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

Introduction: The 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. Methods: We 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. Results: In 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. Discussion: These 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.

Rapid evolutionary adaptation to growth on an 'unfamiliar' carbon source.

BACKGROUND: Cells constantly adapt to changes in their environment. When environment shifts between conditions that were previously encountered during the course of evolution, evolutionary-programmed responses are possible. Cells, however, may also encounter a new environment to which a novel response is required. To characterize the first steps in adaptation to a novel condition, we studied budding yeast growth on xylulose, a sugar that is very rarely found in the wild. RESULTS: We previously reported that growth on xylulose induces the expression of amino acid biosynthesis genes in multiple natural yeast isolates. This induction occurs despite the presence of amino acids in the growth medium and is a unique response to xylulose, not triggered by naturally available carbon sources. Propagating these strains for ~300 generations on xylulose significantly improved their growth rate. Notably, the most significant change in gene expression was the loss of amino acid biosynthesis gene induction. Furthermore, the reduction in amino-acid biosynthesis gene expression on xylulose was tightly correlated with the improvement in growth rate, suggesting that internal depletion of amino-acids presented a major bottleneck limiting growth in xylulose. CONCLUSIONS: We discuss the possible implications of our results for explaining how cells maintain the balance between supply and demand of amino acids during growth in evolutionary 'familiar' vs. 'novel' conditions.

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

The 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.

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.

Improved readout precision of the Bicoid morphogen gradient by early decoding.

Transcription factors (TFs) bind to specific DNA sequences to induce or repress gene expression. Expression levels can be tuned by changing TF concentrations, but the precision of such tuning is limited, since the fraction of time a TF occupies its binding site is subject to stochastic fluctuations. Bicoid (Bcd) is a TF that patterns the early Drosophila embryo by establishing an anterior-to-posterior concentration gradient and activating specific gene targets ("gap genes") in a concentration-dependent manner. Recently, the Bcd gradient and its in-vivo diffusion were quantified in live embryos, raising a quandary: the precision by which the Bcd target genes are defined (one-cell resolution) appeared to exceed the physical limits set by the stochastic binding of Bcd to DNA. We hypothesize that early readout of Bcd could account for the observed precision. Specifically, we consider the possibility that gap genes begin to be expressed earlier than typically measured experimentally, at a time when the distance between the nuclei is large. At this time, the difference in Bcd concentration between adjacent nuclei is large, enabling better tolerance for measurement imprecision. We show that such early decoding can indeed increase the accuracy of gap-gene expression, and that the initial pattern can be stabilized during subsequent divisions.

Physical aspects of precision in genetic regulation.

The process by which transcription factors (TFs) locate specific DNA binding sites is stochastic and as such, is subject to a considerable level of noise. TFs diffuse in the three-dimensional nuclear space, but can also slide along the DNA. It was proposed that this sliding facilitates the TF molecules arriving to their binding site, by effectively reducing the dimensionality of diffusion. However, the possible implications of DNA sliding on the accuracy by which the nuclear concentration of TFs can be estimated were not examined. Here, we calculate the mean and the variance of the number of TFs that bind to their binding site in reduced and partially reduced diffusion dimensionality regimes. We find that a search process which combines three-dimensional diffusion in the nucleus with one-dimensional sliding along the DNA can reduce the noise in TF binding and in this way enables a better estimation of the TF concentration inside the nucleus.