Biology subjects

Platt, A.

Publications and source records attributed to Platt, A..

16 recordsLinked to original sources

bioRxiv2026

A ML-framework for the discovery of next-generation IBD targets using a harmonized single-cell atlas of patient tissue

Target discovery for IBD has traditionally relied on genetic associations, which lack the cellular resolution needed to identify novel, actionable, cell type-specific disease pathways. Here, we describe an integrated analytical and experimental framework that leverages harmonized single-cell data to systematically discover novel therapeutic strategies for IBD. We used AMICA DBTM, Immunais harmonized database of single-cell RNA datasets to construct a harmonized 1 million single-cell atlas of the human intestine. We applied a machine learning framework (Immune Patient Representation, IPR) to identify disease-associated transcriptional programs and cell type-specific gene targets. Candidate targets were prioritized using atlas-derived metrics, refined using custom criteria emphasizing translational actionability, and validated across independent clinical cohorts. Select candidates were evaluated in human primary-cell models reflecting the targets cell-type context. The IPR framework identified 85 disease-associated transcriptional programs and ranked 400 cell type-specific target genes across immune and stromal lineages. Disease-associated programs were interpreted using a structured AI-assisted reasoning framework for structured biological reasoning, linking them to IBD-relevant pathways and guiding the identification of novel, promising gene targets. Functional validation of two cell-type-specific candidates, PTGIR in myeloid cells and IL6ST in fibroblasts, confirmed the reduction of inflammatory and fibrotic pathways linked to IBD pathology. Multi-omic profiling and projection of in vitro phenotypes to patient datasets demonstrated the reversal of disease-associated programs via mechanisms distinct from those of existing biologics. Our single-cell anchored, machine-learning framework integrates in silico discovery with experimental validation, revealing new cell type-specific therapeutic opportunities and supporting a scalable approach for precision target discovery in IBD and other immune-mediated diseases.

bioinformatics↗

bioRxiv2024

Apollo: A comprehensive GPU-powered within-host simulator for viral evolution and infection dynamics across population, tissue, and cell

Modern sequencing instruments bring unprecedented opportunity to study within-host viral evolution in conjunction with viral transmissions between hosts. However, no computational simulators are available to assist the characterization of within-host dynamics. This limits our ability to interpret epidemiological predictions incorporating within-host evolution and to validate computational inference tools. To fill this need we developed Apollo, a GPU-accelerated, out-of-core tool for within-host simulation of viral evolution and infection dynamics across population, tissue, and cellular levels. Apollo is scalable to hundreds of millions of viral genomes and can handle complex demographic and population genetic models. Apollo can replicate real within-host viral evolution; accurately recapturing observed viral sequences from an HIV cohort derived from initial population-genetic configurations. For practical applications, using Apollo-simulated viral genomes and transmission networks, we validated and uncovered the limitations of a widely used viral transmission inference tool.

bioinformatics↗

bioRxiv2023

Misexpression of inactive genes in whole blood is associated with nearby rare structural variants

Gene misexpression is the aberrant transcription of a gene in a context where it is usually inactive. Despite its known pathological consequences in specific rare diseases, we have a limited understanding of its wider prevalence and mechanisms in humans. To address this, we analyzed gene misexpression in 4,568 whole blood bulk RNA sequencing samples from INTERVAL study blood donors. We found that while individual misexpression events occur rarely, in aggregate they were found in almost all samples and over half of inactive genes. Using 2,821 paired whole genome and RNA sequencing samples, we identified that misexpression events are enriched in cis for rare structural variants. We established putative mechanisms through which a subset of SVs lead to gene misexpression, including transcriptional readthrough, transcript fusions and gene inversion. Overall, we develop misexpression as a novel type of transcriptomic outlier analysis and extend our understanding of the variety of mechanisms by which genetic variants can influence gene expression.

genetics↗

bioRxiv2023

Analyses of allele age and impact reveal human beneficial alleles are older than neutral controls

