Altered somatic hypermutation patterns in COVID-19 patients classifies disease severity

IntroductionThe success of the human body in fighting SARS-CoV2 infection relies on lymphocytes and their antigen receptors. Identifying and characterizing clinically relevant receptors is of utmost importance.MethodsWe report here the application of a machine learning approach, utilizing B cell receptor repertoire sequencing data from severely and mildly infected individuals with SARS-CoV2 compared with uninfected controls.ResultsIn contrast to previous studies, our approach successfully stratifies non-infected from infected individuals, as well as disease level of severity. The features that drive this classification are based on somatic hypermutation patterns, and point to alterations in the somatic hypermutation process in COVID-19 patients.DiscussionThese features may be used to build and adapt therapeutic strategies to COVID-19, in particular to quantitatively assess potential diagnostic and therapeutic antibodies. These results constitute a proof of concept for future epidemiological challenges

Tannaitic Literature as a Source for Jewish History: From Simon the Just to Johanan ben Zakkai

This work, which brings forward the historical reality from Tannaitic sources, is a product of the new approach by Dr. S. Zeitlin in his extensive studies which represent an essential revolution in the dealings with the research into the history of the Second Commonwealth. Since Dr. Zeitlin\u27s theory sheds new light on many chapters in the history of this era, I approached this work by collecting the sources and their historical interpretation in order to extend the understanding of Tannaitic sources within their historical background

Natural Diversity in Pentose Fermentation Is Explained by Variations in Histone Deacetylases

SummaryThe extent to which carbon flux is directed toward fermentation versus respiration differs between cell types and environmental conditions. Understanding the basic cellular processes governing carbon flux is challenged by the complexity of the metabolic and regulatory networks. To reveal the genetic basis for natural diversity in channeling carbon flux, we applied quantitative trait loci analysis by phenotyping and genotyping hundreds of individual F2 segregants of budding yeast that differ in their capacity to ferment the pentose sugar xylulose. Causal alleles were mapped to the RXT3 and PHO23 genes, two components of the large Rpd3 histone deacetylation complex. We show that these allelic variants modulate the expression of SNF1/AMPK-dependent respiratory genes. Our results suggest that over close evolutionary distances, diversification of carbon flow is driven by changes in global regulators, rather than adaptation of specific metabolic nodes. Such regulators may improve the ability to direct metabolic fluxes for biotechnological applications

Coordination of Gene Expression and Growth-Rate in Natural Populations of Budding Yeast

<div><p>Cells adapt to environmental changes through genetic mutations that stabilize novel phenotypes. Often, this adaptation involves regulatory changes which modulate gene expression. In the budding yeast, ribosomal-related gene expression correlates with cell growth rate across different environments. To examine whether the same relationship between gene expression and growth rate is observed also across natural populations, we measured gene expression, growth rate and ethanol production of twenty-four wild type yeast strains originating from diverse habitats, grown on the pentose sugar xylulose. We found that expression of ribosome-related genes did not correlate with growth rate. Rather, growth rate was correlated with the expression of amino acid biosynthesis genes. Searching other databases, we observed a similar correlation between growth rate and amino-acid biosyntehsis genes in a library of gene deletions. We discuss the implications of our results for understanding how cells coordinate their translation capacity with available nutrient resources.</p></div

Mode of metabolism during growth on xylulose.

