Browsing by Author "Tambi, Richa"

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Analyzing single cell transcriptome data from severe COVID-19 patients
(2022) Nassir, Nasna; Tambi, Richa; Bankapur, Asma; Karuvantevida, Noushad; Zehra, Binte; Begum, Ghausia; Hameid, Reem Abdel; Ahmed, Awab; Shabestari, Seyed Ali Safizadeh; Hachim, Mahmood Yaseen; Alsheikh-Ali, Alawi; Berdiev, Bakhrom; Al Heialy, Saba; Uddin, Mohammed
SUMMARY: We describe the protocol for identifying COVID-19 severity specific cell types and their regulatory marker genes using single-cell transcriptomics data. We construct COVID-19 comorbid disease-associated gene list using multiple databases and literature resources. Next, we identify specific cell type where comorbid genes are upregulated. We further characterize the identified cell type using gene enrichment analysis. We detect upregulation of marker gene restricted to severe COVID-19 cell type and validate our findings using in silico, in vivo, and in vitro cellular models.
Lack of ethnic diversity in single-cell transcriptomics hinders cell type detection and precision medicine inclusivity
(2023) Kosaji, Noor; Zehra, Binte; Nassir, Nasna; Tambi, Richa; Berdiev, Bakhrom K.; Uddin, Mohammed
Abstract: Perhaps one of the most revolutionary next generation sequencing technologies is single-cell (SC) transcriptomics, which was recognized by Nature in 2013 as the method of the year. SC-technologies delve deep into genomics at the single-cell level, revealing previously restricted, valuable information on the identity of single cells, particularly highlighting their heterogeneity. Understanding the cellular heterogeneity of complex tissue provides insight about the gene expression and regulation across different biological and environmental conditions. This vast heterogeneity of cells and their markers makes identifying populations and sub-clusters especially difficult, even more so in rare cell types limited by the absence of rare sub-population markers. One particularly overlooked challenge is the lack of adequate ethnic representation in single-cell data. As the availability of cell types and their markers grow exponentially through new discoveries, the need to study ethnically driven heterogeneity becomes more feasible, while offering the opportunity to further elaborate ethnicity-related heterogeneity. In this commentary, we will discuss this major single-cell limitation particularly focusing on the repercussions it has on disease research, therapeutic design, and precision medicine.
Large-scale all-atom molecular dynamics alanine-scanning of IAPP octapeptides provides insights into the molecular determinants of amyloidogenicity
(2019-02-21) Tambi, Richa
Abstract: In order to investigate the early phase of the amyloid formation by the short amyloidogenic octapeptide sequence (‘NFGAILSS’) derived from IAPP, we carried out a 100ns all-atom molecular dynamics (MD) simulations of systems that contain 27 peptides and over 30,000 water molecules. The large-scale calculations were performed for the wild type sequence and seven alanine-scanned sequences using AMBER 8.0 on RIKEN’s special purpose MD-GRAPE3 supercomputer, using the all-atom point charge force field ff99, which do not favor β-structures. Large peptide clusters (size 18–26 mers) were observed for all simulations, and our calculations indicated that isoleucine at position 5 played important role in the formation of β-rich clusters. In the oligomeric state, the wild type and the S7A sequences had the highest β-structure content (~14%), as calculated by DSSP, in line with experimental observations, whereas I5A and G3A had the highest helical content (~20%). Importantly, the β-structure preferences of wild type IAPP originate from its association into clusters and are not intrinsic to its sequence. Altogether, the results of this first large-scale, multi-peptide all-atom molecular dynamics simulation appear to provide insights into the mechanism of amyloidogenic and non-amyloidogenic oligomers that mainly corroborate previous experimental observations
Long-Read Sequencing Improves the Detection of Structural Variations Impacting Complex Non-Coding Elements of the Genome
(2021) Begum, Ghausia; Albanna, Ammar; Bankapur, Asma; Berdiev, Bakhrom; Karuvantevida, Noushad; Alhashmi, Deena; Alsheikh-Ali, Alawi; Uddin, Mohammed; Nassir, Nasna; Tambi, Richa
Abstract: The advent of long-read sequencing offers a new assessment method of detecting genomic structural variation (SV) in numerous rare genetic diseases. For autism spectrum disorders (ASD) cases where pathogenic variants fail to be found in the protein-coding genic regions along chromosomes, we proposed a scalable workflow to characterize the risk factor of SVs impacting non-coding elements of the genome. We applied whole-genome sequencing on an Emirati family having three children with ASD using long and short-read sequencing technology. A series of analytical pipelines were established to identify a set of SVs with high sensitivity and specificity. At 15-fold coverage, we observed that long-read sequencing technology (987 variants) detected a significantly higher number of SVs when compared to variants detected using short-read technology (509 variants) (p-value < 1.1020 _ 10􀀀57). Further comparison showed 97.9% of long-read sequencing variants were spanning within the 1–100 kb size range (p-value < 9.080 _ 10􀀀67) and impacting over 5000 genes. Moreover, long-read variants detected 604 non-coding RNAs (p-value < 9.02 _ 10􀀀9), comprising 58% microRNA, 31.9% lncRNA, and 9.1% snoRNA. Even at low coverage, long-read sequencing has shown to be a reliable technology in detecting SVs impacting complex elements of the genome.
