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Serohijos, A.

Publications and source records attributed to Serohijos, A..

16 recordsLinked to original sources

A strong-to-weak interaction shift during microbiome succession is coupled to colonizer-dependent antimicrobial resistance

The outcome of ecological succession is often attributed to the characteristics of the invader or the resident community, but rarely to how the community's interaction network reorganizes during assembly. Here, we track intraspecific lineage dynamics and infer time-resolved community interaction networks using Dynamic Covariance Mapping during ecological invasion of the mouse gut by a chromosomally barcoded, spectinomycin-resistant Escherichia coli K12 colonizer. The network is initially dominated by strong, predominantly inhibitory interactions, but as community diversity recovers, the distribution of interaction strengths contracts toward zero, producing a community increasingly dominated by weak and near-neutral interactions. The dominant eigenvalue of the DCM-inferred interaction matrix moves toward marginal stability predicted for dynamically assembling ecological networks. This pattern replicates across eight independent mice in two experimental cohorts, at both inter- and intra-species resolution. The ecological transition coincides with the reproducible resurgence of Paenibacillaceae to high relative abundance and persistent coexistence with E. coli under continued spectinomycin pressure. Whole-genome sequencing of recovered Paenibacillus macerans isolates identifies recurrent mutations in ribosomal protein S5 region associated with spectinomycin binding and strongly implicating this variant in resistance. Strikingly, under antibiotic pressure but without E. coli K12 invasion, resident Paenibacillaceae never blooms, indicating that expansion of the resistant population depends on the ecological context established by the colonizer. These findings show that gut microbiome succession is accompanied by a reproducible transition from strong toward weak interactions and link this network reorganization to the colonizer-dependent ecological benefit of antimicrobial resistance.

ecology↗

Protein Stability, Turnover Kinetics, and Abundance Constrain the Scaling of Protein Interaction Networks

The propensity of proteins to form oligomers is ultimately dictated by their structural configuration(s). Proteins that persist in a discrete conformational state may form a limited number of specific interactions while those that sample a broader structural ensemble may instead associate with a wider array of partners. These intrinsic tendencies potentially constrain the way proteins navigate wider interaction networks. In this work, we aggregated and surveyed a wide variety of biophysical, biochemical, and cellular descriptors of the S. cerevisiae proteome to identify biases in the connectivity of its protein-protein interaction network. Using mass spectrometry-based interactome measurements and various protein stability estimates, we find that a disproportionate number of abundant, yet unstable binding proteins act as network hubs. Moreover, we show that these features alone can be used to discriminate between hubs and non-hub proteins with high accuracy (AUROC = 0.898). Interestingly, we find that half-lives of hub proteins depend on whether or not they reside within static complexes and/ or whether they interact with molecular chaperones. Finally, we note that the observed connectivity biases associated with abundant, unstable proteins only pertain to network hubs, but not to the bottlenecks that connect them. Together, our findings reveal how the conformational stability of a protein may constrain its context within protein-protein interaction networks.

systems biology↗

ANYI: The ANnotated Yeast Interactome

Although several existing protein-protein interaction (PPI) databases provide yeast PPI data, none unify large-scale network topology information with detailed biophysical, proteostasis, and regulatory annotations in a single protein-centric framework. To address this gap, we developed the ANnotated Yeast Interactome (ANYI), an open, integrated resource that combines experimental yeast PPIs with sixteen feature annotation types, including protein abundance, half-life, disorder content, post-translational modifications, conformational stability, chaperone interactions, sequence, and structure. ANYI integrates 3,927 proteins with 155 annotation features, forming a unified matrix that enables systematic cross-layer analyses. Available via GitHub and Docker Hub with an interactive network browser for broad accessibility, ANYI provides both experienced and beginner computational scientists with tools to investigate the yeast interactome. For example, users can directly test whether highly connected hub proteins exhibit distinct stability, disorder, or proteostasis signatures relative to peripheral nodes. AVAILABILITY AND IMPLEMENTATIONThe code used to assemble ANYI is available on GitHub at https://github.com/NCEMS/energetic-origins-of-PPI-connectivity and the database itself and interactive browser tool are available on Docker Hub as dannissleypsu/anyi-browser:v1.0.2.

