Biology subjects

Scott, C. B.

Publications and source records attributed to Scott, C. B..

4 recordsLinked to original sources

bioRxiv2024

Inhibitory basal ganglia nuclei differentially innervate pedunculopontine nucleus subpopulations and evoke opposite motor and valence behaviors.

The canonical basal ganglia model predicts that the substantia nigra pars reticulata (SNr) and the globus pallidus externa (GPe) will have specific effects on locomotion: the SNr inhibiting locomotion and the GPe enhancing it. In this study, we use in vivo optogenetics to show that the GPe exerts non-canonical effects on locomotion while the SNr has no gross motor impact through inhibition of the PPN. We also show that these structures mediate opposing effects on reward. To understand how these structures differentially modulate the PPN, we use ex vivo whole-cell recording with optogenetics to comprehensively dissect the SNr and GPe synaptic connections to regionally- and molecularly-defined populations of PPN neurons. The SNr inhibits all PPN subtypes, but most strongly inhibits caudal glutamatergic neurons. The GPe selectively inhibits caudal glutamatergic and GABAergic neurons, avoiding both cholinergic and rostral cells. This circuit characterization reveals non-canonical basal ganglia pathways for locomotion and valence. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/606694v2_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@11e6a13org.highwire.dtl.DTLVardef@1c63735org.highwire.dtl.DTLVardef@5282b7org.highwire.dtl.DTLVardef@103da4a_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2024

The battle of the sexes in humans is highly polygenic

Sex-differential selection (SDS), which occurs when the fitness effects of alleles differ between males and females, can have profound impacts on the maintenance of genetic variation, disease risk, and other key aspects of natural populations. Because the sexes mix their autosomal genomes each generation, quantifying SDS is not possible using conventional population genetic approaches. Here, we introduce a novel method that exploits subtle sex differences in haplotype frequencies resulting from SDS acting in the current generation. Using data from 300K individuals in the UK Biobank, we estimate the strength of SDS throughout the genome. While only a handful of loci under SDS are individually significant, we uncover polygenic signals of genome-wide SDS for both viability and fecundity. An interesting life-history tradeoff emerges: alleles that increase viability more in one sex increase fecundity more in the other sex. Lastly, we find evidence of SDS on fecundity acting on alleles affecting arm fat-free mass. Taken together, our findings connect the long-standing evidence of SDS acting on human phenotypes with its impact on the genome. Significance statementSelection often acts differently on females and males, as evidenced by the striking sexual dimorphism found in many taxa. As a result, alleles can have different fitness effects in each sex. Consequences can include higher levels of genetic variation and higher disease burdens in populations. This study introduces a novel method to quantify this sex-differential selection (SDS) and reveals that it acts throughout the human genome. We discovered a life history tradeoff between survival and fecundity in females and males and that SDS on fecundity acts on alleles affecting arm fat-free mass.

evolutionary biology↗

bioRxiv2024

From juveniles to giants: Drivers of holobiont assembly in massive Porites

The fate of coral reefs in response to climate change depends on their ability to adapt to new environments. The coral animal is buffered from environmental stress by its algal endosymbionts and microbial partners (together, the "holobiont"). However, the flexibility of holobiont community assembly is not well understood, making it difficult to estimate its contribution to coral adaptation. To clarify these processes, we genetically profiled holobiont components (coral, algal symbiont, and microbiome) of massive Porites sampled across two size classes (>2m and <30cm) and ecologically distinct reef sites near Orpheus and Pelorus Islands, Australia. We recovered five major genetic clusters in the coral host, with additional splits within them potentially defining up to ten distinct subclusters. We estimated the relative contributions of the host genetic structure, site, and size class to holobiont community composition. Host genetic structure was the primary driver of both Symbiodiniaceae and microbial communities, indicating strong holobiont specificity in cryptic lineages. In addition, the microbial community was associated with reef site and size class, unlike Symbiodiniaceae that were not significantly affected by either factor. As environmentally segregated, cryptic genetic lineages emerge as a common feature of scleractinian corals, these results emphasize that failure to assess cryptic genetic structure of the coral host may lead to dramatic overestimation of holobiont flexibility.

ecology↗

bioRxiv2022

Consensus Label Propagation with Graph Convolutional Networks for Single-Cell RNA Sequencing Cell Type Annotation

MotivationSingle-cell RNA sequencing (scRNA-seq) data, annotated by cell type, is useful in a variety of downstream biological applications, such as profiling gene expression at the single-cell level. However, manually assigning these annotations with known marker genes is both time-consuming and subjective. ResultsWe present a Graph Convolutional Network (GCN) based approach to automate the annotation process. Our process builds upon existing labeling approaches, using state-of-the-art tools to find cells with highly confident label assignments through consensus and spreading these confident labels with a semi-supervised GCN. Using simulated data and two scRNA-seq data sets from different tissues, we show that our method improves accuracy over a simple consensus algorithm and the average of the underlying tools. We also compare our method to a non-parametric neighbor majority approach, showing comparable results. We then demonstrate that our GCN method allows for feature interpretation, identifying important genes for cell type classification. We present our completed pipeline, written in PyTorch, as an end-to-end tool for automating and interpreting the classification of scRNA-seq data. AvailabilityOur code for conducting the experiments in this paper and using our model is available at https://github.com/lewinsohndp/scSHARP Contactd_lewinsohn@coloradocollege.edu Supplementary informationSupplementary data are available at Journal Name online.

bioinformatics↗