Biology subjects

Sela, I.

Publications and source records attributed to Sela, I..

8 recordsLinked to original sources

bioRxiv2024

Biological insights from plasma proteomics of non-small cell lung cancer patients treated with immunotherapy

IntroductionImmune checkpoint inhibitors have made a paradigm shift in the treatment of non-small cell lung cancer (NSCLC). However, clinical response varies widely and robust predictive biomarkers for patient stratification are lacking. Here, we characterize early on-treatment proteomic changes in blood plasma to gain a better understanding of treatment response and resistance. MethodsPre-treatment (T0) and on-treatment (T1) plasma samples were collected from 225 NSCLC patients receiving PD-1/PD-L1 inhibitor-based regimens. Plasma was profiled using aptamer-based technology to quantify approximately 7000 plasma proteins per sample. Proteins displaying significant fold changes (T1:T0) were analyzed further to identify associations with clinical outcomes. Bioinformatic analyses of upregulated proteins were performed to determine potential cell origins and enriched biological processes. ResultsThe levels of 142 proteins were significantly increased in the plasma of NSCLC patients following ICI-based treatments. Soluble PD-1 exhibited the highest increase, with a positive correlation to tumor PD-L1 status. Bioinformatic analysis of the ICI monotherapy dataset revealed a set of 30 upregulated proteins that formed a single, highly interconnected network with CD8A serving as a central hub, suggesting T cell activation during ICI treatment. Notably, the T cell-related network was detected regardless of clinical benefit. Lastly, circulating proteins of alveolar origin were identified as potential biomarkers of limited clinical benefit, possibly due to a link with cellular stress and lung damage. ConclusionsOur study provides insights into the biological processes activated during ICI-based therapy, highlighting the potential of plasma proteomics to identify mechanisms of therapy resistance and potential biomarkers for outcome.

bioinformatics↗

bioRxiv2023

Analytical validation of the PROphet computational model for clinical benefit prediction and decision-making tool in metastatic NSCLC

The blood proteome, consisting of thousands of proteins engaged in various biological processes, acts as a valuable source of potential biomarkers for various medical applications. PROphet is a plasma proteomics-based test that serves as a decision-support tool for non-small cell lung cancer (NSCLC) patients. PROphet combines proteomic profiling using the SomaScan technology and subsequent computational algorithm. PROphet was implemented as a laboratory developed test (LDT). Under the Clinical Laboratory Improvement Amendments (CLIA) and Commission on Office Laboratory Accreditation (COLA) regulations, prior to releasing patient test results, a clinical laboratory located in the United States that employs an LDT must examine the performance characteristics concerning analytical validity. This study describes the experimental and computational analytical validity of the PROphet test, as required by CLIA/COLA. Experimental precision analysis displayed a median coefficient of variation (CV) of 3.9% and 4.7% for intra-plate and inter-plate examination, respectively, and the median accuracy rate between sites was 88%. Computational precision exhibited a high accuracy rate, with 93% of samples displaying complete concordance in results. A cross-platform comparison between SomaScan and other proteomics platforms yielded a median Spearman correlation coefficient of 0.51, affirming the consistency and reliability of the SomaScan platform. Our study presents a robust framework for evaluating the analytical validity of a platform that combines an experimental assay with subsequent computational algorithms. When applied to the PROphet test, strong analytical performance of the test was demonstrated.

systems biology↗

bioRxiv2020

Horizontal gene transfer barrier shapes the evolution of prokaryotic pangenomes

The genomes of bacteria and archaea evolve by extensive loss and gain of genes which, for any group of related prokaryotic genomes, result in the formation of a pangenome with the universal, asymmetrical U-shaped distribution of gene commonality. To elucidate the evolutionary factors that define the specific shape of this distribution, we investigate the fit of simple models of genome evolution to the empirically observed gene commonality distributions and genomes intersections for 33 groups of closely related bacterial genomes. The combined analysis of genome intersections and gene commonality shows that at least one of the two simplifying assumptions that are usually adopted for modeling the evolution of the U-shaped distribution, those of infinitely many genes and constant genome size, is invalid. The violation of both these assumptions stems from the horizontal gene transfer barrier, i.e. the cost of accommodation of foreign genes by prokaryotes.

