Showing papers by "Broad Institute published in 2022"

Improving polygenic prediction in ancestrally diverse populations

Abstract: Polygenic risk scores (PRS) have attenuated cross-population predictive performance. As existing genome-wide association studies (GWAS) have been conducted predominantly in individuals of European descent, the limited transferability of PRS reduces their clinical value in non-European populations, and may exacerbate healthcare disparities. Recent efforts to level ancestry imbalance in genomic research have expanded the scale of non-European GWAS, although most remain underpowered. Here, we present a new PRS construction method, PRS-CSx, which improves cross-population polygenic prediction by integrating GWAS summary statistics from multiple populations. PRS-CSx couples genetic effects across populations via a shared continuous shrinkage (CS) prior, enabling more accurate effect size estimation by sharing information between summary statistics and leveraging linkage disequilibrium diversity across discovery samples, while inheriting computational efficiency and robustness from PRS-CS. We show that PRS-CSx outperforms alternative methods across traits with a wide range of genetic architectures, cross-population genetic overlaps and discovery GWAS sample sizes in simulations, and improves the prediction of quantitative traits and schizophrenia risk in non-European populations.

Transcriptome variation in human tissues revealed by long-read sequencing

Abstract: Regulation of transcript structure generates transcript diversity and plays an important role in human disease1–7. The advent of long-read sequencing technologies offers the opportunity to study the role of genetic variation in transcript structure8–16. In this Article, we present a large human long-read RNA-seq dataset using the Oxford Nanopore Technologies platform from 88 samples from Genotype-Tissue Expression (GTEx) tissues and cell lines, complementing the GTEx resource. We identified just over 70,000 novel transcripts for annotated genes, and validated the protein expression of 10% of novel transcripts. We developed a new computational package, LORALS, to analyse the genetic effects of rare and common variants on the transcriptome by allele-specific analysis of long reads. We characterized allele-specific expression and transcript structure events, providing new insights into the specific transcript alterations caused by common and rare genetic variants and highlighting the resolution gained from long-read data. We were able to perturb the transcript structure upon knockdown of PTBP1, an RNA binding protein that mediates splicing, thereby finding genetic regulatory effects that are modified by the cellular environment. Finally, we used this dataset to enhance variant interpretation and study rare variants leading to aberrant splicing patterns. To understand the contribution of variants to transcript expression regulation, long-read transcriptome data are generated from the GTEx resource, and a new software package to perform allele-specific analysis is developed.

An aluminum hydroxide:CpG adjuvant enhances protection elicited by a SARS-CoV-2 receptor binding domain vaccine in aged mice

Abstract: Global deployment of vaccines that can provide protection across several age groups is still urgently needed to end the COVID-19 pandemic, especially in low- and middle-income countries. Although vaccines against SARS-CoV-2 based on mRNA and adenoviral vector technologies have been rapidly developed, additional practical and scalable SARS-CoV-2 vaccines are required to meet global demand. Protein subunit vaccines formulated with appropriate adjuvants represent an approach to address this urgent need. The receptor binding domain (RBD) is a key target of SARS-CoV-2 neutralizing antibodies but is poorly immunogenic. We therefore compared pattern recognition receptor (PRR) agonists alone or formulated with aluminum hydroxide (AH) and benchmarked them against AS01B and AS03-like emulsion-based adjuvants for their potential to enhance RBD immunogenicity in young and aged mice. We found that an AH and CpG adjuvant formulation (AH:CpG) produced an 80-fold increase in anti-RBD neutralizing antibody titers in both age groups relative to AH alone and protected aged mice from the SARS-CoV-2 challenge. The AH:CpG-adjuvanted RBD vaccine elicited neutralizing antibodies against both wild-type SARS-CoV-2 and the B.1.351 (beta) variant at serum concentrations comparable to those induced by the licensed Pfizer-BioNTech BNT162b2 mRNA vaccine. AH:CpG induced similar cytokine and chemokine gene enrichment patterns in the draining lymph nodes of both young adult and aged mice and enhanced cytokine and chemokine production in human mononuclear cells of younger and older adults. These data support further development of AH:CpG-adjuvanted RBD as an affordable vaccine that may be effective across multiple age groups.

Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease

Abstract: Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)—a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits.

Functional inference of gene regulation using single-cell multi-omics

Abstract: Cells require coordinated control over gene expression when responding to environmental stimuli. Here we apply scATAC-seq and single-cell RNA sequencing (scRNA-seq) in resting and stimulated human blood cells. Collectively, we generate ∼91,000 single-cell profiles, allowing us to probe the cis-regulatory landscape of the immunological response across cell types, stimuli, and time. Advancing tools to integrate multi-omics data, we develop functional inference of gene regulation (FigR), a framework to computationally pair scATAC-seq with scRNA-seq cells, connect distal cis-regulatory elements to genes, and infer gene-regulatory networks (GRNs) to identify candidate transcription factor (TF) regulators. Utilizing these paired multi-omics data, we define domains of regulatory chromatin (DORCs) of immune stimulation and find that cells alter chromatin accessibility and gene expression at timescales of minutes. Construction of the stimulation GRN elucidates TF activity at disease-associated DORCs. Overall, FigR enables elucidation of regulatory interactions across single-cell data, providing new opportunities to understand the function of cells within tissues.

Edge electronic vacancy on ultrathin carbon nitride nanosheets anchoring O2 to boost H2O2 photoproduction

Abstract: The utilization of solar energy for hydrogen peroxide (H2O2) production using graphitic carbon nitride (g-C3N4) under visible light irradiation has attracted increasing interests due to its high efficiency and cost-effectiveness. However, this process is still limited by slow charge carrier migration. In this work, continuous regulation of band structure inside g-C3N4 is obtained by defect engineering through gradient calcination. The H2O2 production rate (4980 μmol g−1 h−1) of nitrogen-defective g-C3N4 is 18 times higher than that of pristine g-C3N4. The π*CN-C signals in X-ray absorption near-edge structure spectrum decline, indicating an increased N-defects. The N-defects with the electronic vacancies in the heptazine intensifies its light-harvesting on g-C3N4 and also improve the selectivity of 2-electron O2 reduction. A quantitative structure-activity relationship between N-defects and band structure is unveiled. This work offers an accessible strategy to design photocatalysts with desirable defect structures for energy conservation.

Edge electronic vacancy on ultrathin carbon nitride nanosheets anchoring O2 to boost H2O2 photoproduction

Abstract: The utilization of solar energy for hydrogen peroxide (H2O2) production using graphitic carbon nitride (g-C3N4) under visible light irradiation has attracted increasing interests due to its high efficiency and cost-effectiveness. However, this process is still limited by slow charge carrier migration. In this work, continuous regulation of band structure inside g-C3N4 is obtained by defect engineering through gradient calcination. The H2O2 production rate (4980 μmol g−1 h−1) of nitrogen-defective g-C3N4 is 18 times higher than that of pristine g-C3N4. The π*C N-C signals in X-ray absorption near-edge structure spectrum decline, indicating an increased N-defects. The N-defects with the electronic vacancies in the heptazine intensifies its light-harvesting on g-C3N4 and also improve the selectivity of 2-electron O2 reduction. A quantitative structure-activity relationship between N-defects and band structure is unveiled. This work offers an accessible strategy to design photocatalysts with desirable defect structures for energy conservation.

Profiling the Landscape of Drug Resistance Mutations in Neosubstrates to Molecular Glue Degraders

Abstract: Targeted protein degradation (TPD) holds immense promise for drug discovery, but mechanisms of acquired resistance to degraders remain to be fully identified. Here, we used clustered regularly interspaced short palindromic repeats (CRISPR)-suppressor scanning to identify mechanistic classes of drug resistance mutations to molecular glue degraders in GSPT1 and RBM39, neosubstrates targeted by E3 ligase substrate receptors cereblon and DCAF15, respectively. While many mutations directly alter the ternary complex heterodimerization surface, distal resistance sites were also identified. Several distal mutations in RBM39 led to modest decreases in degradation, yet can enable cell survival, underscoring how small differences in degradation can lead to resistance. Integrative analysis of resistance sites across GSPT1 and RBM39 revealed varying levels of sequence conservation and mutational constraint that control the emergence of different resistance mechanisms, highlighting that many regions co-opted by TPD are nonessential. Altogether, our study identifies common resistance mechanisms for molecular glue degraders and outlines a general approach to survey neosubstrate requirements necessary for effective degradation.

