Dynamic Evolution of Fibroblasts Revealed by Single-Cell RNA Sequencing of Human Pancreatic Cancer.

Pancreatic cancer remains a high unmet medical need. Understanding the interactions between stroma and cancer cells in this disease may unveil new opportunities for therapeutic intervention.

Systematic multi-trait AAV capsid engineering for efficient gene delivery.

Broadening gene therapy applications requires manufacturable vectors that efficiently transduce target cells in humans and preclinical models. Conventional selections of adeno-associated virus (AAV) capsid libraries are inefficient at searching the vast sequence space for the small fraction of vectors possessing multiple traits essential for clinical translation. Here, we present Fit4Function, a generalizable machine learning (ML) approach for systematically engineering multi-trait AAV capsids.

Protein interaction networks in the vasculature prioritize genes and pathways underlying coronary artery disease.

Population-based association studies have identified many genetic risk loci for coronary artery disease (CAD), but it is often unclear how genes within these loci are linked to CAD. Here, we perform interaction proteomics for 11 CAD-risk genes to map their protein-protein interactions (PPIs) in human vascular cells and elucidate their roles in CAD. The resulting PPI networks contain interactions that are outside of known biology in the vasculature and are enriched for genes involved in immunity-related and arterial-wall-specific mechanisms.

Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases.

Genome-wide association studies (GWASs) are a valuable tool for understanding the biology of complex human traits and diseases, but associated variants rarely point directly to causal genes. In the present study, we introduce a new method, polygenic priority score (PoPS), that learns trait-relevant gene features, such as cell-type-specific expression, to prioritize genes at GWAS loci. Using a large evaluation set of genes with fine-mapped coding variants, we show that PoPS and the closest gene individually outperform other gene prioritization methods, but observe the best overall performance by combining PoPS with orthogonal methods.

Using brain cell-type-specific protein interactomes to interpret neurodevelopmental genetic signals in schizophrenia.

Genetics have nominated many schizophrenia risk genes and identified convergent signals between schizophrenia and neurodevelopmental disorders. However, functional interpretation of the nominated genes in the relevant brain cell types is often lacking. We executed interaction proteomics for six schizophrenia risk genes that have also been implicated in neurodevelopment in human induced cortical neurons.

Protein interaction studies in human induced neurons indicate convergent biology underlying autism spectrum disorders.

Autism spectrum disorders (ASDs) have been linked to genes with enriched expression in the brain, but it is unclear how these genes converge into cell-type-specific networks. We built a protein-protein interaction network for 13 ASD-associated genes in human excitatory neurons derived from induced pluripotent stem cells (iPSCs). The network contains newly reported interactions and is enriched for genetic and transcriptional perturbations observed in individuals with ASDs.

Current advancements of modelling schizophrenia using patient-derived induced pluripotent stem cells.

Schizophrenia (SZ) is a severe psychiatric disorder, with a prevalence of 1-2% world-wide and substantial health- and social care costs. The pathology is influenced by both genetic and environmental factors, however the underlying cause still remains elusive. SZ has symptoms including delusions, hallucinations, confused thoughts, diminished emotional responses, social withdrawal and anhedonia.

Autism genes converge on asynchronous development of shared neuron classes.

Genetic risk for autism spectrum disorder (ASD) is associated with hundreds of genes spanning a wide range of biological functions. The alterations in the human brain resulting from mutations in these genes remain unclear. Furthermore, their phenotypic manifestation varies across individuals.

Endothelial ARHGEF26 is an angiogenic factor promoting VEGF signalling.

Aims: Genetic studies have implicated the ARHGEF26 locus in the risk of coronary artery disease (CAD). However, the causal pathways by which DNA variants at the ARHGEF26 locus confer risk for CAD are incompletely understood. We sought to elucidate the mechanism responsible for the enhanced risk of CAD associated with the ARHGEF26 locus.

is mutated in clonal hematopoiesis and myelodysplastic syndromes and impacts RNA splicing.

Clonal hematopoiesis results from somatic mutations in cancer driver genes in hematopoietic stem cells. We sought to identify novel drivers of clonal expansion using an unbiased analysis of sequencing data from 84,683 persons and identified common mutations in the 5-methylcytosine reader, , as well as in , , and . We also identified these mutations at low frequency in myelodysplastic syndrome patients.

Translating polygenic risk scores for clinical use by estimating the confidence bounds of risk prediction.

