Biology subjects

Dunham, A. S.

Publications and source records attributed to Dunham, A. S..

8 recordsLinked to original sources

bioRxiv2024

A genome-scale single cell CRISPRi map of trans gene regulation across human pluripotent stem cell lines

Population-scale resources of genetic, molecular, and cellular information form the basis for understanding human genomes, charting the heritable basis of disease, and tracing the effects of mutations. Pooled perturbation assays applied to cellular models, probing the effect of many perturbations coupled with an scRNA-seq readout (Perturb-seq), are especially potent references for interpreting disease-linked mutations or gene expression changes. However, the utility of existing maps has been limited by the comprehensiveness of perturbations possible, and the relevance of their cell line context. Here, we present the first genome-scale CRISPR interference (CRISPRi) perturbation map with single-cell RNA sequencing readout across many human genetic backgrounds in human pluripotent cells. To do so, we establish large-scale CRISPRi screening in human induced pluripotent stem cells from healthy donors, using over 20,000 guide RNAs to target 7,226 genes across 34 cell lines from 26 genetic backgrounds, and gather expression data from nearly 2 million cells. We comprehensively map trans expression changes induced by the target knockdowns, which complement co-expression patterns in unperturbed cells and facilitate the functional annotation of target genes to biological processes and complexes. Consistency of targeting protein complex members point to protein complexes as a nexus for aggregating transcriptional variation, revealing novel interaction partners. We characterise variation in perturbation effects across donors, with expression quantitative trait loci linked to higher genetic modulation of perturbation effects but overall low replication of trans effects due to knockdown of their corresponding cis regulators. This study pioneers population-scale CRISPR perturbations with single cell readouts that will fuel foundation models for the future of effective modulation of cellular disease phenotypes.

genetics↗

bioRxiv2022

Global landscape of the host response to SARS-CoV-2 variants reveals viral evolutionary trajectories

A series of SARS-CoV-2 variants of concern (VOCs) have evolved in humans during the COVID-19 pandemic--Alpha, Beta, Gamma, Delta, and Omicron. Here, we used global proteomic and genomic analyses during infection to understand the molecular responses driving VOC evolution. We discovered VOC-specific differences in viral RNA and protein expression levels, including for N, Orf6, and Orf9b, and pinpointed several viral mutations responsible. An analysis of the host response to VOC infection and comprehensive interrogation of altered virus-host protein-protein interactions revealed conserved and divergent regulation of biological pathways. For example, regulation of host translation was highly conserved, consistent with suppression of VOC replication in mice using the translation inhibitor plitidepsin. Conversely, modulation of the host inflammatory response was most divergent, where we found Alpha and Beta, but not Omicron BA.1, antagonized interferon stimulated genes (ISGs), a phenotype that correlated with differing levels of Orf6. Additionally, Delta more strongly upregulated proinflammatory genes compared to other VOCs. Systematic comparison of Omicron subvariants revealed BA.5 to have evolved enhanced ISG and proinflammatory gene suppression that similarly correlated with Orf6 expression, effects not seen in BA.4 due to a mutation that disrupts the Orf6-nuclear pore interaction. Our findings describe how VOCs have evolved to fine-tune viral protein expression and protein-protein interactions to evade both innate and adaptive immune responses, offering a likely explanation for increased transmission in humans. One sentence summarySystematic proteomic and genomic analyses of SARS-CoV-2 variants of concern reveal how variant-specific mutations alter viral gene expression, virus-host protein complexes, and the host response to infection with applications to therapy and future pandemic preparedness.

systems biology↗

bioRxiv2022

High-throughput deep learning variant effect prediction with Sequence UNET

Understanding the consequences of protein coding mutations is important for many applications in biology and medicine. The vast number of possible mutations across species makes comprehensive experimental characterisation impossible, even with recent high-throughput techniques, which means computationally predicting the consequences of variation is essential for many analyses. Previous variant effect prediction (VEP) tools, generally based on evolutionary conservation and protein structure, are often computationally intensive, making them difficult to scale and limiting potential applications. Recent developments in deep learning techniques, including protein language models, and biological data scale have led to a new generation of predictors. These models have improved prediction performance but are still often intensive to run because of slow training steps, hardware requirements and large model sizes. In this work we introduce a new highly scalable deep learning architecture, Sequence UNET, that classifies and predicts variant frequency directly from protein sequence. This model learns to build representations of protein sequence features at a range of scales using a fully convolutional U-shaped compression/expansion architecture. We show that it can generalise to pathogenicity prediction, achieving comparable performance on ClinVar to methods including EVE and ESM-1b at greatly reduced computational cost. We further demonstrate its scalability by analysing the consequences of 8.3 billion variants in 904,134 proteins detected in a large-scale proteomics analysis, showing a link between conservation and protein abundance. Sequence UNET can be run on modest hardware through an easy to use Python package.

