Combined Analysis of Variation in Core, Accessory and Regulatory Genome Regions Provides a Super-Resolution View into the Evolution of Bacterial Populations

The use of whole-genome phylogenetic analysis has revolutionized our understanding of the evolution and spread of many important bacterial pathogens due to the high resolution view it provides. However, the majority of such analyses do not consider the potential role of accessory genes when inferring evolutionary trajectories. Moreover, the recently discovered importance of the switching of gene regulatory elements suggests that an exhaustive analysis, combining information from core and accessory genes with regulatory elements could provide unparalleled detail of the evolution of a bacterial population. Here we demonstrate this principle by applying it to a worldwide multi-host sample of the important pathogenic E. coli lineage ST131. Our approach reveals the existence of multiple circulating subtypes of the major drug-resistant clade of ST131 and provides the first ever population level evidence of core genome substitutions in gene regulatory regions associated with the acquisition and maintenance of different accessory genome elements.Peer reviewe

Border collapse and boundary maintenance: militarisation and the micro-geographies of violence in Israel–Palestine

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.Drawing upon subaltern geopolitics and feminist geography, this article explores how militarisation shapes micro-geographies of violence and occupation in Israel–Palestine. While accounts of spectacular and large-scale political violence dominate popular imaginaries and academic analyses in/of the region, a shift to the micro-scale foregrounds the relationship between power, politics and space at the level of everyday life. In the context of Israel–Palestine, micro-geographies have revealed dynamic strategies for ‘getting by’ or ‘dealing with’ the occupation, as practiced by Palestinian populations in the face of spatialised violence. However, this article considers how Jewish Israelis actively shape the spatial micro-politics of power within and along the borders of the Israeli state. Based on 12 months of ethnographic research in Tel Aviv and West Jerusalem during 2010–2011, an analysis of everyday narratives illustrates how relations of violence, occupation and domination rely upon gendered dynamics of border collapse and boundary maintenance. Here, the borders between home front and battlefield break down at the same time as communal boundaries are reproduced, generating conditions of ‘total militarism’ wherein military interests and agendas are both actively and passively diffused. Through gendering the militarised micro-geographies of violence among Jewish Israelis, this article reveals how individuals construct, navigate and regulate the everyday spaces of occupation, detailing more precisely how macro political power endures.This work was supported by the SOAS, University of London; University of London Central Research Fund

Phylogenetic Distribution and Evolution of Type VI Secretion System in the Genus Xanthomonas

The type VI secretion system (T6SS) present in many Gram-negative bacteria is a contact-dependent apparatus that can directly deliver secreted effectors or toxins into diverse neighboring cellular targets including both prokaryotic and eukaryotic organisms. Recent reverse genetics studies with T6 core gene loci have indicated the importance of functional T6SS toward overall competitive fitness in various pathogenic Xanthomonas spp. To understand the contribution of T6SS toward ecology and evolution of Xanthomonas spp., we explored the distribution of the three distinguishable T6SS clusters, i3*, i3***, and i4, in approximately 1,740 Xanthomonas genomes, along with their conservation, genetic organization, and their evolutionary patterns in this genus. Screening genomes for core genes of each T6 cluster indicated that 40% of the sequenced strains possess two T6 clusters, with combinations of i3*** and i3* or i3*** and i4. A few strains of Xanthomonas citri, Xanthomonas phaseoli, and Xanthomonas cissicola were the exception, possessing a unique combination of i3* and i4. The findings also indicated clade-specific distribution of T6SS clusters. Phylogenetic analysis demonstrated that T6SS clusters i3* and i3*** were probably acquired by the ancestor of the genus Xanthomonas, followed by gain or loss of individual clusters upon diversification into subsequent clades. T6 i4 cluster has been acquired in recent independent events by group 2 xanthomonads followed by its spread via horizontal dissemination across distinct clades across groups 1 and 2 xanthomonads. We also noted reshuffling of the entire core T6 loci, as well as T6SS spike complex components, hcp and vgrG, among different species. Our findings indicate that gain or loss events of specific T6SS clusters across Xanthomonas phylogeny have not been random

