br /> • scRNAseq<br /> • Interferon-Stimulated-Genes (ISGs)<br /> • HSP90

Main FindingsWyler et al. performed bulk and single cell RNA sequencing of three human cell lines at different time points after infection with SARS-CoV-1 or SARS-CoV-2. The cell lines used were H1299 and Calu-3, both epithelial lung cancer cell lines, and Caco-2, a colorectal adenocarcinoma cell line. Permissiveness to SARS-CoV-1/2 was different among cell lines: H1299, which express low ACE2 levels, produced less viral RNA and lower yield of infectious virus than Caco-2 and Calu-3.

Bulk RNA-sequencing showed important differences in host transcriptome responses between the Caco-2 and Calu-3 cell lines. Caco-2 cells exhibited an increase in ER stress genes. In contrast, Calu-3 exhibited a strong induction of Interferon-Stimulated-Genes (ISGs), such as IFNB1, CXCL10, HLA-B, HLA-C. This ISG induction was 2-fold higher for SARS-CoV-2 infection compared to SARS-CoV-1. scRNAseq from Calu-3 cells confirmed the differential ISGs expression. Sars-CoV-2 induced higher expression of IFIT1 and IFIT2 than SARS-CoV-1. Only a cluster of SARS-CoV-2 infected cells showed strong IFNB1 induction. RNA velocity analysis, which can measure the amount of nascent RNA, showed that the induction of ISG was short and transient during viral infection, and preceded Nf-kB signaling target genes activation (IL6, TNF, NFKB1A). A minor increase of ACE2 expression was also noted.

To detect subtler transcriptomic changes not related to the IFN response, the authors analyzed scRNAseq from H1299 cell line, which seem less permissive to infection. HSP90 expression correlates with the amount of viral SARS-CoV-2 RNA, but not with SARS-CoV-1 RNA. A similar induction was found in Calu-3 scRNAseq at early time point. Chemical blocking of the HSP-90 pathway in Calu-3 cells upon viral infection led to a strong reduction of viral replication and expression of the pro-inflammatory genes IL1B and TNF, but interestingly, not of IFIT-2.

Limitations<br /> Although increased transcription of ER stress genes was identified in Caco-2 cells, the authors did not report changes in HSP90 expression in this cell line. This could further indicate whether HSP90 induction is a lung-specific mechanism and could explain COVID19 pathology. Moreover, the relevance of these findings would benefit from the confirmation of HSP90 upregulation in more physiological systems such as primary cells or tissue derived from patients. Furthermore, validation of the role of HSP90AA1 and specificity of 17-AAG using HSP90AA1 knock-out cell would further strengthen these results.

The authors correlate the low susceptibility of H1299 with lower expression of ACE2, but scRNAseq data of H1299 indicates that the majority of cells are infected. Therefore, it is unclear what factors are responsible of H1299 relative resistance to infection.<br /> The authors state that the lack of ISGs induction in Caco-3 could be due to a reduced expression of Pattern Recognition Receptors (PRRs) is this cell line. There might be other differences between cells lines that would explain the contrasting results, rather than the PRR expression. To confirm the role of RNA sensors, the authors could perform targeted experiments, such as genetic deletion of PRR pathway for example. <br /> Figure 1 and Suppl Figure 1 reference in the text seems to have been mixed (S1D wrongly referred as Fig 1B, S1C as 1C, etc.)

Significance<br /> Although this model uses epithelial cancer cell lines, it is of great interest to understand the effect of SARS-CoV-2 infection on lung epithelial cells. Indeed, this study identifies a potential drugable target (HSP90) against SARS-CoV-2 infection, although these findings remains to be confirmed in primary tissues, animal models, or patients. The effect of blocking HSP90 identified here is of important clinical relevance, as it decreases viral replication and the production of cytokines that could be involved in the ARDS pathogenicity.<br /> Analysis of patients with severe COVID-19 showed impaired IFN-I responses compared to mild or moderate cases1, and, together with studies using animal models2, suggest a central role for the dysregulation of IFN-I signaling in COVID-19 pathology. Activation of IFN signaling upon SARS-CoV-2 infection has been observed in lung epithelial organoids3, but other studies indicate a lack of robust IFN type I/III signaling upon SARS-CoV-2 infection compared to influenza A or RSV infection4. The possible discrepancies between the results presented in this preprint and other studies could be explained by different experimental settings, such as the use of different time points, cell lines or MOIs, and warrant further investigations.

References<br /> 1. Impaired type I interferon activity and exacerbated inflammatory responses in severe Covid-19 patients | medRxiv. Accessed May 12, 2020. https://www.medrxiv.org/con...<br /> 2. Boudewijns R, Thibaut HJ, Kaptein SJF, et al. STAT2 signaling as double-edged sword restricting viral dissemination but driving severe pneumonia in SARS-CoV-2 infected hamsters. bioRxiv. Published online April 24, 2020:2020.04.23.056838. doi:10.1101/2020.04.23.056838<br /> 3. Ravindra NG, Alfajaro MM, Gasque V, et al. Single-Cell Longitudinal Analysis of SARS-CoV-2 Infection in Human Bronchial Epithelial Cells. Microbiology; 2020. doi:10.1101/2020.05.06.081695<br /> 4. Blanco-Melo D, Nilsson-Payant BE, Liu W-C, et al. SARS-CoV-2 launches a unique transcriptional signature from in vitro, ex vivo, and in vivo systems. bioRxiv. Published online March 24, 2020:2020.03.24.004655. doi:10.1101/2020.03.24.004655<br /> 5. Zheng H-Y, Zhang M, Yang C-X, et al. Elevated exhaustion levels and reduced functional diversity of T cells in peripheral blood may predict severe progression in COVID-19 patients. Cell Mol Immunol. 2020;17(5):541-543. doi:10.1038/s41423-020-0401-3

Credit<br /> Reviewed by Emma Risson and Bérangère Salomé as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

On 2020-05-25 17:23:49, user Santos Mañes wrote:

This paper has been finally accepted in EMBO Journal. It is open access. So enjoy

On 2020-05-25 11:49:46, user ImmuNoah'sArk wrote:

Awesome study, looking forward to its publication. I've just noticed that in Fig 5 Panel F, the middle plots actually may have been meant for the bottom graph.

On 2020-05-25 11:18:32, user Palle Villesen wrote:

I am concerned with your methodology for testing. You compare 5-10 ratios (or difference in leaf count) using sign tests. This ignores the sample size for each of the 5-10 "replicates". If a sister lineage have many descendants, it should have higher weight than low counts?

Example to show the problem:

library(tidyverse)

x <- tibble(homo = c(3000,2500,500,5,10,15,20,25,30)) %>%<br /> mutate(s = c(60,50,40,0.9,0.9, 0.9, 0.9, 0.9, 0.9)) %>%<br /> mutate(nhomo = round(homo/s)) %>%<br /> mutate(r0h0 = homo/nhomo) %>%<br /> select(homo,nhomo, r0h0, s) %>%<br /> {.}

x %>% arrange(r0h0)<br /> mean(x$r0h0)<br /> median(x$r0h0)<br /> wilcox.test(x = x$homo-x$nhomo, paired = F, mu=0, alternative = "two.sided")

Output

A tibble: 9 x 4

homo nhomo r0h0 s<br /> <dbl> <dbl> <dbl> <dbl><br /> 1 5 6 0.833 0.9<br /> 2 15 17 0.882 0.9<br /> 3 25 28 0.893 0.9<br /> 4 10 11 0.909 0.9<br /> 5 20 22 0.909 0.9<br /> 6 30 33 0.909 0.9<br /> 7 500 12 41.7 40 <br /> 8 2500 50 50 50 <br /> 9 3000 50 60 60

mean(x$r0h0)<br /> [1] 17.44472<br /> median(x$r0h0)<br /> [1] 0.9090909<br /> wilcox.test(x = x$homo-x$nhomo, paired = F, mu=0, alternative = "two.sided")

Wilcoxon signed rank test with continuity correction

data: x$homo - x$nhomo<br /> V = 24, p-value = 0.9054<br /> alternative hypothesis: true location is not equal to 0

On 2020-05-22 16:51:11, user Reviewer#2 wrote:

Isn't there a bias in the statistic by selection? You pick only ancestral nodes, so the recurrent mutation has to occur afterwards, and if there is no other mutation you cannot time this discrepancy. That introduces a (possibly substantial) bias towards ancestral offspring. So the implicit assumption of the RoHo statistic being 1 under neutrality does not seem to hold? That might be the explanation why so many of your medians are substantially below 1.

On 2020-05-25 09:27:44, user Amarinder Singh Thind wrote:

Good work. I was wondering, How it defines amplification and deletion. Any threshold for CNA ?

On 2020-05-25 01:12:51, user François Boucher wrote:

For the theory to hold, that D839Y/N/E mutation they are calling a "European" variation should track to the UK & USA, where most cases of MIS-C were observed…<br /> This following database has only a few examples, limited to Holland, Portugal, Italy and Georgia… maybe they're looking at a different collection of sequences? <br /> https://nextstrain.org/ncov... <br /> https://nextstrain.org/ncov...

On 2020-05-23 15:32:44, user Yoshi Aoki wrote:

I hope that G-CSF is effective to escape MIS-C. https://doi.org/10.1182/blo...

On 2020-05-24 23:56:12, user Sebastian Aguiar Brunemeier wrote:

The drugs they identified are not new mechanisms -- They are all mTOR inhibitors or PI3K inhibitors. This would be more interesting if it found a new MoA drug that slowed aging in cells, and then extended lifespan in some model organism(s).

On 2020-05-24 14:43:06, user david robbe wrote:

A revised version of this manuscript has been published at PNAS the 20th of May 2020, and retitled "Turning the body into a clock: Accurate timing is facilitated by simple stereotyped interactions with the environment"

On 2020-05-24 14:08:29, user msp wrote:

Interesting that you've managed to run Chicago with Hi-C data! (Did I get it right?).

On 2020-05-24 13:19:03, user Yong Zi Tan wrote:

Nice paper! How does this compare with one of the popular fiducial-less alignment software Appion-Promoto? Thank you.

On 2020-05-23 22:26:11, user Misha Monahov wrote:

This code was used to analyze the data: https://github.com/monakhov...

On 2020-05-23 08:33:52, user Are Bayode wrote:

The article is now peer-reviewed and published in PLoS NTD: https://journals.plos.org/p...

On 2020-05-23 00:25:35, user Roli Wilhelm wrote:

Hi, nice study! I'm looking at the metadata that you have uploaded to the NCBI and I cannot find any information on tillage or residue management. Could you please provide this information for samples 1-18 in your supplementary materials? Thank you!

On 2020-05-22 23:58:17, user John N Wilson wrote:

Has anyone looked into if these inserts might be related to HHV-6 instead of HIV? The NK Cell reduction seen in COVID-19 patients seems to be very similar to the same in those infected by HHV-6...

On 2020-05-22 23:54:38, user CleoWang wrote:

????????Recommend my paper on Nature Communications (Chuchu Wang et al. 2020), which might give you some inspirations.

