This is a review of the preprint entitled " Challenging the Raunkiaeran shortfall and the consequences of using imputed databases", by Lucas Jardim, Luis Mauricio Bini, José Alexandre Felizola Diniz-Filho and Fabricio Villalobos. The preprint was originally posted on bioRxiv on October 18, 2016 (https://doi.org/10.1101/081778). Our journal club reviewed this preprint in the meeting of June 6, 2018.

PREreview A role for RNA and DNA:RNA hybrids in the modulation of DNA repair by homologous recombination https://doi.org/10.1101/255976   Summary   D’Alessandro et al. proposes mechanistic advances for the role of damage-induced long-non-coding RNAs (dilncRNAs) in double-strand break (DSB) repair.  The authors have shown that DNA:RNA hybrids can accumulate at targeted DSBs in transcribed and non-transcribed regions.  In addition, damage-induced hybrids accumulate predominantly in S/G2 and are promoted by DSB resection.  Although inhibition of transcription did not reduce resection, the authors reported that it reduced recruitment of HR proteins.  In perhaps their most compelling result, the authors demonstrate that sequence-specific inhibition of dilncRNAs corresponding to a targeted break result in impaired homologous recombination.  Finally, the authors find evidence that RNase H2A is recruited to DSBs in a complex with BRCA2 and interestingly, BRCA2 depletion leads to accumulation of DNA:RNA hybrids at DSBs.  These results lead them to suggest a novel mechanism in which DNA:RNA hybrids promote recruitment of HR factors and RNase H2 in the early stages of repair, followed by degradation of the DNA:RNA hybrid by RNaseH2 and repair of the break via HR.   Positive Feedback   We found the results showing that DR-GFP signal goes down with ASOs around the breaksite to be both surprising and exciting.  It was useful to learn that ASOs can be used to specifically inhibit nuclear RNAs which is distinct from inhibiting cytoplasmic RNAs.  In addition, the dual observations that BRCA2 recruits RNase H2A and siBRCA2 increases hybrid accumulation provide nice support for the authors’ model regarding removal of hybrids at late stages of repair.                The study led us to many questions and future exciting directions this research could take including:   1.     If you only block hybrids on one side of the breaksite, do you get the same reduction in HR? 2.     Does chewing the hybrid up result in a different outcome than unwinding?  Why does BRCA2 bring RNase H2A instead of bringing Senataxin? 3.     Does this mean that BRCA-null tumors have accumulations of DNA:RNA hybrids at DSBs? 4.     BRCA1 and BRCA2 have roles in replication as well – do they also recruit RNase H2A to replication forks by a similar mechanism?     Major Concerns   For the results in Figure 1, it is important to formally demonstrate that the non-genic regions are not being transcribed to claim de novo recruitment of RNA Pol II.  We thought that RNA-seq would be an appropriate control to show this in your system.   We were concerned that the results presented do not fully support the following statement:   “These results show that dilncRNAs, while not affecting DNA-end resection, contribute to the recruitment of HR proteins to the site of damage and, by doing so, they promote HR.”   We felt this statement was an over-interpretation of the data and needs to be softened or supported with more data.  The data show that α-amanitin and DRB treatment lead to less recruitment of HR factors and ASOs lead to less DR-GFP signal.  α-amanitin and DRB lead to massive transcriptional profile changes in the cell, thus it is difficult to extrapolate from their effects.  In addition, α-amanitin and DRB are also two steps removed from the existence of the dilncRNAs at the breaksite.  However, the model would be greatly strengthened by experiments that combine site-specific ChIP at the I-Sce1 break with ASOs showing reductions in recruitment of BRCA1, BRCA2, and RAD51.   We felt the italicized text below represented an assertion that was too strong based on the data,   “Based on our observations that dilncRNAs form DNA:RNA hybrids in S/G2-phase cells and control the recruitment of HR proteins to sites of DNA damage, we sought to test the involvement of DNA:RNA hybrids in the focal accumulation of HR proteins at DSBs.”   Focal accumulation of HR proteins was attenuated but clearly still occurs.   The authors need to show that the ASOs are reducing the abundance of the I-Sce1-induced lncRNAs in the experiments of Figure 3e and f; this was an important missing control.   In Figure 4, the stoichiometry of the in vitro binding is important for the proposed mechanism. Also, a negative control is important to assert specificity of binding.   In Figure 6, if “sorting of the S/G2-phase cells population was necessary since BRCA2 inactivation affects the cell cycle (data not shown)” is true for the experiments in 6d, then it must also be true for the experiments in 6a which appeared to be done in asynchronous cells.   Finally, we felt that the overall model would be strengthened by more discussion of the authors’ previous recent paper, Michelini et al., 2017.  For example, how do DDRNAs fit into this model?  In particular, how does their role differ from the DNA:RNA hybrid?     Minor Concerns   1.     In Fig 1c, it would be more convincing to include a negative control (without I-PpoI) to show the background level. 2.     In Fig 2b, the zoomed fields do not match the squared areas in the upper image. 3.     In Fig 2c and 4b, we found the y-axis metric, “Normalized number of yH2A.X-DNA-RNA hybrids overlaps relative to random” confusing.  It would be clearer the calculation of this metric was included in the figure legend. 4.     In Supplementary Figure 3h, G1 and G2 fractions should be different colors. 5.     Figure 5a and b – should indicate how far away from the breaksite the primers are and what the ‘unrelated’ site is (either in the figure or legends). 6.     It would be less confusing to represent Figure 6c as two different IPs of RNase H2 rather than have two panels of RNase H2A in the same columns. 7.     In Fig 6e, annotation for the blue Pac-Man is missing. Is it CtIP? Also, as pictured, the interaction of RPA and DNA:RNA hybrids is confusing (is RPA directly binding the hybrids?)  We would consider excluding RPA from the model since there is little evidence for when and how RPA is interacting with the DNA:RNA hybrids.              