ASAP - A Webserver for Immunoglobulin-Sequencing Analysis Pipeline

Reproducible and robust data on antibody repertoires are invaluable for basic and applied immunology. Next-generation sequencing (NGS) of antibody variable regions has emerged as a powerful tool in systems immunology, providing quantitative molecular information on antibody polyclonal composition. However, major computational challenges exist when analyzing antibody sequences, from error handling to hypermutation profiles and clonal expansion analyses. In this work, we developed the ASAP (A webserver for Immunoglobulin-Seq Analysis Pipeline) webserver (https://asap.tau.ac.il). The input to ASAP is a paired-end sequence dataset from one or more replicates, with or without unique molecular identifiers. These datasets can be derived from NGS of human or murine antibody variable regions. ASAP first filters and annotates the sequence reads using public or user-provided germline sequence information. The ASAP webserver next performs various calculations, including somatic hypermutation level, CDR3 lengths, V(D)J family assignments, and V(D)J combination distribution. These analyses are repeated for each replicate. ASAP provides additional information by analyzing the commonalities and differences between the repeats (“joint” analysis). For example, ASAP examines the shared variable regions and their frequency in each replicate to determine which sequences are less likely to be a result of a sample preparation derived and/or sequencing errors. Moreover, ASAP clusters the data to clones and reports the identity and prevalence of top ranking clones (clonal expansion analysis). ASAP further provides the distribution of synonymous and non-synonymous mutations within the V genes somatic hypermutations. Finally, ASAP provides means to process the data for proteomic analysis of serum/secreted antibodies by generating a variable region database for liquid chromatography high resolution tandem mass spectrometry (LC-MS/MS) interpretation. ASAP is user-friendly, free, and open to all users, with no login requirement. ASAP is applicable for researchers interested in basic questions related to B cell development and differentiation, as well as applied researchers who are interested in vaccine development and monoclonal antibody engineering. By virtue of its user-friendliness, ASAP opens the antibody analysis field to non-expert users who seek to boost their research with immune repertoire analysis

Automated large-scale prediction of exudative AMD progression using machine-read OCT biomarkers.

Age-related Macular Degeneration (AMD) is a major cause of irreversible vision loss in individuals over 55 years old in the United States. One of the late-stage manifestations of AMD, and a major cause of vision loss, is the development of exudative macular neovascularization (MNV). Optical Coherence Tomography (OCT) is the gold standard to identify fluid at different levels within the retina. The presence of fluid is considered the hallmark to define the presence of disease activity. Anti-vascular growth factor (anti-VEGF) injections can be used to treat exudative MNV. However, given the limitations of anti-VEGF treatment, as burdensome need for frequent visits and repeated injections to sustain efficacy, limited durability of the treatment, poor or no response, there is a great interest in detecting early biomarkers associated with a higher risk for AMD progression to exudative forms in order to optimize the design of early intervention clinical trials. The annotation of structural biomarkers on optical coherence tomography (OCT) B-scans is a laborious, complex and time-consuming process, and discrepancies between human graders can introduce variability into this assessment. To address this issue, a deep-learning model (SLIVER-net) was proposed, which could identify AMD biomarkers on structural OCT volumes with high precision and without human supervision. However, the validation was performed on a small dataset, and the true predictive power of these detected biomarkers in the context of a large cohort has not been evaluated. In this retrospective cohort study, we perform the largest-scale validation of these biomarkers to date. We also assess how these features combined with other EHR data (demographics, comorbidities, etc) affect and/or improve the prediction performance relative to known factors. Our hypothesis is that these biomarkers can be identified by a machine learning algorithm without human supervision, in a way that they preserve their predictive nature. The way we test this hypothesis is by building several machine learning models utilizing these machine-read biomarkers and assessing their added predictive power. We found that not only can we show that the machine-read OCT B-scan biomarkers are predictive of AMD progression, we also observe that our proposed combined OCT and EHR data-based algorithm outperforms the state-of-the-art solution in clinically relevant metrics and provides actionable information which has the potential to improve patient care. In addition, it provides a framework for automated large-scale processing of OCT volumes, making it possible to analyze vast archives without human supervision