On 2020-05-22 20:29:46, user Sinai Immunol Review Project wrote:

Title: Identification of Drugs Blocking SARS-CoV-2 Infection using Human Pluripotent Stem Cell-derived Colonic Organoids

Keywords: <br /> • SARS-CoV-2 drug screening<br /> • colonic organoids<br /> • gastrointestinal complications<br /> • MPA<br /> • QNHC

Main findings:<br /> Gastrointestinal complications have been reported in almost 25% of patients infected with the SARS-CoV-2 virus (1). This study illustrates a high-throughput drug screening platform using Human Pluripotent Stem Cell-derived Colonic Organoids (hPSC-COs) infected with the SARS-CoV-2 pseudo-entry virus. Using single-cell RNA sequencing and phenotypic characterization of hPSC-COs, the authors show endogenous expression of ACE2 in 5 characterized colonic cellular subsets, with the highest expression of ACE2 in KRT20+ enterocytes. hPSC-COs were inoculated with a SARS-CoV-2 pseudo-entry virus, which led to a significant decrease in both KRT20+ enterocytes and ACE2+ cells. The authors also used humanized NOD-scid IL2Rgnull mice transplanted with hPSC-COs to serve as a unique in vivo system for modeling COVID-19. Consistent with in vitro findings, ACE2 was detected in KRT20+ enterocytes from organoid xenografts, and luciferase expression was most highly detected in these populations after local infection with the SARS-CoV-2 pseudo-entry virus. <br /> The authors next used the hPSC-CO system to perform high-throughput screening of drugs from the Prestwick FDA-approved library; they identified eight drugs that could block SARS-CoV-2 pseudo-virus infection, as measured by a reduction of luciferase activity by at least 75%. Of note, mycophenolic acid (MPA) and quinacrine dihydrochloride (QNHC) were drugs that showed specificity to the SARS-CoV-2 pseudo-entry virus, and were also at least 5 times more efficacious than chloroquine at inhibiting viral entry. Additionally, mice treated with MPA prior to local SARS-CoV-2 infection had significantly lower luciferase+ cells in xenografts compared to vehicle-treated mice. Lastly, the authors showed an enrichment of specific chemokines that were upregulated in hPSC-COs infected with the SARS-CoV-2 pseudo-entry virus, consistent with the “cytokine storm” that has been reported in COVID19 patients (2).

Limitations:<br /> While the authors tested efficacy of MPA and QNHC prior to SARS-CoV-2 virus infection, they did not show whether these drugs would have beneficial effects after viral infection. In addition, the effects of QNHC were not investigated in vivo, which would need to be conducted before proceeding to clinical trials. While this study focuses on using MPA and QNHC in colonic organoid-derived systems, it would be important to understand if similar therapeutic mechanisms also exist in infected respiratory epithelial cells, such as lung alveolar epithelial cells and nasal epithelial cells (3).

Significance:<br /> This study reveals advancements in utilizing translational hPSC-COs and humanized mouse models to elucidate key cell types that might allow for enhanced SARS-CoV-2 infection in the gastrointestinal system. The authors’ in vivo system allowed them to study biological effects of cells naturally expressing ACE2, instead of relying on transgenic mouse models expressing the human ACE2 receptor. Moreover, the in vitro hPSC-CO system enabled rapid, high-throughput screening of FDA-approved drugs, which uncovered MPA and QNHC to be promising candidates for SARS-CoV-2 entry inhibition, with greater efficacy than drugs currently being investigated for therapeutic use in COVID-19.

References:<br /> 1. Cheung, K.S., Hung, I.F., Chan, P.P., et al. Gastrointestinal Manifestations of SARS-CoV-2 Infection and Virus Load in Fecal Samples from the Hong Kong Cohort and Systematic Review and Meta-analysis, Gastroenterology (2020), https://doi.org/10.1053/j.g....<br /> 2. Ricardo, J.J. and Manuel .A. COVID-19 cytokine storm: the interplay between inflammation and coagulation. The LANCET (2020), https://doi.org/10.1016/S22...<br /> 3. Sungnak, W., Huang, N., Bécavin, C. et al. SARS-CoV-2 entry factors are highly expressed in nasal epithelial cells together with innate immune genes. Nat Med 26, 681–687 (2020). https://doi.org/10.1038/s41...

Reviewed by Shikha Nayar as part of a project by students, postdocs and faculty at the Immunology Institute of the Icahn School of Medicine, Mount Sinai.

On 2020-05-22 19:51:11, user Kenneth W Witwer wrote:

This preprint confirms some previous findings that miRNA:EV ratios are quite low, and that in some cell culture supernatants (as also suggested elsewhere for biofluids), most miRNAs are found outside EVs. Also that host EV proteins are much less fusogenic than those of viruses, particularly those like VSV.

I think that the greatest disagreements with this manuscript, which includes rigorous approaches, will be around how strongly the conclusions are presented. In my opinion, the authors certainly have a right to be a little provocative in their language, but perhaps some more caveats could be introduced in revision. It's still possible that longer exposure times, different conditions, etc. could lead to uptake with some functional relevance.

A few random comments:

"These experiments also indicated that, depending on individual reporter plasmids, 20–300 miRNA copies per cell reduced the luciferase activity by half (data not shown)."<br /> -Showing these results would greatly strengthen the paper by showing how little miRNA would be needed.

"A higher ratio of EVs per cell led to a reduction of the Renilla luciferase signal probably because a very high EV concentration was toxic to the cells"<br /> -This was quite interesting to me, as we tend to see a trophic effect of EVs in other systems. I am not sure that we can generalize this result.

Regarding Figure 6C: I would prefer to see, additionally, an experiment where miRNA mimics were introduced to the donor cells, not just miRNA-expressing plasmids, to be sure plasmids were not transferred. Although since no effect was observed, this does not affect the current conclusions.

I may have missed it, but where are the viability data? The methods mention viability tests, but I did not see the results. Dying cells may release large amounts of miRNA, and this could greatly affect EV vs non-EV miRNA ratios.

Figure 7A was interesting and puzzling to me. I would have expected that the mini-UC pellet would be the least pure and most "contaminated" with non-EV miRNA, followed by SEC-separated material and then density gradient. If this were the case, one would expect higher miRNA:particle ratios for the UC pellet. However, the UC pellet seems to yield fewer RNAs per particle than the other I'm not sure how much we can read into this, but the result does not seem entirely consistent with the conclusion that more purified EVs have lower RNA:particle ratios. A nice addition to this figure would be to show results from the input, too. There, one would expect many more RNAs per particle compared with the separated fractions (at least for particles in the size range detected by NTA).

On 2020-05-22 13:19:10, user Jan Lötvall wrote:

As Editor in Chief of Journal of Extracellular Vesicles (Impact Factor 11.00) I welcome you to submit the work to our journal for appropriate feedback.

On 2020-05-22 17:20:08, user Andrea Petras wrote:

Very interesting and insightful work! This is an excellent example of how cell guided matrix interactions can be harnessed to engineer niche environments. As a cell biologist, I would love to see something like this done using immune cells to study their changeable phenotypes.

On 2020-05-22 11:35:36, user Sarjeet S. Gill wrote:

This study is not peer reviewed. The results may be true but I am not convinced. So, I am not rushing to buy any new product so that I can look younger.

On 2020-05-21 17:41:37, user Research Explained wrote:

Our team wrote a general public friendly summary of this study and its strengths and weaknesses. Check it out at: https://www.researchexplain...

On 2020-05-15 00:17:27, user Ken B wrote:

These are not new strains ,but are isolates according to Professor Vincent Racaniello. : https://www.virology.ws/202... What's going on!?

On 2020-05-14 08:58:57, user Palle Villesen wrote:

Hi. We discussed your preprint in a journalclub yesterday and have a few comments.

The Sheffield sample is biased to people tested because they had symptoms? (It is not clear from the methods, how the patients were selected?)

The G419 group with symptoms had higher viral load than the D419 genotype.

Wouldn't that indicate LOWER pathogenicity for the G419 mutation? Basically, that a higher viral load of the genotype is tolerated BEFORE you get so sick that you go to the hospital (and get tested)?

Also, the ICU+IP vs. OUT results suggests that G614 is less pathogenic than D614.

If so, this would also explain the improved fitness of the G419 mutation. That it was better at spreading if it caused less severe symptoms while having a higher viral load = better transmission.

Also, we kindly suggest the GLM analysis (including effect sizes and directions) are included in the ms.

On 2020-05-14 04:35:59, user Lev Yampolsky wrote:

if this mutation was indeed causative to faster spreading it would have emerged repeatedly in parallel clades each time with noticeable proliferation. It seems to have emerged twice more without any evidence of proliferation in these two other branches. (According to https://www.gisaid.org/epif.... Moreover, because this substitution occurred so early (January) and the tree is unrooted it is quite hard to be confident what was the ancestral state, D or G. Maybe G was the ancestral state and it mutated to D?

On 2020-05-14 04:26:22, user Lev Yampolsky wrote:

this paper seems to be making the same mistake that the previous publication focusing on orf8: L84S substitution did. Pinpointing an early mutation does not mean finding a faster spreading one. It has a world-wide distribution because it is an early mutation, so lots of clades inherited it. Not (necessarily) because it has a higher R0.

On 2020-05-13 20:38:53, user Brian Foley wrote:

The web site URL in the paper was stated as www.cov.lanl.gov but is actually cov.lanl.gov without the "www". We are very sorry about this, but we were not able to use the URL with www as we had planned as we were writing the paper. Our apologies to anyone who was frustrated by this. It will of course be corrected in the peer-reviewed paper.

On 2020-05-21 16:03:29, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-05-21 15:24:30, user Jinkai Wan wrote:

The version 2 of our paper (Human IgG cell neutralizing monoclonal antibodies block SARS-CoV-2 infection) is currently undergoing bioRxiv screening. We updated the neutralizing experiment data of authentic virus.

On 2020-05-21 14:57:09, user Andy wrote:

Hi there, thank you for performing such a comprehensive analysis of all these deconvolution methods. Just wanted to know if you plan on incorporating CIBERSORTx (Newman et al 2019), which takes scRNA-seq as a direct input and performs a batch correction between scRNA-seq and bulk RNA-seq.

On 2020-05-21 13:47:28, user Alexandru Ioan Voda wrote:

"In an independent sample of n=32 F344 rats, we found that this measure correlated with age at (r=0.93), and related to physical functioning (5.9e-3)*" <- is the last paranthesis a P-value?

On 2020-05-21 12:30:31, user Roberto Pallini wrote:

This study is quite relevant for the scientific community and may anticipate something occurring later on patients, i.e. SARS-CoV-2-induced atrophy of cortical neurons. It is impressive the similarity with the neuronal damage that is typical of Alzheimer' disease.

