Biology subjects

Sulkow, B.

Publications and source records attributed to Sulkow, B..

4 recordsLinked to original sources

bioRxiv2021

Following the Trail of One Million Genomes: Footprints of SARS-CoV-2 Adaptation to Humans

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has accumulated genomic mutations at an approximately linear rate since it first infected human populations in late 2019. Controversies remain regarding the identity, proportion, and effects of adaptive mutations as SARS-CoV-2 evolves from a bat-to a human-adapted virus. The potential for vaccine-escape mutations poses additional challenges in pandemic control. Despite being of great interest to therapeutic and vaccine development, human-adaptive mutations in SARS-CoV-2 are masked by a genome-wide linkage disequilibrium under which neutral and even deleterious mutations can reach fixation by chance or through hitchhiking. Furthermore, genome-wide linkage equilibrium imposes clonal interference by which multiple adaptive mutations compete against one another. Informed by insights from microbial experimental evolution, we analyzed close to one million SARS-CoV-2 genomes sequenced during the first year of the COVID-19 pandemic and identified putative human-adaptive mutations according to the rates of synonymous and missense mutations, temporal linkage, and mutation recurrence. Furthermore, we developed a forward-evolution simulator with the realistic SARS-CoV-2 genome structure and base substitution probabilities able to predict viral genome diversity under neutral, background selection, and adaptive evolutionary models. We conclude that adaptive mutations have emerged early, rapidly, and constantly to dominate SARS-CoV-2 populations despite clonal interference and purifying selection. Our analysis underscores a need for genomic surveillance of mutation trajectories at the local level for early detection of adaptive and immune-escape variants. Putative human-adaptive mutations are over-represented in viral proteins interfering host immunity and binding host-cell receptors and thus may serve as priority targets for designing therapeutics and vaccines against human-adapted forms of SARS-CoV-2.

evolutionary biology↗

bioRxiv2020

Maximum Antigen Diversification in a Lyme Bacterial Population and Evolutionary Strategies to Overcome Pathogen Diversity

Natural populations of microbes and their hosts are engaged in an arms race in which microbes diversify to escape host immunity while hosts evolve novel immunity. This co-evolutionary process, known as the "Red Queen" hypothesis, poses a fundamental challenge to the development of broadly effective vaccines and diagnostics against a diversifying pathogen. Based on surveys of natural allele frequencies and experimental immunization of mice, we show minimal antigenic cross-reactivity among natural variants of the outer surface protein C (OspC), a dominant antigen of a Lyme Disease-causing bacterium (Borrelia burgdorferi). To overcome the challenge of OspC antigenic diversity to clinical development of preventive measures, we implemented a number of evolution-based strategies to broaden OspC immunological cross-reactivity. In particular, the centroid algorithm - a genetic algorithm to minimize sequence differences with natural variants - generated synthetic OspC analogs with the greatest promise as diagnostic and vaccine candidates against diverse Lyme pathogen strains coexisting in the Northeast United States. Mechanistically, we propose a model of runaway maximum antigen di-versification (MAD) mediated by amino-acid variations distributed across hypervariable regions on the OspC molecule. Under the MAD model, evolutionary centroids display high cross-reactivity by occupying the central void in the antigenic space excavated by diversifying natural variants. In contrast to the vaccine design based on concatenated epitopes, the centroid algorithm generates analogs of native antigens and is automated. The MAD model and evolution-inspired antigen designs have broad implications for combating diversifying pathogens driven by pathogen-host coevolution. ImportanceMicrobial pathogens rely on molecular diversity of cell surface antigens to escape host immunity. Vaccines based on one antigen variant often fail to protect the host against pathogens carrying other variants. Here we show evolution-based designs of synthetic antigens that are broadly reactive to all natural variants. The evolutionary analogs of a major surface antigen of a Lyme disease bacterium (Borrelia burgdorferi) showed promise as vaccine candidates against diverse pathogen strains coexisting in the endemic areas of Lyme disease in Northeast United States. Our evolution-based computational design is automated, generates molecular analogs of natural antigens, and opens a novel path to combating fast-evolving microbial pathogens.

microbiology↗

bioRxiv2020

CoV Genome Tracker: tracing genomic footprints of Covid-19 pandemic

SummaryGenome sequences constitute the primary evidence on the origin and spread of the 2019-2020 Covid-19 pandemic. Rapid comparative analysis of coronavirus SARS-CoV-2 genomes is critical for disease control, outbreak forecasting, and developing clinical interventions. CoV Genome Tracker is a web portal dedicated to trace Covid-19 outbreaks in real time using a haplotype network, an accurate and scalable representation of genomic changes in a rapidly evolving population. We resolve the direction of mutations by using a bat-associated genome as outgroup. At a broader evolutionary time scale, a companion browser provides gene-by-gene and codon-by-codon evolutionary rates to facilitate the search for molecular targets of clinical interventions. Availability and ImplementationCoV Genome Tracker is publicly available at http://cov.genometracker.org and updated weekly with the data downloaded from GISAID (http://gisaid.org). The website is implemented with a custom JavaScript script based on jQuery (https://jquery.com) and D3-force (https://github.com/d3/d3-force). Contactweigang@genectr.hunter.cuny.edu, City University of New York, Hunter College Supplementary InformationAll supporting scripts developed in JavaScript, Python, BASH, and PERL programming languages are available as Open Source at the GitHub repository https://github.com/weigangq/cov-browser.

evolutionary biology↗

bioRxiv2018

Phylogeny Recapitulates Learning: Self-Optimization of Genetic Code

Learning algorithms have been proposed as a non-selective mechanism capable of creating complex adaptive systems in life. Evolutionary learning however has not been demonstrated to be a plausible cause for the origin of a specific molecular system. Here we show that genetic codes as optimal as the Standard Genetic Code (SGC) emerge readily by following a molecular analog of the Hebbs rule (\"neurons fire together, wire together\"). Specifically, error-minimizing genetic codes are obtained by maximizing the number of physio-chemically similar amino acids assigned to evolutionarily similar codons. Formulating genetic code as a Traveling Salesman Problem (TSP) with amino acids as \"cities\" and codons as \"tour positions\" and implemented with a Hopfield neural network, the unsupervised learning algorithm efficiently finds an abundance of genetic codes that are more error-minimizing than SGC. Drawing evidence from molecular phylogenies of contemporary tRNAs and aminoacyl-tRNA synthetases, we show that co-diversification between gene sequences and gene functions, which cumulatively captures functional differences with sequence differences and creates a genomic \"memory\" of the living environment, provides the biological basis for the Hebbian learning algorithm. Like the Hebbs rule, the locally acting phylogenetic learning rule, which may simply be stated as increasing phylogenetic divergence for increasing functional difference, could lead to complex and robust life systems. Natural selection, while essential for maintaining gene function, is not necessary to act at system levels. For molecular systems that are self-organizing through phylogenetic learning, the TSP model and its Hopfield network solution offer a promising framework for simulating emerging behavior, forecasting evolutionary trajectories, and designing optimal synthetic systems.

evolutionary biology↗