COVID‐19 pandemic‐related lockdown: response time is more important than its strictness

Abstract The rapid spread of SARS‐CoV‐2 and its threat to health systems worldwide have led governments to take acute actions to enforce social distancing. Previous studies used complex epidemiological models to quantify the effect of lockdown policies on infection rates. However, these rely on prior assumptions or on official regulations. Here, we use country‐specific reports of daily mobility from people cellular usage to model social distancing. Our data‐driven model enabled the extraction of lockdown characteristics which were crossed with observed mortality rates to show that: (i) the time at which social distancing was initiated is highly correlated with the number of deaths, r2 = 0.64, while the lockdown strictness or its duration is not as informative; (ii) a delay of 7.49 days in initiating social distancing would double the number of deaths; and (iii) the immediate response has a prolonged effect on COVID‐19 death toll

PASA: Proteomic analysis of serum antibodies web server.

MotivationA comprehensive characterization of the humoral response towards a specific antigen requires quantification of the B-cell receptor repertoire by next-generation sequencing (BCR-Seq), as well as the analysis of serum antibodies against this antigen, using proteomics. The proteomic analysis is challenging since it necessitates the mapping of antigen-specific peptides to individual B-cell clones.ResultsThe PASA web server provides a robust computational platform for the analysis and integration of data obtained from proteomics of serum antibodies. PASA maps peptides derived from antibodies raised against a specific antigen to corresponding antibody sequences. It then analyzes and integrates proteomics and BCR-Seq data, thus providing a comprehensive characterization of the humoral response. The PASA web server is freely available at https://pasa.tau.ac.il and open to all users without a login requirement

Metal–Ligand Cooperation as Key in Formation of Dearomatized Ni^II–H Pincer Complexes and in Their Reactivity toward CO and CO₂

The unique synthesis and reactivity of [(^RPNP*)­NiH] complexes (<b>1a</b>,<b>b</b>), based on metal–ligand cooperation (MLC), are presented (^RPNP* = deprotonated PNP ligand, R = ⁱPr, ^tBu). Unexpectedly, the dearomatized complexes <b>1a</b>,<b>b</b> were obtained by reduction of the dicationic complexes [(^RPNP)­Ni­(MeCN)]­(BF₄)₂ with sodium amalgam or by reaction of the free ligand with Ni⁰(COD)₂. Complex <b>1b</b> reacts with CO via MLC, to give a rare case of a distorted-octahedral PNP-based pincer complex, the Ni(0) complex <b>3b</b>. Complexes <b>1a</b>,<b>b</b> also react with CO₂ via MLC to form a rare example of η¹ binding of CO₂ to nickel, complexes <b>4a</b>,<b>b</b>. An unusual CO₂ cleavage process by complex <b>4b</b>, involving C–O and C–P cleavage and C–C bond formation, led to the Ni–CO complex <b>3b</b> and to the new complex [(PⁱPr₂NC₂O₂)­Ni­(P­(O)ⁱPr₂)] (<b>5b</b>). All complexes have been fully characterized by NMR and X-ray crystallography

An Approximate Bayesian Computation Approach for Modeling Genome Rearrangements

The inference of genome rearrangement events has been extensively studied, as they play a major role in molecular evolution. However, probabilistic evolutionary models that explicitly imitate the evolutionary dynamics of such events, as well as methods to infer model parameters, are yet to be fully utilized. Here, we developed a probabilistic approach to infer genome rearrangement rate parameters using an Approximate Bayesian Computation (ABC) framework. We developed two genome rearrangement models, a basic model, which accounts for genomic changes in gene order, and a more sophisticated one which also accounts for changes in chromosome number. We characterized the ABC inference accuracy using simulations and applied our methodology to both prokaryotic and eukaryotic empirical datasets. Knowledge of genome-rearrangement rates can help elucidate their role in evolution as well as help simulate genomes with evolutionary dynamics that reflect empirical genomes