On 2020-05-21 09:07:14, user Andre Goffinet wrote:

Human virus goes to ferret, then from ferrets to ferrets, then probably back to human. Could it mutate in between. Recently, Harbin virus generated a disease somewhat distinct from original covid19 (https://www.globaltimes.cn/.... Since Harbin first inoculated several animals, I am afraid virus jumped back from animals to man, in a different form with longer incubation and more directly inflammatory pneumonia. All this is playing with fire, and those experiments must be done with utmost care.

On 2020-05-21 06:32:32, user Myriam Heuertz wrote:

The article is now published here: https://doi.org/10.1002/ajb...

On 2020-05-21 04:58:23, user Wayne Ge wrote:

The results might implicate the importance of the virus collection and preservation, especially for the LAMP based Abbott ID NOW assay that is more sensitive to the degradation of the virus RNA due to its bigger footprint on target with 6 primers sites.<br /> People use RNA viral sample collection filter paper card, http://fortiusbio.com/RNA_S..., for the collection and transpiration of West Nile virus, also the influenza, and lately COVID-19.

On 2020-05-15 18:14:41, user Pandu Bano wrote:

The test method described in this article is flawed. They used VTM eluted sample swabs that were presented to ID NOW after 1-2 hours of collection.

Swab samples eluted in viral transport media (VTM) are not appropriate for use in the Abbott ID NOW - SARS-COV-2 Test according to the product insert sheet that comes with the ID NOW test kit.

ID NOW is supposed be used by patient bed side or closeby with fresh sample swab directly without elution in VTM which will result in decreased delection of low positive samples that are near the limit of detection of the test.

On 2020-05-14 04:28:56, user Wylied wrote:

"Testing at the point of care was not practical."

Point-of-care testing what Abbott ID Now is all about, hence the name. It's a strange study that doesn't attempt to replicate the conditions the test was intended for. That said, it appears that Abbott, at least initially, was unclear about how to obtain optimal results.

On 2020-05-13 20:53:59, user Mirela Stancu wrote:

This study is biased against Abbott ID Now, as it compares NASAL secretions collected by dry swabs for Abbott Rapid test (which by definition contain a lower viral load) with NASOPHARYNGEAL specimens for Chepheid GenExpert (which by definition contain a higher viral load).<br /> In our lab, I've reviewed 100 consecutive tests and compared Abbott ID Now results with RT-PCR (on Roche Cobas) collected at the same time using NASOPHARYNGEAL swabs FOR BOTH test methods. I only had one false negative result with Abbott, and I blame that on poor collection technique.<br /> Your test result is as good as your collection technique and other pre-analytical variables that may be a little difficult to control in a large busy emergency room like one in NYC.<br /> We have instructed the nurses and phlebotomists at our hospitals on the correct collection technique (insert swab deep until the patient flinches or tears, rotate the swab gently 3 times, keep swab in for at least 10 seconds, and then submit it in a sterile test tube for testing as soon as possible after the collection;1-2 hours as per above study is too long), and thus have greatly reduced the risk of a false negative result.

Mirela Stancu, M.D, laboratory director, CharterCare, Providence, RI

On 2020-05-13 20:00:52, user Jj TR wrote:

It is of paramount importance to see the whole report and for it to be peer-reviewed soon. If these findings are corroborated, we cannot rely on the Abbot test as a tool for accurately quantifying Covid-19 infections. This, in turn will affect the safety measures of early reopening.

On 2020-05-20 22:03:18, user Jim wrote:

Name of x-axis should be "Fold change (log2)" instead of "Fold change".

On 2020-05-20 16:25:29, user Ugo Bastolla wrote:

This is a very interesting paper. I think that the observation that children express ACE2 at very low level is extremely important, not only in the context of covid-19.

However, I have concerns on two conclusions:

1) "ACE2 and TMPRSS2 expression in airway epithelial and AT2 cells increases with age". The data presented by the authors in Fig.3g clearly show that ACE2 levels increase from fetal-infant samples to adult age. Nevertheless, the same figure 3g also seems to show a decrease of ACE2 expression from a maximum attained at age class 25-40 for AT2 cells (lungs). There are no error bars in the figures for judging whether the decrease is significant. Nevertheless, a previous paper reported a decrease of ACE2 protein levels in rat lungs (Xie et al. Age- and gender-related difference of ACE2 expression in rat lung. Life Sci. 2006 78:2166) and a recent preprint that analyzed the GTex database confirmed the result in humans (https://www.preprints.org/m.... In Multiciliated cells (fig.3g of the present preprint), it seems that the maximum is attained at lower age (10-25), and there is a secondary peak at age 40-60 that I suspect may be related to smoking, since the authors showed that smoke enhances ACE2 expression, a large fraction of samples were from smokers (50% males and 25% females), and the age 40-60 is likely to be enriched of smokers.<br /> Therefore, it is fair to say that ACE2 expression increases from children to adults, but for adults the present data cannot overturn the previous observation that ACE2 expression decreases at old age.

I admit that I may be biased in this comment since, building on the publication cited above, I proposed a mathematical model that rationalizes the negative correlation between ACE2 expression and lethality (http://arxiv.org/abs/2004.0...:yey5eBgCzVUNqcGhOcOrn8bYbp0 "http://arxiv.org/abs/2004.07224)"). My model isbased on a previous mathematical model that predicts that the virus propagation can be slowed down increasing the receptor level when the viral receptor binding protein has a very fast binding rate as SARS-COV-2 spike has. Notably, the model not only fits SARS-COV-2 lathality across age and gender for different countries, but it also predicts the lethality profile of SARS-COV-1 using the ratio between the binding constants of the two proteins. Based on the results of the present preprint I shall have to modify my paper, but I think that the main results hold: the very low level of ACE2 in children lungs is consistent with the fact that they are unlikely to suffer pneumonia, and the high level in multiciliated cells at ages 10-25 predicts slow viral propagation in the upper respiratory tract.

2) "ACE2 expression is higher in men". This contrast with the above cited papers, which indicate that the expression of ACE2 is higher in women, consistent with the fact that ACE2 is located in the X chromosome of which women have two copies while men have only one. Once again, this observation suggests a negative relationship between ACE2 and lethality, not a positive one as the authors expect. I think that the conclusion that ACE2 expression is higher in men was slightly biased by this expectation and by the fact that in the analyzed data set there were more male smokers (50%) than female ones (25%), and that smoking enhances ACE2 expression. It should be necessary to correct the smoke bias, but I suspect that it was amplified, since the authors introduced an additional fitting parameter that models the interaction between smoke and sex and write that "It should be noted that modeling interaction terms was crucial as their omission resulted in reversed effects for age and sex for particular cell types". In my experience in computational biology, when there are correlated explanatory variables (sex, smoke, interaction between smoke and sex) and the fit is not regularized with ridge regression or some other regularization, it is very likely that the fitted parameter takes a sign that contradicts the physical expectation (for instance, if you do not regularize the fit of B-factors predicted with elastic network model, you can obtain a negative force constant as fitting parameter even if the fit has only two parameters). In this case, the physical expectation is that, since ACE2 is located in the X chromosome, its level is expected to be higher in females than in males. To prove that this expectation is incorrect would require strong evidence.

On 2020-05-13 17:51:23, user Egon Willighagen wrote:

I found a minor typo: AEC2 (PS interesting work!)

On 2020-05-20 15:43:48, user YIGUO ZHANG wrote:

Commentary on Distinct, but Previously Confused, Nrf1 Transcription Factors and Their Functions in Redox Regulation, by Zhu YP, Xiang Y, L'honoré A, Montarras D, Buckingham M, Zhang Y. Dev Cell. 2020 May 18;53(4):377-378. doi: 10.1016/j.devcel.2020.04.022.<br /> PMID: 32428454

On 2020-05-20 13:35:16, user Dominique Weil wrote:

This is an interesting study.<br /> Please note that, in addition to the studies referred to in the manuscript that documented 40S enrichment and 60S depletion in SGs, electron microscopy combined with In Situ Hybridization (ISH-EM) also showed that 18S rRNA is enriched in SGs (1.8-fold compared to the surrounding cytoplasm), while 28S is depleted (3.3 fold) (Souquere et al JCS 2009, PMID:19812307). This is consistent with some translation taking place within SGs.

Furthermore, in EM, SGs show irregular borders and their center is often inhomogeneous, with some area having the same ultrastructure as the surrounding cytoplasm.

Then, is it not tempting to imagine that translation can proceed further in pieces of cytoplasm that are embedded in SGs?

On 2020-05-20 12:39:54, user Crayfarmer wrote:

There is no market for such a small crayfish except in developing countries. Marbled crayfish is on average considerably smaller than Procambarus clarkii. Therefore, culture of marbled crayfish in closed systems will never be profitable.

On 2020-05-20 12:30:22, user Paul Conduit wrote:

Note that this is an updated version of our preprint originally posted in 2019. The changes are largely in response to Reviewers' comments and do not affect our original conclusions.<br /> Paul Conduit

On 2020-05-20 02:39:13, user Sinai Immunol Review Project wrote:

Summary/Main findings: <br /> Zost et al. describe the methodology used to efficiently generate a large library of highly-functional monoclonal antibodies directed against the SARS-CoV-2 spike (S) protein. Several different approaches were used to select the antibodies characterized in this study. Briefly, plasma or serum was obtained from four patients infected with SARS-CoV-2, and ELISA binding assays were used to confirm the presence of reactive antibodies to the prefusion ectodomain of either the SARS-CoV-2 or SARS-CoV S protein. Additional screens were used to assess the presence of antibodies capable of binding to the receptor binding domain (RBD) as well as the entire N-terminal domain (NTD) of the SARS-CoV-2 spike protein. The highest reactivity was seen in binding assays when the antigenic targets were the SARS-CoV-2 spike S2P ectodomain or RBD. SARS-CoV-2 S-specific class-switched memory B cells were isolated from peripheral blood mononuclear cells (PBMCs) via flow cytometry. The two patients whose blood was collected at later stages of convalescence displayed higher frequencies of antigen-specific memory B cells and greater levels of neutralizing antibodies. S2P ectodomain- and RBD-specific memory B cells sorted from these two patients PBMCs were pooled and cultured for one week in wells containing a feeder layer of cells expressing CD40L, IL-21, and BAFF. Approximately 50% of these cells were single-cell sequenced for antibody gene synthesis. The other half were placed in a Berkeley Lights Beacon Optofluidic instrument to further identify, select, and export antigen-reactive B cells prior to single cell antibody sequencing and cloning into immunoglobulin expression vectors. Both approaches yielded a combined total of 386 recombinant SARS-CoV-2 reactive human monoclonal antibodies. Subsequent ELISA and neutralization assays were used to separate these antibodies into five classes based on their cross-reactivity with SARS-CoV and the specific binding domains on the SARS-CoV-2 S protein. Bioinformatic analysis of the immunoglobulin sequences revealed a high degree of relatedness to the inferred unmutated ancestor immunoglobulin genes.

