Biology subjects

Neavin, D.

Publications and source records attributed to Neavin, D..

7 recordsLinked to original sources

bioRxiv2024

Protein Translation Dysregulation and Immune Cell Evasion Define Metastatic Clones in HPV-related Cancer of the Oropharynx

Head and neck cancers, representing the seventh most common malignancy globally, have seen a shift in causative factors from traditional smoking and alcohol use to human papillomavirus (HPV) infection, now accounting for up to 80% of oropharyngeal cancers. We identify the cellular and clonal mechanisms underlying immune avoidance and metastasis by analysing single-cell and spatial genomic data from primary and metastatic cancers. We first map the clonal evolution of malignant cells based on the accumulation of mutations. We identify metastasising clones based on mutational similarity scores between cells in the primary and lymph node metastasis. Genomic analysis of metastasising and non-metastasising clones identified virally mediated protein translation relief (P=4.24x10-24) pathway underlying metastatic expansion. We show that in metastatic clones, this process is driven through upregulation of transition-initiating factors, EIF4E (P=1.5x10-13) and EIFG1 (P<2.22x10-16), and suppression of regulatory kinases EIF4EBP1 (P=2.1x10), EIF2AK2 (P<2.22x10-16), and EIF2S1 (P<2.22x10-16). We subsequently identify that metastatic clones have a corresponding downregulation of the JAK/STAT pathway and immunoproteasome genes PSMB8 (P<2.22x10- 16) and PSMB9 (P<2.22x10-16), suggesting these clones escape immune surveillance through decreased INF inflammatory response and antigen presentation. We validate these results using spatial RNA-seq data, where metastatic cancer clones show decreased cell-to-cell interactions with CD4 T-effector memory cells (CD4TEM) (P=0.0077), CD8 T-exhausted cells (CD8Ex) (P=0.0191), and innate lymphoid cells (ILC) (P=0.04). Finally, we demonstrate that the upregulation of cap-independent translational drives cell proliferation in metastatic clones through the expression of translation initiation factors (EIF4G1: P<2.22x10-16). Our results provide evidence of the mechanisms by which virally induced cancer clones lead to advanced disease and poor prognosis in patients.

cancer biology↗

bioRxiv2024

Deep sequencing of proteotoxicity modifier genes uncovers a Presenilin-2/beta-amyloid-actin genetic risk module shared among alpha-synucleinopathies

Whether neurodegenerative diseases linked to misfolding of the same protein share genetic risk drivers or whether different protein-aggregation pathologies in neurodegeneration are mechanistically related remains uncertain. Conventional genetic analyses are underpowered to address these questions. Through careful selection of patients based on protein aggregation phenotype (rather than clinical diagnosis) we can increase statistical power to detect associated variants in a targeted set of genes that modify proteotoxicities. Genetic modifiers of alpha-synuclein ([a]S) and beta-amyloid (A{beta}) cytotoxicity in yeast are enriched in risk factors for Parkinsons disease (PD) and Alzheimers disease (AD), respectively. Here, along with known AD/PD risk genes, we deeply sequenced exomes of 430 [a]S/A{beta} modifier genes in patients across alpha-synucleinopathies (PD, Lewy body dementia and multiple system atrophy). Beyond known PD genes GBA1 and LRRK2, rare variants AD genes (CD33, CR1 and PSEN2) and A{beta} toxicity modifiers involved in RhoA/actin cytoskeleton regulation (ARGHEF1, ARHGEF28, MICAL3, PASK, PKN2, PSEN2) were shared risk factors across synucleinopathies. Actin pathology occurred in iPSC synucleinopathy models and RhoA downregulation exacerbated [a]S pathology. Even in sporadic PD, the expression of these genes was altered across CNS cell types. Genome-wide CRISPR screens revealed the essentiality of PSEN2 in both human cortical and dopaminergic neurons, and PSEN2 mutation carriers exhibited diffuse brainstem and cortical synucleinopathy independent of AD pathology. PSEN2 contributes to a common-risk signal in PD GWAS and regulates [a]S expression in neurons. Our results identify convergent mechanisms across synucleinopathies, some shared with AD.

genomics↗

bioRxiv2022

Pitfalls and opportunities for applying PEER factors in single-cell eQTL analyses

