Biology subjects

Vitkup, D.

Publications and source records attributed to Vitkup, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Multiple molecular and cellular properties jointly affect protein and site-specific evolutionary rates

Protein evolutionary rates vary widely across proteins and among sites within proteins, reflecting multiple molecular, cellular, and functional constraints. While protein-level properties, such as expression and essentiality, and site-level structural and functional constraints, are known to influence evolutionary rates, how these constraints combine across scales to determine site-specific evolutionary rates remains unclear. Moreover, because many protein features are strongly correlated, it is difficult to disentangle their individual contributions to evolutionary rate variance, and unified predictive models that integrate these properties are still lacking. Here, we use neural networks to predict protein evolutionary rates across multiple scales based on multiple molecular and cellular features. At the protein level, integrating molecular and cellular descriptors explains substantial variance in evolutionary rates across proteins in multiple eukaryotic species, including nearly 50% of the variance in humans and substantial fractions of the variance in other eukaryotic species. The model also allows us to identify proteins whose evolutionary rates deviate from expectations based on their molecular and cellular properties. At the site level, we found that structural and functional features explain a comparable fraction of the variance in relative evolutionary rates. By integrating protein-level and site-level predictors, the model explains up to 37% of the variance in site-specific evolutionary rates across proteins. Our analysis demonstrates that constraints at these two scales combine largely additively, with protein-level properties setting the overall evolutionary context and site-level properties shaping variation within proteins. Together, these results provide a quantitative framework for understanding protein evolution across biological scales.

evolutionary biology↗

bioRxiv2025

Macroecological Laws Can Naturally Arise from Chaotic Internal Species Dynamics

Macroecological relationships that connect various statistical descriptors of long-term and short-term species dynamics represent some of the most general laws in ecology and biology. These macroecological laws have been observed across diverse ecologies of plants and animals, and more recently, also in microbiota. Yet it remains unclear why strikingly similar macroecological relationships often arise in very different biological communities and various environmental contexts. Here, we investigated whether chaotic internal dynamics in spatially heterogeneous communities could underlie multiple macroecological relationships. Our analyses reveal that very general constraints on species interactions and spatial migration parameters can simultaneously lead to multiple macroecological laws found in microbial ecosystems without requiring external sources of noise. Our study also identifies the mechanistic origins of many empirically observed macroecological relationships, such as Taylors law, anomalous abundance diffusion, the Laplace distribution of short-term abundance changes, and the distribution of species residence times. Overall, we demonstrate how macroecological laws can arise from interaction-driven chaotic dynamics and common ecological constraints, thereby providing a unifying explanation for their widespread prevalence in nature.

ecology↗

bioRxiv2025

Autism Mutations Preferentially Target Distributed Brain Circuits and Cell Types Associated with Sensory Integration and Decision Making

Autism spectrum disorder (ASD) is a highly heritable psychiatric condition characterized by difficulties in social communication and stereotypic, repetitive behaviors. Genetics studies have discovered many dozens of genes with causal roles in autism, and functional analyses have demonstrated that ASD-associated mutations affect a diverse range of brain regions and cell types. However, the precise mechanisms by which these genetic alterations lead to autism-related phenotypes remain unclear. Psychiatric cognitive and behavioral traits are believed to arise from dysfunction of specific brain circuits formed by anatomically and functionally connected brain areas. To identify the circuits and cell types primarily affected by ASD mutations, we developed an unbiased approach, GENCIC, which computationally integrates genome-wide genetic data with a brain-wide spatial mouse transcriptome and an anatomical mesoscale connectome. Applying this approach to ASD reveals a convergence of mutations on cohesive brain circuits that are enriched in long-distance connections and span both cortical and subcortical brain structures. Furthermore, our analysis of brain-wide single-cell spatial transcriptomics shows that the heterogeneity of circuit structures affected in ASD is matched by the substantial diversity of strongly impacted circuit cell types. Notably, the implicated circuits and cell types play a central role in the integration of multimodal sensory and emotional information and in decision-making based on this information. We also find that different circuit structures exhibit distinct vulnerability patterns that correlate with cognitive phenotypes in ASD. Overall, our study demonstrates how ASD-related genetic mutations impact multiple levels of brain organization, ultimately disrupting functional circuits that drive core autism-related behaviors.