Seipin forms a flexible cage at lipid droplet formation sites

Abstract: Abstract Lipid droplets (LDs) form in the endoplasmic reticulum by phase separation of neutral lipids. This process is facilitated by the seipin protein complex, which consists of a ring of seipin monomers, with a yet unclear function. Here, we report a structure of S. cerevisiae seipin based on cryogenic-electron microscopy and structural modeling data. Seipin forms a decameric, cage-like structure with the lumenal domains forming a stable ring at the cage floor and transmembrane segments forming the cage sides and top. The transmembrane segments interact with adjacent monomers in two distinct, alternating conformations. These conformations result from changes in switch regions, located between the lumenal domains and the transmembrane segments, that are required for seipin function. Our data indicate a model for LD formation in which a closed seipin cage enables triacylglycerol phase separation and subsequently switches to an open conformation to allow LD growth and budding.

Genome-wide analysis of somatic noncoding mutation patterns in cancer

Abstract: We established a genome-wide compendium of somatic mutation events in 3949 whole cancer genomes representing 19 tumor types. Protein-coding events captured well-established drivers. Noncoding events near tissue-specific genes, such as ALB in the liver or KLK3 in the prostate, characterized localized passenger mutation patterns and may reflect tumor-cell-of-origin imprinting. Noncoding events in regulatory promoter and enhancer regions frequently involved cancer-relevant genes such as BCL6, FGFR2, RAD51B, SMC6, TERT, and XBP1 and represent possible drivers. Unlike most noncoding regulatory events, XBP1 mutations primarily accumulated outside the gene's promoter, and we validated their effect on gene expression using CRISPR-interference screening and luciferase reporter assays. Broadly, our study provides a blueprint for capturing mutation events across the entire genome to guide advances in biological discovery, therapies, and diagnostics.

High-dimensional gene expression and morphology profiles of cells across 28,000 genetic and chemical perturbations

Abstract: Cells can be perturbed by various chemical and genetic treatments and the impact on gene expression and morphology can be measured via transcriptomic profiling and image-based assays, respectively. The patterns observed in these high-dimensional profile data can power a dozen applications in drug discovery and basic biology research, but both types of profiles are rarely available for large-scale experiments. Here, we provide a collection of four datasets with both gene expression and morphological profile data useful for developing and testing multimodal methodologies. Roughly a thousand features are measured for each of the two data types, across more than 28,000 chemical and genetic perturbations. We define biological problems that use the shared and complementary information in these two data modalities, provide baseline analysis and evaluation metrics for multi-omic applications, and make the data resource publicly available ( https://broad.io/rosetta/ ).

Designing sensitive viral diagnostics with machine learning

Abstract: Design of nucleic acid-based viral diagnostics typically follows heuristic rules and, to contend with viral variation, focuses on a genome's conserved regions. A design process could, instead, directly optimize diagnostic effectiveness using a learned model of sensitivity for targets and their variants. Toward that goal, we screen 19,209 diagnostic-target pairs, concentrated on CRISPR-based diagnostics, and train a deep neural network to accurately predict diagnostic readout. We join this model with combinatorial optimization to maximize sensitivity over the full spectrum of a virus's genomic variation. We introduce Activity-informed Design with All-inclusive Patrolling of Targets (ADAPT), a system for automated design, and use it to design diagnostics for 1,933 vertebrate-infecting viral species within 2 hours for most species and within 24 hours for all but three. We experimentally show that ADAPT's designs are sensitive and specific to the lineage level and permit lower limits of detection, across a virus's variation, than the outputs of standard design techniques. Our strategy could facilitate a proactive resource of assays for detecting pathogens.

