Biology subjects

Oak, N.

Publications and source records attributed to Oak, N..

4 recordsLinked to original sources

bioRxiv2023

Overlapping representations of food and social stimuli in VTA dopamine neurons

Dopamine neurons of the ventral tegmental area (VTADA) respond to food and social stimuli and contribute to both forms of motivation. However, it is unclear if the same or different VTADA neurons encode these different stimuli. To address this question, we performed 2-photon calcium imaging in mice presented with food and conspecifics, and found statistically significant overlap in the populations responsive to both stimuli. Both hunger and opposite-sex social experience further increased the proportion of neurons that respond to both stimuli, implying that modifying motivation for one stimulus affects responses to both stimuli. In addition, single-nucleus RNA sequencing revealed significant co-expression of feeding- and social-hormone related genes in individual VTADA neurons. Taken together, our functional and transcriptional data suggest overlapping VTADA populations underlie food and social motivation.

neuroscience↗

bioRxiv2020

Ancestry-Specific Predisposing Germline Variants in Cancer

BackgroundCancer risk differs across ancestries and these differences may result from differing prevalence of inherited genetic predisposition. Yet, most germline genomic studies performed to date have focused on individuals of European ancestry. Ancestry-specific analyses of germline genomes are required to inform cancer genetic risk and prognosis for each ancestral group. Here, we investigate potentially germline pathogenic variants in cancer predisposition genes (CPG) and their somatic effects in patients across diverse ancestral backgrounds. MethodsWe performed a retrospective analysis of germline genomic data of 9,899 patients from 33 cancer types generated by The Cancer Genome Atlas (TCGA) project along with matching somatic genomic and transcriptomic data. By collapsing pathogenic and likely pathogenic variants to the gene level, we analyzed the association between variants in CPGs and cancer types within each ancestry. We also identified ancestry- specific predisposing variants and their associated somatic two-hit events and gene expression levels. ResultsRecent genetic ancestry analysis classified the cohort of 9,899 cancer cases into individuals of primarily European, (N = 8,184, 82.7%), African (N = 966, 9.8%), East Asian (N = 649, 6.6%), South Asian (N=48, 0.5%), Native/Latin American (N=41, 0.4%), and admixed (N=11, 0.1%) ancestries. In the African ancestry, we discovered a potentially novel association of BRCA2 in lung squamous cell carcinoma (OR = 41.4 [95% CI, 6.1-275.6]; FDR = 0.002) along with the previously identified association of BRCA2 in ovarian serous cystadenocarcinoma (OR=8.5 [95% CI, 1.5-47.4]; FDR=0.045). Similarly, in the East Asian ancestry, we discovered one previously known association of BRIP1 in stomach adenocarcinoma (OR=12.8 [95% CI, 1.8-90.84]; FDR=0.038). Rare variant burden analysis further identified 7 suggestive associations for cancer-gene pairs in African ancestry individuals previously well described in European ancestry including SDHB in pheochromocytoma and paraganglioma, ATM in prostate adenocarcinoma, VHL in kidney renal clear cell carcinoma, FH in kidney renal papillary cell carcinoma, and PTEN in uterine corpus endometrial carcinoma. Loss of heterozygosity was identified for 7 out of the 15 African ancestry carriers of predisposing variants. Further, tumors from the SDHB or BRCA2 carriers showed simultaneous allelic specific expression and low gene expression of their respective affected genes; and FH splice-site variant carriers showed mis-splicing of FH. ConclusionWhile several predisposing genes are shared across patients, many pathogenic variants are found to be ancestry-specific and trigger somatic effects. Analysis of larger diverse ancestries genomic cohorts are required to pinpoint ancestry- specific genetic predisposition to inform personalized diagnosis and screening strategies.

genomics↗

bioRxiv2017

Landscape of human miRNA variation and conservation using Annotative Database of miRNA Elements, ADmiRE

MicroRNAs (miRNAs) are the most abundant class of non-coding RNAs that regulate expression of >60% genes and are frequently deregulated in many human diseases. Sequence variants in miRNAs are expected to have a high impact on miRNA function. However, the lack of miRNA variant annotation and prioritization guidelines has hampered this analysis from whole genome/exome sequencing (WGS/WES) studies. Through the development of an Annotative Database of miRNA Elements, ADmiRE workflow, we re-annotated the publicly available population dataset of gnomAD 15,596 WGS and 123,136 WES and describe 26,094 precursor-miRNA variants. AdmiRE annotates twice the miRNA variants predicted by existing tools which prioritize variation relative to protein coding regions. We provide the allele frequency distribution of miRNA variation which is comparable to variation in exonic regions. This distribution is similar for miRNAs located in the intragenic and intergenic genomic context. Moreover, high confidence miRNAs (designated by miRBase) harbor less variation (the majority contributed by rare variants) compared with the remaining miRNAs. We identify 279 miRNAs highly constrained with little or no variation in gnomAD. We further describe the evolutionary conservation of miRNAs across 100 vertebrates and identify 434 highly conserved miRNAs. We demonstrate that these constraint and conservation metrics (now incorporated into the ADmiRE workflow) characterize miRNAs previously implicated in human diseases. In conclusion, through the development of ADmiRE, we comprehensively analyze the landscape of miRNA sequence variation in large human population datasets and provide miRNA vertebrate conservation scores to aid future studies of miRNA variation in human diseases.

genomics↗

bioRxiv2017

Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines

BackgroundThe American College of Medical Genetics and American College of Pathologists (ACMG/AMP) variant classification guidelines for clinical reporting are widely used in diagnostic laboratories for variant interpretation. The ACMG/AMP guidelines recommend complete concordance of predictions among all in silico algorithms used without specifying the number or types of algorithms. The subjective nature of this recommendation contributes to discordance of variant classification among clinical laboratories and prevents definitive classification of variants.\n\nResultsUsing 14,819 benign or pathogenic missense variants from the ClinVar database, we compared performance of 25 algorithms across datasets differing in distinct biological and technical variables. There was wide variability in concordance among different combinations of algorithms with particularly low concordance for benign variants. We also identify a previously unreported source of error in variant interpretation where in silico predictions are opposite to the evidence provided by other sources. We identified recently developed algorithms with high predictive power and robust to variables like disease mechanism, gene constraint and mode of inheritance, although poorer performing algorithms are more frequently used based on review of the clinical genetics literature (2011-2017).\n\nConclusionsOur analyses identify algorithms with high performance characteristics independent of underlying disease mechanisms. We describe combinations of algorithms with increased concordance that should improve in silico algorithm usage during assessment of clinically relevant variants using the ACMG/AMP guidelines.

bioinformatics↗