systems biology↗

bioRxiv2025

Rare mutations implicate CGE interneurons as a vulnerable axis of cognitive deficits across psychiatric disorders

Neuropsychiatric disorders such as autism spectrum disorder (ASD) and schizophrenia (SCZ) share genetic risk factors, including genes affected by rare high-penetrance single nucleotide variants (SNVs) and copy number variants (CNVs). ASD and SCZ exhibit both overlapping and distinct clinical phenotypes. Cognitive deficits and intellectual disability--critical predictors of long-term outcomes--are common to both conditions. To investigate shared and disorder-specific neurobiological impact of highly penetrant rare mutations in ASD and SCZ, we analyzed human single-nucleus whole-brain sequencing data to identify strongly affected brain cell types. Our analysis revealed caudal ganglionic eminence (CGE)-derived GABAergic interneurons as a key nexus for cognitive deficits across these disorders. Notably, genes within 22q11.2 deletions, known to confer a high risk for SCZ, ASD, and cognitive impairment, showed a strong expression bias toward vasoactive intestinal peptide-expressing cells (VIP+) among CGE subtypes. To explore perturbations of VIP+ GABAergic interneurons in the 22q11.2 deletion syndrome in vivo, we examined their activity in the Df(16)A+/- mouse model during a spatial navigation task and observed reduced activity along with altered responses to random rewards. At the population level, VIP+ interneurons exhibited impaired spatial encoding and diminished subtype-specific activity suggesting deficient disinhibition in CA1 microcircuits in the hippocampus, a region essential for learning and memory. Overall, these results demonstrate the crucial role of CGE-derived interneurons in mediating cognitive processes that are disrupted across a range of psychiatric and neurodevelopmental disorders.

neuroscience↗

bioRxiv2025

Percolation and lifestyle transition in microbial metabolism

Microbial species exhibit a remarkable diversity in their metabolic properties, genome composition, and ecological distribution. A central challenge of systems biology is to understand the relationships between genomic, metabolic, and phenotypic properties of bacteria. However, it is currently not well understood how the structure of metabolic network defines and reflects the lifestyle of diverse bacterial species across the tree of life. By analyzing thousands of genome-scale metabolic models of bacteria, we found a percolation-like transition in their ability to grow on independent carbon sources at around 800 metabolic reactions or about 2000 protein-coding genes. The observed transition is characterized by significant changes in metabolic network functional connectivity primarily associated with the completion of central carbon metabolism and the TCA cycle. Strikingly, experimentally observed phenotypic properties of bacteria also exhibit two markedly different regimes below and above the transition. Species with metabolic network sizes below the transition are typically obligate symbionts and require complex minimal media for their growth. In contrast, species with networks above the transition are primarily free- living generalists. The observed percolation transition is also reflected in multiple other genomic properties, such as a substantial decrease in the fraction of regulatory genes below the transition and higher evolvability for new metabolic phenotypes above the transition. Furthermore, we find that the distribution of bacterial genome sizes from unbiased environmental metagenomic sequencing also reflects genomic clusters corresponding to the observed transition. Overall, our work identifies two qualitatively different regimes in microbial metabolism and lifestyles characterized by distinct structural and functional properties of their metabolic networks.

