Biology subjects

Snir, S.

Publications and source records attributed to Snir, S..

9 recordsLinked to original sources

bioRxiv2025

Evolution of gene order in prokaryotes is driven primarily by gene gain and loss

Evolution of bacterial and archaeal genomes is highly dynamic including extensive gene gain via horizontal gene transfer and gene loss as well as different types of genome rearrangements, such as inversions and translocations, so that gene order is not highly conserved even among closely related organisms. We sought to quantify the contributions of different genome dynamics processes to the evolution of the gene order relying on the recently developed "jump" model of gene translocation. The jump model has been completely solved analytically and provides the exact distribution of syntenic gene block lengths (SBL) in compared genomes based on gene translocations alone. Comparing the SBL distribution predicted by the jump model with the distributions empirically observed for multiple groups of closely related bacterial and archaeal genomes, we obtained robust estimates of the genome rearrangement to gene flux (gain and loss) ratio. In most groups of bacteria and archaea, this ratio was found to be on the order of 0.1 indicating that the loss of synteny in the evolution of bacteria and archaea is driven primarily by gene gain and loss rather than by gene translocation. SignificanceEvolution of bacterial and archaeal genomes is a highly dynamic process that includes extensive gene gain via horizontal gene transfer and gene loss as well as different types of genome rearrangements, so that gene order is not highly conserved even among closely related organisms. We developed a theoretical framework to quantify the contributions of different genome dynamics processes to the evolution of the gene order and found that in most groups of bacteria and archaea, the genome rearrangement to gene flux (combined gain and loss) is on the order of 0.1. Thus, the loss of genomic synteny in the evolution of bacteria and archaea appears to be driven primarily by gene gain and loss rather than by gene translocation.

evolutionary biology↗

bioRxiv2025

Untying Rates of Gene Gain and Loss Leads to a New Phylogenetic Approach

The advent of the genomic era has produced an incredible wealth and resolution of molecular data, posing an unprecedented challenge for molecular systematics, necessitating novel techniques and paradigms. Consequently, whole genome approaches were developed to extract the evolutionary signal by taking advantage of a larger amount of data. In parallel and in light of the understanding that in prokaryotes, genome dynamics (GD) events, primarily gene gain and loss, provide a significantly richer signal than point mutations in ubiquitous housekeeping genes, GD-based approaches were suggested. However, proper modeling of these data and the processes generating them has lagged in their pace of accumulation, both because of a lack of deep understanding and because of technical difficulties. Among the central hurdles of accurate modeling of real data is the relaxation of rate constancy, particularly the untying of gain and loss rates. This relaxation violates key assumptions such as constant genome sizes, gene set, and model reversibility and has vast implications for implementation. This work presents a generic stochastic model, the two-ratio process (TRP), which encompasses and deals with these complications. As a special case, it contains the Poissonian process with different gene gain and loss rates as a form of the Birth-Death process with varying population sizes. The lack of reversibility invalidates traditional phylogenetic approaches, yielding a novel two-stage phylogenetic approach in which accurate, bidirectional parameters are first inferred for triplets and later combined by a special cherry-picking method to a complete tree. We show by algebraic techniques that this method is theoretically statistically consistent. The method implemented by the software TDDR (Triplets Directed Distances Reconstruction) was applied to synthetic data, showing an advantage over other approaches handling similar data but without the same model assumption. We also applied it to the Alignable Tight Genomic Clusters (ATGC) Database, which showed a high adequacy to the observed data. The TDDR code is available on GitHub: https://github.com/YoavDvir/TDDR.

bioinformatics↗

bioRxiv2024

Privacy Preserving Epigenetic PaceMaker Stronger Privacy and Improved Efficiency

DNA methylation data plays a crucial role in estimating chronological age in mammals, offering real-time insights into an individuals aging process. The Epigenetic Pacemaker (EPM) model allows inference of the biological age as deviations from the population trend. Given the sensitivity of this data, it is essential to safeguard both inputs and outputs of the EPM model. In a recent study by Goldenberg et al., a privacy-preserving approach for EPM computation was introduced, utilizing Fully Homomorphic Encryption (FHE). However, their method had limitations, including having high communication complexity and being impractical for large datasets Our work presents a new privacy preserving protocol for EPM computation, analytically improving both privacy and complexity. Notably, we employ a single server for the secure computation phase while ensuring privacy even in the event of server corruption (compared to requiring two non-colluding servers in Goldenberg et al.). Using techniques from symbolic algebra and number theory, the new protocol eliminates the need for communication during secure computation, significantly improves asymptotic runtime and and offers better compatibility to parallel computing for further time complexity reduction. We have implemented our protocol, demonstrating its ability to produce results similar to the standard (insecure) EPM model with substantial performance improvement compared to Goldenberg et al. These findings hold promise for enhancing data security in medical applications where personal privacy is paramount. The generality of both the new approach and the EPM, suggests that this protocol may be useful to other uses employing similar expectation maximization techniques.