Using latent variables in gene expression data can help correct spurious correlations due to unobserved confounders and increase statistical power for expression Quantitative Trait Loci (eQTL) detection. Probabilistic Estimation of Expression Residuals (PEER) is a widely used statistical method that has been developed to remove unwanted variation and improve eQTL discovery power in bulk RNA-seq analysis. However, its performance has not been largely evaluated in single-cell eQTL data analysis, where it is becoming a commonly used technique. Potential challenges arise due to the structure of single-cell data, including sparsity, skewness, and mean-variance relationship. Here, we show by a series of analyses that this method requires additional quality control and data transformation steps on the pseudo-bulk matrix to obtain valid PEER factors. By using a population-scale single-cell cohort (OneK1K, N = 982), we found that generating PEER factors without further QC or transformation on the pseudo-bulk matrix could result in inferred factors that are highly correlated (Pearsons correlation r = 0.626[~]0.997). Similar spurious correlations were also found in PEER factors inferred from an independent dataset (induced pluripotent stem cells, N = 31). Optimization of the strategy for generating PEER factors and incorporating the improved PEER factors in the eQTL association model can identify 9.0[~]23.1% more eQTLs or 1.7%[~]13.3% more eGenes. Sensitivity analysis showed that the pattern of change between the number of eGenes detected and PEER factors fitted varied significantly for different cell types. In addition, using highly variable genes (e.g., top 2000) to generate PEER factors could achieve similar eGenes discovery power as using all genes but save considerable computational resources ([~]6.2-fold faster). We provide diagnostic guidelines to improve the robustness and avoid potential pitfalls when generating PEER factors for single-cell eQTL association analyses.

bioinformatics↗

bioRxiv2022

Demuxafy: Improvement in droplet assignment by integrating multiple single-cell demultiplexing and doublet detection methods

Recent innovations in droplet-based single-cell RNA-sequencing (scRNA-seq) have provided the technology necessary to investigate biological questions at cellular resolution. With the ability to assay thousands of cells in a single capture, pooling cells from multiple individuals has become a common strategy. Droplets can subsequently be assigned to a specific individual by leveraging their inherent genetic differences, and numerous computational methods have been developed to address this problem. However, another challenge implicit with droplet-based scRNA-seq is the occurrence of doublets - droplets containing two or more cells. The inaccurate assignment of cells to individuals or failure to remove doublets contribute unwanted noise to the data and result in erroneous scientific conclusions. Therefore, it is essential to assign cells to individuals and remove doublets accurately. We present a new framework to improve individual singlet classification and doublet removal through a multi-method intersectional approach. We developed a framework to evaluate the enhancement in donor assignment and doublet removal through the consensus intersection of multiple demultiplexing and doublet detecting methods. The accuracy was assessed using scRNA-seq data of [~]1.4 million peripheral blood mononucleated cells from 1,034 unrelated individuals and [~]90,000 fibroblast cells from 81 unrelated individuals. We show that our approach significantly improves droplet assignment by separating singlets from doublets and classifying the correct individual compared to any single method. We show that the best combination of techniques varies under different biological and experimental conditions, and we present a framework to optimise cell assignment for a given experiment. We offer Demuxafy (https://demultiplexing-doublet-detecting-docs.readthedocs.io/en/latest/index.html) - a framework built-in Singularity to provide clear, consistent documentation of each method and additional tools to simplify and improve demultiplexing and doublet removal. Our results indicate that leveraging multiple demultiplexing and doublet detecting methods improves accuracy and, consequently, downstream analyses in multiplexed scRNA-seq experiments.

genomics↗

bioRxiv2021

Transcriptomic and proteomic retinal pigment epithelium signatures of age-related macular degeneration.