systems biology↗

bioRxiv2025

Integrated model of the protein molecular clock across mammalian species

One of the foundational concepts in molecular evolution is the protein molecular clock [1-3], encapsulating the observation that proteins tend to accumulate amino acid substitutions at approximately constant and protein-specific rates over long evolutionary timescales. According to the neutral theory of molecular evolution, the majority of protein substitutions have neutral or nearly neutral effects on species fitness. The neutral theory suggests the existence of a strong generation time effect, positing that species with shorter generation times should accumulate protein substitutions faster per unit time compared to species with longer generation times. However, earlier studies failed to detect a significant generation time effect for amino acid substitutions in vertebrates, raising questions about core tenets of the neutral theory. In this study, we take advantage of the recently accumulated evolutionary and genomics data to investigate the scaling of substitution rates with generation time among many dozens of mammalian species and thousands of proteins. Our findings reveal a strong generation time effect both for synonymous and non-synonymous substitutions. We demonstrate that the empirically observed generation time effect is fully consistent with several mechanisms, such as functional and mutation rate selection. Building on these results, we develop an integrated model of protein evolution that incorporates changes in substitution rates due to variation in species generation times throughout their evolutionary history. Overall, our analysis shows that protein evolution is consistent with neutral or nearly neutral theories and explains time-like behavior of the protein molecular clock across deep phylogenetic lineages.

evolutionary biology↗

bioRxiv2024

Ecological Scaling of Temporal Fluctuations with Bacterial Abundance in Gut Microbiota Depends on Functional Properties of Individual Microbial Species and Bacterial Communities

Macroecological relationships that describe various statistical associations between species abundances, their spatial, and temporal variability are among the most general laws in ecology and biology. One of the most commonly observed relationships is a power-law scaling between means and variances of temporal species abundances, known in ecology as Taylors law. Taylors law has been observed across many ecosystems, from diverse plant and animal ecosystems to complex microbial communities. While many mathematical models have been proposed to explain the potential origins of Taylors law, what determines its scaling exponents across species and ecosystems is not understood. Here, we use temporal trajectories of human and baboon gut microbiota to analyze the relationship between functional properties of individual bacterial species and microbial communities with the scaling of species-specific and community-level Taylors law. The species Taylor law characterizes - for each individual species - the relationship between the species temporal abundance means and temporal abundance variances across host organisms. On the other hand, community-level Taylors law characterizes - in each host organism - the scaling across multiple species between their temporal abundance means and temporal abundance variances. For community Taylors law, we find that the power law scaling is strongly associated with the microbiota abundance of certain nutrient-degrading enzymes in the gut. Notably, our results demonstrate that the availability of enzymes metabolizing starch glycogen significantly increases Taylors law scaling. We also find that species Taylors law depends on the individual species functional properties. Specifically, we observe lower Taylors law scaling for species with larger metabolic networks, for species that are able to grow on a larger number of carbon sources, and for species with particular metabolic functions, such as glutamine and folate metabolism. Overall, our study reveals that Taylors law scaling is strongly associated with the functional capabilities of bacterial communities and individual microbial species biosynthetic properties, which are likely related to their ecological roles in the gut microbiota.

microbiology↗

bioRxiv2024

Functional optimization in distinct tissues and conditions constrains the rate of protein evolution

Understanding the main determinants of protein evolution is a fundamental challenge in biology. Despite many decades of active research, the molecular and cellular mechanisms underlying the substantial variability of evolutionary rates across cellular proteins are not currently well understood. It also remains unclear how protein molecular function is optimized in the context of multicellular species and why many proteins, such as enzymes, are only moderately efficient on average. Our analysis of genomics and functional datasets reveals in multiple organisms a strong inverse relationship between the optimality of protein molecular function and the rate of protein evolution. Furthermore, we find that highly expressed proteins tend to be substantially more functionally optimized. These results suggest that cellular expression costs lead to more pronounced functional optimization of abundant proteins, and that the purifying selection to maintain high levels of functional optimality significantly slows protein evolution. We observe that in multicellular species both the rate of protein evolution and the degree of protein functional efficiency are primarily affected by expression in several distinct cell types and tissues. Specifically, in developed neurons with upregulated synaptic processes in animals and in young and fast-growing tissues in plants. Overall, our analysis reveals how various constraints from the molecular, cellular, and species levels of biological organization jointly affect the rate of protein evolution and the level of protein functional adaptation.

