Biology subjects

Rezenman, S.

Publications and source records attributed to Rezenman, S..

3 recordsLinked to original sources

bioRxiv2023

Differential proteome diversification in yeast populations: modes of short-term adaptation and fitness outcomes

Short-term proteomic adaptations serve as an initial line of defence, allowing populations to cope with environmental changes before long-term genetic alterations occur. Using a representative set of genes, we examined how stress affects gene expression variability for different types and levels of abiotic stresses and how this influences population-level adaptation. Our data reveal that, depending on the nature of the stress, two distinct modes of response can be employed. In one, the levels of most proteins vary between individuals, leading to varied fitness levels in the population. In the other, a more limited range of expression is seen, and fitness is more even. This suggests different levels of complexity and plasticity in adaptation to different types of stress.

systems biology↗

bioRxiv2023

CoSMIC - A hybrid approach for large-scale, high-resolution microbial profiling of novel niches

Standard microbial profiling based on 16S rRNA (16S) sequencing suffers from a lack of primer universality, primer biases and often yields low resolution. We introduce Comprehensive Small Ribosomal Subunit Mapping and Identification of Communities (CoSMIC), addressing these challenges, especially in unexplored niches. CoSMIC begins with long-read sequencing of the full-length 16S gene, amplified by generic Locked Nucleic Acid primers over pooled samples, thus augmenting reference databases with novel niche-specific gene sequences. Subsequently, CoSMIC amplifies multiple non-consecutive variable regions along the gene, followed by short-read sequencing of each sample. Data from the different regions are integrated using the SMURF framework, alleviating primer biases and providing de-facto full gene resolution. Using a mock community, CoSMIC identified full-length 16S genes with significantly higher specificity and sensitivity while dramatically increasing resolution compared to standard methods. Evaluating CoSMIC across environmental samples provided higher accuracy and unprecedented resolution while detecting thousands of novel full-length 16S sequences.

molecular biology↗

bioRxiv2022

gUMI-BEAR, a modular, unsupervised population barcoding method to track variants and evolution at high resolution

Cellular lineage tracking provides a means to observe population makeup at the clonal level, allowing exploration of heterogeneity, evolutionary and developmental processes and individual clones relative fitness. It has thus contributed significantly to understanding microbial evolution, organ differentiation and cancer heterogeneity, among others. Its use, however, is limited because existing methods are highly specific, expensive, labour-intensive, and, critically, do not allow the repetition of experiments. To address these issues, we developed gUMI-BEAR (genomic Unique Molecular Identifier Barcoded Enriched Associated Regions), a modular, cost-effective method for tracking populations at high resolution. We first demonstrate the systems application and resolution by applying it to track tens of thousands of Saccharomyces cerevisiae lineages growing together under varying environmental conditions applied across multiple generations, revealing fitness differences and lineage-specific adaptations. Then, we demonstrate how gUMI-BEAR can be used to perform parallel screening of a huge number of randomly generated variants of the Hsp82 gene. We further show how our method allows isolation of variants, even if their frequency in the population is low, thus enabling unsupervised identification of modifications that lead to a behaviour of interest.

evolutionary biology↗