bioinformatics↗

bioRxiv2023

The Multi-State Epigenetic Pacemaker enables the identification of combinations of factors that influence DNA methylation

Epigenetic clocks, DNA methylation based predictive models of chronological age, are often utilized to study aging associated biology. Despite their widespread use, these methods do not account for other factors that also contribute to the variability of DNA methylation data. For example, many CpG sites show strong sex-specific or cell type specific patterns that likely impact the predictions of epigenetic age. To overcome these limitations, we developed a multidimensional extension of the Epigenetic Pacemaker, the Multi-State Epigenetic Pacemaker (MSEPM). We show that the MSEPM is capable of accurately modeling multiple methylation associated factors simultaneously, while also providing site specific models that describe the per site relationship between methylation and these factors. We utilized the MSEPM with a large aggregate cohort of blood methylation data to construct models of the effects of age, sex and cell type heterogeneity on DNA methylation. We found that these models capture a large faction of the variability at thousands of DNA methylation sites. Moreover, we found modeled sites that are primarily affected by aging and no other factors. Among these, those that lose methylation over time are enriched for CTCF transcription factor chip peaks, while those that gain methylation over time are enriched for REST transcription factor chip peaks. Both transcription factors are associated with transcriptional maintenance and suggest a general dysregulation of transcription with age that is not impacted by sex or cell type heterogeneity. In conclusion, the MSEPM is capable of accurately modeling multiple methylation associated factors and the models produced can illuminate site specific combinations of factors that affect methylation dynamics.

bioinformatics↗

bioRxiv2022

Gene Transfer based Phylogenetics: Analytical Expressions and Additivity via Birth/Death Theory

The genomic era has opened up vast opportunities in molecular systematics, one of which is deciphering the evolutionary history in fine detail. Under this mass of data, analysing the point mutations of standard markers is too crude and slow for fine-scale phylogenetics. Nevertheless, genome dynamics events provide far richer information. The synteny index (SI) between a pair of genomes combines gene order and gene content information, allowing the comparison of genomes of unequal gene content, together with order considerations of their common genes. Recently, genome dynamics has been modelled as a continuous-time Markov process, and gene distance in the genome as a birth-death-immigration process. Nevertheless, due to complexities arising in this setting such as overlapping neighbourhoods and other confounding factors, no precise and provably consistent estimators could be derived. Here, we extend this modelling approach by using techniques from birth-death theory to derive explicit expressions of the systems probabilistic dynamics in the form of rational functions of the model parameters. This, in turn, allows us to infer analytically the expected distances between organisms based on a transformation of their SI. Despite the complexity of the expressions obtained, we establish additivity of this estimated evolutionary distance (a desirable property yielding phylogenetic consistency). Applying the new measure in simulation studies shows that it attains very accurate results in realistic settings and even under model extensions. In the real-data realm, we applied the new formulation to unique data structure that we constructed - the ordered orthology DB - based on a new version of the EggNOG database, to construct a tree with more than 4.5K taxa. The resulted tree was compared it with a NCBI taxonomy for these organisms. To the best of our knowledge, this is the largest gene-order-based tree constructed and it overcomes flaws found in previous approaches.

evolutionary biology↗

bioRxiv2021

The Epigenetic Pacemaker is a more sensitive tool than penalized regression for identifying moderators of epigenetic aging

Epigenetic clocks, DNA methylation based chronological age prediction models, are commonly employed to study age related biology. The error between the predicted and observed age is often interpreted as a form of biological age acceleration and many studies have measured the impact of environmental and other factors on epigenetic age. Epigenetic clocks are fit using approaches that minimize the error between the predicted and observed chronological age and as a result they reduce the impact of factors that may moderate the relationship between actual and epigenetic age. Here we compare the standard methods used to construct epigenetic clocks to an evolutionary framework of epigenetic aging, the epigenetic pacemaker (EPM) that directly models DNA methylation as a function of a time dependent epigenetic state. We show that the EPM is more sensitive than epigenetic clocks for the detection of factors that moderate the relationship between actual age and epigenetic state (ie epigenetic age). Specifically, we show that the EPM is more sensitive at detecting sex and cell type effects in a large aggregate data set and in an example case study is more sensitive sensitive at detecting age related methylation changes associated with polybrominated biphenyl exposure. Thus we find that the pacemaker provides a more robust framework for the study of factors that impact epigenetic age acceleration than traditional clocks based on linear regression models.