As part of our first Live PREreview Journal Club (#LivePREJC), we discussed the bioRxiv preprint "A Measure of Open Data: A Metric and Analysis of Reusable Data Practices in Biomedical Data Resources" by Seth Carbon,  Robin Champieux, Julie McMurry,  Lilly Winfree,  Letisha R Wyatt, and Melissa Haendel. doi: https://doi.org/10.1101/282830.  We were joined by two of the preprint authors: Robin Champieux and Lilly Winfree.  Below is a summary of our discussion. 

Aged blood inhibits hippocampal neurogenesis and activates microglia through VCAM1 at the blood-brain barrier Hanadie Yousef1, Cathrin J Czupalla2, Davis Lee3, Ashley Burke1, Michelle Chen4, Judith Zandstra1, Elisabeth Berber1,3, Benoit Lehallier1, Vidhu Mathur1, Ramesh V Nair5, Liana Bonanno1, Taylor Merkel1, Markus Schwaninger6, Stephen Quake4, Eugene C Butcher2,3, Tony Wyss-Coray1,3*  bioRxiv preprint first posted online Jan. 3, 2018; doi: http://dx.doi.org/10.1101/242198. Humanity has been seeking fountain of youth through the ages, and nowadays many promising findings against aging have been made public. However, with increasing life expectance, cognitive decline continues to be one of the most concerning health challenges. Aged-related neurodegeneration, typically in hippocampus, is responsible for many geriatric brain diseases such as Alzheimer’s disease. The hippocampus subserves learning and memory, and is always the first region of brain to suffer injury \citep{Castellano_2017}. Due to its vulnerability to adverse effect of aging, hippocampus has become one of the most important targets of attempts to remit aging damages to the brain. Previous studies have discovered the rejuvenation effects of young blood as it can revitalize hippocampus function in aged mice treated with plasma of young mice \citep{Castellano_2017,Villeda_2011}. Conversely, some studies also reveled that besides natural aging, hippocampus deterioration could also be driven by treatments of plasma from aged individuals\citep{Villeda_2011,Rebo_2016} . Early in 2005, an experiment through vascular anastomoses of young and aged mice has proposed the idea that old plasma relies on systemic inhibitory factors to degenerate organs, including the brain, in young mice \citep{Conboy_2005}.  A recent research carried out by \citet{Yousef_2018} in Stanford University also observed sufficient capacity of aged plasma to trigger aging phenotypes in young brains, primarily in neurogenesis suppression and microglia activation of the hippocampus. Activated microglia is known to be a chronic source of diverse neurotoxins that leads to neuronal function loss, particularly in aged brain and neurodegenerative diseases \citep{Lull_2010}. These two phenomena are considered to be cellular hallmarks of brain aging. \citet{Yousef_2018} hypothesized that deterioration of hippocampus function in aged circulatory environment is mediated by blood-brain barrier (BBB). Because BBB separates brain from blood and protects brain parenchyma from harmful factors in the circulating milieu. In their paper, \citet{Yousef_2018}explained that factors in blood affect brain cells through behaviour of brain endothelial cells (BECs) constituting the BBB.  To determine the proteins involved in age-related changes in BECs, \citet{Yousef_2018} compared the plasma proteomic differences between healthy aging control groups and identified 31 protein factors related significantly with age. Among them soluble form of Vascular Cell Adhesion Molecule 1 (sVCAM1) has performed the most strongly positive correlation with age. However, aged plasma depleted of sVCAM1 did not display obviously less detrimental effects on young brain, indicating that sVCAM1 is not the driving factor of aging phenotype. \citep{Yousef_2018} explained the high quantity of sVCAM1 as a result of high membrane bound Vascular Cell Adhesion Molecule 1 (VCAM1) because of the constitutive shedding of VCAM1 from BBB to plasma \citep{Garton_2003,SINGH_2005}\citep{SINGH_2005}. This is in line with the higher expression level of VCAM1, according to higher Vcam1 mRNA concentration detected in aged BECs compared to young ones. \citep{Yousef_2018}discovered that cultured BECs treated with aged plasma performed considerably higher level of VCAM1 than those treated with young plasma. Similar results can be observed in vivo when infusing aged plasma into young mice. VCAM1 is membrane bound on the luminal (blood-facing) side of the BBB and facilitates leukocyte tethering that leads to sustained inflammation of the brain \citep{Yousef_2018}. Meanwhile, they also discovered that VCAM1 is upregulated in response to inflammatory effects. Many studies show that aging and aged-related disease are companied by a certain degree of inflammation \citep{Pizza_2011}. In nervous system, neuroinflammation refers to the increase of activated microglia while in circulatory system it refers to vascular inflammatory changes. \citet{Yousef_2018} suggested that inflamed BECs rely on signally factors transmitting through the BBB to induce subsequent inflammatory response in brain parenchyma. And this signaling is induced by the interaction of VCAM1 and leukocyte. However at this point of their research the signals are not identified.  