Frequency of disturbance alters diversity, function, and underlying assembly mechanisms of complex bacterial communitiesEzequiel Santillan, Hari Seshan, Florentin Constancias, Daniela I. Drautz-Moses, Stefan Wuertz, May 4th 2018, bioRxiv[doi: https://doi.org/10.1101/313585] Understanding the effects of disturbance on ecosystem function and diversity has many potential applications in microbial ecology and human disease biology. In this paper, the authors tackled the long-standing question of how different disturbance frequencies affect bacterial community diversity and function. To do so, activated-sludge communities within laboratory-scale microcosms were exposed to toxic 3-chloroaniline (3-CA) at varying frequencies. Ecosystem function and community diversity were measured weekly by measuring biomass and organic carbon, ammonia, and toxin removal as proxies for ecosystem function and T-RFLP 16S rRNA gene fingerprinting and shotgun metagenomics were performed to examine variation in bacterial diversity and community composition. This work is an excellent example of integrating genomic and functional analysis, thereby providing a more thorough understanding of the effects of disturbance frequency on microbial community diversity and function. Interestingly, both genetic methods yielded similar results, suggesting that the less expensive gene fingerprinting method could be sufficient when sequencing resources are limited. We particularly commend the use of multiple alpha-diversity measurements and the inclusion of abundance-related indices, which are less method dependent and allow results to be compared between studies. Ultimately, the authors propose the "Intermediate Stochasticity Hypothesis,” which suggests that stochastic processes produce higher diversity assemblages at intermediate disturbance frequencies while deterministic processes produce lower diversity assemblages at low and high disturbance frequencies. Overall, this paper is a fascinating and substantial contribution to microbial ecology. There are, however, a few issues that we feel could be improved in future versions of the manuscript.   Major concerns:This comment is unique to the preprint. The manuscript references multiple figures available in the supplementary materials, but these materials were not made available as part of the preprint. This hindered our ability to understand the fine points of the experiments. We encourage the authors to upload the supplementary materials to bioRxiv. 1. Figure 2 is an integral figure to the manuscript because it showcases the effects of 3-CA disturbance frequencies on community performance, namely organic carbon and toxin removal (plots A, C)  and nitrification products (plots B, D). In the Materials and Methods section (lines 353-356), the authors state that these parameters were measure weekly, which leads to the assumption that data is available for days 7, 15, 21, and 35, even though only the data from days 7 and 35 are included in the figure (is there T0 data?). The results section refers to supplementary figures S2 and S3 in addition to Figure 2, so these supplementals may portray the data of interest. However, since these data are so important to the overall conclusions, we believe it should be available in the main text. One way to accomplish this could be to have one plot per variable with time on the x-axis and different colors for each disturbance frequency. The number of plots could be reduced by not including Volatile Suspended Solids (VSS) results in the main text. In Figure 2A, the COD removal and 3-CA removal is not monotonously decreasing relative to the disturbance frequency (specifically, level 2 and 4). We figured that this was due to the number of days since the disturbance being different for each disturbance frequency at measurement time on day 7. We encourage the authors to mention and explain this in the text, as this was a puzzling feature of the results for us for some time. It also calls into question the appropriateness of the weekly measurements, especially given that some disturbance level will be highly correlated to this rhythm of measurement (level 1 disturbance will always happen on the same day of the measurements, while level 2 and others will drift). 2. Along with disturbance frequency, varied intensity and duration of disturbance and differing sampling frequencies (e.g- data collection every two days or  bi-weekly, larger spread of intermediate disturbance levels) might produce a different pattern of microbial community diversity and function. Questions we can ask are: would the system reach the observed IDH pattern at an early stage? Would the intermediate levels still follow the IDH model? We would be very interested in the authors opinions on these topics, perhaps in the discussion section. 

A review of the Biorxiv preprint entitled: Links between environment, diet, and the hunter-gatherer microbiomeGabriela K Fragiadakis, Samuel A Smits, Erica D Sonnenburg, William Van Treuren, Gregor Reid, Rob Knight, Alphaxard Manjurano, John Changalucha, Maria Gloria Dominguez-Bello, Jeff Leach, Justin L SonnenburgPosted to Bioarxiv on May 15, 2018doi: https://doi.org/10.1101/319673

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.