Jing Li, Ph.D.
Institute of Biosciences and Technology (IBT), Texas A&M University Health Science Center
Jing Li is a postdoctoral researcher at the Institute of Biosciences and Technology (IBT), Texas A&M University Health Science Center. He received a Ph.D. in Biomedical Engineering from Purdue University before starting the position at IBT. His previous work resulted in several publications including topics in single cell discrete, mechano-dynamic models that focused on intracellular cytoskeletal dynamics during cell morphogenesis. The modeling components include cytoskeletal proteins such as actin filaments and microtubules. He has been proactively pursing research with the goal of developing multiscale model for single and collective cell dynamics, which also requires integrative data analysis across various spatiotemporal scales. He is currently committed to single cell spatial transcriptomics study which involves the development of an innovative mapping technique to understand patterning of cardiac progenitor cells during early embryonic stages in mice. He has started serving as a steering committee member at the Gulf Coast Consortia (GCC), for the GCC Single Cell Omics Scholars Program.
Abstract:
Multi-omic Modeling of Candidate Regulatory Elements to Decipher Congenital Heart Disease
Congenital heart defects (CHD) arise upon dysregulation of highly synchronized, chronological sequence of proliferation and differentiation of cardiac progenitor cells (CPCs). The CPCs are present in both the first and second heart field (SHF). As the SHF contributes entirely to the outflow tract, mutations in the SHF-related genes lead to major arterial pole defects which account for one third of congenital heart defects in newborn children. Understanding the cellular behavior and the underlying molecular mechanisms are of vital importance, not only for early detection but also for precise therapy in a timely manner. Leveraging the PCGC, CDDRC and our SHF single cell RNA-seq datasets, we aim to develop a comprehensive metric for quantitative analysis of SHF cell’s migratory level and epithelial to mesenchymal (EMT) transitional status. Further, we will perform spatiotemporal analysis to identify and validate specific patterns of genes and genotypes associated with SHF-derived CHD as seen in the PCGC and CDDRC databases.
Richa Naveed Ahamed, Ph.D.
Centre for Applied and Translational Genomics (CATG), Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU), Dubai Health, Dubai, UAE
Richa is a Computational Biologist currently working as a Data Scientist at the Centre for Applied and Translational Genomics (CATG) at Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU), Dubai, UAE. She completed her Ph.D. in Bioinformatics at SASTRA University, India, followed by a JSPS Postdoctoral fellowship. Her first postdoctoral research was conducted at the Tokyo University of Agriculture and Technology (TUAT), Japan, where she focused on in silico protein structural analysis using machine learning and molecular dynamics. Subsequently, she joined MBRU as a Postdoctoral Fellow in a lab co-led by Dr. Bakhrom Berdiev and Dr. Mohammed Uddin, where she delved into genomics with a particular focus on single-cell transcriptomics. Among her contributions, she uncovered the molecular convergence of clinically relevant mutations associated with the etiology of Brugada Syndrome. Furthermore, she also carried out a comprehensive analysis integrating congenital heart disease (CHD) genomics with single-cell transcriptomics, identifying the most commonly mutated CHD genes and highlighting their genetic diversity. Richa enjoys exploring various types of omics data, utilizing existing tools, and creating innovative applications to tackle complex biological problems. She is particularly interested in investigating biological disorders using a multi-omic approach.
Abstract:
Comprehensive Investigation of Post-Zygotic Mosaic Variants in Congenital Heart Disease
Postzygotic Mosaicism (PZM) occurs as a result of mutations arising after fertilization, which results in two or more distinct cell populations (at least one of them carrying mutation) within an individual. These distinct cell populations can be present in one or more tissues. PZM has been linked to cancer, developmental syndromes, neurodevelopmental disorders, autoinflammatory diseases, and atrial fibrillation. In this work I aim at understanding the contribution of mosaic mutations to congenital heart disease (CHD) utilizing the WES and WGS data available from the PCGC CHD cohort. I will recall the WES/WGS data to identify the pathogenic mosaic SNV and CNVs using an array of bioinformatics tools including EM-Mosaic, Mutect2, MosaicHunter, MosaicForecast, Horizon and DeepMosai on the Biodata Catalyst SevenBridges workspace. Overall, systematic evaluation of PZM and their involvement in CHD will allow us to distinguish between mosaic and germline mutations, gain knowledge about the timing of mutations, evaluate the likelihood of recurrence in families, enhance our understanding of the disease genetics, and facilitate more precise diagnoses and personalized treatment strategies.