evolutionary biology↗

bioRxiv2019

Homologous recombination substantially delays sequence but not gene content divergence of prokaryotic populations

Evolution of bacterial and archaeal genomes is a highly dynamic process that involves extensive gain and loss of genes. Therefore, phylogenetic trees of prokaryotes can be constructed both by the traditional sequence-based methods (gene trees) and by comparison of gene compositions (genome trees). Comparing the branch lengths in gene and genome trees with identical topologies for 34 clusters of closely related bacterial and archaeal genomes, we found that the terminal branches of gene trees were systematically compressed compared to those of genome trees. Thus, sequence evolution seems to be significantly delayed with respect to genome evolution by gene gain and loss. The extent of this delay widely differs among bacterial and archaeal lineages. We develop and explore mathematical models demonstrating that the delay of sequence divergence can be explained by sequence homogenization that is caused by homologous recombination. Once evolving genomes become isolated by barriers that impede homologous recombination, gene and genome evolution processes settle into parallel trajectories, and genomes diverge, resulting in speciation. This model of prokaryotic genome evolution gives a mechanistic explanation of our previous finding that archaeal genomes contain a class of genes that turn over rapidly, before significant sequence divergence occurs, and provides a framework for correcting phylogenetic trees, to make them consistent with the dynamics of gene turnover.

evolutionary biology↗

bioRxiv2019

Crossing fitness valleys via double substitutions within codons

Single nucleotide substitutions in protein-coding genes can be divided into synonymous (S), with little fitness effect, and non-synonymous (N) ones that alter amino acids and thus generally have a greater effect. Most of the N substitutions are affected by purifying selection that eliminates them from evolving populations. However, additional mutations of nearby bases can modulate the deleterious effect of single substitutions and thus might be subject to positive selection. To elucidate the effects of selection on double substitutions in all codons, it is critical to differentiate selection from mutational biases. We approached this problem by comparing the fractions of double substitutions within codons to those of the equivalent double S substitutions in adjacent codons. Under the assumption that substitutions occur one at a time, all within-codon double substitutions can be represented as \"ancestral-intermediate-final\" sequences and can be partitioned into 4 classes: 1) SS: S intermediate - S final, 2) SN: S intermediate - N final, 3) NS: N intermediate - S final, 4) NN: N intermediate - N final. We found that the selective pressure on the second substitution markedly differs among these classes of double substitutions. Analogous to single S substitutions, SS evolve neutrally whereas, analogous to single N substitutions, SN are subject to purifying selection. In contrast, NS show positive selection on the second step because the original amino acid is recovered. The NN double substitutions are heterogeneous and can be subject to either purifying or positive selection, or evolve neutrally, depending on the amino acid similarity between the final or intermediate and the ancestral states. The general trend is that the second mutation compensates for the deleterious effect of the first one, resulting in frequent crossing of valleys on the fitness landscape.

evolutionary biology↗

bioRxiv2018

Genome plasticity, a key factor of evolution in prokaryotes

In prokaryotic genomes, the number of genes that belong to distinct functional classes shows apparent universal scaling with the total number of genes [1-5] (Fig. 1). This scaling can be approximated with a power law, where the scaling power can be sublinear, near-linear or super-linear. Scaling laws are robust under various statistical tests [4], across different databases and for different gene classifications [1-5]. Several models aimed at explaining the observed scaling laws have been proposed, primarily, based on the specifics of the respective biological functions [1, 5-8]. However, a coherent theory to explain the emergence of scaling within the framework of population genetics is lacking. We employ a simple mathematical model for prokaryotic genome evolution [9] which, together with the analysis of 34 clusters of closely related microbial genomes [10], allows us to identify the underlying forces that dictate genome content evolution. In addition to the scaling of the number of genes in different functional classes, we explore gene contents divergence to characterize the evolutionary processes acting upon genomes [11]. We find that evolution of the gene content is dominated by two factors that are specific to a functional class, namely, selection landscape and genome plasticity. Selection landscape quantifies the fitness cost that is associated with deletion of a gene in a given functional class or the advantage of successful incorporation of an additional gene. Genome plasticity, that can be considered a measure of evolvability, reflects both the availability of the genes of a given functional class in the external gene pool that is accessible to the evolving microbial population, and the ability of microbial genomes to accommodate these genes. The selection landscape determines the gene loss rate, and genome plasticity is the principal determinant of the gene gain rate.\n\nO_FIG O_LINKSMALLFIG WIDTH=197 HEIGHT=200 SRC=\"FIGDIR/small/357400_fig1.gif\" ALT=\"Figure 1\">\nView larger version (39K):\norg.highwire.dtl.DTLVardef@6df3e2org.highwire.dtl.DTLVardef@a69e8dorg.highwire.dtl.DTLVardef@f36a80org.highwire.dtl.DTLVardef@d519c9_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Scaling laws for all functional classes of the COGs. The number of genes in a given COG category is plotted against the total number of genes. Each point represents one genome from the analyzed set of 1490 genomes. The scaling is fitted to a power law which is indicated by a solid red line. The fitted scaling exponent is indicated in parentheses.\n\nC_FIG