genetics↗

bioRxiv2021

Hibernation slows epigenetic aging in yellow-bellied marmots

Species that hibernate live longer than would be expected based solely on their body size. Hibernation is characterized by long periods of metabolic suppression (torpor) interspersed by short periods of increased metabolism (arousal). The torpor-arousal cycles occur multiple times during hibernation, and it has been suggested that processes controlling the transition between torpor and arousal states cause aging suppression. Metabolic rate is also a known correlate of longevity, we thus proposed the hibernation-aging hypothesis whereby aging is suspended during hibernation. We tested this hypothesis in a well-studied population of yellow-bellied marmots (Marmota flaviventer), which spend 7-8 months per year hibernating. We used two approaches to estimate epigenetic age: the epigenetic clock and the epigenetic pacemaker. Variation in epigenetic age of 149 samples collected throughout the life of 73 females were modeled using generalized additive mixed models (GAMM), where season (cyclic cubic spline) and chronological age (cubic spline) were fixed effects. As expected, the GAMM using epigenetic ages calculated from the epigenetic pacemaker was better able to detect nonlinear patterns in epigenetic age change over time. We observed a logarithmic curve of epigenetic age with time, where the epigenetic age increased at a higher rate until females reached sexual maturity (2-years old). With respect to circannual patterns, the epigenetic age increased during the summer and essentially stalled during the winter. Our enrichment analysis of age-related CpG sites revealed pathways related to development and cell differentiation, while the season-related CpGs enriched pathways related to central carbon metabolism, immune system, and circadian clock. Taken together, our results are consistent with the hibernation-aging hypothesis and may explain the enhanced longevity in hibernators.

evolutionary biology↗

bioRxiv2018

Human Epigenetic Aging is Logarithmic with Time Across the Entire Lifespan

It is well established that organisms undergo epigenetic changes both during development and aging. Developmental changes have been extensively studied to characterize the differentiation of stem cells into diverse lineages. Epigenetic changes during aging have been characterized by multiple epigenetic clocks, that allow the prediction of chronological age based on methylation status. Despite their accuracy and utility, epigenetic age biomarkers leave many questions about epigenetic aging unanswered. Specifically, they do not permit the unbiased characterization of non-linear epigenetic aging trends across entire life spans, a critical question underlying this field of research. Here we a provide an integrated framework to address this question. Our model, inspired from evolutionary models, is able to account for acceleration/deceleration in epigenetic changes by fitting an individuals model age, the epigenetic age, which is related to chronological age in a non-linear fashion. We have devised a two stage procedure leveraging these model ages to infer aging trends over the entire lifespan of a population. Application of this procedure to real data measured across broad age ranges, from before birth to old age, and from two tissue types, suggests a universal logarithmic trend characterizes epigenetic aging across entire lifespans. This observation may have important implications for the development and application of future, more accurate, aging biomarkers.

genomics↗

bioRxiv2017

A Phylogenomic Study Quantifies Competing Mechanisms for Pseudogenization in Prokaryotes

BackgroundPseudogenes are non-functional sequences in the genome with homologous sequences that are functional (i.e. genes). They are abundant in eukaryotes where they have been extensively investigated, while in prokaryotes they are significantly scarcer and less well studied. Here we conduct a comprehensive analysis of the evolution of pseudogenes in prokaryotes. For this analysis we consider a broad range of bacteria, but also focus on the leprosy pathogen Mycobacterium leprae, which contains an unusually large number of pseudogenes which comprise approximately 40% of its entire genome.\n\nResultsWe have developed an informatics-based approach to characterize the evolution of pseudogenes. This approach combines tools from phylogenomics, genomics, and transcriptomics. The results we obtain suggest the presence of two mechanisms for pseudogene formation: failed horizontal gene transfer events and disruption of native genes.\n\nConclusionsWe conclude that while in most bacteria the former is most likely responsible for the majority of pseudogenization events, in mycobacteria, and in particular in M. leprae with its exceptionally high pseudogene numbers, the latter predominates. We believe that our study sheds new light on the evolution of pseudogenes in bacteria, by utilizing new methodologies that are applied to the unusually abundant M. leprae pseudogenes and their orthologs. As such, we anticipate that it will be of broad interest to both evolutionary biologists as well as microbiologists.

evolutionary biology↗