To determine the significance of VCAM1 in brain degeneration driven by aged plasma, \citet{Yousef_2018}deleted Vcam1 gene from young mice and then infused aged or young plasma into these mice. Results of both treatments presented a similar level of neurogenesis and equally infinitesimal quantity of activated microglia. Therefore, \citet{Yousef_2018} indicated that Vcam1 deletion is effective to eliminate unfavorable effects on hippocampus caused by aged plasma. \citet{Yousef_2018}hypothesized that Vcam1 deletion abrogates negative effects of aged plasma by interfering VCAM1 and leukocyte interaction. To test this hypothesis, they systemically administrated monoclonal VCAM1 antibody into young mice. With VCAM1 antibody treatment, the neurogenesis inhibition and microglia activation triggered by aged plasma treatment were both prevented, while the increase in VCAM1 expression remained unaffected. More excitingly, treatment of VCAM1 antibody also improved neurogenesis and reduced active microglia number in naturally aged mice. These results revealed that antibody blockage of VCAM1 could effectively mitigate adverse impact of aged plasma and could also rejuvenate aged brains. Based on the above findings, \citet{Yousef_2018} proposed a model explaining the mechanism of how aged plasma influence brain cell behaviour through VCAM1 at the BBB and how VCAM1 antibody inhibits the influences (Fig.1).  This research is a further proof of previous studies on inhibitory effects of aged blood on young brains. Inspiringly, \citet{Yousef_2018}proposed a model of the mechanism that aged plasma trigger aged-related brain damage through a specific protein at the blood-brain barrier. In traditional treatments of neurodegeneration diseases, blood brain barrier remains the biggest obstacle of therapeutic interference to the brain. Importantly, \citet{Yousef_2018} revealed the possibility to ameliorated age-related neurogenesis decline and microglia activity via noninvasive modulation of proteins at blood brain barrier. Therefore, their research provided a possible alternative therapy to combat hippocampus neurodegeneration by molecular regulation through circulate system.  The most exciting finding of this paper is that they discovered a particular protein—VCAM1 antibody that reverse age-related brain dysfunction in vivo, which has a promising future to clinical application. From the perspective of medicinal development, this VCAM1 antibody molecule has already been approved in treatments for Multiple Sclerosis (MS) and Crohn’s Disease \citep{Polman_2006}, supporting the feasibility and safety of this antibody. With multiple previous researches1-4, this is one further step towards human rejuvenation and the ‘fountain of youth’. However, many details of the proposed mechanism behind this process remain unclear.  In their proposed model of VCAM1 modulation, signals leading to neurodegeneration are considered to be induced by leukocyte and VCAM1 interactions through VLA-4 integrin \citep{Yousef_2018}. However, systemic administration of VLA-4 antibody into aged mice showed different results compared to VCAM1 antibody treatment \citep{Yousef_2018}. VLA-4 intervention only reduced active microglia and did not affect neurogenesis. This result may indicate that leukocytes bind to VCAM1 through multiple receptors or pathways. Alternatively it might suggest that the signaling is not necessarily triggered by VCAM1 via leukocyte interaction, but possibly depend on other VCAM1 behaviour. Moreover, the inhibitory factors in aged plasma as well as the signal factors through blood brain barrier involved in this model are not yet identified in this paper. As there some factors remain unknown in this mechanism, the proposed model by \citet{Yousef_2018} still needed to be verified through further researches.  \citet{Yousef_2018}constructed the linkage between VCAM1 and brain crossing the blood brain barrier, which indicates VCAM1 as potential target for age-associated brain disease treatment. This research also provided impressive evidence for medical implication for strategies to alleviate, or even reverse age-associated neurodegeneration. Although there are some questions left in this research, it is still of great importance to future academic studies and clinical application.