bioinformatics↗

Gene Interaction Network Architecture of Human Polygenic Traits Reveals Domain-Specific Connectivity and Evolutionary Pressures

Human polygenic phenotypes arise from complex interactions among genes within regulatory networks. To gain insights into the structural and evolutionary characteristics of these networks, we analyzed gene interaction networks across 4756 human polygenic phenotypes, contrasting the network properties of genes associated with polygenic phenotypes against those of non-associated genes. Our results indicate that genes associated with polygenic phenotypes exhibit a significantly higher connectivity in their corresponding gene-interaction networks, with connectivity varying markedly across different trait domains. Notably, genes within highly connected networks are enriched for immune-related biological processes, whereas genes in less connected networks are more involved in neurogenesis. Furthermore, we found that genes embedded in highly connected networks are, on average, under weaker selective constraints than those in lowly connected networks. Overall, our findings provide a systematic analysis of gene interaction networks underlying human polygenic traits and reveal how selective constraints vary with network connectivity. These insights offer a framework for prioritizing genes based on their connectivity within trait-associated networks. To support this effort, we developed the online portal http://netpolygen.com enabling researchers to generate and explore hypotheses by identifying genes that are both highly associated and highly connected across thousands of polygenic phenotypes.

evolutionary biology↗

HP1-enhanced chromatin compaction stabilizes a synthetic metabolic circuit in yeast

Chromatin compaction defines genome topology, evolution, and function. The Saccharomycotina subphylum, including the fermenting yeast Saccharomyces cerevisiae have a decompacted genome, possibly because they lost two genes mediating a specific histone lysine methylation and histone binding protein heterochromatin protein 1 (HP1). This decompaction may result in the higher-than-expected mutation and meiotic recombination rates observed in this species. To test this hypothesis, we retro-engineered S. cerevisiae to compact the genome by expressing the HP1 homologue of Schizosaccharomyces pombe SpSwi6 and H3K9 methyltransferase SpClr4. The resulting strain had significantly more compact chromatin and reduced rates of mutation and meiotic recombination. The increased genomic stability significantly prolongs the optogenetic control of an engineered strain designed to grow only in blue light. This result demonstrates the potential of our approach to enhance the stability of strains for metabolic engineering and other synthetic biology applications, which are prone to lose activities due to genetic instability.

synthetic biology↗

Doblin: Inferring dominant clonal lineages from DNA barcoding time-series

MotivationThe lineage dynamics and history of cells in a population reflect the interplay of evolutionary forces they experience, namely mutation, drift, and selection. When the population is polyclonal, lineage dynamics also manifest the extent of clonal competition among co-existing mutational variants. If the population exists in a community of other species, the lineage dynamics could also reflect the populations ecological interaction with the rest of the community. Recent advances in high-resolution lineage tracking via DNA barcoding, coupled with next-generation sequencing of bacteria, yeast, and mammalian cells, allow for precise quantification of clonal dynamics in these organisms. ResultsIn this work, we introduce Doblin, an R suite for identifying dominant barcode lineages based on high-resolution lineage tracking data. We first benchmarked Doblins accuracy using lineage data from evolutionary simulations, showing that it recovers clones identity and relative fitness in the simulation. Subsequently, we applied Doblin to analyze clonal dynamics in laboratory evolutions of E. coli populations undergoing antibiotic treatment and in colonization experiments of the gut microbial community. Doblins versatility allows it to be applied to lineage time-series data across different experimental setups. Availability and implementationThe source code and data are available at https://github.com/dagagf/doblin.

bioinformatics↗

Non-consecutive enzyme interactions within TCA cycle supramolecular assembly regulate carbon-nitrogen metabolism

