Biology subjects

Wright, E. S.

Publications and source records attributed to Wright, E. S..

5 recordsLinked to original sources

bioRxiv2023

Mind the gap: A systematic review and meta-analysisof how social memory is studied

Social recognition is crucial for survival in social species, and necessary for group living, selective reproduction, pair bonding, and dominance hierarchies. Mice and rats are the most commonly used animal models in social memory research, however current paradigms do not account for the complex social dynamics they exhibit in the wild. To assess the range of social memories being studied, we conducted a systematic analysis of neuroscience articles testing the social memory of mice and rats published within the past two decades and analyzed their methods. Our results show that despite these rodents rich social memory capabilities, the majority of social recognition papers explore short-term memories and short-term familiarity levels with minimal exposure between subject and familiar stimuli - a narrow type of social memory. We have identified several key areas currently understudied or underrepresented: kin relationships, mates, social ranks, sex variabilities, and the effects of aging. Additionally, reporting on social stimulus variables such as housing history, strain, and age, is limited, which may impede reproducibility. Overall, our data highlight large gaps in the diversity of social memories studied and the effects social variables have on social memory mechanisms.

neuroscience↗

bioRxiv2022

Multi-factorial examination of amplicon sequencing workflows from sample preparation to bioinformatic analysis

The development of sequencing technologies to evaluate bacterial microbiota composition has allowed new insights into the importance of microbial ecology. However, the variety of methodologies used among amplicon sequencing workflows leads to uncertainty about best practices as well as reproducibility and replicability among microbiome studies. Using a bacterial mock community composed of 37 soil isolates, we performed a comprehensive methodological evaluation of 540 workflows, each with a different combination of methodological factors spanning sample preparation to bioinformatic analysis to define sources of artifacts that affect sensitivity, specificity, and biases in the resulting compositional profiles. Of the 540 workflows examined, those using the V4-V4 primer set enabled the highest level of concordance between the original mock community and resulting microbiome sequence composition. Use of a high-fidelity polymerase, or a lower-fidelity polymerase with increased PCR elongation time limited chimera formation. Bioinformatic pipelines presented a trade-off between the fraction of distinct community members identified (sensitivity) and fraction of correct sequences (specificity). DADA2 and QIIME2 assembled V4-V4 reads amplified by Taq polymerase resulted in the highest specificity (100%), but only identified 52% of mock community members. Using mothur to assemble and denoise V4-V4 reads resulted in detection of 75% of mock community members among the resulting sequences, albeit with marginally lower specificity (99.5%). Optimization of microbiome workflows is critical for accuracy and to support reproducibility and replicability among microbiome studies. These aspects will help reveal the guiding principles of microbial ecology and impact the translation of microbiome research to human and environmental health.

microbiology↗

bioRxiv2021

Parallel evolution between genomic segments of seasonal human influenza viruses reveals RNA-RNA relationships

The influenza A virus (IAV) genome consists of eight negative-sense viral RNA (vRNA) segments that are selectively assembled into progeny virus particles through RNA-RNA interactions. To identify relationships between vRNA segments, we examined parallel evolution between vRNA segments of seasonal human IAV, finding that evolutionary relationships between vRNA segments differ between subtypes and antigenically-shifted strains. Intersegmental relationships were distinct between H3N2 and H1N1 viruses, but largely conserved over time in H3N2 viruses. However, parallel evolution of vRNA segments diverged between H1N1 strains isolated before and after the 2009 pandemic. Surprisingly, intersegmental relationships were not driven solely by protein sequence, which is potentially indicative of RNA-RNA driven coevolution. Colocalization of highly coevolved vRNA segments was enriched over other pairs at the nuclear periphery during a productive viral infection. This study illustrates how phylogenetics can be applied to interrogate putative RNA interactions underlying selective assembly of IAV.

microbiology↗

bioRxiv2020

SARS-CoV-2 genome evolution exposes early human adaptations

The set of mutations observed at the outset of the SARS-CoV-2 pandemic may illuminate how the virus will adapt to humans as it continues to spread. Viruses are expected to quickly acquire beneficial mutations upon jumping to a new host species. Advantageous nucleotide substitutions can be identified by their parallel occurrence in multiple independent lineages and are likely to result in changes to protein sequences. Here we show that SARS-CoV-2 is acquiring mutations more slowly than expected for neutral evolution, suggesting purifying selection is the dominant mode of evolution during the initial phase of the pandemic. However, several parallel mutations arose in multiple independent lineages and may provide a fitness advantage over the ancestral genome. We propose plausible reasons for several of the most frequent mutations. The absence of mutations in other genome regions suggests essential components of SARS-CoV-2 that could be the target of drug development. Overall this study provides genomic insights into how SARS-CoV-2 has adapted and will continue to adapt to humans. SUMMARYIn this study we sought signals of evolution to identify how the SARS-CoV-2 genome has adapted at the outset of the COVID-19 pandemic. We find that the genome is largely undergoing purifying selection that maintains its ancestral sequence. However, we identified multiple positions on the genome that appear to confer an adaptive advantage based on their repeated evolution in independent lineages. This information indicates how SARS-CoV-2 will evolve as it diversifies in an increasing number of hosts.

evolutionary biology↗

bioRxiv2018

Stochastic exits from dormancy give rise to heavy-tailed distributions of descendants in bacterial populations

Variance in reproductive success is a major determinant of the degree of genetic drift in a population. While many plants and animals exhibit high variance in their number of progeny, far less is known about these distributions for microorganisms. Here, we used a strain barcoding approach to quantify variability in offspring number among replicate bacterial populations and developed a Bayesian method to infer the distribution of descendants from this variability. We applied our approach to measure the offspring distributions for 5 strains of bacteria from the genus Streptomyces after germination and growth in a homogenous laboratory environment. The distributions of descendants were heavy-tailed, with a few cells effectively \"winning the jackpot\" to become a disproportionately large fraction of the population. This extreme variability in reproductive success largely traced back to initial populations of spores stochastically exiting dormancy, which provided early-germinating spores with an exponential advantage. In simulations with multiple dormancy cycles, heavy-tailed distributions of descendants decreased the effective population size by many orders of magnitude and led to allele dynamics differing substantially from classical population genetics models with matching effective population size. Collectively, these results demonstrate that extreme variability in reproductive success can occur even in growth conditions that are far more homogeneous than the natural environment. Thus, extreme variability in reproductive success might be an important factor shaping microbial population dynamics with implications for predicting the fate of beneficial mutations, interpreting sequence variability within populations, and explaining variability in infection outcomes across patients.

bioinformatics↗