bioRxiv articleThe cellular NMD pathway restricts Zika virus infection and is targeted by the viral capsid protein

Recent demographic histories and genetic diversity across pinnipeds are shaped by anthropogenic interactions and mediated by ecology and life-history  Martin Adam Stoffel, Emily Humble, Karina Acevedo-Whitehouse, Barbara L. Chilvers, Bobette Dickerson, Fillipo Galimberti, Neil Gemmell, Simon D. Goldsworthy, Hazel J. Nichols, Oliver Krueger, Sandra Negro, Amy Osborne, Anneke J. Paijmans, Teresa Pastor, Bruce C. Robertson, Simona Sanvito, Jennifer Schultz, Aaron B.A. Shafer, Jochen B.W. Wolf, Joseph I. Hoffman,  April 12, 2018, version 1, bioRxiv doi: https://doi.org/10.1101/293894Firstly, we thank the authors for their work and for posting it as a preprint on bioRxiv. This work endeavored to evaluate the occurrence and intensity of population bottlenecks in a large number of pinniped species that have been differentially affected by human exploitation. Population bottlenecks can decrease genetic diversity and adversely affect the ability of a species to adapt to modern habitat loss and climate change. Because historical data is sparse and unreliable, the authors applied population genetics methods to a large, multi-species dataset to detect and evaluate past population bottlenecks and then compared the results to life-history traits and current conservation status for each species. The results indicate that 11 of the species included in the analysis experienced a population bottleneck and that land-breeding pinnipeds are more likely to have experienced a bottleneck than ice-breeding pinnipeds. While there was not an overall relationship between IUCN status and past bottleneck events, bottleneck events were detected for 4 of the 6 endangered species included in the study. The breadth of this study is especially important, as it represents a first effort to apply these methods across 30 species in a single analysis. Our overall impression is that this was a large project using an extensive amount of data from multiple sources, which then had to be standardized in order to be analyzed in a novel way. This paper highlights the benefits of open science and open data, as data from multiple studies was reused and analyzed in a far broader context than any single study on a single population or species.This manuscript is exceptionally well-written and uses clear language, making it both easy and enthralling to read. However, there are a few small mistakes that caused some confusion. Particularly, the caption of Figure 4 switches the descriptions for Panels A and B. Additionally, the figures in the main text are numbered 1:4, 6; it appears that Figure 5 may have been moved to the supplemental materials, but the main figure numbering was not adjusted accordingly. All of the figures in the manuscript are very attractive and make good use of consistent coloring. Figure 1 nicely summarizes many of the main findings of the paper. This figure would be even better if Panel A utilized a 2-color scale like Panels B and C. Additionally since Pbot and Pneut are complementary, we suggest that only Pbot needs to be included in Panel C, which will reduce the size of the figure and make it easier to interpret. Figure 2 is very clear and intuitive and nicely illustrates the intensity of population bottlenecks for different species. Additionally, the pinniped drawings are beautiful and the use of original artwork in the paper is commendable. We feel that Figure 4, which displays the expected correlation between global abundance and allelic richness does not necessarily need to be included in the main text. Conversely, we feel that Supplementary Table 1, which contains the sample size, number of microsatellite loci, and citation for each species' dataset, is important for readers to have available in the main text. Overall, the authors did an outstanding job applying population genetics techniques appropriately. In particular, the authors made very good use of the program STRUCTURE. This program was used to detect population substructure and if detected, the largest genetic cluster was selected for inclusion in ensuing analyses. This important step prevents false detection of bottlenecks, which can be a common mistake. It is also appreciated that the authors chose to examine allelic richness rather than allelic frequency. We were left with a few lingering questions about the methods, however. First, we are curious if the authors acquired raw electropherograms or pre-interpreted genotypes for the published datasets used and if there were any measures taken to control for observer bias in interpreting microsatellite genotypes, such as preparing and running samples from other labs and assessing whether similar conclusions were reached. We were also curious about the justification for grouping IUCN categories into "concern" ('near threatened,' 'vulnerable,' and 'endangered') and "least concern" ('least concern'), especially since the IUCN Red List Categories and Criteria groups 'near threatened' with 'least concern' and explicitly distinguishes 'vulnerable,' 'endangered,' and 'critically endangered' as the "threatened" categories. We wonder how the results presented in Figure 6 would be affected by moving the species designated as 'near threatened' out of the "concern" category.Lastly, we would have appreciated a more extensive discussion. For example, the authors describe ice-breeding species as experiencing less historical exploitation than the land-breeding species, but ice-breeding species are likely more susceptible to negative impacts from climate change in the recent past and into the future. The implications of the findings of the study combined with change in anthropogenic disturbance patterns and continuing disturbance  in these habitats into the future could be addressed in the discussion. We are also very interested in what the authors perceive as weaknesses in the approaches used, if there may be alternative interpretations of the results, and especially what future studies they would suggest based on their results and conclusions. Again, it was a great pleasure reading this impressive work. We hope that our comments are useful to the authors and we look forward to reading the final version when it is published. Thank you!The OIST Ecology and Evolution Preprint Journal Club 