<p>(A) Mean expression of all genes of each of the 24 strains from our collection grown on xylulose, compared to existing expression data for a wild type yeast strain grown on various carbon sources. Colors represent the Pearson correlation coefficient for each of these comparisons. (B) Difference (log ratio) between expression on xylulose and on glucose of genes participating in glycolysis and fermentation (G+F), the PPP, the TCA cycle and the respiratory chain (RC). Genes for which data from less than 14 strains existed were omitted. The complete list of genes can be found in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088801#pone.0088801.s007" target="_blank">Table S2</a></b>. Each column represents data from an individual strain. Ethanol production level on xylulose of the respective strain is shown above, for each <i>S. cerevisiae</i> (dark gray) and <i>S. paradoxus</i> (light gray) strain. The columns are sorted according to the level of ethanol production. Same format matrices but for absolute expression levels on glucose and xylulose are shown in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088801#pone.0088801.s002" target="_blank">Fig. S2A</a></b> and <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088801#pone.0088801.s003" target="_blank">Fig. S3A</a></b>, respectively. (C) Mean difference (log ratio) between expression on xylulose and on glucose over all genes participating in glycolysis and fermentation (G+F), the TCA cycle and the respiratory chain (RC) for each of the 12 <i>S. cerevisiae</i> (blue) and 12 <i>S. paradoxus</i> (red) strains, vs. ethanol production levels. Same plots but for absolute expression levels on glucose and xylulose are shown in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088801#pone.0088801.s002" target="_blank">Fig. S2B</a></b> and <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088801#pone.0088801.s003" target="_blank">Fig. S3B</a></b>, respectively. (D) Detailed description of the PPP including metabolic intermediates and genes encoding for the enzymes participating in the pathway. The color of the gene name indicates whether its expression (absolute level) on xylulose across all 24 strains is positively correlated (blue), negatively correlated (red) or not correlated (black) with the level of ethanol production (using a correlation threshold of c = ±0.3). Induction/reduction in expression of each gene on xylulose, compared to glucose, averaged over all strains, is indicated by <b>+</b> (induction), <b>-</b> (reduction) and <b>0</b> (equal expression) signs (using a log-difference threshold of ±0.2).</p

Expression response to various environmental conditions and genetic perturbations.

<p>Expression of RP genes vs. amino-acid biogenesis genes for a set of deletion mutation strains and strains treated with various drugs (blue), a laboratory strain grown in various environmental conditions consisting of temperature and osmotic shocks, amino-acid starvation, nitrogen depletion, addition of hydrogen-peroxide/menadione/DTT (red), a wild type strain grown on a variety of carbon sources, including ethanol, galactose, glucose, mannose, raffinose and sucrose (orange) and all 24 strains from our yeast collection grown on xylulose (log ratio between expression on xylulose and on glucose, green). We note that expression data was obtained using different microarrays but are nevertheless compared due to normalization of the data and the fact that expression differences between conditions are used.</p

Phenotypic diversity during growth on glucose or xylulose.

<p>(A,B) Doubling times (A) and ethanol production levels (B) of all wt <i>S. cerevisiae</i> (blue) and <i>S. paradoxus</i> (red) yeast strains in our collection during growth on glucose vs. growth on xylulose. Dashed lines in (A) delimit the boundaries of doubling times for each carbon source. (C) Schematic representation of xylulose metabolism. Intracellular xylulose is phosphorylated to xylulose-5-phsophate, an intermediate of the PPP. The sugar phosphate is metabolized via the PPP and subsequently enters glycolysis through two common metabolic intermediates shared by these pathways – Fructose-6-phosphate and Glyceraldehyde-3-phosphate.</p

Relation between growth rate and expression of typically growth rate-related genes during growth on xylulose across natural strains.

<p>(A,B) Difference (log ratio) between expression on xylulose and on glucose of genes belonging to the RP, Ribi, proteasome (pro) and amino acid biosynthesis (AA) gene groups (A), and genes induced or repressed during the ESR (B). Genes for which data from less than 14 strains existed were omitted. The complete list of genes can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088801#pone.0088801.s007" target="_blank">Table S2</a>. Each column represents data from an individual strain. Growth rate on xylulose of the respective strain is shown above, for each <i>S. cerevisiae</i> (dark gray) and <i>S. paradoxus</i> (light gray) strain. The columns are sorted according to growth rate. Same format matrices but for absolute expression levels on glucose and xylulose are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088801#pone.0088801.s004" target="_blank">Fig. S4A</a>, B and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088801#pone.0088801.s005" target="_blank">Fig. S5A</a>, B, respectively. (C) Mean difference (log ratio) between expression on xylulose and on glucose over all genes belonging to the RP, Ribi and amino acid biosynthesis (AA) gene groups, as well as genes which are induced and genes which are repressed during the ESR, for each of the 12 <i>S. cerevisiae</i> (blue) and 12 <i>S. paradoxus</i> (red) strains, vs. growth rate. Same plots but for absolute expression levels on glucose and xylulose are shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088801#pone.0088801.s004" target="_blank">Fig. S4C</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088801#pone.0088801.s005" target="_blank">Fig. S5C</a>, respectively.</p

Additional file 3: of Rapid evolutionary adaptation to growth on an ‘unfamiliar’ carbon source

Correlation between expression change of various gene groups and growth change. (PDF 299 kb