Wenxing Li, Ph.D.
Department of Systems Biology, Columbia University
liwenxing2016@gmail.com; wl2907@cumc.columbia.edu
Brief Biography
Wenxing Li is a postdoctoral research scientist at the Department of Systems Biology, Columbia University. He received a Ph.D. in Neurobiology from the Kunming Institute of Zoology, Chinese Academy of Sciences, China. His research interests include multi-omics data integration analysis of complex human diseases and the development of bioinformatics analysis tools. He has much research experience in bulk and single-cell multi-omics data analysis, machine learning, and human genetics. His current research focuses on analyzing de novo and rare inherited variants of congenital heart disease (CHD) and developing the single-cell foundation model to discover new risk genes and genetic etiology of developmental diseases.
Abstract:
Deep learning models to integrate single-cell cardiac development in genetic analysis of congenital heart disease
The interaction and regulation of genes in different cell types at specific stages is critical for cardiac development. Genetic mutations can disrupt this process and lead to congenital heart disease (CHD). We aim to build a single-cell foundation model for cardiac development. Our model uses attention neural networks, tokenize genes using single-cell expression and ATAC-seq data, ESM embeddings, and gene regulatory regions, and is trained by predicting masked genes. The model can predict the impact of dosage changes of a single gene to other genes and global transcriptomic state of key cell types during development. Additionally, we will integrate the model in statistical analysis of genetic data from PCGC CHD cohorts to infer novel CHD risk genes and cell types during development which are sensitive to dosage changes of the risk genes, and nominate candidates for mechanistic studies using animal models.
Mohammad Arafat Hussain
Brief Biography
Mohammad Arafat Hussain is a Postdoctoral Research Fellow in the Fetal-Neonatal Neuroimaging and Developmental Science Center (FNNDSC) at Boston Children’s Hospital/Harvard Medical School. His research focuses on deep learning techniques for medical image analysis. Dr. Hussain completed his Ph.D. in Biomedical Engineering at the University of British Columbia, Vancouver, where he specialized in cancer detection and analysis using CT imaging and deep learning models. Currently, his work is centered on premature aging in adolescents and young adults (AYAs) with congenital heart disease (CHD). His research aims to identify brain regions showing accelerated aging and assess its severity using explainable artificial intelligence (XAI) on brain MRI data. He is also investigating how demographic, genomic, clinical, and parental factors contribute to these changes. By analyzing the relationship between brain morphometry changes and genetic variants from exome and genome data, his work seeks to improve early prediction and targeted interventions for neurocognitive outcomes in AYAs with CHD, addressing critical clinical needs in patient care.
Abstract:
Exploring Premature Brain Aging in Adolescents and Young Adults with Congenital Heart Disease: An Explainable AI and Genomic Analysis of MRI Data
Congenital heart disease (CHD) affects 1% of newborns, with 85% surviving to adulthood. However, ~50% of survivors have neurologic differences, including altered brain structure and accelerated brain aging. Our key research questions are: (1) Which brain regions show premature aging and its severity? (2) How do these changes vary by demographic, genomic, clinical, and parental factors? We will use explainable artificial intelligence (XAI) on MRI data to quantify brain morphometry changes in adolescents and young adults (AYAs) with CHD. We will also analyze how these changes relate to genetic variants from exome and genome data in NHLBI BioData Catalyst. This approach aims to improve early prediction and targeted intervention for neurocognitive outcomes in AYAs with CHD, addressing key clinical needs and enhancing patient care.