              REQUEST A LIVE-STREAMED PREreview JOURNAL CLUBAt PREreview, we want to take preprint journal clubs to the next level. Live-streamed PREreview journal clubs (LivePREJCs) are hosted via online community calls, allowing anyone with internet or phone-in capabilities to join the discussion. This format promotes inclusivity by following a structure that provides a means to join the discussion silently in written form and vocally. You can request our help to organize a live-streamed preprint journal club by clicking on the link above and filling the form. Here is a list of current and past LivePREJCs.Live-streamed PREreview journal clubs are:Inclusive: anyone, anywhere in the world with a internet or phone connection can joinInformative: you can learn more about the topic by listening to/reading the comments of other researchers in the field and even the authors themselves (if invited)Efficient: if preprint authors are present, they receive feedback in real time. Also, we restrict the live journal clubs to 1 hour to keep the feedback focused and efficientCollaborative: the format encourages contributions from all participants regardless of input style preference, i.e. both vocal and silent writing (etherpadding)Fun: even though the discussions are kept professional and centered around providing constructive feedback to the preprint authors, they are a fun way to meet other people interested in the field.How to get started:Choose a preprint you wish to discuss at a LivePREJC (find out more about what preprints are and where to find them).Find a few other scientists or researchers, preferably at different career levels and from different institutions, who would like to participate in the LivePREJC. Our team will help you recruit more participants if you cannot find them on your own.Fill out this form to formally request a LivePREJC and our logistic support.If you are not the preprint author(s), you can contact the corresponding author(s) and let them know about the LivePREJC. If you don't feel comfortable doing this, please let us know and we will do it on your behalf. You can also choose not to include the author(s) in the discussion. What to expect during the call:LivePREJCs are usually hosted by one or two members of the PREreview team: we will ensure that the conversation around the preprint runs smoothly and stays on time (1 hour); they will take notes and encourage others to take notes on a collaborative etherpad (see below); importantly, they will set the tone for a productive and respectful conversation according to the PREreview code-of-conduct.Preprint authors interested in having their preprint discussed on a LivePREJC, will have the option to be present (recommended). We advise authors to find 5-10 participants (not all the participants need to be experts in the field). Our team will help coordinate the call and, if the authors request it, help recruit more researchers in the field by advertising the call on social media using the #LivePREJC hashtag and any other hashtag related to the research field of the preprint discussed.Once the preprint authors have identified the participants, we will send out a short email with instructions on how to join the LivePREJC and with a copy of the preprint. Participants will be encouraged to read the preprint before the LivePREJC, to keep the discussion short and focused.Authors will be asked to remain in ‘listening mode’ – except when asked a question by participants - until the last 10 minutes reserved for this discussion. This will encourage participants to express their constructive feedback freely and stimulate a productive discussion.Participants will be given the opportunity to give feedback both vocally (with notes taken by one of the PREreview team) and in written form (via collaborative note-taking on a public etherpad that we will set up for each LivePREJC). Here is an example of an etherpad template that will be used for the LivePREJCs.

This is a review of Brendolise et al. bioRxiv 293050; doi: https://doi.org/10.1101/293050 posted on April 1, 2018. This paper adds to a current body of research detailing the resistance mechanism triggered by the Pseudomonas syringe pv. tomato effector HopQ1 in the model plant Nicotiana benthamiana. This plant can be used as a source of novel disease resistance genes against plant pathogens.

This is a preprint review from our group's journal club. We reviewed the following manuscript: CREAM: Clustering of genomic REgions Analysis Method

Homology-directed repair of a defective glabrous gene in Arabidopsis with Cas9-based gene targeting  [Florian Hahn, Marion Eisenhut, Otho Mantegazza, Andreas P.M. Weber, January 5, 2018, BioRxiv] [https://doi.org/10.1101/243675]Overview and take-home messages:    Hahn et al. have compared the efficiencies of two different methods that have been previously reported to enhance the frequency of homologous recombination in plants. The paper has focused on testing a viral replicon system with two different enzymes, nuclease and nickase, as well as an in planta gene targeting (IPGT) system in  Arabidopsis thaliana. Interestingly, authors have chosen GLABROUS1 (GL1), a regulator of trichome formation, as a visual marker to detect Cas9 activity and therefore homologous recombination. A 10 bp deletion in the coding region of GL1 gene produces plants devoid of trichomes. Out of the two methods in planta gene targeting approach successfully restored trichome formation in less than 0.2% of ~2,500 plants screened, whereas the method based on viral replicon machinery did not manage to restore trichome formation at all. This manuscript is of high quality, experiments are well designed and executed. However, there are some concerns that could be addressed in the next preprint or print version. Below are some feedback and suggestions that we hope will improve the manuscript.

Review of:Evolutionary dynamics of bacteria in the gut microbiome within and across hostsNandita R. Garud*, Benjamin H. Good*, Oskar Hallatschek, Katherine S.Pollard*co-first authorsOriginally published on biorxivdoi: https://doi.org/10.1101/210955

This is a preprint journal club review of "Exotic species dominate due to niche overlap in a complex grassland community" by Lawrence H. Uricchio, S. Caroline Daws, Erin R. Spear and Erin A. Mordecai. Te preprint was originally posted on bioRxiv on January 15, 2018 (DOI:   https://doi.org/10.1101/253518) https://www.biorxiv.org/content/early/2018/01/24/253518. Our group Biotic Interactions and Global Change (BIGC) reviewed this paper in February 2018.