Critical Analysis:<br /> This study characterizes a robust repertoire of SARS-CoV-2 spike-specific antibodies. The authors begin to shed light on the binding sites of these antibodies by describing the domains on the spike protein to which these antibodies react. However, in order to more fully capture the mechanism of neutralization for the leading therapeutic candidates, it will be important to further characterize the specific epitopes and structural binding modes. This is especially important since many of the antibodies identified in this study will not directly interfere with the RBD/ACE2 interaction and therefore likely act through another mechanism such as destabilizing the spike prefusion conformation. Another interesting observation raised in this study is that, as seen with Ebola, patients do not possess a high frequency of memory B cells expressing neutralizing antibodies until later in convalescence. However, given the small number of patients in the study, a larger sample is needed to confirm this conclusion. While this study presents a comprehensive class of candidate antibodies for therapeutic development there is still much needed data describing the protective potential of these antibodies in animal models challenged with SARS-CoV-2, as the authors assert as well. Finally, as synergy has been observed in strong B cell response for other coronaviruses and the fact that antibody cocktails are an effective treatment platform to prevent mutation escape, it would be helpful to know whether specific combinations of these monoclonal antibodies enhance neutralization and in vivo protection.

Relevance/Implications:<br /> In conclusion, this study presents a robust analysis of the specific B cell response to SARS-CoV-2 in a small number of individuals, and describes practical techniques to isolating a large and diverse panel of human monoclonal antibodies. In addition to revealing potential therapeutic antibody candidates for COVID-19, the authors provide additional information as to the complicated and inconsistent observations of antibody cross-reactivity and cross-neutralization in the context of SARS-CoV and SARS-CoV-2. Information on conserved and highly potent neutralizing targets of antibody responses will be critical down the road as we evaluate the immunogenicity of vaccine candidates. Meanwhile, the information in this study can be directly applied to the therapeutic antibody pipeline for SARS-CoV-2 and the methodologies described here can be adapted for similar emerging pathogens in the future.

On 2020-05-20 00:12:59, user A. Andreoni wrote:

This is definitely a really nice work, very comprehensive and providing insights from multiple points of view on a problem that has been puzzling researchers in this field for a while. Well done! I see that in the twitter feed many people are reporting: “When experiment and theory disagrees, it's not always the theory that's wrong”, and although I see where it’s coming from, I would suggest caution. I do not think that previous experiments were “wrong”: there might be the possibility that they were not interpreted correctly. And this pre-print shows, partly, why and how. On this topic, I have some commentary that I would like to share here below (apologies in advance for the length).

I understand that the article is trying to provide an accurate, unequivocal experimental response to what seem to be controversial findings from several research groups, in a field where experimental results often do not match the thermodynamic calculations on the system. It is also my understanding that, coming from the same research groups, this pre-print is trying to clarify the findings from their earlier publication, Riedel et al., 2015, Nature.<br /> The effort and amount of work presented here is impressive, and the results are quite compelling. In the big picture, this work is very important in the process of trying to solve the debate on whether diffusion is enhanced, or not during catalysis.<br /> However, what is presented here seems to somehow leave aside experimental evidence and knowledge on FCS and fluorescent dyes that were reported in the literature even before the previous paper (Riedel et al., 2015, Nature) was published. I will try to make my case here to the best of my knowledge, and I will be happy to discuss and revise any inaccuracies or anything that I might have missed and could help understanding the choices of the authors.<br /> This is the main concern that I bring forward: most of the field working on “catalysis enhanced diffusion” appears to be using Fluorescence Correlation Spectroscopy (FCS) to measure diffusion coefficients with a required accuracy that ignores the intrinsic flaws of the technique itself, which have been known for a (debatably) long number of years. Part of this concern is addressed in Gunther et al, 2018, AccChemRes, although not, in my view, to its full extent.

Is FCS accurate and precise enough for the task? My answer is: it depends on several considerations.<br /> 1. Precision-wise that should be possible, as long as alignment of the setup is at its best (see: Enderlein et al, 2004, Current Pharmaceutical Biotechnologiy; Enderlein, 2005, Journal of Fluorescence) and care is taken to use high precision coverslips, properly set the correction collar of the objective, focus consistently at the same distance from the glass/buffer interface, use an appropriate combination of power and recording time, and perform well-designed controls.<br /> 2. Point 1 holds if we assume that once the reaction is started (substrate is added), (almost) every single enzyme will be always caught in the act of catalysis while going through the confocal volume, however, what is most likely to happen is that only a fraction of the enzymes will be observed during catalysis and therefore with their diffusion coefficient enhanced. To analyze the data, two different scenarios might be faced:<br /> a. Try and use a two-components diffusion model to fit the data; this might work if the difference between the diffusion times/coefficients of the different species in solution is at least ~1.6-fold (see Ruttinger et al, 2010, Journal of Fluorescence; Meseth et al, 1999, <br /> Biophysical Journal)<br /> b. It could also be that only one decay will be discernible, where the time constant is the weighted average between the species involved (enzymes "caught" in catalysis and enzymes which aren’t). By increasing the substrate concentration, the contribution to the time constant of enzymes undergoing catalysis will increase and therefore the overall FCS decay will shift toward shorter times. This is what is observed in Riedel et al., 2015, Nature , as well as other similar publications investigating the same problem using the same technique.<br /> 3. Point 2 would generate clean data to be analyzed in either of the two ways suggested if we were in an ideal world. That is, with an ideal fluorescent dye. However, this is (very) rarely the case, which brings me to my second main argument.

What about the photophysics of the dyes used? Fluorescent dyes are far from perfect, and even though amazing steps forward were made by organic chemists, there are still considerations to be made nevertheless. And it seemed that these were (point blank) ignored prior to the commentary of Gunther et al, 2018, AccChemRes about the use of FCS in this particular field. Here are some examples:<br /> 1. Most dyes have complex photophysics occurring in the us to ms time scale (triplet states, blinking), and this has been amply and thoroughly documented since the resurgence of FCS in the modern microscopy era, and more so with the advent of super-resolution microscopy, which makes direct use of these properties in some of its implementations. Research groups that worked on this are Rigler, Schwille, Widengren, Eggeling, Tinnefeld, Sauer, Seidel, De Schryver, Hofkens, Enderlein just to name a few. Even the dye ATTO655 which seemed to be, at first, not plagued by triplet-state issues detectable in FCS, turned out not to be exempt from them, if proper conditions are not met (see Vogelsang et al, 2009, PNAS).<br /> 2. The photophysics of a large number of fluorescent dyes often used in FCS experiments is affected by organic compounds in various way, especially so upon excitation to their first excited state, at which point they could be considered essentially radicals. Fluorophores are often subject to redox reactions and this has been documented (work from Rigler, Widengren, Schwille using FCS, and Tinnefeld, Sauer, among others, for single molecule super-resolution):<br /> a. An example is presented in Widengren et al, 2007, Journal of Phys Chem A, where the effect of a series of redox chemicals on the apparent diffusion time of dyes is shown. It’s worth noting that one of the chemicals, n-propyl-gallate, is chemically similar to pNPP, used in this pre-print, and in Riedel et al.<br /> b. In Vogelsang et al, 2008, Angewandte Chemie and Vogelsang et al, 2009, PNAS, ATTO dyes are investigated and it is quite clear from their findings that in one way or the other (depending on conditions) most of these dyes blink and some time-constants are provided in the papers.<br /> c. A side note: in Riedel et al, 2015, Nature, not all the substrates are potentially redox active, however there are other properties to be taken into account such as the changes in refractive index that some substrates (e.g.: urea) might cause and that will introduce artifacts that has to be corrected for (see Enderlein et al, 2005, ChemPhysChem).<br /> 3. A few miscellaneous things: I was trying to find back papers where the triplet state (us region) of ATTO647N is discussed/observed in FCS but did not have any luck, however I recall using it myself for FCS measurements and it is quite clear that there is a triplet state to account for (~5-20 us region). On a different aspect, I understand that when looking at, for example Widengren et al, 2007, Journal of Phys Chem A one might argue that the changes in apparent diffusion time there work the opposite of what observed in the field of diffusion-enhanced catalysis, but it does make a point that intermolecular reactions that are not necessarily faster than diffusion do influence the observed diffusion coefficient. Furthermore, a more illustrative example might be found in Andreoni et al, 2017, Chemistry – A EurJ where redox chemicals do reduce the apparent diffusion time of a fluorescently labeled protein (Figure S6) and it is necessary to consider the redox nature of dye and reactants in order to find a physical explanation to the apparently odd phenomenon.

Provided all the considerations above, here is my last argument: data analysis. Although the focus might be on Riedel et al, 2015, Nature, I find this to be the most puzzling aspect in most of the publications that I looked at in the field of catalysis-enhance diffusion. Either because some authors do not show the data (e.g.: Muddana et al, 2010, JACS; Jee et al, 2018, PNAS; Illien et al, 2007, Nano Letters), or because when analyzing the data, it seems that the authors do not take into account the known literature on dyes photophysics and they claim that FCS data were analyzed “using a model accounting for diffusion only (Gdiff)”. Given the context, Riedel et al, 2015, Nature, probably provides for the first time a glance at how the data look like and here are some observations:<br /> 1. In Figure 1 of the manuscript the data with their fit to a diffusion-only equation are presented. Although not strictly necessary to see what is going on, the residuals are also shown, and make further observations easier:<br /> a. It is clear that the residuals are not randomly distributed, and this should already raise questions. Even ignoring everything regarding the photophysical aspects of dyes during FCS experiments, there is evidence that the model does not describe the data.<br /> b. I understand that, supposedly, the diffusion time constant in FCS data might be described by the half-decay region of the curve and that is why close-ups of the plots on those areas are presented. However, this ignores completely the influence that other phenomena might have on the decay in a specific time region, and does not attempt to describe the phenomena occurring in the sample in a more comprehensive way.<br /> c. This is just a doubt: the data are presented here normalized, and I wonder if the fitting was performed on the normalized, or on the non-normalized data. Statistical weighing when fitting FCS data is very important (variance is not constant, see Wohland et al, 2001, Biophysical Journal; Saffarian, 2003, Biophysical Journal), and hopefully it was taken into account, because it would introduce bias if fitting was performed on normalized data.<br /> 2. It is quite clear that to properly fit the data, addition of components to the fitting equation would have benefited the quality of the analysis, without necessarily incurring in a problem of overfitting (which I acknowledge might be an issue with FCS data). There are known and explainable physical phenomena underlying the need for multiple components (triplets, blinking, possibly multiple species?). Fitting of FCS data is indeed a controversial subject because overfitting is not uncommon, especially when not all the underlying processes are known and clearly explainable, or proper care is not take to ensure that the measuring setup is properly aligned and aberrations are minimized. However, achieving proper chi-square minimization would have cleared doubts on the reliability of the fitting:<br /> a. He et al, 2012, Analytical Chemistry provide a nice framework to use a Bayesian approach to fit the data with the statistically most likely model. Applications to “real world data” are presented in Guo et al, 2012, Analytical Chemistry and Sun et al, 2015, Analytical Chemistry<br /> b. Another possible approach would be to use the maximum entropy method proposed by Sengupta et al, 2003, Biophysical Journal, which would be very suitable here since the working hypothesis assumes distributions of diffusion times in the sample.