Benchmarking of SpCas9 variants enables deeper base editor screens of BRCA1 and BCL2

Abstract: Numerous rationally-designed and directed-evolution variants of SpCas9 have been reported to expand the utility of CRISPR technology. Here, we assess the activity and specificity of WT-Cas9 and 10 SpCas9 variants by benchmarking their PAM preferences, on-target activity, and off-target susceptibility in cell culture assays with thousands of guides targeting endogenous genes. To enhance the coverage and thus utility of base editing screens, we demonstrate that the SpCas9-NG and SpG variants are compatible with both A > G and C > T base editors, more than tripling the number of guides and assayable residues. We demonstrate the performance of these technologies by screening for loss-of-function mutations in BRCA1 and Venetoclax-resistant mutations in BCL2, identifying both known and new mutations that alter function. We anticipate that the tools and methodologies described here will facilitate the investigation of genetic variants at a finer and deeper resolution for any locus of interest.

Cell type-specific inference of differential expression in spatial transcriptomics

Abstract: A central problem in spatial transcriptomics is detecting differentially expressed (DE) genes within cell types across tissue context. Challenges to learning DE include changing cell type composition across space and measurement pixels detecting transcripts from multiple cell types. Here, we introduce a statistical method, cell type-specific inference of differential expression (C-SIDE), that identifies cell type-specific DE in spatial transcriptomics, accounting for localization of other cell types. We model gene expression as an additive mixture across cell types of log-linear cell type-specific expression functions. C-SIDE's framework applies to many contexts: DE due to pathology, anatomical regions, cell-to-cell interactions and cellular microenvironment. Furthermore, C-SIDE enables statistical inference across multiple/replicates. Simulations and validation experiments on Slide-seq, MERFISH and Visium datasets demonstrate that C-SIDE accurately identifies DE with valid uncertainty quantification. Last, we apply C-SIDE to identify plaque-dependent immune activity in Alzheimer's disease and cellular interactions between tumor and immune cells. We distribute C-SIDE within the R package https://github.com/dmcable/spacexr .

Neurons burdened by DNA double-strand breaks incite microglia activation through antiviral-like signaling in neurodegeneration

Abstract: DNA double-strand breaks (DSBs) are linked to neurodegeneration and senescence. However, it is not clear how DSB-bearing neurons influence neuroinflammation associated with neurodegeneration. Here, we characterize DSB-bearing neurons from the CK-p25 mouse model of neurodegeneration using single-nucleus, bulk, and spatial transcriptomic techniques. DSB-bearing neurons enter a late-stage DNA damage response marked by nuclear factor κB (NFκB)-activated senescent and antiviral immune pathways. In humans, Alzheimer's disease pathology is closely associated with immune activation in excitatory neurons. Spatial transcriptomics reveal that regions of CK-p25 brain tissue dense with DSB-bearing neurons harbor signatures of inflammatory microglia, which is ameliorated by NFκB knockdown in neurons. Inhibition of NFκB in DSB-bearing neurons also reduces microglia activation in organotypic mouse brain slice culture. In conclusion, DSBs activate immune pathways in neurons, which in turn adopt a senescence-associated secretory phenotype to elicit microglia activation. These findings highlight a previously unidentified role for neurons in the mechanism of disease-associated neuroinflammation.

Morphology and gene expression profiling provide complementary information for mapping cell state