Induced pluripotent stem cells generated from patients with geographic atrophy as well as healthy individuals were differentiated to retinal pigment epithelium (RPE) cells. By integrating transcriptional profiles of 127,659 RPE cells generated from 43 individuals with geographic atrophy and 36 controls with genotype data, we identified 439 expression Quantitative Trait (eQTL) loci in cis that were associated with disease status and specific to subpopulations of RPE cells. We identified loci linked to two genes with known associations with geographic atrophy - PILRB and PRPH2, in addition to 43 genes with significant genotype x disease interactions that are candidates for novel genetic associations for geographic atrophy. On a transcriptome-only level, we identified molecular pathways significantly upregulated in geographic atrophy-RPE including in extracellular cellular matrix reorganisation, neurodegeneration, and mitochondrial functions. We subsequently implemented a large-scale proteomics analysis, confirming modification in proteins associated with these pathways. We also identified six significant protein (p) QTL that regulate protein expression in the RPE cells and in geographic atrophy - two of which share variants with cis-eQTL. Transcriptome-wide association analysis identified genes at loci previously associated with age-related macular degeneration. Further analysis conditional on disease status, implicated statistically significant RPE-specific eQTL. This study uncovers important differences in RPE homeostasis associated with geographic atrophy.

neuroscience↗

bioRxiv2021

Village in a dish: a model system for population-scale hiPSC studies

The mechanisms by which DNA alleles contribute to disease risk, drug response, and other human phenotypes are highly context-specific, varying across cell types and under different conditions. Human induced pluripotent stem cells (hiPSCs) are uniquely suited to study these context-dependent effects, but to do so requires cell lines from hundreds or potentially thousands of individuals. Village cultures, where multiple hiPSC lines are cultured and differentiated together in a single dish, provide an elegant solution for scaling hiPSC experiments to the necessary sample sizes required for population-scale studies. Here, we show the utility of village models, demonstrating how cells can be assigned back to a donor line using single cell sequencing, and addressing whether line-specific signaling alters the transcriptional profiles of companion lines in a village culture. We generated single cell RNA sequence data from hiPSC lines cultured independently (uni-culture) and in villages at three independent sites. We show that the transcriptional profiles of hiPSC lines are highly consistent between uni- and village cultures for both fresh (0.46 < R < 0.88) and cryopreserved samples (0.46 < R < 0.62). Using a mixed linear model framework, we estimate that the proportion of transcriptional variation across cells is predominantly due to donor effects, with minimal evidence of variation due to culturing in a village system. We demonstrate that the genetic, epigenetic or hiPSC line-specific effects on gene expression are consistent whether the lines are uni- or village-cultured (0.82 < R < 0.94). Finally, we identify the consistency in the landscape of cell states between uni- and village-culture systems. Collectively, we demonstrate that village methods can be effectively used to detect hiPSC line-specific effects including sensitive dynamics of cell states.

genomics↗

bioRxiv2019

Acylcarnitine Metabolomic Profiles Inform Clinically-Defined Major Depressive Phenotypes

BackgroundAcylcarnitines have important functions in mitochondrial energetics and {beta}-oxidation, and have been implicated to play a significant role in metabolic functions of the brain. This retrospective study examined whether plasma acylcarnitine profiles can help biochemically distinguish the three phenotypic subtypes of major depressive disorder (MDD)--(core depression (CD+), anxious depression (ANX+), and neurovegetative symptoms of melancholia (NVSM+))--following treatment with a selective serotonin reuptake inhibitor (SSRI).\n\nMethodsDepressed outpatients (n=240) from the Mayo Clinic Pharmacogenomics Research Network were treated with citalopram or escitalopram for eight weeks. Plasma samples collected at baseline and eight weeks post-treatment were profiled for multiple-, short-, medium- and long-chain acylcarnitine levels using AbsoluteIDQ(R)p180-Kit and LC-MS. Linear mixed effects models were used to examine whether acylcarnitine levels discriminate the clinical phenotypes at baseline or eight weeks post-treatment, and whether temporal changes in acylcarnitine profiles differ between groups.\n\nResultsAt baseline, significantly lower concentrations of short- and long-chain acylcarnitines were found in CD+ and NVSM+ compared to ANX+, and the short-chain acylcarnitines remained lower after eight weeks. At eight weeks, the medium- and long-chain acylcarnitines were significantly lower in NVSM+ compared to ANX+. Regarding changes baseline to week eight, short-chain acylcarnitine levels significantly increased in CD+ and ANX+, and medium- and long-chain acylcarnitines significantly decreased in NVSM+ and CD+.\n\nConclusionsIn depressed patients treated with SSRIs, {beta}-oxidation and mitochondrial energetics as evaluated by levels and changes in acylcarnitines may provide the biochemical basis of the clinical heterogeneity of MDD, especially when combined with clinical characteristics.

biochemistry↗