evolutionary biology↗

bioRxiv2022

Cancer tissue of origin constrains the growth and metabolism of metastases

Metastases arise from a subset of cancer cells that disseminate from the primary tumor; however, the factors that contribute to proliferation of cancer cells in a secondary site are incompletely understood. The ability of cancer cells to thrive in a new tissue site is influenced by genetic and epigenetic changes that are important for disease initiation and progression, but these factors alone do not predict if and where cancers metastasize. Specific cancer types metastasize to consistent subsets of tissues, suggesting that factors associated with the primary tumor influence the tissue environments where cancers can grow. Using pancreatic cancer as a model, we find that primary and metastatic tumors share many metabolic similarities and that the tumor initiating capacity and proliferation of both primary- and metastasis-derived cells is favored in the primary site relative to the metastatic site. Moreover, propagating lung or liver metastatic cells in vivo to enrich for tumor cells adapted to grow in the lung or the liver does not enhance their relative ability to form large tumors in those sites, change their preference to grow in the primary site, nor stably alter some aspects of their metabolism relative to primary tumors. We also analyzed primary liver and lung cancer cells and find that these cells also exhibit a preference to grow in their primary site relative to metastatic sites. Together, these data suggest that the cancer tissue-of-origin influences the metabolism of both primary and metastatic tumors and may impact whether cancer cells can thrive in a metastatic site.

cancer biology↗

bioRxiv2021

Multiple macroecological laws do characterize various aspects of microbiota dynamics

In this letter we address the potential confusion related to our recent demonstration that multiple macroecological laws describe short- and long-term dynamics of microbial communities. Specifically, we clarify that these laws, similarly to many other relationships observed in nature, are characterized not just by the existence of scaling, but also by certain characteristic values of the scaling exponents. By performing proper statistical analysis, we demonstrate that the relationships sensitive to temporal bacterial dynamics are not reproduced in the shuffled data. We also discuss that there is no clear evidence in the data that macroecological relationships in microbiota are primarily driven by external or environmental factors. Proper statistical analyses of the data suggest that the dynamics of gut microbiota, even on a constant diet, contains rich temporal structure. Therefore, it is likely that complex and non-linear internal dynamics may be primarily responsible for the observed macroecological laws in microbiota and other ecological communities.

microbiology↗

bioRxiv2020

Cancer cells depend on environmental lipids for proliferation when electron acceptors are limited

It is not well understood how physiological environmental conditions and nutrient availability influence cancer cell proliferation. Production of oxidized biomass, which requires regeneration of the cofactor NAD+, can limit cancer cell proliferation1-5. However, it is currently unclear which specific metabolic processes are constrained by electron acceptor availability, and how they affect cell proliferation. Here, we use computational and experimental approaches to demonstrate that de novo lipid biosynthesis can impose an increased demand for NAD+ in proliferating cancer cells. While some cancer cells and tumors synthesize a substantial fraction of their lipids de novo6, we find that environmental lipids are crucial for proliferation in hypoxia or when the mitochondrial electron transport chain is inhibited. Surprisingly, we also find that even the reductive glutamine carboxylation pathway to produce fatty acids is impaired when cancer cells are limited for NAD+. Furthermore, gene expression analysis of 34 heterogeneous tumor types shows that lipid biosynthesis is strongly and consistently negatively correlated with hypoxia, whereas expression of genes involved in lipid uptake is positively correlated with hypoxia. These results demonstrate that electron acceptor availability and access to environmental lipids can play an important role in determining whether cancer cells engage in de novo lipogenesis to support proliferation.

cancer biology↗

bioRxiv2019

Ability of the misfolding avoidance hypothesis to explain protein evolutionary rates in the light of empirical evidence