A classic population genetic prediction is that alleles experiencing directional selection should swiftly traverse allele frequency space, leaving detectable reductions in genetic variation in linked regions. However, despite this expectation, identifying clear footprints of beneficial allele passage has proven to be surprisingly challenging. We addressed the basic premise underlying this expectation by estimating the ages of large numbers of beneficial and deleterious alleles in a human population genomic data set. Deleterious alleles were found to be young, on average, given their allele frequency. However, beneficial alleles were older on average than non-coding, non-regulatory alleles of the same frequency. This finding is not consistent with directional selection and instead indicates some type of balancing selection. Among derived beneficial alleles, those fixed in the population show higher local recombination rates than those still segregating, consistent with a model in which new beneficial alleles experience an initial period of balancing selection due to linkage disequilibrium with deleterious recessive alleles. Alleles that ultimately fix following a period of balancing selection will leave a modest soft sweep impact on the local variation, consistent with the overall paucity of species-wide hard sweeps in human genomes. Impact StatementAnalyses of allele age and evolutionary impact reveal that beneficial alleles in a human population are often older than neutral controls, suggesting a large role for balancing selection in adaptation.

evolutionary biology↗

bioRxiv2022

Influences of rare protein-coding genetic variants on the human plasma proteome in 50,829 UK Biobank participants

Combining human genomics with proteomics is becoming a powerful tool for drug discovery. Associations between genetic variants and protein levels can uncover disease mechanisms, clinical biomarkers, and candidate drug targets. To date, most population-level proteogenomic studies have focused on common alleles through genome-wide association studies (GWAS). Here, we studied the contribution of rare protein-coding variants to 1,472 plasma proteins abundances measured via the Olink Explore 1536 assay in 50,829 UK Biobank human exomes. Through a variant-level exome-wide association study (ExWAS), we identified 3,674 rare and significant protein quantitative trait loci (pQTLs), of which 76% were undetected in a prior GWAS performed on the same cohort, and we found that rare pQTLs are less likely to be random in their variant effect annotation. In gene-based collapsing analyses, we identified an additional 166 significant gene-protein pQTL signals that were undetected through single-variant analyses. Of the total 456 protein-truncating variant (PTV)-driven cis-pQTLs in the gene-based collapsing analysis, 99.3% were associated with decreased protein levels. We demonstrate how this resource can identify allelic series and propose biomarkers for several candidate therapeutic targets, including GRN, HSD17B13, NLRC4, and others. Finally, we introduce a new collapsing analysis framework that combines PTVs with missense cis-pQTLs that are associated with decreased protein abundance to bolster genetic discovery statistical power. Our results collectively highlight a considerable role for rare variation in plasma protein abundance and demonstrate the utility of plasma proteomics in gene discovery and unravelling mechanisms of action.

genomics↗

bioRxiv2022

Genetic architecture and evolution of color variation in American black bears

Color variation is a frequent evolutionary substrate for camouflage in small mammals but the underlying genetics and evolutionary forces that drive color variation in natural populations of large mammals are mostly unexplained. The American black bear, Ursus americanus, exhibits a range of colors including the cinnamon morph which has a similar color to the brown bear, U. arctos, and is found at high frequency in the American southwest. Reflectance and chemical melanin measurements showed little distinction between U. arctos and cinnamon U. americanus individuals. We used a genome-wide association for hair color as a quantitative trait in 151 U. americanus individuals and identified a single major locus (P < 10-13). Additional genomic and functional studies identified a missense alteration (R153C) in Tyrosinase-related protein 1 (TYRP1) that impaired protein localization and decreased pigment production. Population genetic analyses and demographic modeling indicated that the R153C variant arose 9.36kya in a southwestern population where it likely provided a selective advantage, spreading both northwards and eastwards by gene flow. A different TYRP1 allele, R114C, contributes to the characteristic brown color of U. arctos, but is not fixed across the range. HIGHLIGHTSO_LIThe cinnamon morph of American black bears and brown bears have different missense mutations in TYRP1 that account for their similar coloration C_LIO_LITYRP1 variants in American black bears and brown bears are loss-of-function alleles associated with impaired protein localization to melanosomes C_LIO_LIIn American black bears, the variant causing the cinnamon morph arose 9,360 years ago in the western lineage where it provides an adaptive advantage, and has spread northwards and eastwards by migration C_LI

evolutionary biology↗

bioRxiv2020

Surveying the contribution of rare variants to the genetic architecture of human disease through exome sequencing of 177,882 UK Biobank participants