microbiology↗

bioRxiv2018

A transmissible RNA pathway in honey bees

One of the characteristics of RNA interference (RNAi) is systemic spread of the silencing signal among cells and tissues throughout the organism. Systemic RNAi, initiated by double-stranded RNA (dsRNA) ingestion, has been reported in diverse invertebrates, including honey bees, demonstrating environmental RNA uptake that undermines homologous gene expression. However, the question why any organism would take up RNA from the environment has remained largely unanswered. Here, we report on horizontal RNA flow among honey bees mediated by secretion and ingestion of worker and royal jelly diets. We show that ingested dsRNA spreads through the bees hemolymph associated with a protein complex. The systemic dsRNA is secreted with the jelly and delivered to larvae via ingestion. Furthermore, we demonstrate that transmission of jelly-secreted dsRNA to larvae is biologically active and triggers gene knockdown that lasts into adulthood. Finally, RNA extracted from worker and royal jellies harbor differential naturally occurring RNA populations. Some of these RNAs corresponded to honey bee protein coding genes, transposable elements, non-coding RNA and exogenous viruses. These results reveal an inherent property of honey bees to share RNA among individuals and generations. Thus, our findings suggest a transmissible RNA pathway, playing a role in social immunity and epigenetic dynamics among honey bees and potentially other closely interacting organisms.\n\nSIGNIFICANCEHoney bees are eusocial insects, living in a colony that is often described as a superorganism. RNA mobility among cells of an organism has been documented in plants and animals. Here we show that RNA spreads further in honey bees, and is horizontally transferred between individuals and across generations. We found that honey bees share biologically active RNA through secretion and ingestion of worker and royal jellies. Such RNA initiated RNA interference, which is a known defense mechanism against viral infection. Furthermore, we characterized diverse RNA profiles of worker and royal jelly, including fragmented viral RNA. Our findings demonstrate a transmissible RNA pathway with potential roles in social immunity and epigenetic signaling among members of the hive.

molecular biology↗

bioRxiv2017

Estimation Of Universal And Taxon-Specific Parameters Of Prokaryotic Genome Evolution

Our recent study on mathematical modeling of microbial genome evolution indicated that, on average, genomes of bacteria and archaea evolve in the regime of mutation-selection balance defined by positive selection coefficients associated with gene acquisition that is counter-acted by the intrinsic deletion bias. This analysis was based on the strong assumption that parameters of genome evolution are universal across the diversity of bacteria and archaea, and yielded extremely low values of the selection coefficient. Here we further refine the modeling approach by taking into account evolutionary factors specific for individual groups of microbes using two independent fitting strategies, an ad hoc hard fitting scheme and an hierarchical Bayesian model. The resulting estimate of the mean selection coefficient of s[~]10-10 associated with the gain of one gene implies that, on average, acquisition of a gene is beneficial, and that microbial genomes typically evolve under a weak selection regime that might transition to strong selection in highly abundant organisms with large effective population sizes. The apparent selective pressure towards larger genomes is balanced by the deletion bias, which is estimated to be consistently greater than unity for all analyzed groups of microbes. The estimated values of s are more realistic than the lower values obtained previously, indicating that global and group-specific evolutionary factors synergistically affect microbial genome evolution that seems to be driven primarily by adaptation to existence in diverse niches.

evolutionary biology↗