Biology subjects

Torkamani, A.

Publications and source records attributed to Torkamani, A..

11 recordsLinked to original sources

bioRxiv2025

Advanced Melissopalynology For Ensuring Honey Authenticity And Quality: Insights From Diverse Floral Resources as Diverse Natural Component to Apitherapy

As the global honey market experiences exponential growth, ensuring the authenticity and quality of honey has become a critical concern. This study employs melissopalynology, the analysis of pollen in honey, to ascertain both the botanical and geographical origins of honey samples. Through the examination of honey samples representing a diverse range of plant families, the research highlights the prevalence of Asteraceae and Fabaceae. The findings emphasize the pivotal role of melissopalynology in safeguarding honey quality, facilitating fair pricing, and detecting adulteration. This research not only enhances the authentication cycle for monofloral honey but also contributes significantly to the creation of a comprehensive melissopalynological database, underpinning future studies in honey authenticity and quality control.

plant biology↗

bioRxiv2025

Allele Specific Expression Quality Control Fills Critical Gap in Transcriptome Assisted Rare Variant Interpretation

Allele-specific expression (ASE) captures the functional impact of genetic variation on transcription, offering a high-resolution view of cis-regulatory effects, but its quality can be diminished by technical, biological, and analysis artifacts. We introduce aseQC, a statistical framework that quantifies sample-level ASE quality in terms of the overall expected extra-binomial variation to exclude uncharacteristically noisy samples in a cohort to improve robustness of downstream analyses. Applying aseQC to a dataset of rare mendelian muscular disorders, successfully identified previously annotated low-quality cases demonstrating clinical genomic utility. When applied to 15,253 samples in extensively quality controlled GTEx project data, aseQC uncovered 563 low-quality samples that exhibit excessive allelic imbalance. We identify these to be associated with specific processing dates but not otherwise described adequately by any other quality control measures and metadata available in GTEx data. We show that these low-quality samples lead to 23.6 and 31.6 -fold increased ASE, and splicing outliers, degrading the performance of transcriptome analysis for rare variant interpretation. In contrast, we did not observe any adverse effect associated with inclusion of these samples in common-variant analysis using quantitative traits loci mapping. By enabling quick and reliable assessment of sample quality, aseQC presents a critical step for identifying subtle quality issues that remain critical for a successful analysis of rare variant effects using transcriptome data.

bioinformatics↗

bioRxiv2024

AoUPRS: A Cost-Effective and Versatile PRS Calculator for the All of Us Program

BackgroundThe All of Us (AoU) Research Program provides a comprehensive genomic dataset to accelerate health research and medical breakthroughs. Despite its potential, researchers face significant challenges, including high costs and inefficiencies associated with data extraction and analysis. AoUPRS addresses these challenges by offering a versatile and cost-effective tool for calculating polygenic risk scores (PRS), enabling both experienced and novice researchers to leverage the AoU dataset for significant genomic discoveries. ResultsAoUPRS is implemented in Python and utilizes the Hail framework for genomic data analysis. It offers two distinct approaches for PRS calculation: the Hail MatrixTable (MT) and the Hail Variant Dataset (VDS). The MT approach provides a dense representation of genotype data, while the VDS approach offers a sparse representation, significantly reducing computational costs. In performance evaluations, the VDS approach demonstrated a cost reduction of up to 99.51% for smaller scores and 85% for larger scores compared to the MT approach. Both approaches yielded similar predictive power, as shown by logistic regression analyses of PRS for coronary artery disease, atrial fibrillation, and type 2 diabetes. The empirical cumulative distribution functions (ECDFs) for PRS values further confirmed the consistency between the two methods. ConclusionsAoUPRS is a versatile and cost-effective tool that addresses the high costs and inefficiencies associated with PRS calculations using the AoU dataset. By offering both dense and sparse data processing approaches, AoUPRS allows researchers to choose the approach best suited to their needs, facilitating genomic discoveries. The tools open-source availability on GitHub, coupled with detailed documentation and tutorials, ensures accessibility and ease of use for the scientific community.

bioinformatics↗

bioRxiv2022

Genome-wide analysis of Structural Variants in Parkinson's Disease using Short-Read Sequencing data