Enzymes of the core energy metabolism pathways tend to assemble into transient supramolecular complexes, yet the functional significance of the interactions within these complexes, particularly between enzymes catalyzing non-consecutive reactions, remains unclear. Here, by co-localizing two non-consecutive enzymes of the TCA cycle from B. subtilis, malate dehydrogenase (MDH) and isocitrate dehydrogenase (ICD), in highly crowded liquid-liquid phase separated droplets we discovered that MDH-ICD interaction causes an enhancement of ICD catalytic rate and an apparent sequestration of its reaction product, 2-oxoglutarate. Theory suggests that the observed phenomena are explained by the MDH-mediating clustering of ICD molecules. In vivo validation with targeted GC-MS and 13C tracer analyses revealed that when B. subtilis is grown on glucose and ammonia, overexpression of MDH leads to accumulation of 2-oxoglutarate with a concomitant reduction of fluxes flowing through both the catabolic and anabolic branches of the carbon-nitrogen intersection occupied by 2-oxoglutarate, resulting in impeded ammonium assimilation and reduced biomass production. Our findings thus suggest that in B. subtilis the MDH-ICD interaction is an important coordinator of carbon-nitrogen metabolism, thereby expanding the list of types of functionally understood unconventional enzyme-enzyme interactions.

microbiology↗

The fitness effects of codon composition of the horizontally transferred antibiotic resistance genes intensify at sub-lethal antibiotic levels.

The rampant variability in codon bias existing between bacterial genomes is expected to interfere with horizontal gene transfer (HGT), a phenomenon that drives bacterial adaptation. However, delineating the constraints imposed by codon bias on functional integration of the transferred genes is complicated by multiple genomic and functional barriers controlling HGT, and by the dependence of the evolutionary outcomes of HGT on the hosts environment. Here, we designed an experimental system in which codon composition of the transferred genes is the only variable triggering fitness change of the host. We replaced E. colis chromosomal folA gene encoding dihydrofolate reductase, an essential enzyme that constitutes a target for trimethoprim, with combinatorial libraries of synonymous codons of folA genes from trimethoprim-sensitive Listeria grayi and trimethoprim-resistant Neisseria sicca. The resulting populations underwent selection at a range of trimethoprim concentrations, and the ensuing changes in variant frequencies were used to infer the fitness effects of the individual combinations of codons. We found that when HGT causes overstabilization of the 5-end mRNA, the fitness contribution of mRNA folding stability dominates over that of codon optimality. The 5-end overstabilization can also lead to mRNA accumulation outside of the polysome, thus preventing the decay of the foreign transcripts despite the codon composition-driven reduction in translation efficiency. Importantly, the fitness effects of mRNA stability or codon optimality become apparent only at sub-lethal levels of trimethoprim individually tailored for each library, emphasizing the central role of the hosts environment in shaping the codon bias compatibility of horizontally transferred genes.

microbiology↗

Intra- and inter-species interactions drive early phases of invasion in mice gut microbiota

The stability and dynamics of ecological communities are dictated by interaction networks typically quantified at the level of species.1-10 But how such networks are influenced by intra-species variation (ISV) is poorly understood.11-14 Here, we use ~500,000 chromosomal barcodes to track high-resolution intra-species clonal lineages of Escherichia coli invading mice gut with the increasing complexity of gut microbiome: germ-free, antibiotic-perturbed, and innate microbiota. By co-clustering the dynamics of intra-species clonal lineages and those of gut bacteria from 16S rRNA profiling, we show the emergence of complex time-dependent interactions between E. coli clones and resident gut bacteria. With a new approach, dynamic covariance mapping (DCM), we differentiate three phases of invasion in susceptible communities: 1) initial loss of community stability as E. coli enters; 2) recolonization of some gut bacteria; and 3) recovery of stability with E. coli coexisting with resident bacteria in a quasi-steady state. Comparison of the dynamics, stability and fitness from experimental replicates and different cohorts suggest that phase 1 is driven by mutations in E. coli before colonization, while phase 3 is by de novo mutations. Our results highlight the transient nature of interaction networks in microbiomes driven by the persistent coupling of ecological and evolutionary dynamics. One-Sentence SummaryHigh-resolution lineage tracking and dynamic covariance mapping (DCM) define three distinct phases during early gut microbiome invasion.

evolutionary biology↗

Pangenome sequence evolution within human gut microbiomes is explained by gene-specific rather than host-specific selective pressures