I know all this was long, and mainly not focused on this pre-print, but this pre-print sparks one major question: were the data, in previous publications on catalysis-enhanced diffusion, treated differently, analyzed in a more suitable way, what would they tell us?<br /> It seems to me a non-trivial oversight to go the great length of using very complex measurements (ABEL trap, SPT) to verify previous findings, without addressing the analysis of previously published results. Furthermore, a review of the FCS literature would have already raised concerns on trying to use yet again the same technique as previously (as done in Riedel et al). Methodologies to mitigate the issues (dyes photophysics, other artifacts) preventing from measuring accurate diffusion coefficients by fluctuation spectroscopy were already proposed, most of them introducing an additional “ruler” in the system: 2-focus FCS (Schwille; Enderlein), scanning-FCS (Schwille), RICS (Gratton). They are also less cumbersome to realize and less “invasive” than the ABEL trap: could, for example, the introduction of the fairly intense external electric field influence the experiments in this case?

I am glad to see that in this pre-print controls were shown, which were not provided earlier, such as the effect of substrate on the apparent diffusion of the dye and other enzymes. Focusing on this pre-print there are still few questions that would be useful if they were addressed by the authors:<br /> • In the ABEL trap experiments, did they have to use viscosity-increasing additives, such as glycerol or PEG? If so, what’s the concentration and were the same conditions used for the FCS and the SPT experiments?<br /> • In Figure 2, the authors report the Ds extracted from one single, 300s long FCS experiment and show the standard deviation calculated from there. However, it would be more meaningful to obtain an error from repeated experiments, where a new sample is used each time. This would account for reproducibility of methodological variables, such as refocusing, coverslip-to-coverslip variations and so on.<br /> • In the FCS setup description, the pinhole used is 100 um but no indication is provided regarding the total magnification of the system (objective+tube lens): how many Airy units is the pinhole?<br /> • To verify the blinking behavior, the authors used Trolox + "oxygen removal" to reverse the effect of pNPP. I am not aware of photophysical studies on JF646, thus my questions are mainly focused on ATTO647N: this dye is known to blink in the absence of oxygen, but I cannot find any information on the blinking behavior in the presence of it. Now, introduction of pNPP in solution induces blinking, is this in oxygen-free environment? Or is it in the presence of oxygen? Did the author try to use Trolox without removing oxygen? Did they quantify the TX/TQ ratio (see Cordes et al, 2009, JACS)? Since we are talking photophysics here, is pNPP likely to behave as a reducing or oxidizing agent in this case? There should be a more clear presentation of the different conditions (similar to what is done for JF646 in Figure 5) to help the reader navigate through them.<br /> • It is nice to see that Monte-Carlo simulations were performed to study the effect of blinking on diffusion. However, I have two notes here:<br /> o Without the need of simulations, it would have been possible to simply produce FCS data from an autocorrelation function containing 2 terms (Gdiff*Gblink), maybe using local gaussian noise to emulate uncertainty, and see if the introduction of Gblink (~10ms) would affect the mean diffusion time observed, and how.<br /> o I notice in Figure S8 B that the fitting of the blue curve (+3mM pNPP) is not quite following the data above 7ms: is it reasonable to suspect that’s because the data actually should be described by two components (Gdiff*Gblink)? Did the authors try to simulate different % of blinking occurring, and then tried to retrieve both the blinking and the diffusion time constant? If this worked, it would make an intriguing case for re-visiting previous data in light of these new findings.

If anyone reads this, thanks for your attention and I hope this won’t be taken too harshly, I am just trying to use this space to share my view, and open a discussion about this in a constructive way.<br /> Best of luck to the authors if they already submitted the paper for publication, and thanks for sharing this on bioRxiv!

On 2020-05-19 22:16:09, user Sophie Gryseels wrote:

A corrected and improved version of this study is out now in PNAS: https://www.pnas.org/conten...

On 2020-05-19 17:49:18, user Professor Navarre wrote:

Thanks so much for your thorough review of the paper! We appreciate your comments and will incorporate many of your suggestions into the submission!

William Navarre

On 2020-05-15 13:57:39, user UAB Journal Club wrote:

Bacterial Pathogenesis and Physiology Journal Club<br /> The University of Alabama at Birmingham<br /> Summer Rogue team 2020

Review of “The Salmonella LysR family regulator, RipR, activates the SPI-13 encoded itaconate degradation cluster”<br /> Hersch et al.

Summary

In this manuscript, the authors show in novel ways that the dicarboxylic acid itaconate, produced by macrophages, has bactericidal effects under the physiological conditions of the macrophage phagosome (e.g. low pH). Additionally, the authors show that pathogens which are adapted to resist these macrophages and the conditions of the phagosome, such as Salmonella, sense the itaconate and express an itaconate degradation protein under the regulation of the ripR gene, to resist this bactericidal effect.

Overall the manuscript is a thorough example of the host-microbe warfare that occurs during infection. The work is detailed and the conclusions drawn are well supported by the data, but there are a few things that would be helpful to clarify.

Minor Comments

Text sizes in legends are inconsistent.

Explanation of the methods to measure itaconate degradation and its acidification (that used in Fig 1 B) is lacking. Increasing explanation in legend or in main text would be helpful to understand the biochemical complexity.

Emphasis on the macrophage experiments should be increased. There was a lot of detail included in the biochemical experiments but this seemed to fade off in the macrophage results section. These results are arguably the most translational and would draw the most diverse audience.

Overall, I think a bit more could be done here to give the paper more substance. There are only four figures, one of which is a diagram of an operon and could be merged with another figure. Supplemental figure 4 could possibly be added to the primary figures. Supplemental figures 2 and 3 also seem like they could be important enough to be used as actual figures.

It’s mentioned that homologs of RipABC are shown to degrade itaconate, would it be possible to repeat that study using these proteins to show they’re involved? Future study maybe?

Not sure if the operon diagram (Figure 2) is substantial enough to stand as its own figure. This could become a panel in Figure 1 or 3.

Major Comments or Lack of Clarity

The order of figures as associated with the results text is a bit jarring. For example, Figure 2 and 3 are mentioned before Figure 1.

Paragraph describing statistics lacking from methods section. A brief description of figure-specific analyses have been included in the figure legends however the software and overall specifics should be included in a more broad section of the methods.

Was there a difference in THP-1 vs J774 survival following Salmonella challenge which would possibly interfere with the phagocytosis? Additionally, it would be interesting if there was one or two kinetic experiments with this phagocytosis as the cell lines may take different amounts of time to phagocytose the bacteria. Why specifically choose J774 mouse macrophages instead of another cell line, like RAW264? Have you compared the pH of J774 mouse macrophages to that of THP-1 human macrophages?

IL-4/IL-13 stimulation was stated to be done with 100U/mL of each. This is below the standard of 200U/mL (or 20ng/mL) and may have affected overall result of M2 polarization.

Why is it that deleting rpoS makes a bigger difference in survival than deleting IRO?

Why do you think survival of ΔRPO or ΔripR Salmonella strains wasn’t impacted compared to wildtype in mouse macrophages (Supplemental Figure 5)? Wouldn’t you expect the lack of ability to degrade itaconate to cause these strains to be killed quicker?

The conclusion that succinate is bactericidal is overstated. It looks like Salmonella just isn't that happy at a pH of 4.4. In the paper the authors cite (ref 34) for succinate being bactericidal, but it looks like succinate increases inside of macrophages stimulate a more robust inflammatory response-- not a direct killing of bacteria by succinate.

Figure-specific comments:

Figure 1: methods for panel B are lacking detail. How was pH adjusted? is this pH monitored as itaconate breaks down, possibly altering overall pH?

Figure 2: No major comments

Figure 3: No major comments. Text sizes in legends and axis labels seem inconsistent

Figure 4: No major comments. Addition of a detailed statistics section in the methods would make the legend to this figure less wordy (the description of the box and whisker plots takes away from overall data impact of this figure).

On 2020-05-19 17:37:38, user James Mitchell wrote:

I think you are missing a few classes in Pezizomycotina; there are not currently 13, but at least 17: Arthoniomycetes, Candelariomycetes, Collemopsidiomycetes, Coniocybomycetes, Dothideomycetes, Eurotiomycetes, Geoglossomycetes, Laboulbeniomycetes, Lecanoromycetes, Leotiomycetes, Lichinomycetes, Orbiliomycetes, Pezizomycetes, Sareomycetes, Sordariomycetes, Xylobotryomycetes, and Xylonomycetes.

On 2020-05-19 16:26:43, user Peter Neubauer wrote:

Dear Ms. Chory, dear Mr. Esvelt,

We have just recognized your nice contribution to the field of automated bioengineering. Your interesting case study illustrates well the impressive possibilities when harnessing the power of automated liquid handling stations. This is a field where our group is also working since almost ten years. We did not only build an automated platform for bioprocess development, but also developed a turbidostat to simultaneously transform cells on the Hamilton MicrolabSTAR. The latter approach is similar to what you have described in your paper, although focusing on a different application. We have worked for a long time on the alternatives to operate the Hamilton, Tecan, and other devices in one single experiment simultaneously. Here are some papers that we think might enrich your manuscript with related research:

1. Anane et al. (2019). A model-based framework for parallel scale-down fed-batch cultivations in mini-bioreactors for accelerated phenotyping. Biotechnol Bioeng 116, 2906–2918. doi: 10.1002/bit.27116.
2. Cruz Bournazou et al. (2017). Online optimal experimental re-design in robotic parallel fed-batch cultivation facilities. Biotechnol Bioeng 114, 610–619. doi: 10.1002/bit.26192.
3. Dörr & Bornscheuer (2018). Program-Guided Design of High-Throughput Enzyme Screening Experiments and Automated Data Analysis/Evaluation. Methods in molecular biology 1685, 269–282. doi: 10.1007/978-1-4939-7366-8_16.
4. Haby et al. (2019). Integrated Robotic Mini Bioreactor Platform for Automated, Parallel Microbial Cultivation With Online Data Handling and Process Control. SLAS Technol 24, 569–582. doi: 10.1177/2472630319860775.
5. Hanset al. (2018). Automated Cell Treatment for Competence and Transformation of Escherichia coli in a High-Throughput Quasi-Turbidostat Using Microtiter Plates. Microorganisms. 6. doi: 10.3390/microorganisms6030060.
6. Horinouchiet al. (2014). Development of an automated culture system for laboratory evolution. J Lab Autom 19, 478–482. doi: 10.1177/2211068214521417.