Single-cell reconstruction and mutation enrichment analysis identifies dysregulated cardiomyocyte and endothelial cells in congenital heart disease
(2023) Tambi, Richa; Bente, Zehra; Nandkishore, Sharon; Sharafat, Shermin; Kader, Faiza; Nassir, Nasna; Mohamed, Nesrin; Ahmed, Awab; Abdel Hameid, Reem; Alasrawi, Samah; Alsheikh-Ali, Alawi; Uddin, Mohammed; Berdiev, Bakhrom K
Abstract: Congenital heart disease (CHD) is one of the most prevalent neonatal congenital anomalies. To catalog the putative candidate CHD risk genes, we collected 16,349 variants [single-nucleotide variants (SNVs) and Indels] impacting 8,308 genes in 3,166 CHD cases for a comprehensive meta-analysis. Using American College of Medical Genetics (ACMG) guidelines, we excluded the 0.1% of benign/likely benign variants and the resulting dataset consisted of 83% predicted loss of function variants and 17% missense variants. Seventeen percent were de novo variants. A stepwise analysis identified 90 variant-enriched CHD genes, of which six (GPATCH1, NYNRIN, TCLD2, CEP95, MAP3K19, and TTC36) were novel candidate CHD genes. Single-cell transcriptome cluster reconstruction analysis on six CHD tissues and four controls revealed upregulation of the top 10 frequently mutated genes primarily in cardiomyocytes. NOTCH1 (highest number of variants) and MYH6 (highest number of recurrent variants) expression was elevated in endocardial cells and cardiomyocytes, respectively, and 60% of these gene variants were associated with tetralogy of Fallot and coarctation of the aorta, respectively. Pseudobulk analysis using the single-cell transcriptome revealed significant (P < 0.05) upregulation of both NOTCH1 (endocardial cells) and MYH6 (cardiomyocytes) in the control heart data. We observed nine different subpopulations of CHD heart cardiomyocytes of which only four were observed in the control heart. This is the first comprehensive meta-analysis combining genomics and CHD single-cell transcriptomics, identifying the most frequently mutated CHD genes, and demonstrating CHD gene heterogeneity, suggesting that multiple genes contribute to the phenotypic heterogeneity of CHD. Cardiomyocytes and endocardial cells are identified as major CHD-related cell types.
Single-cell transcriptome identifies FCGR3B upregulated subtype of alveolar macrophages in patients with critical COVID-19
(2021) Nassir, Nasna; Tambi, Richa; Bankapur, Asma; Al Heialy, Saba; Karuvantevida, Noushad; Zehra, Binte; Begum, Ghausia; Hameid, Reem Abdel; Ahmed, Awab; Shabestari, Seyed Ali Safizadeh; Kandasamy, Richard K; Loney, Tom; Tayoun, Ahmad Abou; Nowotny, Norbert; Hachim, Mahmood Yaseen; Berdiev, Bakhrom; Alsheikh-Ali, Alawi; Uddin, Mohammed
Summary: Understanding host cell heterogeneity is critical for unraveling disease mechanism. Utilizing large-scale single-cell transcriptomics, we analyzed multiple tissue specimens from patients with life-threatening COVID-19 pneumonia, compared with healthy controls. We identified a subtype of monocyte-derived alveolar macrophages (MoAMs) where genes associated with severe COVID-19 comorbidities are significantly upregulated in bronchoalveolar lavage fluid of critical cases. FCGR3B consistently demarcated MoAM subset in different samples from severe COVID-19 cohorts and in CCL3L1-upregulated cells from nasopharyngeal swabs. In silico findings were validated by upregulation of FCGR3B in nasopharyngeal swabs of severe ICU COVID-19 cases, particularly in older patients and those with comorbidities. Additional lines of evidence from transcriptomic data and in vivo of severe COVID-19 cases suggest that FCGR3B may identify a specific subtype of MoAM in patients with severe COVID-19 that may present a novel biomarker for screening and prognosis, as well as a potential therapeutic target.