Pangenomes - the cumulative set of genes encoded by a species - arise from evolutionary forces including horizontal gene transfer (HGT), drift, and selection. The relative importance of drift and selection in shaping pangenome structure has been recently debated, and the role of sequence evolution (point mutations) within mobile genes has been largely ignored, with studies focusing mainly on patterns of gene presence or absence. The effects of drift, selection, and HGT on pangenome evolution likely depends on the time scale being studied, ranging from ancient (e.g., between distantly related species) to recent (e.g., within a single animal host), and the unit of selection being considered (e.g., the gene, whole genome, microbial species, or human host). To shed light on pangenome evolution within microbiomes on relatively recent time scales, we investigate the selective pressures acting on mobile genes using a dataset that previously identified such genes in the gut metagenomes of 176 Fiji islanders. We mapped the metagenomic reads to mobile genes to call single nucleotide variants (SNVs) and calculate population genetic metrics that allowed us to infer deviations from a neutral evolutionary model. We found that mobile gene sequence evolution varied more by gene family than by human social attributes, such as household or village membership, suggesting that selection at the level of gene function is most relevant on these short time scales. Patterns of mobile gene sequence evolution could be qualitatively recapitulated with a simple evolutionary simulation, without the need to invoke an adaptive advantage of mobile genes to their bacterial host genome. This suggests that, at least on short time scales, a majority of the pangenome need not be adaptive. On the other hand, a subset of gene functions including defense mechanisms and secondary metabolism showed an aberrant pattern of molecular evolution, consistent with species-specific selective pressures or negative frequency-dependent selection not seen in prophages, transposons, or other gene categories. That mobile genes of different functions behave so differently suggests stronger selection at the gene level, rather than at the genome level. While pangenomes may be largely adaptive to their bacterial hosts on longer evolution time scales, here we show that, on shorter "human" time scales, drift and gene-specific selection predominate.

microbiology↗

RNASeq analysis identifies non-canonical role of STAT2 and IRF9 in the regulation of a STAT1-independent antiviral and immunoregulatory transcriptional program induced by IFNβ and TNFα

IFN{beta} typically induces an antiviral and immunoregulatory transcriptional program through the activation of ISGF3 (STAT1, STAT2 and IRF9) transcriptional complexes. The response to IFN{beta} is context-dependent and is prone to crosstalk with other cytokines, such as TNF IFN{beta} and TNF synergize to drive a specific delayed transcriptional program. Previous observation led to the hypothesis that an alternative STAT1-independent pathway involving STAT2 and IRF9 might be involved in gene induction by the combination of IFN{beta} and TNF. Using genome wide transcriptional profiling by RNASeq, we found that the costimulation with IFN{beta} and TNF induces a broad antiviral and immunoregulatory transcriptional program independently of STAT1. Additionally, STAT2 and IRF9 are involved in the regulation of only a subset of these STAT1-independent genes. Consistent with the growing literature, STAT2 and IRF9 act in concert to regulate a subgroup of these genes. Unexpectedly, STAT2 and IRF9 were also engaged in specific independent pathways to regulate distinct sets of IFN{beta} and TNF-induced genes. Altogether these observations highlight the existence of distinct previously unrecognized non-canonical STAT1-independent, but STAT2 and/or IRF9-dependent pathways in the establishment of a delayed antiviral and immunoregulatory transcriptional program in conditions where elevated levels of both IFN{beta} and TNF are present.

immunology↗

SodaPop: A computational suite for simulating the dynamics of asexual populations

MotivationSimulating protein evolution with realistic constraints from population genetics is essential in addressing problems in molecular evolution, from understanding the forces shaping the evolutionary landscape to the clinical challenges of antibiotic resistance, viral evolution and cancer.\n\nResultsTo address this need, we present SodaPop, a new forward-time simulator of large asexual populations aimed at studying their structure, dynamics and the distribution of fitness effects with flexible assumptions on the fitness landscape. SodaPop integrates biochemical and biophysical properties in a cell-based, object-oriented framework and provides an efficient, open-source toolkit for performing large-scale simulations of protein evolution.\n\nAvailability and implementationSource code and binaries are freely available at https://github.com/louisgt/SodaPop under the GNU GPLv3 license. The software is implemented in C++ and supported on Linux, Mac OS/X and Windows.\n\nContactadrian.serohijos@umontreal.ca\n\nSupplementary informationSupplementary information is available on the Github project page.