With kind regards<br /> Peter Neubauer<br /> Head of Bioprocess Engineering <br /> TU Berlin, Germany

On 2020-05-19 16:15:16, user Peter Neubauer wrote:

Dear Ms Chory, Dear Mr Esvelt,

We have just recognized your nice contribution to the field of automated bioengineering. Your interesting case study illustrates well the impressive possibilities when harnessing the power of automated liquid handling stations. This is a field where our group is also working since almost ten years. We did not only build an automated platform for bioprocess development, but also developed a turbidostat to simultaneously transform cells on the Hamilton MicrolabSTAR. The latter approach is similar to what you have described in your paper, although focusing on a different application. We have worked for a long time on the alternatives to operate the Hamilton, Tecan, and other devices in one single experiment simultaneously. Here are some papers that we think might enrich your manuscript with related research:

1. Anane et al. (2019). A model-based framework for parallel scale-down fed-batch cultivations in mini-bioreactors for accelerated phenotyping. Biotechnol Bioeng 116, 2906–2918. doi: 10.1002/bit.27116.
2. Cruz Bournazou et al. (2017). Online optimal experimental re-design in robotic parallel fed-batch cultivation facilities. Biotechnol Bioeng 114, 610–619. doi: 10.1002/bit.26192.
3. Dörr & Bornscheuer (2018). Program-Guided Design of High-Throughput Enzyme Screening Experiments and Automated Data Analysis/Evaluation. Methods in molecular biology 1685, 269–282. doi: 10.1007/978-1-4939-7366-8_16.
4. Haby et al. (2019). Integrated Robotic Mini Bioreactor Platform for Automated, Parallel Microbial Cultivation With Online Data Handling and Process Control. SLAS Technol 24, 569–582. doi: 10.1177/2472630319860775.
5. Hanset al. (2018). Automated Cell Treatment for Competence and Transformation of Escherichia coli in a High-Throughput Quasi-Turbidostat Using Microtiter Plates. Microorganisms. 6. doi: 10.3390/microorganisms6030060.
6. Horinouchiet al. (2014). Development of an automated culture system for laboratory evolution. J Lab Autom 19, 478–482. doi: 10.1177/2211068214521417.

With kind regards<br /> Peter Neubauer<br /> Head of Bioprocess Engineering <br /> TU Berlin, Germany

On 2020-05-19 15:12:38, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-05-14 16:34:14, user Anita Bandrowski wrote:

"Hi, we're trying to improve preprints using automated screening tools. Here's some stuff that our tools found. If we're right then you might want to look at your text, but if we're not then we'd love it if you could take a moment to reply and let us know so we can improve the way our tools work. Have a nice day.

Specifically, your paper (DOI:10.1101/2020.03.22.002204); was checked for the presence of transparency criteria such as blinding, which may not be relevant to all papers, as well as research resources such as statistical software tools, cell lines, and open data.

We did not detect information on sex as a biological variable, which is particularly important given known sex differences in COVID-19 (Wenham et al, 2020).

We also screened for some additional NIH & journal rigor guidelines:<br /> IACUC/IRB: not detected ; randomization of experimental groups: not detected ; reduction of experimental bias by blinding: not detected ; analysis of sample size by power calculation: not detected .

We found that you used the following key resources: cell lines (3), software (3) . We recommend using RRIDs so that others can tell exactly what research resources you used. You can look up RRIDs at rrid.site

Thank you for sharing your data.

More specific comments and a list of suggested RRIDs can be found by opening the Hypothes.is window on this manuscript, direct link https://hyp.is/9Mu_XI-sEeqF...<br /> References cited: https://tinyurl.com/y7fpsvzy"

On 2020-05-13 16:54:46, user Anita Bandrowski wrote:

Hi, we're trying to improve preprints using automated screening tools. Here's some stuff that our tools found. If we're right then you might want to look at your text, but if we're not then we'd love it if you could take a moment to reply and let us know so we can improve the way our tools work. Have a nice day.

Specifically, your paper (DOI:10.1101/2020.03.22.002204); was checked for the presence of transparency criteria such as blinding, which may not be relevant to all papers, as well as research resources such as statistical software tools, cell lines, and open data.

We did not detect information on sex as a biological variable, which is particularly important given known sex differences in COVID-19 (Wenham et al, 2020).

We also screened for some additional NIH & journal rigor guidelines:<br /> IACUC/IRB: not detected ; randomization of experimental groups: not detected ; reduction of experimental bias by blinding: not detected ; analysis of sample size by power calculation: not detected .

We found that you used the following key resources: cell lines (3), software (3) . We recommend using RRIDs so that others can tell exactly what research resources you used. You can look up RRIDs at rrid.site

Thank you for sharing your data.

More specific comments and a list of suggested RRIDs can be found by opening the Hypothes.is window on this manuscript, direct link https://hyp.is/9Mu_XI-sEeqF...<br /> References cited: https://tinyurl.com/y7fpsvzy"

On 2020-05-10 18:25:09, user Alina wrote:

"We have already instigated a programme to determine if this deletion also occurs in human clinical isolates and are currently examining the evolution of the S glycoprotein deletant virus in human cell lines to determine whether the furin cleavage site is essential for infection of human cells." - Exciting! Looking forward to your findings.

On 2020-05-19 12:27:22, user Francisco M. Goycoolea wrote:

Dear colleagues, I was very happy to read your paper and see that your work agrees with our observations in MDCK-C7 cells. We used nanoencapsulated capsaicin and demonstrated that it induces migration and directedness associated with calcium influx. We attributed it to the TRPV4 Ca channel and the resulting mechanical stimulation (as seen in previous works). <br /> https://journals.plos.org/p...<br /> Hope you get your preprint published soon. Regards, Francisco M. Goycoolea

On 2020-05-19 10:20:56, user Vikas Bansal wrote:

We have published with the similar name. Please have a look here https://github.com/imsb-uke...

On 2020-05-19 08:23:40, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-05-19 00:55:54, user Fraser Lab wrote:

I am posting this review on behalf of a student from a class at UCSF on peer review: https://fraserlab.com/peer_... . The student wishes to remain anonymous. I will be happy to act as an intermediary for any correspondence.

This manuscript by Wang et al., uses tagged PKD-2 extracellular vesicles (EVs) in C. Elegans to explore the potential role of EVs in directional transfer from one organism to another.

Overall, they identify a mechanoresponsive nature of certain male sensory cilia to release EVs, which are then found to be specifically located on the vulva of his mating partner.

The authors provide compelling evidence that the male tail sensory cilia can respond to global pressure to release EVs, in that the usage of agarose-coated coverslips and slides was a robust way to perturb the forces that a male nematode feels when mounted.

Separately, they also provided evidence of directional transfer of EV cargo from male to hermaphrodite C. elegans during mating. Specifically, showing that in inseminated hermaphrodites, there was highly localized deposition of the male-specific PKD-2-carrying EVs along the hermaphrodite vulva. Though, this study was limited by the inability to perturb EV budding and determine causality between EVs and presence of PKD-2 on hermaphrodite vulvas.

The major success of this paper was in their ability to tag and visualize EVs, and use this technique to identify a candidate mechanism of release for extracellular vesicles. All in all, this paper opens a door for determining potential biological functions for extracellular vesicles, which has been largely elusive in the field.

Minor points:<br /> Figure 1B could benefit from having an inseminated control image, to visualize which signals are present as autofluorescence<br /> It was unclear how many worms were imaged in the directional transfer experiment, but having that number would be important in establishing reproducibility

On 2020-05-18 20:22:10, user Kenneth W. Witwer wrote:

The question of whether viral RNAs can be transferred between cells without infectious virions is interesting and important, especially for positive sense RNA viruses. It was thus good to see this investigation on SARS-CoV-2. However, after careful review, I have numerous concerns about the interpretations presented in this preprint. The cytoplasmic delivery of RNA is not demonstrated here, nor is the presence and purity of "exosomes" or even a broader population of extracellular vesicles (EVs) demonstrated in this study. EV or exosome-specific separation methods are not used, and EVs are not characterized according to minimal requirements. Please see the consensus guidelines of the International Society for Extracellular Vesicles (Théry and Witwer, et al, JEV, 2018, https://www.tandfonline.com..., https://www.tandfonline.com..., as well as EV-TRACK (evtrack.org).

My criticisms in detail:<br /> 1) Exosomes are extracellular vesicles of endosomal origin. The assayed tetraspanins are typically found on exosomes but also on other types of EVs. It is thus not clear that the separated particles are exosomes.<br /> 2) The separation technology is polymer-based precipitation. This is not specific to EVs or exosomes. It will precipitate other particles, too, including ribonucleoprotein particles. No cellular or other markers are examined to assess purity.<br /> 3) The principle of the fluorescent labeling approach, which is used to support uptake of particles, is unclear, nor are minimal controls shown. The dye appears to stain proteins, but proteins would be present in/on many extracellular particles, not just EVs. Similar to lipid dyes, which are promiscuous and transferrable (see https://www.tandfonline.com..., extensive controls would be needed to show that this dye labels only EVs and nothing else...and that its presence in the cell indicates EV uptake. No such controls are shown.<br /> 4) While dye and apparently RNA uptake are confirmed, it is not clear that cytoplasmic delivery has occurred. These entities could be present in endosomal vesicles taken up by the cell. RNA could also be present in particles attached to the cell surface.<br /> 5) RNase digestion on its own does not prove that RNAs are found inside EVs. It must be combined with protease digestion to expose protein-protected RNAs.<br /> 6) The qPCR background cutoff is 40 cycles. This is quite late, especially since the performance of the assays is not shared. For transparency and rigor, please share the raw Cq data.<br /> 7) This point is less important than the above, but although "viral genes" are mentioned, only small amplicons of viral sequences are detected, and protein products are not assayed. Furthermore, these sequences are from codon-optimized sequences, not the actual viral sequences. The sequence differences are very large for the sequences recognized by these primers. This raises the question of whether the actual viral sequences would be packaged in the same way.

In summary, this study does not demonstrate cytoplasmic delivery of viral RNA via so-called "exosomes" or EVs in general.

On 2020-05-18 17:00:11, user Mikhail Pogorelyy wrote:

Now published at eLife <br /> https://elifesciences.org/a...

On 2020-05-18 16:18:00, user Anita Bandrowski wrote:

"Hi, we're trying to improve preprints using automated screening tools. Here's some stuff that our tools found. If we're right then you might want to look at your text, but if we're not then we'd love it if you could take a moment to reply and let us know so we can improve the way our tools work. Have a nice day.

Specifically, your paper (DOI:10.1101/2020.04.03.023846); was checked for the presence of transparency criteria such as blinding, which may not be relevant to all papers, as well as research resources such as statistical software tools, cell lines, and open data.

We did not detect information on sex as a biological variable, which is particularly important given known sex differences in COVID-19 (Wenham et al, 2020).

We also screened for some additional NIH & journal rigor guidelines:<br /> IACUC/IRB: not detected ; randomization of experimental groups: not detected ; reduction of experimental bias by blinding: not detected ; analysis of sample size by power calculation: not detected .

We found that you used the following key resources: cell lines (1) . We recommend using RRIDs to improve so that others can tell exactly what research resources you used. You can look up RRIDs at rrid.site

We did not find a statement about open data. We also did not find a statement about open code. Researchers are encouraged to share open data when possible (see Nature blog).