Abstract: •Cell Painting (morphology) and L1000 (mRNA) are complementary profiling assays •Cell Painting is more reproducible but with more batch effect than L1000 •Cell Painting offers higher sample diversity but lower feature diversity than L1000 •Some drugs and pathways are captured and predicted better by one assay or the other Morphological and gene expression profiling can cost-effectively capture thousands of features in thousands of samples across perturbations by disease, mutation, or drug treatments, but it is unclear to what extent the two modalities capture overlapping versus complementary information. Here, using both the L1000 and Cell Painting assays to profile gene expression and cell morphology, respectively, we perturb human A549 lung cancer cells with 1,327 small molecules from the Drug Repurposing Hub across six doses, providing a data resource including dose-response data from both assays. The two assays capture both shared and complementary information for mapping cell state. Cell Painting profiles from compound perturbations are more reproducible and show more diversity but measure fewer distinct groups of features. Applying unsupervised and supervised methods to predict compound mechanisms of action (MOAs) and gene targets, we find that the two assays not only provide a partially shared but also a complementary view of drug mechanisms. Given the numerous applications of profiling in biology, our analyses provide guidance for planning experiments that profile cells for detecting distinct cell types, disease phenotypes, and response to chemical or genetic perturbations. Morphological and gene expression profiling can cost-effectively capture thousands of features in thousands of samples across perturbations by disease, mutation, or drug treatments, but it is unclear to what extent the two modalities capture overlapping versus complementary information. Here, using both the L1000 and Cell Painting assays to profile gene expression and cell morphology, respectively, we perturb human A549 lung cancer cells with 1,327 small molecules from the Drug Repurposing Hub across six doses, providing a data resource including dose-response data from both assays. The two assays capture both shared and complementary information for mapping cell state. Cell Painting profiles from compound perturbations are more reproducible and show more diversity but measure fewer distinct groups of features. Applying unsupervised and supervised methods to predict compound mechanisms of action (MOAs) and gene targets, we find that the two assays not only provide a partially shared but also a complementary view of drug mechanisms. Given the numerous applications of profiling in biology, our analyses provide guidance for planning experiments that profile cells for detecting distinct cell types, disease phenotypes, and response to chemical or genetic perturbations.

Population structure discovery in meta-analyzed microbial communities and inflammatory bowel disease using MMUPHin

Abstract: Microbiome studies of inflammatory bowel diseases (IBD) have achieved a scale for meta-analysis of dysbioses among populations. To enable microbial community meta-analyses generally, we develop MMUPHin for normalization, statistical meta-analysis, and population structure discovery using microbial taxonomic and functional profiles. Applying it to ten IBD cohorts, we identify consistent associations, including novel taxa such as Acinetobacter and Turicibacter, and additional exposure and interaction effects. A single gradient of dysbiosis severity is favored over discrete types to summarize IBD microbiome population structure. These results provide a benchmark for characterization of IBD and a framework for meta-analysis of any microbial communities.

Genetic analysis of right heart structure and function in 40,000 people

Abstract: Congenital heart diseases often involve maldevelopment of the evolutionarily recent right heart chamber. To gain insight into right heart structure and function, we fine-tuned deep learning models to recognize the right atrium, right ventricle and pulmonary artery, measuring right heart structures in 40,000 individuals from the UK Biobank with magnetic resonance imaging. Genome-wide association studies identified 130 distinct loci associated with at least one right heart measurement, of which 72 were not associated with left heart structures. Loci were found near genes previously linked with congenital heart disease, including NKX2-5, TBX5/TBX3, WNT9B and GATA4. A genome-wide polygenic predictor of right ventricular ejection fraction was associated with incident dilated cardiomyopathy (hazard ratio, 1.33 per standard deviation; P = 7.1 × 10-13) and remained significant after accounting for a left ventricular polygenic score. Harnessing deep learning to perform large-scale cardiac phenotyping, our results yield insights into the genetic determinants of right heart structure and function.

The 22q11.2 region regulates presynaptic gene-products linked to schizophrenia

Abstract: It is unclear how the 22q11.2 deletion predisposes to psychiatric disease. To study this, we generated induced pluripotent stem cells from deletion carriers and controls and utilized CRISPR/Cas9 to introduce the heterozygous deletion into a control cell line. Here, we show that upon differentiation into neural progenitor cells, the deletion acted in trans to alter the abundance of transcripts associated with risk for neurodevelopmental disorders including autism. In excitatory neurons, altered transcripts encoded presynaptic factors and were associated with genetic risk for schizophrenia, including common and rare variants. To understand how the deletion contributed to these changes, we defined the minimal protein-protein interaction network that best explains gene expression alterations. We found that many genes in 22q11.2 interact in presynaptic, proteasome, and JUN/FOS transcriptional pathways. Our findings suggest that the 22q11.2 deletion impacts genes that may converge with psychiatric risk loci to influence disease manifestation in each deletion carrier.