The UK Biobank (UKB) represents an unprecedented population-based study of 502,543 participants with detailed phenotypic data and linkage to medical records. While the release of genotyping array data for this cohort has bolstered genomic discovery for common variants, the contribution of rare variants to this broad phenotype collection remains relatively unknown. Here, we use exome sequencing data from 177,882 UKB participants to evaluate the association between rare protein-coding variants with 10,533 binary and 1,419 quantitative phenotypes. We performed both a variant-level phenome-wide association study (PheWAS) and a gene-level collapsing analysis-based PheWAS tailored to detecting the aggregate contribution of rare variants. The latter revealed 911 statistically significant gene-phenotype relationships, with a median odds ratio of 15.7 for binary traits. Among the binary trait associations identified using collapsing analysis, 83% were undetectable using single variant association tests, emphasizing the power of collapsing analysis to detect signal in the setting of high allelic heterogeneity. As a whole, these genotype-phenotype associations were significantly enriched for loss-of-function mediated traits and currently approved drug targets. Using these results, we summarise the contribution of rare variants to common diseases in the context of the UKB phenome and provide an example of how novel gene-phenotype associations can aid in therapeutic target prioritisation.

genomics↗

bioRxiv2020

A broad exome study of the genetic architecture of asthma reveals novel patient subgroups

IntroductionAsthma risk is a complex interplay between genetic susceptibility and environment. Despite many significantly-associated common variants, the contribution of rarer variants with potentially greater effect sizes has not been as extensively studied. We present an exome-based study adopting 24,576 cases and 120,530 controls to assess the contribution of rare protein-coding variants to the risk of early-onset or all-comer asthma. MethodsWe performed case-control analyses on three genetic units: variant-, gene- and pathway-level, using sequence data from the Scandinavian Asthma Genetic Study and UK Biobank participants with asthma. Cases were defined as all-comer asthma (n=24,576) and early-onset asthma (n=5,962). Controls were 120,530 UK Biobank participants without reported history of respiratory illness. ResultsVariant-level analyses identified statistically significant variants at moderate-to-common allele frequency, including protein-truncating variants in FLG and IL33. Asthma risk was significantly increased not only by individual, common FLG protein-truncating variants, but also among the collection of rare-to-private FLG protein-truncating variants (p=6.8x10-7). This signal was driven by early-onset asthma and did not correlate with circulating eosinophil levels. In contrast, a single splice variant in IL33 was significantly protective (p=8.0x10-10), while the collection of remaining IL33 protein-truncating variants showed no class effect (p=0.54). A pathway-based analysis identified that protein-truncating variants in loss-of-function intolerant genes were significantly enriched among individuals with asthma. ConclusionsAccess to the full allele frequency spectrum of protein-coding variants provides additional clarity about the potential mechanisms of action for FLG and IL33. Beyond these two significant drivers, we detected a significant enrichment of protein-truncating variants in loss-of-function intolerant genes.

genomics↗

bioRxiv2020

Phantom histories of misspecified pasts

AO_SCPLOWBSTRACTC_SCPLOWThe observation that even a tiny sample of genome sequences from a natural population contains a plethora of information about the history of the population has enticed researchers to use these data to fit complex demographic histories and make detailed inference about the changes a population has experienced through time. Unfortunately, the standard assumptions required to make these inferences are often violated by natural populations in such ways as to produce specious results. This paper examines two phenomena of particular concern: when a sample is drawn from a single sub-population of a larger meta-population these models infer a spurious recent population decline, and when a genome contains loci under weak or recessive purifying selection these models infer a spurious recent population expansion.