For more than a decade the misfolding avoidance hypothesis (MAH) and related theories have dominated evolutionary discussions aimed at explaining the variance of molecular clock across cellular proteins. In this study we use various experimental data to further investigate the consistency of the MAH predictions with empirical evidence. We also critically discuss experimental results that motivated the MAH development and that are often viewed as evidence of its major contribution to constraining protein evolution. We demonstrate, in Escherichia coli and Homo sapiens, the lack of a substantial negative correlation between protein evolutionary rates and Gibbs free energies of unfolding, a direct measure of protein stability. We then analyze multiple new genome-scale datasets describing protein aggregation and interaction propensities, which are likely optimized in evolution to alleviate deleterious effects associated with toxic protein misfolding and misinteractions. Our results demonstrate that the propensity of proteins to aggregate, the fraction of charged amino acids, and protein stickiness do correlate with protein abundances. Nevertheless, across multiple organisms and datasets we do not observe substantial correlations between proteins aggregation- and stability-related properties and evolutionary rates. Therefore, diverse empirical data support the conclusion that the MAH and similar hypotheses are unlikely to play a major role in mediating a strong negative correlation between protein expression and molecular clock, and thus in explaining the variability of evolutionary rates across cellular proteins. Significance statementEvolutionary rates vary substantially across cellular proteins. Understanding the nature of molecular clock and its variability across proteins is a foundational question in molecular evolution. The popular and currently dominant theory to explain the molecular clock variability is the misfolding avoidance hypothesis (MAH). The role of the MAH is currently under active debate. In the manuscript we discuss how to appropriately test the MAH based on available empirical data, and then rigorously test the hypothesis using more than a dozen of new genome-wide datasets of protein stability and aggregation propensities. Our results suggest that the MAH is unlikely to play a major role in explaining the variability of molecular clock across proteins.

evolutionary biology↗

bioRxiv2018

Macroecological relationships in the dynamics of gut microbiota

The gut microbiome is now widely recognized as a dynamic ecosystem that plays an important role in health and disease1. While current sequencing technologies make it possible to estimate relative abundances of host-associated bacteria over time2, 3, the biological processes governing their dynamics remain poorly understood. Therefore, as in other ecological systems4, 5, it is important to identify quantitative relationships describing global aspects of gut microbiota dynamics. Here we use multiple high-resolution time series data obtained from humans and mice6-8 to demonstrate that despite their inherent complexity, gut microbiota dynamics can be characterized by several robust scaling relationships. Interestingly, these patterns are highly similar to those previously observed across diverse ecological communities and economic systems, including the temporal fluctuations of animal and plant populations9-12 and the performance of publicly traded companies13. Specifically, we find power law relationships describing short- and long-term changes in gut microbiota abundances, species residence and return times, and the connection between the mean and variance of species abundances. The observed scaling relationships are altered in mice receiving different diets and affected by context-specific perturbations in humans. We use these macroecological relationships to reveal specific bacterial taxa whose dynamics are significantly affected by dietary and environmental changes. Overall, our results suggest that a quantitative macroecological framework will be important for characterizing and understanding complex dynamics of microbial communities.

microbiology↗

bioRxiv2018

Quantification of human gut microbiota variability using replicate sampling and spike-in sequencing

Metagenomic sequencing has enabled detailed investigation of diverse microbial communities, but understanding their spatiotemporal dynamics remains an important challenge. Here we present DIVERS, a widely applicable method based on replicate sampling and spike-in sequencing that quantifies the contributions of temporal dynamics, spatial sampling variability and technical noise to the variances and covariances of absolute bacterial abundances. Using high resolution time series profiling, we apply DIVERS to the human gut microbiome. Our method reveals complex spatiotemporal dynamics of individual gut bacteria and unmasks key features of their behavior hidden from previous analyses.

microbiology↗

bioRxiv2018

Intellectual phenotypes in autism strongly correlate with gene dosage changes and exon locations of truncating mutations