I started writing this memo while on an airplane, flying back from sunny San Diego. While definitely one of the highlights of the trip, the sunshine was not the reason for my visit to Southern California. Instead, I was there with hundreds of other auditory neuroscientists from all over the world to attend the 41th MidWinter Meeting of the Association for Research in Otolaryngology (ARO). 

This is a review of the preprint "Frequent lack of repressive capacity of promoter DNA methylation identified through genome-wide epigenomic manipulation" by Ethan Edward Ford,  Matthew R. Grimmer,  Sabine Stolzenburg,  Ozren Bogdanovic,  Alex de Mendoza,  Peggy J. Farnham,  Pilar Blancafort, and  Ryan Lister.The preprint was originally posted on bioRxiv on September 20, 2017 (DOI: https://doi.org/10.1101/170506). 

Medicago truncatula Zinc-Iron Permease6 provides zinc to rhizobia-infected nodule cells [Isidro Abreu, Angela Saez, Rosario Castro-Rodriguez, Viviana Escudero, Benjamin Rodriguez-Haas,  Marta Senovilla, Camille Laure, Daniel Grolimund, Manuel Tejada-Jimenez, Juan Imperial, Manuel Gonzalez-Guerrero , January 24, 2017 (preprint),  September 21, 2017 (in print), BioRxiv & Wiley-Blackwell]

