Biology subjects

Weissman, M.

Publications and source records attributed to Weissman, M..

4 recordsLinked to original sources

bioRxiv2026

The effects of hitchhiking and repulsion of deleterious alleles on linkage disequilibrium during a selective sweep

Deleterious mutations are abundant in nature. Consequently, when an adaptive mutation arises, it will likely land on a haplotype with several deleterious mutations. While deleterious mutations may hitchhike with the adaptive variant, deleterious mutations are less likely to be found on the same haplotype with each other due to repulsion (i.e. Hill Robertson interference). Both hitchhiking and repulsion shape patterns of linkage disequilibrium (LD), but their simultaneous impact on LD have not been fully understood, leaving a gap in accurate inference of selection from genomic data. Here, we investigate patterns of LD during a sweep using simulations across a wide range of evolutionary parameters. On average, LD is elevated between pairs of intermediate frequency nonsynonymous variants relative to synonymous variants. This occurs because nonsynonymous variants, which are presumably deleterious, are more likely to rise to high frequency via hitchhiking rather than drift and thus will be predominantly found on the sweeping haplotype. However, we find that this elevation does not occur across all distances: when nonsynonymous variants are either too close to each other or too far from the adaptive locus, repulsion between deleterious variants results in lower LD among nonsynonymous variants. This results in a characteristic "criss-crossing" of LD. We find that criss-crossing is a feature of empirical data from Clostridioides difficile, a pathogen that has undergone several selective sweeps. However, some sweeps show elevation of nonsynonymous LD over synonymous LD across all distances. These sweeps have been previously identified to bear complex adaptations, and moreover, harbor elevated nucleotide diversity. Consistent with this, we demonstrate that lack of criss-crossing can arise when adaptation proceeds via epistatic, multi-locus architecture or introgression.

genetics↗

bioRxiv2024

Balancing the risks of mating: biogeographic evidence of cleistogamy as a bet hedging strategy

Cleistogamy is a mating system in which plants produce some proportion of closed, autonomously self-pollinating flowers. Cleistogamous flowers differ from chasmogamous flowers, which are open flowers capable of outcrossing. Both dimorphic cleistogamy (cleistogamous and chasmogamous flowers produced on the same plant) and complete cleistogamy occur. Cleistogamy has been hypothesized to be a bet hedging strategy for reducing risk in the face of unpredictable pollinator availability. However, conflicting results across species and challenges connecting theory to data have prevented researchers from proving that cleistogamy is bet hedging. To test the bet hedging hypothesis, we investigated the distribution of over 400 cleistogamous species through biogeographical analyses. We find that cleistogamy is more prevalent in cooler, more variable environments. Additionally, we find that among cleistogamous species, complete cleistogamy is more likely to occur in warmer, more stable, tropical and subtropical environments. We hypothesize that the difference in distribution between complete and dimorphic cleistogamy may be driven by the opposing forces of selection to increase cleistogamy proportion and extinction risk, which we test using a heuristic Markov transition model. We conclude that the distribution of cleistogamy suggests that the strategy has evolved in variable environments, consistent with expectations for bet hedging.

evolutionary biology↗

bioRxiv2022

Clonally heritable gene expression imparts a layer of diversity within cell types

Cell types can be classified based on shared patterns of transcription. Variability in gene expression between individual cells of the same type has been ascribed to stochastic transcriptional bursting and transient cell states. We asked whether long-term, heritable differences in transcription can impart diversity within a cell type. Studying clonal human lymphocytes and mouse brain cells, we uncover a vast diversity of heritable transcriptional states among different clones of cells of the same type in vivo. In lymphocytes we show that this diversity is coupled to clone specific chromatin accessibility, resulting in distinct expression of genes by different clones. Our findings identify a source of cellular diversity, which may have important implications for how cellular populations are shaped by selective processes in development, aging and disease.

systems biology↗

bioRxiv2020

Distinct Predictive Patterns of Antidepressant Response to Sertraline, Placebo and Bupropion using Pre-Treatment Reward Processing as examined through Functional MRI and Deep Learning: Key Results from the EMBARC Study

ImportanceThe lack of antidepressant-specific biomarkers to inform treatment selection is a key obstacle in the treatment of Major Depressive Disorder. Quantitative measurements of reward processing neural activity with task-based functional magnetic resonance imaging (fMRI) may allow prediction of individual outcomes for specific antidepressants. ObjectiveTo build and validate predictors of individual outcomes for sertraline, bupropion, and placebo using reward processing task-based fMRI, clinical assessments, and deep learning. DesignThis is a secondary analysis of data from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, a placebo-controlled, double-blind randomized clinical trial. The study ran from July 2011 to December 2015 and this analysis was performed between December 2018 and July 2019. SettingThe EMBARC study was conducted at 4 academic medical centers. ParticipantsA random sample of 296 un-medicated participants meeting DSIM-IV criteria for depression was enrolled. InterventionSubjects were randomized to sertraline or placebo during phase 1. In phase 2, non-responders to placebo were treated with sertraline and non-responders to sertraline were treated with bupropion. Each phase lasted for 8 weeks. Main outcomes and measuresThe primary outcome was the change in the 17-item Hamilton Rating Scale for Depression ({Delta}HAMD) after 8 weeks of treatment. Task-related brain activation measures, computed from pre-treatment reward processing task fMRI, and clinical measurements were used to train deep learning predictive models for each treatment group. ResultsThe current analysis includes 222 participants with complete imaging, clinical, and treatment outcome data (146 female, mean age 16.4 {+/-} 13.4 years). The predictive model for sertraline, trained on 106 participants, achieved an R2 of 35% (95% CI 20-50%, p < 10-3) in predicting {Delta}HAMD and a number-needed-to-treat (NNT) of 4.31 in predicting remission. The placebo model, trained on 116 participants, achieved an R2 of 23% (95% confidence interval of 11-37%, p < 10-3) and an NNT of 2.78. The bupropion model achieved an R2 of 37% (95% CI 12-61%, p < 10-3) and an NNT of 2.35. Reward processing activity in regions such as the medial frontal cortex, insula, and thalamus were important predictors of sertraline outcome, while the anterior cingulate cortex, striatum, insula, and thalamus were important regions for predicting bupropion outcome. These are consistent with previously reported findings, while regions not previously implicated such as the temporal pole and paracentral lobule were also found to be predictive. Conclusions and relevanceThese findings demonstrate the utility of reward processing measurements and deep learning in predicting individual antidepressant outcomes with high accuracy. They also present potential composite biomarkers for these treatments based on neuroimaging and clinical features. Trial registrationClinicalTrials.gov identifier: NCT01407094 KEY POINTSO_ST_ABSQuestionC_ST_ABSCan pre-treatment fMRI measurements of reward processing, in combination with multi-dimensional clinical assessments, be used to form personalized predictions of antidepressant outcomes? FindingsDeep learning predictive models trained on pre-treatment reward processing task-based neuroimaging and clinical data from a randomized clinical trial were able to explain up to 37% of the variance in individual treatment outcomes and predict remission with NNT of 2-4. Specific clinical variables and brain regions with reward processing activity important for the model predictions were identified, which may form composite biomarkers of antidepressant response. MeaningQuantitative measures of reward processing, as identified through deep learning, may move us closer to a precision medicine approach that enables clinicians to select the appropriate antidepressant for each patient with greater certainty.

bioinformatics↗