genomics↗

bioRxiv2020

Identification of a novel missense variant in SPDL1 associated with idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a fatal disorder characterised by progressive, destructive lung scarring. Despite significant progress, the genetic determinants of this disease remain incompletely defined. Using next generation sequencing data from 752 individuals with sporadic IPF and 119,055 controls, we performed both variant- and gene-level analyses to identify novel IPF genetic risk factors. Our variant-level analysis revealed a novel rare missense variant in SPDL1 (NM_017785.5 p.Arg20Gln; p = 2.4 x 10-7, odds ratio = 2.87). This signal was independently replicated in the FinnGen cohort (combined p = 2.2 x 10-20), firmly associating this variant as a novel IPF risk allele. SPDL1 encodes Spindly, a protein involved in mitotic checkpoint signalling during cell division that has not been previously described in fibrosis. Our results highlight a novel mechanism underlying IPF, providing the potential for new therapeutic discoveries in a disease of great unmet need.

genetics↗

bioRxiv2018

Adaptive landscape of protein variation in human exomes

The human genome contains hundreds of thousands of missense mutations. However, only a handful of these variants are known to be adaptive, which implies that adaptation through protein sequence change is an extremely rare phenomenon in human evolution. Alternatively, existing methods may lack the power to pinpoint adaptive variation. We have developed and applied an Evolutionary Probability Approach (EPA) to discover candidate adaptive polymorphisms (CAPs) through the discordance between allelic evolutionary probabilities and their observed frequencies in human populations. EPA reveals thousands of missense CAPs, which suggest that a large number of previously optimal alleles had experienced a reversal of fortune in the human lineage. We explored non-adaptive mechanisms to explain CAPs, including the effects of demography, mutation rate variability, and negative and positive selective pressures in modern humans. Our analyses suggest that a large proportion of CAP alleles have increased in frequency due to beneficial selection. This conclusion is supported by the facts that a vast majority of adaptive missense variants discovered previously in humans are CAPs, and that hundreds of CAP alleles are protective in genotype-phenotype association data. Our integrated phylogenomic and population genetic EPA approach predicts the existence of thousands of signatures of non-neutral evolution in the human proteome. We expect this collection to be enriched in beneficial variation. EPA approach can be applied to discover candidate adaptive variation in any protein, population, or species for which allele frequency data and reliable multispecies alignments are available.

evolutionary biology↗

bioRxiv2018

Age distributions of rare lineages reveal recent demographic history and selection

The age of an allele of a given frequency offers insight into both its function and origin, and the distribution of ages of alleles in a population conveys significant information about its history. The rarer the allele the more likely it is to reveal functional biological insight and the more recent the historical revelation. By measuring the length of the haplotype shared between an individual carrying a rare variant and its closest relative not carrying the variant we are able to approximate the age of the variant and can apply this method even when only a single copy of a variant has been sampled in a population. Applying this technique to rare variants in a large population sample from the United Kingdom, we identify historical migration from West Africa approximately 400 years ago, evidence of direct selection against novel protein-altering rare variants in individual biological pathways, continued negative frequency dependent selection on protein-altering variants in olfactory transducers and the innate immune system, and map the impact of background selection on the most recent portions of the sample genealogy.

genomics↗

bioRxiv2017

Recent African gene flow responsible for excess of old rare genetic variation in Great Britain