This report was prepared by Vienna Biocenter Summer Class 2017 PhD students as a part of their Priming Your PhD training. Please see the instructors and participating students’ names at the end of the review.   Shoemaker et al. performed a systematic genome wide CRISPR screen and identified a catalogue of factors involved in mammalian autophagy. They validated their findings by further characterizing one of the newly identified autophagy-related factors in their screen. They showed that TMEM41B, an integral ER-membrane protein is involved in maturation of the phagophore. Furthermore, using NBR1 as an autophagy substrate, they discovered a number of genes involved in a novel non-canonical autophagy pathway, which is independent of ATG7. The comprehensive results obtained in this study make the picture of mammalian autophagy more complete, and provide entry points for future studies that will dissect alternative autophagy pathways and the molecular mechanism underlying the role of TMEM41B in canonical autophagy. Genome wide CRISPR-screen provides a state of the art, unbiased approach to discover unknown genes in the autophagy process. They have developed a sensitive autophagic flux measurement reporter using the tandem-fluorescent reporters. In addition to the commonly used LC3B, they have also used known cargo receptors to identify autophagy regulators. Using this reporter system as a readout for a genome-wide screen is opening up the possibility to systematically dissect complex cellular autophagy networks. The authors were able to validate their screening approach by recovering almost all known ATG factors. Interestingly, the screen also identified a number of completely new candidates. While the authors have initially setup screens to address the pathways involved in the five major autophagy receptors, their last screening setup also allows to dissect the pathway involved in the highly debated ATG7-independent lysosomal targeting. We find this particularly interesting as is significantly contributes to our general understanding of how the network of autophagy receptors is organized. Furthermore, the description of ATG-independent alternative autophagy pathways questions the use of ATG7 KO genotypes as a control in autophagy experiments. As autophagy may still be active in ATG7 KO, this should be of concern for future experimental designs. The authors extensively stress-tested their reporter systems prior to performing the screen. They have also integrated all the reporters in the same loci to prevent expression level differences. The authors, in most cases, transparently provide numbers of cells used in the experimental setup and respective controls. Throughout the paper the authors confirm and cross-reference results obtained from other researchers in the field. They have used a broad range of experimental tools to validate their screen and results. Overall, the manuscript is of very high quality. Below are some suggestions that we hope will improve the manuscript. 1.     The authors have chosen a 60% interval for their FACS sorting range. It would be useful if they provide the original FACS plots (RFP/GFP ratio), in order for us to see how the distribution of non-infected and library-infected populations look like. This would explain why screening for 60% of the population is necessary/reasonable. Although they have used a published sgRNA library, it would have been useful if they provided more information such as how many sgRNAs/gene are in the initial library, library representation in the transduced cell pool, replicate correlation plots. In addition, previous CRISPR screen papers have used fold changes for quantifying their hits. It would be useful if the authors explain the rationale behind beta scores and give more detail on beta score calculation. 2.     There is also no information about Cas9 clonality in the K562 cells which were used for the screen. This would be useful for readers. 3.     