More specific comments and a list of suggested RRIDs can be found by opening the Hypothes.is window on this manuscript, direct link https://hyp.is/U4WZvo-tEeqx...<br /> References cited: https://tinyurl.com/y7fpsvzy"

On 2020-05-18 16:17:13, user Anita Bandrowski wrote:

"Hi, we're trying to improve preprints using automated screening tools. Here's some stuff that our tools found. If we're right then you might want to look at your text, but if we're not then we'd love it if you could take a moment to reply and let us know so we can improve the way our tools work. Have a nice day.

Specifically, your paper (DOI:10.1101/2020.03.02.972935); was checked for the presence of transparency criteria such as blinding, which may not be relevant to all papers, as well as research resources such as statistical software tools, cell lines, and open data.

We did not detect information on sex as a biological variable, which is particularly important given known sex differences in COVID-19 (Wenham et al, 2020).

We also screened for some additional NIH & journal rigor guidelines:<br /> IACUC/IRB: detected ; randomization of experimental groups: not detected ; reduction of experimental bias by blinding: not detected ; analysis of sample size by power calculation: not detected .

We found that you used the following key resources: antibodies (2), cell lines (3), software (8) . We recommend using RRIDs to improve so that others can tell exactly what research resources you used. You can look up RRIDs at rrid.site

We did not find a statement about open data. We also did not find a statement about open code. Researchers are encouraged to share open data when possible (see Nature blog).

More specific comments and a list of suggested RRIDs can be found by opening the Hypothes.is window on this manuscript, direct link https://hyp.is/puNh0o-sEeqg...<br /> References cited: https://tinyurl.com/y7fpsvzy"

On 2020-05-18 16:16:27, user Anita Bandrowski wrote:

"Hi, we're trying to improve preprints using automated screening tools. Here's some stuff that our tools found. If we're right then you might want to look at your text, but if we're not then we'd love it if you could take a moment to reply and let us know so we can improve the way our tools work. Have a nice day.

Specifically, your paper (DOI:10.1101/2020.03.02.972927); was checked for the presence of transparency criteria such as blinding, which may not be relevant to all papers, as well as research resources such as statistical software tools, cell lines, and open data.

We did not detect information on sex as a biological variable, which is particularly important given known sex differences in COVID-19 (Wenham et al, 2020).

We also screened for some additional NIH & journal rigor guidelines:<br /> IACUC/IRB: detected ; randomization of experimental groups: not detected ; reduction of experimental bias by blinding: not detected ; analysis of sample size by power calculation: not detected .

We found that you used the following key resources: antibodies (1) cell lines (2) . We recommend using RRIDs to improve so that others can tell exactly what research resources you used. You can look up RRIDs at rrid.site

We did not find a statement about open data. We also did not find a statement about open code. Researchers are encouraged to share open data when possible (see Nature blog).

More specific comments and a list of suggested RRIDs can be found by opening the Hypothes.is window on this manuscript, direct link https://hyp.is/pn6YiI-sEeqN...<br /> References cited: https://tinyurl.com/y7fpsvzy"

On 2020-05-18 16:15:38, user Anita Bandrowski wrote:

"Hi, we're trying to improve preprints using automated screening tools. Here's some stuff that our tools found. If we're right then you might want to look at your text, but if we're not then we'd love it if you could take a moment to reply and let us know so we can improve the way our tools work. Have a nice day.

Specifically, your paper (DOI:10.1101/2020.03.10.986711); was checked for the presence of transparency criteria such as blinding, which may not be relevant to all papers, as well as research resources such as statistical software tools, cell lines, and open data.

We did not detect information on sex as a biological variable, which is particularly important given known sex differences in COVID-19 (Wenham et al, 2020).

We also screened for some additional NIH & journal rigor guidelines:<br /> IACUC/IRB: not detected ; randomization of experimental groups: not detected ; reduction of experimental bias by blinding: not detected ; analysis of sample size by power calculation: not detected .

We found that you used the following key resources: cell lines (1) software (2) . We recommend using RRIDs to improve so that others can tell exactly what research resources you used. You can look up RRIDs at rrid.site

We did not find a statement about open data. We also did not find a statement about open code. Researchers are encouraged to share open data when possible (see Nature blog).

We also found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

More specific comments and a list of suggested RRIDs can be found by opening the Hypothes.is window on this manuscript, direct link https://hyp.is/xbw2sI-sEeqf...<br /> References cited: https://tinyurl.com/y7fpsvzy"

On 2020-05-18 16:14:36, user Anita Bandrowski wrote:

"Hi, we're trying to improve preprints using automated screening tools. Here's some stuff that our tools found. If we're right then you might want to look at your text, but if we're not then we'd love it if you could take a moment to reply and let us know so we can improve the way our tools work. Have a nice day.

Specifically, your paper (DOI:10.1101/2020.04.14.041228); was checked for the presence of transparency criteria such as blinding, which may not be relevant to all papers, as well as research resources such as statistical software tools, cell lines, and open data.

We did not detect information on sex as a biological variable, which is particularly important given known sex differences in COVID-19 (Wenham et al, 2020).

We also screened for some additional NIH & journal rigor guidelines:<br /> IACUC/IRB: not detected ; randomization of experimental groups: not detected ; reduction of experimental bias by blinding: not detected ; analysis of sample size by power calculation: not detected .

We found that you used the following key resources: cell lines (2) . We recommend using RRIDs to improve so that others can tell exactly what research resources you used. You can look up RRIDs at rrid.site

We did not find a statement about open data. We also did not find a statement about open code. Researchers are encouraged to share open data when possible (see Nature blog).

We also found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

More specific comments and a list of suggested RRIDs can be found by opening the Hypothes.is window on this manuscript, direct link https://hyp.is/vCb_gI-tEeqa...<br /> References cited: https://tinyurl.com/y7fpsvzy"

On 2020-05-18 14:51:54, user Black wrote:

1. Why not use LC3/ATG8 6KO cells for your study to rule out the compensation effect by other homologs?

2. Do other LC3/ATG8 homologs get phosphorylated by STK4 at the similar position?

On 2020-05-18 14:30:58, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-05-12 01:10:48, user chandrika senthil wrote:

This is a great addition to the COVID-19 mouse models. Congrats for the authors!! <br /> It is remarkably interesting to see the different findings from previously published hACE2 by Bao et al.,2020 (The Pathogenicity of SARS-CoV-2 in hACE2 Transgenic Mice) and I wonder why the Authors failed to discuss the difference in the outcome of an identical experiment, where the hACE2 mice were infected with WT SARS CoV2 intranasally. Further these authors suggesting that 40 % mortality was driven by viral neuro-invasion by 5 dpi, whereas in the previous experiment (Bao et al.,) those mice were kept up until 14 dpi with no mortality and no viral RNA in brain tissues. <br /> Dinnon et al., did not mentioned the age of the hACE2 mice that used in the experiment. Could age be a contributing factor for the neuro-invasion in hACE2 mice?

On 2020-05-18 14:02:51, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-05-18 13:37:49, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-05-18 13:31:12, user Jessica C Kissinger wrote:

My research group and collaborators are pleased to share our research on the complex & novel mitochondrial genome of Toxoplasma and related parasites with the larger parasite and evolution communities @WiParasitology @ISEPprotists @CTEGD. We welcome your feedback.

On 2020-05-18 09:03:11, user Moin wrote:

Thanks for this fabulous article and the info about amoeba virus was great!

On 2020-05-18 08:41:56, user Bala Chandramouli wrote:

This work reveals interesting changes happening by D614G substitution on the spike protein.The change in conformation seen within 150 ns itself is an important hint on the protein quick response to G614. A replica sampling could help to confirm this result (as also suggested by <br /> Andrea). You may mention the specific forcefield type of Amber used for modelling the protein in the methods. I suppose it is ff14SB. It would be interesting to add additional geometric descriptor, for instance, an angle between NTD and RBD highlighting the relative opening between the segments in the two variants (Fig 4A). However, the distance estimation itself clearly confirms the clear difference in the two variants.

On 2020-05-16 09:13:37, user standardito wrote:

A great paper about the spreading of a naturally selected variant of SARS-CoV-2.

On 2020-05-15 16:00:28, user Emiliano Trucchi wrote:

We would like to report a graphical mistake in Figure 2B: the same plot showing the diffusion of the two spike variants in NY was inadvertently inserted as inset in both the NY and the WA main plots. The WA inset is then missing. This will be amended in a corrected version of the manuscript to be uploaded asap. Sorry for the inconvenience! If you spot other mistakes, please let us know!

On 2020-05-15 10:32:28, user Andrea Coletta wrote:

Due to the large conformational change observed in the first 150ns, I think it would be best advised to run several replica of each system starting from a "stable" confromation (e.g. one after 150ns).

On 2020-05-18 08:25:53, user Yuval Kolodny wrote:

The described method, to extract intracellular and measure its isotopic composition, could be extremely useful for many interesting applications. We were looking for exactly such a method to study (in the field of Quantum Biology) the dynamics in chiral channels. Can you tell whether this method is applicable to other cell types?

On 2020-05-18 03:55:52, user Joshua Mylne wrote:

This work is published in the Journal of Natural Products after peer review now, but title has changed "Cyclic Peptides in Seed of Annona muricata Are Ribosomally Synthesized": https://dx.doi.org/10.1021/...

On 2020-05-17 18:12:10, user Isha Shingare wrote:

Hi,<br /> in this article, you have discussed about the negative controls used in microbiome studies, how about the usage of positive controls used in microbiome study?

On 2020-05-17 17:46:56, user Guest wrote:

The NMR data is of low quality!

On 2020-05-17 12:03:23, user Alexei Tsygvintsev wrote:

The final version of this manuscript was published. The reference of the article is:<br /> J Proteins Proteom 11, 11–16 (2020). <br /> https://doi.org/10.1007/s42...

On 2020-05-17 04:59:32, user Fraser Lab wrote:

I am posting this review on behalf of a student from a class at UCSF on peer review: https://fraserlab.com/peer_... . The student wishes to remain anonymous. I will be happy to act as an intermediary for any correspondence.

The goal of this paper is to engineer an optogenetic circuit that provides low noise and allows single-cell gene expression control in mammalian cells. The authors 1) engineered the light-inducible tuner (LITer) system and illustrated that incorporating negative feedback into optogenetic circuits can drastically reduce noise, and 2) applied the LITer system to achieve expression control of KRAS gene and explored the biological functions of KRAS in cell proliferation.

The authors characterized the LITer system using different light intensities and illumination conditions, exploring the dose-response, the linearity, and the efficiency of the system. One other important and distinct feature of the optogenetic system is reversibility that can provide flexible control of gene expression. The authors didn’t mention or do any characterization of the reversibility of the system in the paper. Adding a reversibility test would thoroughly highlight a potential advantage of the LITer system.

By comparing the performance of the LITer system with a benchmark tool LightOn, the authors observed 4- to 5-fold noise reduction in the LITer system. The authors proposed three sources of the noise reduction, including negative feedback, fast kinetics of LOV2, and the advantage of monomer over dimer. I think the authors could provide more evidence about each of their hypotheses. For example, to validate how the kinetics of optogenetic tools affect the noise reduction, they can use computational modeling to explore the different kinetic parameters, combining with experimental validation to see how changing those parameters will affect the performance of the system.

