Adaptive evolution within the gut microbiome of individual people, bioRxiv, 2017-10-25
AbstractIndividual bacterial lineages stably persist for years in the human gut microbiome1–3. However, the potential of these lineages to adapt during colonization of healthy people is not well understood2,4. Here, we assess evolution within individual microbiomes by sequencing the genomes of 602 Bacteroides fragilis isolates cultured from 12 healthy subjects. We find that B. fragilis within-subject populations contain substantial de novo nucleotide and mobile element diversity, which preserve years of within-person evolutionary history. This evolutionary history contains signatures of within-person adaptation to both subject-specific and common selective forces, including parallel mutations in sixteen genes. These sixteen genes are involved in cell-envelope biosynthesis and polysaccharide utilization, as well as yet under-characterized pathways. Notably, one of these genes has been shown to be critical for B. fragilis colonization in mice5, indicating that key genes have not already been optimized for survival in vivo. This lack of optimization, given historical signatures of purifying selection in these genes, suggests that varying selective forces with discordant solutions act upon B. fragilis in vivo. Remarkably, in one subject, two B. fragilis sublineages coexisted at a stable relative frequency over a 1.5-year period despite rapid adaptive dynamics within one of the sublineages. This stable coexistence suggests that competing selective forces can lead to B. fragilis niche-differentiation even within a single person. We conclude that B. fragilis adapts rapidly within the microbiomes of individual healthy people, providing a new route for the discovery of key genes in the microbiome and implications for microbiome stability and manipulation.
biorxiv evolutionary-biology 200-500-users 2017Bioconda A sustainable and comprehensive software distribution for the life sciences, bioRxiv, 2017-10-23
AbstractWe present Bioconda (<jatsext-link xmlnsxlink=httpwww.w3.org1999xlink ext-link-type=uri xlinkhref=httpsbioconda.github.io>httpsbioconda.github.io<jatsext-link>), a distribution of bioinformatics software for the lightweight, multiplatform and language-agnostic package manager Conda. Currently, Bioconda offers a collection of over 3000 software packages, which is continuously maintained, updated, and extended by a growing global community of more than 200 contributors. Bioconda improves analysis reproducibility by allowing users to define isolated environments with defined software versions, all of which are easily installed and managed without administrative privileges.
biorxiv bioinformatics 500+-users 2017Transcriptome-wide association studies opportunities and challenges, bioRxiv, 2017-10-23
Transcriptome-wide association studies (TWAS) integrate GWAS and gene expression datasets to find gene-trait associations. In this Perspective, we explore properties of TWAS as a potential approach to prioritize causal genes, using simulations and case studies of literature-curated candidate causal genes for schizophrenia, LDL cholesterol and Crohn’s disease. We explore risk loci where TWAS accurately prioritizes the likely causal gene, as well as loci where TWAS prioritizes multiple genes, some of which are unlikely to be causal, because they share the same variants as eQTLs. We illustrate that TWAS is especially prone to spurious prioritization when using expression data from tissues or cell types that are less related to the trait, due to substantial variation in both expression levels and eQTL strengths across cell types. Nonetheless, TWAS prioritizes candidate causal genes at GWAS loci more accurately than simple baselines based on proximity to lead GWAS variant and expression in trait-related tissue. We discuss current strategies and future opportunities for improving the performance of TWAS for causal gene prioritization. Our results showcase the strengths and limitations of using expression variation across individuals to determine causal genes at GWAS loci and provide guidelines and best practices when using TWAS to prioritize candidate causal genes.
biorxiv genetics 100-200-users 2017Mapping the human brain's cortical-subcortical functional network organization, bioRxiv, 2017-10-20
Understanding complex systems such as the human brain requires characterization of the system's architecture across multiple levels of organization - from neurons, to local circuits, to brain regions, and ultimately large-scale brain networks. Here we focus on characterizing the human brain's large-scale network organization, as it provides an overall framework for the organization of all other levels. We developed a highly principled approach to identify cortical network communities at the level of functional systems, calibrating our community detection algorithm using extremely well-established sensory and motor systems as guides. Building on previous network partitions, we replicated and expanded upon well-known and recently-identified networks, including several higher-order cognitive networks such as a left-lateralized language network. We expanded these cortical networks to subcortex, revealing 358 highly-organized subcortical parcels that take part in forming whole-brain functional networks. Notably, the identified subcortical parcels are similar in number to a recent estimate of the number of cortical parcels (360). This whole-brain network atlas - released as an open resource for the neuroscience community - places all brain structures across both cortex and subcortex into a single large-scale functional framework, with the potential to facilitate a variety of studies investigating large-scale functional networks in health and disease.
biorxiv neuroscience 0-100-users 2017RNA velocity in single cells, bioRxiv, 2017-10-20
AbstractRNA abundance is a powerful indicator of the state of individual cells, but does not directly reveal dynamic processes such as cellular differentiation. Here we show that RNA velocity—the time derivative of RNA abundance—can be estimated by distinguishing unspliced and spliced mRNAs in standard single-cell RNA sequencing protocols. We show that RNA velocity is a vector that predicts the future state of individual cells on a timescale of hours. We validate the accuracy of RNA velocity in the neural crest lineage, demonstrate its use on multiple technical platforms, reconstruct the branching lineage tree of the mouse hippocampus, and measure RNA kinetics in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.
biorxiv genomics 100-200-users 2017Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain by scGESTALT, bioRxiv, 2017-10-20
ABSTRACTHundreds of cell types are generated during development, but their lineage relationships are largely elusive. Here we report a technology, scGESTALT, which combines cell type identification by single-cell RNA sequencing with lineage recording by cumulative barcode editing. We sequenced ~60,000 transcriptomes from the juvenile zebrafish brain and identified more than 100 cell types and marker genes. We engineered an inducible system that combines early and late barcode editing and isolated thousands of single-cell transcriptomes and their associated barcodes. The large diversity of edited barcodes and cell types enabled the generation of lineage trees with hundreds of branches. Inspection of lineage trajectories identified restrictions at the level of cell types and brain regions and helped uncover gene expression cascades during differentiation. These results establish scGESTALT as a new and widely applicable tool to simultaneously characterize the molecular identities and lineage histories of thousands of cells during development and disease.
biorxiv developmental-biology 100-200-users 2017