Parkinsons disease is a complex neurodegenerative disorder, affecting approximately one million individuals in the USA alone. A significant proportion of risk for Parkinsons disease is driven by genetics. Despite this, the majority of the common genetic variation that contributes to disease risk is unknown, in-part because previous genetic studies have focussed solely on the contribution of single nucleotide variants. Structural variants represent a significant source of genetic variation in the human genome. However, because assay of this variability is challenging, structural variants have not been cataloged on a genome-wide scale, and their contribution to the risk of Parkinsons disease remains unknown. In this study, we 1) leveraged the GATK-SV pipeline to detect and genotype structural variants in 7,772 short-read sequencing data and 2) generated a subset of matched whole-genome Oxford Nanopore Technologies long-read sequencing data from the PPMI cohort to allow for comprehensive structural variant confirmation. We detected, genotyped, and tested 3,154 "high-confidence" common structural variant loci, representing over 412 million nucleotides of non-reference genetic variation. Using the long-read sequencing data, we validated three structural variants that may drive the association signals at known Parkinsons disease risk loci, including a 2kb intronic deletion within the gene LRRN4. Further, we confirm that the majority of structural variants in the human genome cannot be detected using short-read sequencing alone, encompassing on average around 4 million nucleotides of inaccessible sequence per genome. Therefore, although these data provide the most comprehensive survey of the contribution of structural variants to the genetic risk of Parkinsons disease to date, this study highlights the need for large-scale long-read datasets to fully elucidate the role of structural variants in Parkinsons disease.

genetics↗

bioRxiv2021

Rapid, Reference-Free Human Genotype Imputation with Denoising Autoencoders

Genotype imputation is a foundational tool for population genetics. Standard statistical imputation approaches rely on the co-location of large whole-genome sequencing-based reference panels, powerful computing environments, and potentially sensitive genetic study data. This results in computational resource and privacy-risk barriers to access to cutting-edge imputation techniques. Moreover, the accuracy of current statistical approaches is known to degrade in regions of low and complex linkage disequilibrium. Artificial neural network-based imputation approaches may overcome these limitations by encoding complex genotype relationships in easily portable inference models. Here we demonstrate an autoencoder-based approach for genotype imputation, using a large, commonly-used reference panel, and spanning the entirety of human chromosome 22. Our autoencoder-based genotype imputation strategy achieved superior imputation accuracy across the allele-frequency spectrum and across genomes of diverse ancestry, while delivering at least 4-fold faster inference run time relative to standard imputation tools.

bioinformatics↗

bioRxiv2020

Identification of an N-acetylneuraminic acid-presenting bacteria isolated from a healthy human microbiome

N-acetylneuraminic acid is the most abundant sialic acid in humans and is generally expressed as the terminal sugar on intestinal mucus glycans. Several pathogenic bacterial species harvest sialic acid from the mucus, diet, as well as other intestinal sources and display this sugar on their own surface to evade sialic acid-binding immunoglobulin-type lectin (Siglec)-mediated host immune surveillance. While previous studies have identified bacterial enzymes associated with sialic acid catabolism, no reported methods permit the selective labeling, tracking, and quantitation of sialic acid-presenting microbes within complex multi-microbial systems. Here, we apply an interdisciplinary approach combining metabolic labeling, click chemistry, metagenomic, and whole-genome sequencing to selectively track and identify sialic acid-presenting microbes from a cultured healthy human fecal microbiome. We isolated and identified a new strain of Escherichia coli that incorporates sialic acid onto its own surface. Analysis of the sequence data reveals that this E. coli strain encodes for the NanT, NeuA, and NeuS genes necessary for harvesting environmental sialic acid and generating the capsular polysialic acid. We envision that this method is applicable to the detection and quantitation of sialic acid-presenting bacteria from human, animal, and environmental microbiomes, as well as investigating the importance of other carbohydrates to commensal and pathogenic bacteria.

microbiology↗

bioRxiv2020

Large-scale pathway-specific polygenic risk, transcriptomic community networks and functional inferences in Parkinson disease

Polygenic inheritance plays a central role in Parkinson disease (PD). A priority in elucidating PD etiology lies in defining the biological basis of genetic risk. Unraveling how risk leads to disruption will yield disease-modifying therapeutic targets that may be effective. Here, we utilized a high-throughput and hypothesis-free approach to determine biological pathways underlying PD using the largest currently available cohorts of genetic data and gene expression data from International Parkinsons Disease Genetics Consortium (IPDGC) and the Accelerating Medicines Partnership - Parkinsons disease initiative (AMP-PD), among other sources. We placed these insights into a cellular context. We applied large-scale pathway-specific polygenic risk score (PRS) analyses to assess the role of common variation on PD risk in a cohort of 457,110 individuals by focusing on a compilation of 2,199 publicly annotated gene sets representative of curated pathways, of which we nominate 46 pathways associated with PD risk. We assessed the impact of rare variation on PD risk in an independent cohort of whole-genome sequencing data, including 4,331 individuals. We explored enrichment linked to expression cell specificity patterns using single-cell gene expression data and demonstrated a significant risk pattern for adult dopaminergic neurons, serotonergic neurons, and radial glia. Subsequently, we created a novel way of building de novo pathways by constructing a network expression community map using transcriptomic data derived from the blood of 1,612 PD patients, which revealed 54 connecting networks associated with PD. Our analyses highlight several promising pathways and genes for functional prioritization and provide a cellular context in which such work should be done.