bioinformatics↗

bioRxiv2021

Towards a structurally resolved human protein interaction network

All cellular functions are governed by complex molecular machines that assemble through protein-protein interactions. Their atomic details are critical to the study of their molecular mechanisms but fewer than 5% of hundreds of thousands of human interactions have been structurally characterized. Here, we test the potential and limitations of recent progress in deep-learning methods using AlphaFold2 to predict structures for 65,484 human interactions. We show that higher confidence models are enriched in interactions supported by affinity or structure-based methods and can be orthogonally confirmed by spatial constraints defined by cross-link data. We identify 3,137 high confidence models, of which 1,371 have no homology to a known structure, from which we identify interface residues harbouring disease mutations, suggesting potential mechanisms for pathogenic variants. We find groups of interface phosphorylation sites that show patterns of co-regulation across conditions, suggestive of coordinated tuning of multiple interactions as signalling responses. Finally, we provide examples of how the predicted binary complexes can be used to build larger assemblies. Accurate prediction of protein complexes promises to greatly expand our understanding of the atomic details of human cell biology in health and disease.

bioinformatics↗

bioRxiv2021

A structural biology community assessment of AlphaFold 2 applications

Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods have led to protein structure predictions that have reached the accuracy of experimentally determined models. While this has been independently verified, the implementation of these methods across structural biology applications remains to be tested. Here, we evaluate the use of AlphaFold 2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modelling of interactions; and modelling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modelled when compared to homology modelling, identifying structural features rarely seen in the PDB. AF2-based predictions of protein disorder and protein complexes surpass state-of-the-art tools and AF2 models can be used across diverse applications equally well compared to experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life science research.

biophysics↗

bioRxiv2019

Population Structure, Stratification and Introgression of Human Structural Variation in the HGDP.

Structural variants contribute substantially to genetic diversity and are important evolutionarily and medically, yet are still understudied. Here, we present a comprehensive analysis of deletions, duplications, insertions, inversions and non-reference unique insertions in the Human Genome Diversity Project (HGDP-CEPH) panel, a high-coverage dataset of 911 samples from 54 diverse worldwide populations. We identify in total 126,018 structural variants (25,588 <100 bp in size), of which 78% are novel. Some reach high frequency and are private to continental groups or even individual populations, including a deletion in the maltase-glucoamylase gene MGAM involved in starch digestion, in the South American Karitiana and a deletion in the Central African Mbuti in SIGLEC5, potentially leading to immune hyperactivity. We discover a dynamic range of copy number expansions and find cases of regionally-restricted runaway duplications, for example, 18 copies near the olfactory receptor OR7D2 in East Asia and in the clinically-relevant HCAR2 in Central Asia. We identify highly-stratified putatively introgressed variants from Neanderthals or Denisovans, some of which, like a deletion within AQR in Papuans, are almost fixed in individual populations. Finally, by de novo assembly of 25 genomes using linked-read sequencing we discover 1631 breakpoint-resolved unique insertions, in aggregate accounting for 1.9 Mb of sequence absent from the GRCh38 reference. These insertions show population structure and some reside in functional regions, illustrating the limitation of a single human reference and the need for high-quality genomes from diverse populations to fully discover and understand human genetic variation.

genomics↗

bioRxiv2018

The impact of the genetic background on gene deletion phenotypes in Saccharomyces cerevisiae

Loss-of-function (LoF) mutations associated with disease dont manifest equally in different individuals, a phenomenon known as incomplete penetrance. The impact of the genetic background on incomplete penetrance remains poorly characterized. Here, we systematically assessed the changes in gene deletion phenotypes for 3,786 gene knockouts in four Saccharomyces cerevisiae strains and 38 conditions. We observed 16% to 42% of deletion phenotypes changing between pairs of strains with a small fraction conserved in all strains. Conditions causing higher WT growth differences and the deletion of pleiotropic genes showed above average changes in phenotypes. We further illustrate how these changes affect the interpretation of the impact of genetic variants across 925 yeast isolates. These results show the high degree of genetic background dependencies for LoF phenotypes.

genetics↗

bioRxiv2017

A delay-deterministic model for inferring fitnesseffects from time-resolved genome sequence data

A common challenge arising from the observation of an evolutionary system over time is to infer the magnitude of selection acting upon a specific genetic variant, or variants, within the population. The inference of selection may be confounded by the effects of genetic drift in a system, leading to the development of inference procedures to account for these effects. However, recent work has suggested that deterministic models of evolution may be effective in capturing the effects of selection even under complex models of demography, suggesting the more general application of deterministic approaches to inference. Responding to this literature, we here note a case in which a deterministic model of evolution may give highly misleading inferences, resulting from the non-deterministic properties of mutation in a finite population. We propose an alternative approach which corrects for this error, which we denote the delay-deterministic model. Applying our model to a simple evolutionary system we demonstrate its performance in quantifying the extent of selection acting within that system. We further consider the application of our model to sequence data from an evolutionary experiment. We outline scenarios in which our model may produce improved results for the inference of selection, noting that such situations can be easily identified via the use of a regular deterministic model.

evolutionary biology↗