Biology subjects

Washburne, A.

Publications and source records attributed to Washburne, A..

7 recordsLinked to original sources

bioRxiv2022

Endonuclease fingerprint indicates a synthetic origin of SARS-CoV-2

To prevent future pandemics, it is important that we understand whether SARS-CoV-2 spilled over directly from animals to people, or indirectly in a laboratory accident. The genome of SARS-COV-2 contains a peculiar pattern of unique restriction endonuclease recognition sites allowing efficient dis- and re-assembly of the viral genome characteristic of synthetic viruses. Here, we report the likelihood of observing such a pattern in coronaviruses with no history of bioengineering. We find that SARS-CoV-2 is an anomaly, more likely a product of synthetic genome assembly than natural evolution. The restriction map of SARS-CoV-2 is consistent with many previously reported synthetic coronavirus genomes, meets all the criteria required for an efficient reverse genetic system, differs from closest relatives by a significantly higher rate of synonymous mutations in these synthetic-looking recognitions sites, and has a synthetic fingerprint unlikely to have evolved from its close relatives. We report a high likelihood that SARS-CoV-2 may have originated as an infectious clone assembled in vitro. Lay SummaryTo construct synthetic variants of natural coronaviruses in the lab, researchers often use a method called in vitro genome assembly. This method utilizes special enzymes called restriction enzymes to generate DNA building blocks that then can be "stitched" together in the correct order of the viral genome. To make a virus in the lab, researchers usually engineer the viral genome to add and remove stitching sites, called restriction sites. The ways researchers modify these sites can serve as fingerprints of in vitro genome assembly. We found that SARS-CoV has the restriction site fingerprint that is typical for synthetic viruses. The synthetic fingerprint of SARS-CoV-2 is anomalous in wild coronaviruses, and common in lab-assembled viruses. The type of mutations (synonymous or silent mutations) that differentiate the restriction sites in SARS-CoV-2 are characteristic of engineering, and the concentration of these silent mutations in the restriction sites is extremely unlikely to have arisen by random evolution. Both the restriction site fingerprint and the pattern of mutations generating them are extremely unlikely in wild coronaviruses and nearly universal in synthetic viruses. Our findings strongly suggest a synthetic origin of SARS-CoV2.

bioengineering↗

bioRxiv2019

Ecological and evolutionary drivers of hemoplasma infection and bacterial genotype sharing in a Neotropical bat community

Most emerging pathogens can infect multiple species, underscoring the importance of understanding the ecological and evolutionary factors that allow some hosts to harbor greater infection prevalence and share pathogens with other species. However, our understanding of pathogen jumps is primarily based around viruses, despite bacteria accounting for the greatest proportion of zoonoses. Because bacterial pathogens in bats (Order: Chiroptera) can have conservation and human health consequences, studies that examine the ecological and evolutionary drivers of bacterial prevalence and barriers to pathogen sharing are crucially needed. We here studied hemotropic Mycoplasma spp. (i.e., hemoplasmas) across a species-rich bat community in Belize over two years. Across 469 bats spanning 33 species, half of individuals and two-thirds of species were hemoplasma positive. Infection prevalence was higher for males and for species with larger body mass and colony sizes. Hemoplasmas displayed high genetic diversity (21 novel genotypes) and strong host specificity. Evolutionary patterns supported co-divergence of bats and bacterial genotypes alongside phylogenetically constrained host shifts. Bat species centrality to the network of shared hemoplasma genotypes was phylogenetically clustered and unrelated to prevalence, further suggesting rare--but detectable--bacterial sharing between species. Our study highlights the importance of using fine phylogenetic scales when assessing host specificity and suggests phylogenetic similarity may play a key role in host shifts for not only viruses but also bacteria. Such work more broadly contributes to increasing efforts to understand cross-species transmission and epidemiological consequences of bacterial pathogens.

ecology↗

bioRxiv2019

Microbial community-level features linked to divergent carbon flows during early litter decomposition in a constant environment

During plant litter decomposition in soils, carbon has two general fates: return to the atmosphere via microbial respiration or transport into soil where long-term storage may occur. Discovering microbial community features that drive carbon fate from litter decomposition may improve modeling and management of soil carbon. This concept assumes there are features (or underlying processes) that are widespread among disparate communities, and therefore amenable to modeling. We tested this assumption using an epidemiological approach in which two contrasting patterns of carbon flow in laboratory microcosms were delineated as functional states and diverse microbial communities representing each state were compared to discover shared features linked to carbon fate. Microbial communities from 206 soil samples from the southwestern United States were inoculated on plant litter in microcosms, and carbon flow was measured as cumulative carbon dioxide (CO2) and dissolved organic carbon (DOC) after 44 days. Carbon flow varied widely among the microcosms, with a 2-fold range in cumulative CO2 efflux and a 5-fold range in DOC quantity. Bacteria, not fungi, were the strongest drivers of DOC variation. The most significant community-level feature linked to DOC abundance was bacterial richness--the same feature linked to carbon fate in human-gut microbiome studies. This proof-of-principle study under controlled conditions suggests common features driving carbon flow in disparate microbial communities can be identified, motivating further exploration of underlying mechanisms that may influence carbon fate in natural ecosystems.