genetics↗

bioRxiv2020

Decoding the transcriptome of atherosclerotic plaque at single-cell resolution

Atherogenesis involves an interplay of inflammation, tissue remodeling and cellular transdifferentiation (CTD), making it especially difficult to precisely delineate its pathophysiology. Here we examine the single-cell transcriptome of entire atherosclerotic core (AC) plaques and patient-matched proximal adjacent (PA) portions of carotid artery tissue from patients undergoing carotid endarterectomy. We use a novel tissue dissociation strategy, single-cell RNA sequencing, and systems-biology approaches to analyze the transcriptional profiles of six main cell populations and identify key gene drivers of pathogenic biological processes in vascular smooth muscle cells (VSMCs) and endothelial cells (ECs). Our results reveal an anatomic continuum whereby PA cells promote and respond to inflammatory processes and eventually transition through CTD into matrix-secreting cells in the AC. Inflammatory signaling in PA ECs is driven by IL6, while TNFa signaling defines inflammation in both PA ECs and VSMCs. Furthermore, we identify POSTN, SPP1 and IBSP in AC VSMCs, and ITLN1, SCX and S100A4 in AC ECs as key drivers of CTD in the atherosclerotic core. These results establish an anatomic framework for atherogenesis and suggest a site-specific strategy for disruption of disease progression.

genomics↗

bioRxiv2019

High throughput Characterization of KCNB1 variants Associated with Developmental and Epileptic Encephalopathy

Pathogenic variants in KCNB1, encoding the voltage-gated potassium channel Kv2.1, are associated with developmental and epileptic encephalopathies (DEE). Previous functional studies on a limited number of KCNB1 variants indicated a range of molecular mechanisms by which variants affect channel function, including loss of voltage sensitivity, loss of ion selectivity, and reduced cell-surface expression. We evaluated a series of 17 KCNB1 variants associated with DEE or neurodevelopmental disorder (NDD) to rapidly ascertain channel dysfunction using high-throughput functional assays. Specifically, we investigated the biophysical properties and cell-surface expression of variant Kv2.1 channels expressed in heterologous cells using high-throughput automated electrophysiology and immunocytochemistry-flow cytometry. Pathogenic variants exhibited diverse functional defects, including altered current density and shifts in the voltage-dependence of activation and/or inactivation, as homotetramers or when co-expressed with wild-type Kv2.1. Quantification of protein expression also identified variants with reduced total Kv2.1 expression or deficient cell-surface expression.\n\nOur study establishes a platform for rapid screening of functional defects of KCNB1 variants associated with DEE and other NDDs, which will aid in establishing KCNB1 variant pathogenicity and may enable discovery of targeted strategies for therapeutic intervention based on molecular phenotype.

genetics↗

bioRxiv2018

Identification of essential regulatory elements in the human genome

The identification of essential regulatory elements is central to the understanding of the consequences of genetic variation. Here we use novel genomic data and machine learning techniques to map essential regulatory elements and to guide functional validation. We train an XGBoost model using 38 functional and structural features, including genome essentiality metrics, 3D genome organization and enhancer reporter STARR-seq data to differentiate between pathogenic and control non-coding genetic variants. We validate the accuracy of prediction by using data from tiling-deletion-based and CRISPR interference screens of activity of cis-regulatory elements. In neurodevelopmental disorders, the model (ncER, non-coding Essential Regulation) maps essential genomic segments within deletions and rearranged topologically associated domains linked to human disease. We show that the approach successfully identifies essential regulatory elements in the human genome.

genomics↗

bioRxiv2017

Moving Beyond Clinical Risk Scores with a Mobile App for the Genomic Risk of Coronary Artery Disease

Primary prevention of coronary artery disease (CAD) is important for individuals at increased risk, and largely consists of healthy lifestyle modifications and initiation of medications when appropriate - including statins. Defining the inherent risk for any given individual typically relies on traditional risk factors established decades ago by the Framingham Heart Study. Unfortunately, recent studies have indicated that these traditional clinical risk factors systematically overestimate the risk of CAD across all major ancestries. This has increased the number of patients that would be eligible for statin therapy for the primary prevention of CAD but would likely receive little benefit and potentially incur negative consequences. On the other hand, researchers have demonstrated that genetic factors can effectively identify a subset of high risk individuals, and that the benefit from statin therapy is greatest among individuals with the highest genetic risk score (GRS). These individuals also receive the greatest absolute benefit from healthy lifestyle choices, being able to titrate their risk to normal levels despite high genetic predisposition. However, it is not yet possible for the average individual to discover their genetic risk because no tools are currently available to make such a determination. Here, we present a free mobile app - MyGeneRank - that can provide this information. Individuals may choose to use this knowledge to complement traditional risk assessments, and make critical decisions regarding lifelong statin therapy and lifestyle changes. As of 1/25/2017, MyGeneRank is currently in closed beta and will soon be available to the public.

genomics↗