Population genomic studies can reveal the allele frequencies at millions of SNPs, with the numbers of observed low frequency SNPs increasing as more genomes are sequenced. Rare alleles tend to be younger than common alleles and are especially useful for studying demographic history, selection and heritability1,2. However, allele frequency can be a poor proxy for allele age, as genetic drift and natural selection can lead to alleles that are both rare and old. In order to allow joint assessments of allele frequency and allele age, a new estimator of allele age was developed that can be applied to variants of the lowest observed frequencies (singletons). By examining the geographic and age distribution of very rare variants in a large genomic sample from the UK3, we identify new evidence of gene flow from Africa into the ancestors of the modern UK population. A substantial proportion of variants with observed frequencies as low as 1.4 x 10-4 are orders of magnitude older than can be explained without African gene flow and are found at much higher frequencies within modern African populations. We estimate that African populations contributed approximately 1.2% of the UK gene pool and did so approximately 400 years ago. These findings are relevant both to our understanding of human history and to the nature of rare variation segregating within populations: a variant that is rare because it is a recent mutation in the direct ancestor of the population will have had a very different evolutionary history than an ancient one that has persisted at high frequencies in a diverged population and only recently arrived through migration.

genomics↗

bioRxiv2017

Time-conditional properties of branches in coalescent gene trees

Coalescent gene trees have proven to be a powerful framework for formulating and solving problems in population genetics both in theory and practice. Using them, geneticists have been able to generate expectations for many attributes of a random sample of genotypes from a population given a model of the history of the population. This paper derives three new properties of coalescent gene trees that will help characterize the present-day impacts of historical events. Considering a single branch sampled at a given time ts in the past, it presents distributions describing 1) the length of time a branch sampled ts generations in the past had existed at the time of sampling, 2) the length of time that branch continues from time ts towards the present, and3) the the probability that the branch is ancestral to x individuals in a modern sample.

genetics↗

bioRxiv2017

T cell activation and the HLA locus associate with latent infections of human African trypanosomiasis

Infections by many pathogens can result in a wide range of phenotypes, from severe to mild, or even asymptomatic. Understanding the genetic basis of these phenotypes can lead to better tools to treat patients or detect reservoirs. To identify human genetic factors that contribute to symptoms diversity, we examined the range of disease severities caused by the parasite T. b. gambiense, the primary cause of human African trypanosomiasis (HAT). We analyzed the transcriptomes of immune cells from both symptomatic HAT cases and individuals with latent infections. Our analysis identified several genes and pathways that associated with the latent phenotype, primarily suggesting increased T and B cell activation in HAT patients relative to latent infections. We also used these transcriptome data to conduct an exome-wide single nucleotide polymorphism (SNP) association study. This suggested that SNPs in the human major histocompatibility locus (HLA) associate with severity, supporting the transcription data and suggesting that T cell activation is a determining factor in outcome. Finally, to establish if T cell activation controls disease severity, we blocked co-stimulatory dependent T cell activation in an animal model for HAT. This showed that reducing T cell activation during trypanosome infection improves symptoms and reduces parasitemia. Our data has used a combination of transcriptome-wide analysis and an in vivo model to reveal that T cell activation and the HLA locus associate with the development of symptoms during HAT. This may open new avenues for the development of new therapeutics and prognostics.

genetics↗

bioRxiv2017

Protein Evolution Depends On Multiple Distinct Population Size Parameters

That population size affects the fate of new mutations arising in genomes, modulating both how frequently they arise and how efficiently natural selection is able to filter them, is well established. It is therefore clear that these distinct roles for population size that characterize different processes should affect the evolution of proteins and need to be carefully defined. Empirical evidence is consistent with a role for demography in influencing protein evolution, supporting the idea that functional constraints alone do not determine the composition of coding sequences.\n\nGiven that the relationship between population size, mutant fitness and fixation probability has been well characterized, estimating fitness from observed substitutions is well within reach with well-formulated models. Molecular evolution research has, therefore, increasingly begun to leverage concepts from population genetics to quantify the selective effects associated with different classes of mutation. However, in order for this type of analysis to provide meaningful information about the intra- and inter-specific evolution of coding sequences, a clear definition of concepts of population size, what they influence, and how they are best parameterized is essential.\n\nHere, we present an overview of the many distinct concepts that \"population size\" and \"effective population size\" may refer to, what they represent for studying proteins, and how this knowledge can be harnessed to produce better specified models of protein evolution.

evolutionary biology↗