Biology subjects

Scarpino, S. V.

Publications and source records attributed to Scarpino, S. V..

8 recordsLinked to original sources

bioRxiv2024

Rates of evolution differ between cell types identified by single-cell RNAseq in threespine stickleback

Rates of evolutionary change vary by gene. While some broad gene categories are highly conserved with little divergence over time, others undergo continuous selection pressure and are highly divergent. Here, we combine single-cell RNA sequencing (scRNAseq) with evolutionary genomics to understand whether certain cell types exhibit faster evolutionary divergence (using their characteristic genes), than other types of cells. Merging scRNAseq with population genomic data, we show that cell types differ in the rate at which their characteristic genes evolve, as measured by allele frequency divergence among many populations (FST) and between species (dN/dS ratios). Neutrophils, B cells, and fibroblasts exhibit elevated FST at characteristic genes, while eosinophils in the intestine and thrombocytes in the head kidney exhibit lower FST than the average for 1000 random genes. Gene network centrality also differed between immune- and non-immune-associated genes, and closeness centrality was positively related to gene FST. These results highlight the value of merging single cell RNA sequencing technology with evolutionary population genomic data, and reveal that genes which define immune cell types exhibit especially rapid evolution.

evolutionary biology↗

bioRxiv2021

A family of fitness landscapes modeled through gene regulatory networks

Over 100 years, Fitness landscapes have been a powerful metaphor for understanding the evolution of biological systems. These landscapes describe how genotypes are connected to each other and are related according to relative fitness. Despite the high dimensionality of such real-world landscapes, empirical studies are often limited in their ability to quantify the fitness of different genotypes beyond point mutations, while theoretical works attempt statistical/mechanistic models to reason the overall landscape structure. However, most classical fitness landscape models overlook an instinctive constraint that genotypes leading to the same phenotype almost certainly share the same fitness value, since the information of genotype-phenotype mapping is rarely incorporated. Here, we investigate fitness landscape models through the lens of Gene Regulatory Networks (GRNs), where the regulatory products are computed from multiple genes and collectively treated as the phenotypes. With the assumption that regulatory mediators/products exhibit binary states, we prove topographical features of GRN fitness landscape models such as accessibility and connectivity insensitive to the choice of the fitness function. Furthermore, using graph theory, we deduce a mesoscopic structure underlying GRN fitness landscape models that retains necessary information for evolutionary dynamics with minimal complexity. We also propose an algorithm to construct such a mesoscopic backbone which is more efficient than the brute-force approach. Combined, this work provides mathematical implications for fitness landscape models with high-dimensional genotype-phenotype mapping, yielding the potential to elucidate empirical landscapes and their resulting evolutionary processes in a manner complementary to existing computational studies.

evolutionary biology↗

bioRxiv2021

The Population Genetics of Gene Regulatory Networks

The evolution of diverse phenotypes both involves and is constrained by molecular interaction networks. When these networks influence patterns of expression, we refer to them as gene regulatory networks (GRNs). Here, we develop a quasi-species model of GRN evolution. With this model, we prove that-across a broad spectrum of viability and mutation functions-the dynamics converge to a stationary distribution over GRNs. Next, we show from first principles how the frequency of GRNs at equilibrium will be proportional to each GRNs eigenvector centrality in the genotype network. Finally, we determine the structural characteristics of GRNs that are favored in response to a range of selective environments and mutational constraints. Our work connects GRN evolution to quasi-species models, and thus can provide a mechanistic explanation for the topology of GRNs experiencing various evolutionary forces.

evolutionary biology↗

bioRxiv2020

Reproductive Barriers as a Byproduct of Gene Network Evolution

Molecular analyses of closely related taxa have increasingly revealed the importance of higher-order genetic interactions in explaining the observed pattern of reproductive isolation between populations. Indeed, both empirical and theoretical studies have linked the process of speciation to complex genetic interactions. Gene Regulatory Networks (GRNs) capture the inter-dependencies of gene expression and encode information about an individuals phenotype and development at the molecular level. As a result, GRNs can-in principle-evolve via natural selection and play a role in non-selective, evolutionary forces. Here, we develop a network-based model, termed the pathway framework, that considers GRNs as a functional representation of coding sequences. We then simulated the dynamics of GRNs using a simple model that included natural selection, genetic drift, and sexual reproduction and found that reproductive barriers can develop rapidly between allopatric populations experiencing identical selection pressure. Further, we show that alleles involved in reproductive isolation can predate the allopatric separation of populations and that the number of interacting loci involved in genetic incompatibilities, i.e., the order, is often high simply as a by-product of the networked structure of GRNs. Finally, we discuss how results from the pathway framework are consistent with observed empirical patterns for genes putatively involved in post-zygotic isolation. Taken together, this study adds support for the central role of gene networks in speciation and in evolution more broadly.