Autism spectrum disorders (ASD) are a group of related neurodevelopmental diseases displaying significant genetic and phenotypic heterogeneity1-4. Despite recent progress in understanding ASD genetics, the nature of phenotypic heterogeneity across probands remains unclear5, 6. Notably, likely gene-disrupting (LGD) de novo mutations affecting the same gene often result in substantially different ASD phenotypes. Nevertheless, we find that truncating mutations that affect the same exon frequently lead to strikingly similar intellectual phenotypes in unrelated ASD probands. Analogous patterns are observed for two independent proband cohorts and several other important ASD-associated phenotypes. We find that exons biased towards prenatal and postnatal expression preferentially contribute to ASD cases with lower and higher IQ phenotypes, respectively. These results suggest that exons, rather than genes, often represent a unit of effective phenotypic impact for truncating mutations in autism. The observed phenotypic effects are likely mediated by nonsense-mediated decay (NMD) of splicing isoforms, with autism phenotypes usually triggered by relatively mild (15-30%) decreases in overall gene dosage. We find that each gene with recurrent ASD mutations can be described by a parameter, phenotype dosage sensitivity (PDS), which characterizes the quantitative relationship between changes in a genes dosage and changes in a given disease phenotype. We further demonstrate analogous relationships between LGD mutations and changes in gene expression across human tissues. Therefore, similar phenotypic patterns may be also observed in multiple other systems and genetic disorders.

genetics↗

bioRxiv2017

Molecular function limits divergent protein evolution on planetary timescales

Functional conservation is known to constrain protein evolution. Nevertheless, the long-term divergence patterns of proteins maintaining the same molecular function and the possible limits of this divergence have not been explored in detail. We investigate these fundamental questions by characterizing the divergence between ancient protein orthologs with conserved molecular function. Our results demonstrate that the decline of sequence and structural similarities between such orthologs significantly slows down after ~1-2 billion years of independent evolution. As a result, their sequence and structural similarities have not substantially decreased for the past billion years. The effective divergence limit (>25% sequence identity) is not primarily due to protein sites universally conserved in all linages. Instead, less than four amino acid types are accepted, on average, per site in orthologs strictly conserving their molecular function. Our analysis also reveals different divergence patterns for protein sites with experimentally determined small and large fitness effects of mutations.

evolutionary biology↗

bioRxiv2017

Avoidance of toxic misfolding does not explain the sequence constrains of highly expressed proteins across organisms

The avoidance of cytotoxic effects associated with protein misfolding has been proposed as a dominant constraint on the sequence evolution and molecular clock of highly expressed proteins. Recently, Leuenberger et al. developed an elegant experimental approach to measure protein thermal stability at the proteome scale. The collected data allow us to rigorously test the predictions of the misfolding avoidance hypothesis that highly expressed proteins have evolved to be more stable, and that maintaining thermodynamic stability significantly constrains their evolution. Notably, careful re-analysis of the Leuenberger et al. data across four different organisms reveals no substantial correlation between protein stability and protein abundance. Therefore, the key predictions of the misfolding toxicity and related hypotheses are not supported by available empirical data. The data also suggest that, regardless of protein expression, protein stability does not substantially affect the protein molecular clock across organisms.

evolutionary biology↗

bioRxiv2017

Receptor-Based Mechanism of Relative Sensing in Mammalian Signaling Networks

Detecting relative rather than absolute changes in external signals enables cells to make decisions in fluctuating environments and diverse biological contexts. However, how mammalian signaling networks store the memories of past stimuli and use them to compute relative signals is not well understood. Using the growth factor-activated PI3K-Akt signaling pathway, we develop computational and analytical models, and experimentally validate a novel mechanism of relative sensing in mammalian cells. This non-transcriptional mechanism relies on a new form of cellular memory, where cells effectively encode past stimulation levels in the abundance of cognate receptors on the cell surface. We show the robustness and specificity of the relative sensing for two physiologically important ligands, epidermal growth factor (EGF) and hepatocyte growth factor (HGF), and across wide ranges of background stimuli. The described memory and sensing mechanism could play a role in multiple other sensory cascades where stimulation leads to a proportional reduction in the abundance of cell surface receptors.

systems biology↗