A promise of genomics in precision medicine is to provide individualized genetic risk predictions. Polygenic risk scores (PRS), computed by aggregating effects from many genomic variants, have been developed as a useful tool in complex disease research. However, the application of PRS as a tool for predicting an individual's disease susceptibility in a clinical setting is challenging because PRS typically provide a relative measure of risk evaluated at the level of a group of people but not at individual level.

Coexpression network architecture reveals the brain-wide and multiregional basis of disease susceptibility.

Gene networks have yielded numerous neurobiological insights, yet an integrated view across brain regions is lacking. We leverage RNA sequencing in 864 samples representing 12 brain regions to robustly identify 12 brain-wide, 50 cross-regional and 114 region-specific coexpression modules. Nearly 40% of genes fall into brain-wide modules, while 25% comprise region-specific modules reflecting regional biology, such as oxytocin signaling in the hypothalamus, or addiction pathways in the nucleus accumbens.

Genoppi is an open-source software for robust and standardized integration of proteomic and genetic data.

Combining genetic and cell-type-specific proteomic datasets can generate biological insights and therapeutic hypotheses, but a technical and statistical framework for such analyses is lacking. Here, we present an open-source computational tool called Genoppi (lagelab.org/genoppi) that enables robust, standardized, and intuitive integration of quantitative proteomic results with genetic data.

Systematic auditing is essential to debiasing machine learning in biology.

Biases in data used to train machine learning (ML) models can inflate their prediction performance and confound our understanding of how and what they learn. Although biases are common in biological data, systematic auditing of ML models to identify and eliminate these biases is not a common practice when applying ML in the life sciences. Here we devise a systematic, principled, and general approach to audit ML models in the life sciences.

Leadership.

We asked group leaders how they foster mutually reinforcing research productivity and psychological safety in their teams.

Cohesin mutations alter DNA damage repair and chromatin structure and create therapeutic vulnerabilities in MDS/AML.

The cohesin complex plays an essential role in chromosome maintenance and transcriptional regulation. Recurrent somatic mutations in the cohesin complex are frequent genetic drivers in cancer, including myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). Here, using genetic dependency screens of stromal antigen 2-mutant (STAG2-mutant) AML, we identified DNA damage repair and replication as genetic dependencies in cohesin-mutant cells.

Comprehensive characterization of amino acid positions in protein structures reveals molecular effect of missense variants.

Interpretation of the colossal number of genetic variants identified from sequencing applications is one of the major bottlenecks in clinical genetics, with the inference of the effect of amino acid-substituting missense variations on protein structure and function being especially challenging. Here we characterize the three-dimensional (3D) amino acid positions affected in pathogenic and population variants from 1,330 disease-associated genes using over 14,000 experimentally solved human protein structures. By measuring the statistical burden of variations (i.

Prediction of cancer driver genes through network-based moment propagation of mutation scores.

Motivation: Gaining a comprehensive understanding of the genetics underlying cancer development and progression is a central goal of biomedical research. Its accomplishment promises key mechanistic, diagnostic and therapeutic insights. One major step in this direction is the identification of genes that drive the emergence of tumors upon mutation.

TCF12 haploinsufficiency causes autosomal dominant Kallmann syndrome and reveals network-level interactions between causal loci.

Dysfunction of the gonadotropin-releasing hormone (GnRH) axis causes a range of reproductive phenotypes resulting from defects in the specification, migration and/or function of GnRH neurons. To identify additional molecular components of this system, we initiated a systematic genetic interrogation of families with isolated GnRH deficiency (IGD). Here, we report 13 families (12 autosomal dominant and one autosomal recessive) with an anosmic form of IGD (Kallmann syndrome) with loss-of-function mutations in TCF12, a locus also known to cause syndromic and non-syndromic craniosynostosis.

BraInMap Elucidates the Macromolecular Connectivity Landscape of Mammalian Brain.

Connectivity webs mediate the unique biology of the mammalian brain. Yet, while cell circuit maps are increasingly available, knowledge of their underlying molecular networks remains limited. Here, we applied multi-dimensional biochemical fractionation with mass spectrometry and machine learning to survey endogenous macromolecules across the adult mouse brain.

Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight adipocyte biology.

Population-based biobanks with genomic and dense phenotype data provide opportunities for generating effective therapeutic hypotheses and understanding the genomic role in disease predisposition. To characterize latent components of genetic associations, we apply truncated singular value decomposition (DeGAs) to matrices of summary statistics derived from genome-wide association analyses across 2,138 phenotypes measured in 337,199 White British individuals in the UK Biobank study. We systematically identify key components of genetic associations and the contributions of variants, genes, and phenotypes to each component.