In the validation experiments, the authors state “mock treated cells” as negative control – does this mean that no sgRNA is transduced? If so, this is not an applicable negative control, as biological response to transduction and DNA damage repair by introducing an sgRNA is not addressed. Therefore, using a sgRNA targeting a gene desert would be the correct control. 4.     Information how they normalized to 1.0 in Fig 3A is missing, as well as additional labels on Y-Axis that would enable the reader to understand whether this is a linear or log-scale. 5.     In Figure 6, Stx17 and one of the cargo receptors could have been nice controls. Additionally, it would have been nice to have EM images of autophagosomes in TMEM41B KO cells. 6.     Due to the design of the screen, one would have expected to get some hits related to lysosomal acidification. The authors did not mention this in the results or discussion. It would be nice to at least discuss this. Similarly, the authors should compare and contrast DeJesus et al., 2017 eLife paper in the discussion. This paper uses a similar CRISPR approach and looks for autophagy regulators. 7.     In the introduction, it would have been beneficial to introduce the SQSTM1 receptors in a bit more detail, since the screen is based on them.   8.     It would be useful if they clarified why they decided to use TMEM41B, since it wasn’t a very strong hit for all the receptors tested. 9.     The description of the cell line used for the screen was unclear; were single clones or a bulk population used? 10.  Figure S6 is missing; the supplementary figures go from Figure S5 to S7. 11.  In the text, RAB7A and HOPS are mentioned while referring to figure 7E. However, these factors cannot be found in the figure. 12.  The last paragraph of the results section refers to an analysis about genetic interactions between ATG7 and several factors, but no figure is ever referenced for this section. 13.  Figure 3A: in the legend, more information could be given as to what exactly is plotted and less regarding the supplementary data. It is difficult to determine the scale of the Y-axis as only two values are given.   PhD Students: Krista Briedis, Alexander Bykov, Claudia Ctortecka, Melanie         De Almeida, Philipp Dexheimer, Joachim Garbrecht, Sarah Gruenbacher, Bence Hajdusits, Felix Holstein, Bhagyshree Jamge, Friederike Leesch, Joanna Nowacka, Mina Petrovic, Anna Schmuecker, Monika Steininger, Pietro Tardivo, Szu-Hsien Wu Facilitators: Fumiyo Ikeda, Yasin Dagdas      

Here is how it all started. Two researchers and ASAPbio Ambassadors met at a Mozilla Working Open Workshop in April 2017. A PhD student (Daniela) and a postdoctoral fellow (Sam) decided to volunteer some of their time to develop guidelines to help researchers from all around the world start preprint journal clubs. We believed this would have contributed positively to spreading the word and value of preprints in the scientific community, as well as helped early-career researchers master their skills in peer review.During the Mozilla Science Global Sprint, June 2-3 2017, we wrote our application to the first Mozilla Science Mini-Grant. We asked for enough money to support 20 beta testers by covering the cost of snacks and beverage for two preprint journal clubs. And we were awarded!Since then, a lot has happened, including starting PREreview thank to the help of the Authorea team and the support of many others who share our mission.Since July, our application has been posted on our project GitHub, but we wanted to have it on PREreview as well. So below is our full proposal. Thank you!

This is a preprint journal club review of "Evolutionary responses to conditionality in species interactions across environmental gradients" by Anna M O'Brien, Ruairidh J.H. Sawers, Jeffrey Ross-Ibarra, Sharon Y Strauss. The preprint was originally posted on bioRxiv on December 10, 2017 (DOI: https://doi.org/10.1101/031195) https://www.biorxiv.org/content/early/2017/12/10/031195.Our group Biotic Interactions and Global Change (BIGC) reviewed this paper on January, 2017 .