The origins and functional effects of postzygotic mutations throughout the human life span

INTRODUCTIONMutation lays the foundation for genetics, evolution, and our very existence and demise. Historically, genetics has focused on inherited variants and has only recently begun to examine the genetic changes that occur after fertilization, known as postzygotic mutations(PZMs). This bias is partially because of technological limitations and the simplifying assumption that all cells in a multicellular organism share the same genome.RATIONALEMost PZM research has been single-tissue studies. An exciting next generation of PZM studies now examine PZMs across multiple tissues within an individual. However, the relatively small number of individuals and tissue types examined thus far have limited the ability to ascribe sources of mutation variation among individuals or to provide detailed descriptions of embryonic mutations that occur after the first few cell divisions.RESULTSTo expand the field’s knowledge of the origins and functional consequences of PZMs, we sought to answer four key questions: (i) Are PZMs detectable? (ii) Where do PZMs occur? (iii) When do PZMs occur? (iv) When do PZMs contribute to phenotypic variation?We developed a suite of methods called Lachesis to detect single-nucleotide DNA PZMs from bulk RNA sequencing(RNA-seq) data. We applied these methods to the final major release of the NIH Genotype-Tissue Expression (GTEx) project—a catalog of 17,382 samples derived from 948 donors across 54 diverse tissues and cell types—to generate one of the largest and most diverse catalogs of PZMs in normal individuals.PZMs were pervasive and highly variable among donors and tissues. Nearly half of the variation in mutation burden among tissue samples was explained by technical and biological effects, such as age and tissue type. We also found that 9% of this variation was attributed to donor-specific effects. This means that there may be systematic differences among individuals in the number of mutations that they carry due to genetic and/or environmental effects, even after controlling for age. The types of mutations, i.e., mutation spectra, were also variable across tissues, which suggests that mutational mechanisms may be different across tissues.To estimate when PZMs occur during development, we first identified putative prenatal PZMs in the catalog and then mapped them to a developmental tree. Mutation burden and spectra varied throughout prenatal development, with early embryogenesis being the most mutagenic.Finally, to investigate the functional consequences of PZMs, we compared the predicted deleteriousness and selection strength on PZMs across space and time. We found that the predicted functional impact of PZMs varies during prenatal development and across tissues, and we identified a class of low-frequency prenatal mutations apparently more deleterious than all other forms of human genetic variation considered. The deleteriousness of germline mutations decreased through the lifecycle: testicular germ cells carried more deleterious mutations than ejaculated sperm and sperm resulting in viable offspring.CONCLUSIONIn this work, we present methods for detecting PZMs and a comprehensive and diverse atlas of PZMs in normal development and aging. Akin to how expansive surveys of normal germline variation are immensely beneficial for human and medical genetics, this catalog contributes to our understanding of normal postzygotic variation so that abnormal variation can be identified and interpreted. Uncovering the effects of these PZMs on human health and disease is an exciting and valuable endeavor.</p

Spatially organized multicellular immune hubs in human colorectal cancer.

Immune responses to cancer are highly variable, with mismatch repair-deficient (MMRd) tumors exhibiting more anti-tumor immunity than mismatch repair-proficient (MMRp) tumors. To understand the rules governing these varied responses, we transcriptionally profiled 371,223 cells from colorectal tumors and adjacent normal tissues of 28 MMRp and 34 MMRd individuals. Analysis of 88 cell subsets and their 204 associated gene expression programs revealed extensive transcriptional and spatial remodeling across tumors. To discover hubs of interacting malignant and immune cells, we identified expression programs in different cell types that co-varied across tumors from affected individuals and used spatial profiling to localize coordinated programs. We discovered a myeloid cell-attracting hub at the tumor-luminal interface associated with tissue damage and an MMRd-enriched immune hub within the tumor, with activated T&nbsp;cells together with malignant and myeloid cells expressing T&nbsp;cell-attracting chemokines. By identifying interacting cellular programs, we reveal the logic underlying spatially organized immune-malignant cell networks

Emergent achievement segregation in freshmenlearning community networks

A common assumption about Freshmen Learning Communities (FLCs) is that academic relationships contribute to students’ success. This study investigates how students inlearning communities connect with fellow students for friendship and academic support. Longitudinal social network data across the first year, collected from 95 Dutch students in eight FLCs, measure both social and academic relational choices within and beyond the FLCs. Using stochastic actor-based models, the study tests two competing hypotheses. The alignment hypothesis states that students connect with their similar-achieving friends for both academic and social support, leading to an alignment of both types of networks over time. In contrast, the duality hypothesis states dissimilarity between academic support networks and friendship networks: students should connect with better-achieving fellow students for academic support and to more similar peers for friendship. The data support the alignment hypothesis but not the duality hypothesis; in addition, they show evidence of achievement segregation in FLCs: the higher the students’ achievement level, the more they connect with other students for both academic support and friendship, relating in particular to peers with a similarly high achievement level. The results suggest that lower-achieving students are excluded from the support provided by higher achieving students and instead ask similar lower achievers for support. They thus cannot benefit optimally from the academic integration FLC offer. The article concludes with recommendations of how to support students in an FLC so that they can reach optimal achievement potential

Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease

Long non-coding RNA (lncRNA) genes have well-established and important impacts on molecular and cellular functions. However, among the thousands of lncRNA genes, it is still a major challenge to identify the subset with disease or trait relevance. To systematically characterize these lncRNA genes, we used Genotype Tissue Expression (GTEx) project v8 genetic and multi-tissue transcriptomic data to profile the expression, genetic regulation, cellular contexts, and trait associations of 14,100 lncRNA genes across 49 tissues for 101 distinct complex genetic traits. Using these approaches, we identified 1,432 lncRNA gene-trait associations, 800 of which were not explained by stronger effects of neighboring protein-coding genes. This included associations between lncRNA quantitative trait loci and inflammatory bowel disease, type 1 and type 2 diabetes, and coronary artery disease, as well as rare variant associations to body mass index.[Display omitted]•29% of lncRNA genes with eQTLs show tissue-specific genetic regulation•Co-expression networks and single-cell data provide annotations for 94% of lncRNAs•Rare variants near lncRNA expression outliers impact complex traits, like BMI•We identify 800 lncRNA-trait relationships not explained by protein-coding genesA systematic analysis of NIH Genotype Tissue Expression (GTEx) project data provides insights into lncRNA expression patterns and functions, explores the impact of genetic variation on lncRNAs, and connects lncRNAs to complex traits and human disease