Biology subjects

Plata, G.

Publications and source records attributed to Plata, G..

8 recordsLinked to original sources

bioRxiv2025

Niche dimensionality drives microbial community structure

Niche dimensionality links environmental complexity to ecosystem structure. Although niche theory is often invoked in investigations of microbiomes, most models assume very high-dimensional coexistence, effectively sidestepping the role of dimensionality. However, direct estimates of niche dimensionality of microbiome have been lacking. Here we use joint species distribution modeling (JSDM) to infer niche dimensionality from relative abundance data alone. In paired 16S rRNA-metabolomics datasets, inferred dimensionality closely tracked the complexity of the metabolic environment, validating our abundance-only approach. Across nearly 200 human gut microbiome studies, lower dimensionality coincided with greater inter-species competition (metabolic niche overlap) and reduced biodiversity, consistent with the macroecological niche dimensionality hypothesis. Consumer/resource simulations reproduced these empirical relationships when both species-intrinsic metabolic tradeoffs and species-extrinsic environmental tradeoffs were imposed, with the latter dominating. Dimensionality was reduced in stressed or diet-simplified microbiomes and correlated broadly with ecological stress markers; negatively with prevalence of generalists and positively with microbial load. Together, these results establish niche dimensionality as a measurable and previously overlooked driver of microbial community structure.

ecology↗

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↗

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

SMbiot: A Shared Latent Model for Microbiomes and their Hosts

The collective nature of the variation in host associated microbial communities suggest that they exhibit low dimensional characteristics. To identify these lower dimensional descriptors, we propose SMbiot (pronounced SIM BY OT): a Shared Latent Model for Microbiomes and their hosts. In SMbiot, latent variables embed host-specific microbial communities in a lower dimensional space and the corresponding features reflect controlling axes that dictate community compositions. Using data from different animal hosts, organ sites, and microbial kingdoms of life, we show that SMbiot identifies a small number of host-specific latent variables that accurately capture the compositional variation in host associated microbial communities. By using the same latents to describe hosts phenotypic states and the host-associated microbiomes, we show that the latent space embedding is informed by host physiology as well as the associated microbiomes. Importantly, SMbiot enables the quantification of host phenotypic differences associated with altered microbial community compositions in a host-specific manner, underscoring the context specificity of host-microbiome associations. SMbiot can also predict missing host metadata or microbial community compositions. This way, SMbiot is a concise quantitative method to understand the low dimensional collective behavior of host-associated microbiomes.

systems biology↗

bioRxiv2022

Interactions between time on diet, antibiotic treatment, and liver abscess development on the fecal microbiome of beef cattle

Liver abscesses caused by polymicrobial infections of the liver are a widespread problem in feedlot cattle production. There are currently no effective methods for the early detection of liver abscesses or to predict antibiotic efficacy. Although gene expression and microbiome differences have been reported in the rumen of abscessed and normal animals, liver abscess biomarkers using less invasive tools can facilitate managing of the disease in the field. Here we report the results of two studies measuring the fecal microbiome composition of steers that did or did not develop liver abscesses, with or without antibiotic treatment, along a 7-month feeding period on a high-concentrate diet. Our results indicate a limited impact of liver abscesses or tylosin on fecal microbiome composition, with time on diet explaining most variance in the fecal microbiome. Interestingly, in both studies, antibiotic treatment led to larger differences in both the composition and variance of the fecal microbiomes between abscessed and normal animals compared to controls. These differences were limited to specific sampling times in each of the two studies. Although multiple amplicon sequence variants with differential abundances according to liver abscess state were identified, there was no overlap between the two studies. Our results suggests that early fecal biomarkers of liver abscess susceptibility might be developed, especially for animals receiving preventative antibiotics, but the fecal abundance of individual microorganisms may not be a robust predictor across different sampling times or diet regimes.

microbiology↗

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↗

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↗