To test their new tool, the authors adapted the LITer system to control the expression of KRAS, where they showed that the level of KRAS and the downstream ERK phosphorylation could be fine regulated by different light intensities. By doing a cell count experiment, they found that after light illumination, the cell number decreases. The authors thus concluded that low KRAS level may maximize cell growth, while higher KRAS may lead to senescence. Several controls and supporting experiments could strengthen this claim:

They could measure KRAS and phosphorylated ERK concentration in parental cells to make sure the range of KRAS level in LITer-KRAS cells is comparable with that of the parental cells. <br /> They could perform cell number counting before the light stimulation as a control to make sure cells of different cell types or treated with different light intensities have the same initial count. Otherwise, the cell number count after illumination isn’t normalized. Moreover, they showed in the supplement the cell counting results under different light intensities from 0 up to 500 g.s. The number of parental cells fluctuates a lot under different light conditions, which makes their claim that light does not affect the growth of parental cells questionable. The authors tried to normalize the cell count results by calculating the ratio of the LITer-KRAS cells to the parental cells, but they didn’t do any statistical analysis. <br /> They pointed out in the paper that they tried to validate that the observed effects were due to KRAS induction and not light alone by using chemical inductions. Quite interestingly they only showed that doxycycline can induce KRAS expression and phospho-ERK, but didn’t put any cell proliferation results. Adding these results might be a convincing argument, if they also observe decreased cell number after doxycycline induction.

In general, the major success of the paper is that they engineered and characterized the LITer system that showed significant noise reduction compared to the benchmark LightOn system. Moreover, through computational modeling they found the reason for the high expression level of the system and made further improvements leading to LITer2.0, which shows lower basal expression level and better linearity. The major weakness of the paper is that they lack some important control and supporting experiments to support their conclusion about the biological functions of KRAS.

In summary, the LITer system that the authors engineered will allow precise and spatiotemporal control of gene expression for biological researches, with potential improvement to the dynamic fold change by using more efficient optogenetic tools.

Minor points:<br /> 1. Why not use the same duration of illumination for both LITer and LightOn systems when doing the comparison? Both systems seem to reach saturation after 12h illumination.<br /> 2. The deterministic and stochastic model seem to have similar results according to the paper. Is there a reason why they want to use both methods for computational modeling?<br /> 3. It would help the reader understand the circuit they engineered better if they can put the detailed gene circuits shown in supporting Figure S1 into Figure 1 and 3 in the paper.<br /> 4. The x-axes of Figure 2J and Figure 5H should be 0, 50, and 100 since the unit is percent (%).<br /> 5. In the supplement where they explain the deterministic models, R should stand for ???????????????? or Tet Repressor but not TetR Repressor.

On 2020-05-17 00:04:37, user John Rakus wrote:

Hello,

I would appreciate any suggestions on improving this manuscript. I am a faculty member at a primarily undergraduate institution, we have decent resources but we are definitely not a research-intensive university. I submitted this manuscript to an ACS journal but it was ultimately rejected despite several rounds of revision. I realize the content is not necessarily groundbreaking - and I would like to follow it up in greater detail - but I do believe the work is novel and of publication quality. The reviews at each step were split, minor revisions vs do not publish. The latter reviewer (I assume it was the same individual at each resubmission) was apparently not satisfied with any additions, alterations and edits that I provided.

If anyone has any ideas and suggestions about improving this paper without performing substantial additional experimentation, I would be extremely grateful.

My thanks,

John R.

On 2020-05-16 15:21:44, user Jennifer H wrote:

What is salient to the mouse besides big overhead scary things? This is the first of our efforts to find out and we're excited to see what other labs will starting finding too!

On 2020-05-16 11:33:19, user Edward Kazehaya wrote:

So what is the implication of this work in simple English? <br /> Thanks in advance.

On 2020-05-15 21:49:49, user Lou Altamura wrote:

What tests were performed to evaluate potential cytotoxicity of the disinfectant to target cells. If cytotoxicity was observed, how was this mitigated and were disinfectant neutralization controls performed?

On 2020-05-15 20:48:04, user Adam Smith wrote:

Nice paper! Seeking a little clarification as I prepare to give it a test run. In Appendix 3, beta0 is passed as g0 \* K, but in the oSCR::scrdesignGA documentation, it indicates this should be passed as log(g0 \* K). And, more confusingly, the default value is -0.2 \* 5. The negative value suggests the log is involved, but it's unclear from the default whether that is log(g0) \* K or log(g0 \* K).

On 2020-05-15 09:06:25, user OxImmuno Literature Initiative wrote:

https://www.immunology.ox.a...

On 2020-05-14 21:14:41, user Yang Xu wrote:

First look at accuracy over 90%. I was impressed. However, after a closer look, I realized the classification is tissue specific (around 30 tissues) instead of cell type specific (over 600 cell types). Instead of accuracy, why not show F1 score for each class?

On 2020-05-14 17:36:40, user miaomiao706 wrote:

new discovery about Nucleolin

On 2020-05-14 16:43:23, user Kifayat Hussain wrote:

Please let me know to which protein the nab binds , make sure it doesn't bind to spike protein that will have specifity for ace2 receptor as wel thus destruction of ace2 bearing cells in humans

On 2020-05-14 16:42:11, user Anita Bandrowski wrote:

"Hi, we're trying to improve preprints using automated screening tools. Here's some stuff that our tools found. If we're right then you might want to look at your text, but if we're not then we'd love it if you could take a moment to reply and let us know so we can improve the way our tools work. Have a nice day.

Specifically, your paper (DOI:10.1101/2020.02.17.951335); was checked for the presence of transparency criteria such as blinding, which may not be relevant to all papers, as well as research resources such as statistical software tools, cell lines, and open data.

We did not detect information on sex as a biological variable, which is particularly important given known sex differences in COVID-19 (Wenham et al, 2020).

We also screened for some additional NIH & journal rigor guidelines:<br /> IACUC/IRB: not detected ; randomization of experimental groups: not detected ; reduction of experimental bias by blinding: not detected ; analysis of sample size by power calculation: not detected .

We found that you used the following key resources: antibodies (1) cell lines (1) software (6) . We recommend using RRIDs to improve so that others can tell exactly what research resources you used. You can look up RRIDs at rrid.site

Thank you for sharing your data.

More specific comments and a list of suggested RRIDs can be found by opening the Hypothes.is window on this manuscript, direct link https://hyp.is/fNe3-I-sEeqC...<br /> References cited: https://tinyurl.com/y7fpsvzy"

On 2020-05-14 16:35:22, user Anita Bandrowski wrote:

"Hi, we're trying to improve preprints using automated screening tools. Here's some stuff that our tools found. If we're right then you might want to look at your text, but if we're not then we'd love it if you could take a moment to reply and let us know so we can improve the way our tools work. Have a nice day.

Specifically, your paper (DOI:10.1101/2020.02.10.936898); was checked for the presence of transparency criteria such as blinding, which may not be relevant to all papers, as well as research resources such as statistical software tools, cell lines, and open data.

We did not detect information on sex as a biological variable, which is particularly important given known sex differences in COVID-19 (Wenham et al, 2020).

We also screened for some additional NIH & journal rigor guidelines:<br /> IACUC/IRB: not detected ; randomization of experimental groups: not detected ; reduction of experimental bias by blinding: not detected ; analysis of sample size by power calculation: not detected .

We found that you used the following key resources: cell lines (8), software (4) . We recommend using RRIDs so that others can tell exactly what research resources you used. You can look up RRIDs at rrid.site

We did not find a statement about open data. We also did not find a statement about open code. Researchers are encouraged to share open data when possible (see Nature blog).

More specific comments and a list of suggested RRIDs can be found by opening the Hypothes.is window on this manuscript, direct link https://hyp.is/b2aE0o-sEeqm...<br /> References cited: https://tinyurl.com/y7fpsvzy"

On 2020-05-13 16:53:24, user Anita Bandrowski wrote:

"Hi, we're trying to improve preprints using automated screening tools. Here's some stuff that our tools found. If we're right then you might want to look at your text, but if we're not then we'd love it if you could take a moment to reply and let us know so we can improve the way our tools work. Have a nice day.

Specifically, your paper (DOI:10.1101/2020.02.10.936898); was checked for the presence of transparency criteria such as blinding, which may not be relevant to all papers, as well as research resources such as statistical software tools, cell lines, and open data.

We did not detect information on sex as a biological variable, which is particularly important given known sex differences in COVID-19 (Wenham et al, 2020).

We also screened for some additional NIH & journal rigor guidelines:<br /> IACUC/IRB: not detected ; randomization of experimental groups: not detected ; reduction of experimental bias by blinding: not detected ; analysis of sample size by power calculation: not detected .

We found that you used the following key resources: cell lines (8), software (4) . We recommend using RRIDs so that others can tell exactly what research resources you used. You can look up RRIDs at rrid.site

We did not find a statement about open data. We also did not find a statement about open code. Researchers are encouraged to share open data when possible (see Nature blog).

More specific comments and a list of suggested RRIDs can be found by opening the Hypothes.is window on this manuscript, direct link https://hyp.is/b2aE0o-sEeqm...<br /> References cited: https://tinyurl.com/y7fpsvzy"

On 2020-05-14 16:33:21, user Anita Bandrowski wrote:

"Hi, we're trying to improve preprints using automated screening tools. Here's some stuff that our tools found. If we're right then you might want to look at your text, but if we're not then we'd love it if you could take a moment to reply and let us know so we can improve the way our tools work. Have a nice day.

Specifically, your paper (DOI:10.1101/2020.01.31.929042); was checked for the presence of transparency criteria such as blinding, which may not be relevant to all papers, as well as research resources such as statistical software tools, cell lines, and open data.

We did not detect information on sex as a biological variable, which is particularly important given known sex differences in COVID-19 (Wenham et al, 2020).

We also screened for some additional NIH & journal rigor guidelines:<br /> IACUC/IRB: not detected ; randomization of experimental groups: not detected ; reduction of experimental bias by blinding: not detected ; analysis of sample size by power calculation: not detected .

We found that you used the following key resources: cell lines (2) . We recommend using RRIDs to improve so that others can tell exactly what research resources you used. You can look up RRIDs at rrid.site

Thank you for sharing your data.

More specific comments and a list of suggested RRIDs can be found by opening the Hypothes.is window on this manuscript, direct link https://hyp.is/Yt9Z0o-sEeqF...<br /> References cited: https://tinyurl.com/y7fpsvzy"

On 2020-05-14 16:09:56, user Carlo Camilloni wrote:

Hi, I find quite remarkable that the fraction of native contacts we found in 2009 for the intermediate state is close to what you found here. I would be really curios to see the contact maps of your intermediate state in the paper, I think it would provide a clearer picture of the structural features you are observing. Anyway, great work!