bioinformatics↗

Inferring the selection window in antimicrobial resistance using deep mutational scanning data and biophysics-based fitness models

Mutant-selection window (MSW) hypothesis in antimicrobial resistance implies a range for antimicrobial concentration that promotes selection of single-step resistant mutants. Since the inception and experimental verification, MSW has been at the forefront of strategies to minimize development of antimicrobial resistance (AR). Setting the upper and lower limits of MSW requires an understanding of the dependence of selection coefficient of arising mutations to antimicrobial concentration. In this work, we employed a biophysics-based and experimentally calibrated fitness model to estimate MSW in the case of Ampicillin and Cefotaxime resistance in E.coli TEM-1 beta lactamase. In line with experimental observations, we show that selection is active at very low levels of antimicrobials. Furthermore, we elucidate the dependence of MSW to catalytic efficiency of mutants, fraction of mutants in the population and discuss the role of population genetic parameters such as population size and mutation rate. Altogether, our analysis and formalism provide a predictive model of MSW with direct implications in the design of dosage strategies.

evolutionary biology↗

Highly expressed genes evolve under strong epistasis from a proteome-wide scan in E. coli

Epistasis or the non-additivity of mutational effects is a major force in protein evolution, but it has not been systematically quantified at the level of a proteome. Here, we estimated the extent of epistasis for 2,382 genes in E. coli using several hundreds of orthologs for each gene within the class Gammaproteobacteria. We found that the average epistasis is ~41% across genes in the proteome and that epistasis is stronger among highly expressed genes. This trend is quantitatively explained by the prevailing model of sequence evolution based on minimizing the fitness cost of protein unfolding and aggregation. Our results highlight the coupling between selection and epistasis in the long-term evolution of a proteome.

evolutionary biology↗

Estimating The Contribution Of Folding Stability To Non-Specific Epistasis In Protein Evolution

The extent of non-additive interaction among mutations or epistasis reflects the ruggedness of the fitness landscape, the mapping of genotype to reproductive fitness. In protein evolution, there is strong support for the importance and prevalence of epistasis, but whether there is a universal mechanism behind epistasis remains unknown. It is also unclear which of the biophysical properties of proteins--folding stability, activity, binding affinity, and dynamics--have the strongest contribution to epistasis. Here, we determine the contribution of selection for folding stability to epistasis in protein evolution. By combining theoretical estimates of the rates of molecular evolution and protein folding thermodynamics, we show that simple selection for folding stability implies that at least ~30% to ~60% of among amino acid substitutions would have experienced epistasis. Additionally, our model predicts substantial epistasis at marginal stabilities therefore linking epistasis to the strength of selection. Estimating the contribution of governing factors in molecular evolution such as protein folding stability to epistasis will provide a better understanding of epistasis that could improve methods in molecular evolution.

genetics↗

Tuning the course of evolution on the biophysical fitness landscape of an RNA virus

Predicting viral evolution remains a major challenge with profound implications for public health. Viral evolutionary pathways are determined by the fitness landscape, which maps viral genotype to fitness. However, a quantitative description of the landscape and the evolutionary forces on it remain elusive. Here, we apply a biophysical fitness model based on capsid folding stability and antibody binding affinity to predict the evolutionary pathway of norovirus escaping a neutralizing antibody. The model is validated by experimental evolution in bulk culture and in a drop-based microfluidics device, the \"Evolution Chip\", which propagates millions of independent viral sub-populations. We demonstrate that along the axis of binding affinity, selection for escape variants and drift due to random mutations have the same direction. However, along folding stability, selection and drift are opposing forces whose balance is tuned by viral population size. Our results demonstrate that predictable epistatic tradeoffs shape viral evolution.

evolutionary biology↗