evolutionary biology↗

bioRxiv2019

Google searches accurately forecast RSV hospitalizations

BackgroundHospitalization of children with respiratory syncytial virus (RSV) is common and costly. Traditional sources of hospitalization data, useful for public health decision-makers and physicians to make decisions, are themselves costly to acquire and are subject to delays from gathering to publication. Here we use Google searches for RSV as a proxy for RSV hospitalizations.\n\nMethodsSearches for \"RSV\" and numbers of RSV hospitalizations in WA, MD, FL, and CT were examined from 2004-2018. Running correlation coefficients and phase angles between search and hospitalizations were calculated. Various machine learning models were compared to assess the ability of searches to forecast hospitalizations. Using search data from all 50 US states, we use K-means clustering to identify RSV transmission clusters. We calculate the timing of the optimal timing of RSV prophylaxis initiation as the week beginning the 24-week period covering 95% of all RSV cases.\n\nResultsHigh correlations (> 0.95) and low phase differences were seen between counts of hospitalizations and search volume in WA, MD, FL, and CT. Searching for RSV began in FL and radiated outward and three distinct transmission clusters were identified: the south and northeast, the northwest and Appalachia, and the center of the country. Calculated initiation dates for prophylaxis closely followed those calculated using traditional data sources (correlation = 0.84).\n\nConclusionsThis work validates searches as a proxy for RSV hospitalizations. Search query surveillance of RSV is a rapid and no-cost addition to traditional RSV hospitalization surveillance and may be useful for medical and public health decision-making.

epidemiology↗

bioRxiv2017

Automated collection of pathogen-specific diagnostic data for real-time syndromic epidemiological studies

Health-care and public health professionals rely on accurate, real-time monitoring of infectious diseases for outbreak preparedness and response. Early detection of outbreaks is improved by systems that are pathogen-specific. We describe a system, FilmArray(R) Trend, for rapid disease reporting that is syndrome-based but pathogen-specific. Results from a multiplex molecular diagnostic test are sent directly to a cloud database. www.syndromictrends.com presents these data in near real-time. Trend preserves patient privacy by removing or obfuscating patient identifiers. We summarize the respiratory pathogen results, for 20 organisms from 344,000 patient samples acquired as standard of care testing over the last four years from 20 clinical laboratories in the United States. The majority of pathogens show influenza-like seasonality, rhinovirus has fall and spring peaks and adenovirus and bacterial pathogens show constant detection over the year. Interestingly, the rate of pathogen co-detections, on average 7.7%, matches predictions based on the relative abundance of organisms present.

epidemiology↗

bioRxiv2016

The risk of sustained sexual transmission of Zika is underestimated

Pathogens can follow more than one transmission route during outbreaks - from needle sharing plus sexual transmission of HIV to small droplet aerosol plus fomite transmission of influenza. Thus, controlling an infectious disease outbreak often requires characterizing the risk associated with multiple mechanisms of transmission. For example, during the Ebola virus outbreak in West Africa, weighing the relative importance of funeral versus health care worker transmission was essential to stopping disease spread [1]. Strategic policy decisions regarding interventions must rely on accurately characterizing risks associated with multiple transmission routes. The ongoing Zika virus outbreak challenges our conventional methodologies for translating case-counts into route-specific transmission risk. Critically, most approaches will fail to accurately estimate the risk of seeing sustained sexual transmission of a pathogen that is primarily vectored by a mosquito - ...

epidemiology↗

bioRxiv2016

Topological features of gene regulatory networks predict patterns of natural diversity in environmental response

Molecular interactions affect the evolution of complex traits. For instance, adaptation may be constrained by pleiotropic or epistatic effects, both of which will be reflected in the structure of molecular interaction networks. To date, empirical studies investigating the role of molecular interactions in phenotypic evolution have been idiosyncratic, offering no clear patterns. Here, we investigated the network topology of genes putatively involved in local adaptation to two abiotic stressors--drought and cold--in Arabidopsis thaliana. Our findings suggest that the gene-interaction topologies for both cold and drought stress response are non-random, with genes that show genetic variation in drought response (GxE) being significantly more peripheral and cold response genes being significantly more central than genes not involved in either response. We suggest that the observed topologies reflect different constraints on the genetic pathways involved in the assayed phenotypes. The approach presented here may inform predictive models linking genetic variation in molecular signaling networks with phenotypic variation, specifically traits involved in environmental response.\n\nSignificance StatementOur study focuses on genes whose transcriptional activity exhibits genetic variation in response to the environment, or \"GxE.\" GxE is a widely observed phenomenon of critical importance to understanding the genotype-to-phenotype map, the evolution of natural populations, medical genetics, population response to climate change, and agricultural improvement. We investigated expression GxE in plant responses to two abiotic cues: cold and drought. We found that genes showing genetically variable response to cold stress are centrally located in regulatory networks whereas genes showing genetically variable response to drought stress are peripherally located in regulatory networks. This result suggests that selection is presented with vastly different mutational landscapes for shaping evolutionary or breeding response to these two important climatic factors

evolutionary biology↗