microbiology↗

bioRxiv2019

Rare Microbial Taxa Emerge When Communities Collide: Freshwater and Marine Microbiome Responses to Experimental Seawater Intrusion

Whole microbial communities regularly merge with one another, often in tandem with their environments, in a process called community coalescence. Such events allow us to address a central question in ecology - what processes shape community assembly. We used a reciprocal transplant and mixing experiment to directly and independently unravel the effects of environmental filtering and biotic interactions on microbiome success when freshwater and marine communities coalesce. The brackish treatment and community mixing resulted in strong convergence of microbiome structure and function toward the marine. Brackish exposure imposed a 96% taxa loss from freshwater and 66% loss from marine microbiomes, which was somewhat counterbalanced by the emergence of tolerant rare taxa. Community mixing further resulted in 29% and 49% loss from biotic interactions between freshwater and marine microbiomes, offset somewhat by mutualistically-assisted rare microbial taxa. Our study emphasizes the importance of the rare biosphere as a critical component of community resilience.

ecology↗

bioRxiv2018

Percolation models of pathogen spillover

A series of logical events must occur for a pathogen to spill over from animals to people. The pathogen must be present in an animal reservoir, it must be shed from the reservoir into the environment or be transferred from the reservoir to a vector, it must persist in the environment or vector until contact with a human or amplifier host, and it must successfully enter, colonize, and reproduce within the human. These events each represent a barrier the pathogen must cross to successfully infect a human. Percolation models of pathogens completing the series of barriers or logical events can connect models of spillover risk with standard tools for statistical inference.\n\nHere, we develop percolation-based models of spillover risk and a theoretical framework for managing spillover as an inextricably multilevel process. Through analysis and simulation, we show that estimated associations between level-specific covariates and spillover events will err towards associations from dominant pathway to spillover, a potential problem if there are alternative pathways to spillover with different associations with covariates. Furthermore, estimated associations between covariates and spillover will better reflect associations between covariates and success probabilities of bottleneck events with the highest pathogen attrition rates in the data observed. If one agrees with a percolation model for spillover, then GLMs should not be used to estimate relative importance of various levels. We recommend always using nonlinear models for predicting spillover risk with quantitative covariates and discuss why switching regression models may be well suited for avoiding some obvious pitfalls in predicting spillover from alternative pathways or wildlife reservoirs. Finally, we demonstrate how percolation models formalize an intuitive management paradigm for mitigating risk in the inherently multilevel process of pathogen spillover.

epidemiology↗

bioRxiv2018

Phylogenetic factorization of mammalian viruses complements trait-based analyses and guides surveillance efforts

Predicting which novel microorganisms may spill over from animals to humans has become a major priority in infectious disease biology. However, there are few tools to help assess the zoonotic potential of the enormous number of potential pathogens, the majority of which are undiscovered or unclassified and may be unlikely to infect or cause disease in humans. We adapt a new biological machine learning technique - phylofactorization - to partition viruses into clades based on their non-human host range and whether or not there exist evidence they have infected humans. Our cladistic analyses identify clades of viruses with common within-clade patterns - unusually high or low propensity for spillover. Phylofactorization by spillover yields many clades of viruses containing few to no representatives that have spilled over to humans, including the families Papillomaviridae and Herpesviridae, and the genus Parvovirus. Removal of these non-zoonotic clades from previous trait-based analyses changed the relative significance of traits determining spillover due to strong associations of traits with non-zoonotic clades. Phylofactorization by host breadth yielded clades with unusually high host breadth, including the family Togaviridae. We identify putative life-history traits differentiating clades host breadth and propensities for zoonosis, and discuss how these results can prioritize sequencing-based surveillance of emerging infectious diseases.

epidemiology↗

bioRxiv2017

Phylofactorization - theory and challenges

Data from biological communities are composed of species connected by the phylogeny. A greedy algorithm phylofactorization - was developed to construct an isometric log-ratio transform whose balances correspond to edges along which traits arose, controlling for previously made inferences.\n\nIn this paper, the general theory of phylofactorization is presented as a graph-partitioning algorithm. A special case-regression phylofactorization-chooses coordinates based on sequential maximization of objective functions from regression on \"contrast\" variables such as an isometric log-ratio transform. The connections between regression phylofactorization and other methods is discussed, including matrix factorization, hierarchical regression, factor analysis and latent variable models. Open challenges in the statistical analysis of phylofactorization are presented, including criteria for choosing the number of factors and approximating null-distributions of commonly used test statistics and objective functions. As a graph-partitioning algorithm, cross-validation of phylo factorization across datasets requires graph-topological considerations, such as how to deal with novel nodes and edges and whether or not to control for partition order. Overcoming these challenges can accelerate our analysis of phylogenetically-structured data and allow annotations of edges in an online tree of life.

evolutionary biology↗