Single-cell reconstruction and mutation enrichment analysis identifies dysregulated cardiomyocyte and endothelial cells in congenital heart disease.

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.NEW & NOTEWORTHY Congential heart disease (CHD) is one of the most prevalent neonatal congenital anomalies. We present a comprehensive analysis combining genomics and CHD single-cell transcriptome. Our study identifies 90 potential candidate CHD risk genes of which 6 are novel. The risk genes have heterogenous expression suggestive of multiple genes contributing to the phenotypic heterogeneity of CHD. Cardiomyocytes and endocardial cells are identified as major CHD-related cell types.

Lack of ethnic diversity in single-cell transcriptomics hinders cell type detection and precision medicine inclusivity.

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.

Overlapping pathogenic de novo CNVs in neurodevelopmental disorders and congenital anomalies impacting constraint genes regulating early development.

Neurodevelopmental disorders (NDDs) and congenital anomalies (CAs) are rare disorders with complex etiology. In this study, we investigated the less understood genomic overlap of copy number variants (CNVs) in two large cohorts of NDD and CA patients to identify de novo CNVs and candidate genes associated with both phenotypes. We analyzed clinical microarray CNV data from 10,620 NDD and 3176 CA cases annotated using Horizon platform of GenomeArc Analytics and applied rigorous downstream analysis to evaluate overlapping genes from NDD and CA CNVs. Out of 13,796 patients, only 195 cases contained 218 validated de novo CNVs. Eighteen percent (31/170) de novo CNVs in NDD cases and 40% (19/48) de novo CNVs in CA cases contained genomic overlaps impacting developmentally constraint genes. Seventy-nine constraint genes (10.1% non-OMIM entries) were found to have significantly enriched genomic overlap within rare de novo pathogenic deletions (P value = 0.01, OR = 1.58) and 45 constraint genes (13.3% non-OMIM entries) within rare de novo pathogenic duplications (P value = 0.01, OR = 1.97). Analysis of spatiotemporal transcriptome demonstrated both pathogenic deletion and duplication genes to be highly expressed during the prenatal stage in human developmental brain (P value = 4.95 X 10-6). From the list of overlapping genes, EHMT1, an interesting known NDD gene encompassed pathogenic deletion CNVs from both NDD and CA patients, whereas FAM189A1, and FSTL5 are new candidate genes from non-OMIM entries. In summary, we have identified constraint overlapping genes from CNVs (including de novo) in NDD and CA patients that have the potential to play a vital role in common disease etiology.

Analyzing single cell transcriptome data from severe COVID-19 patients.

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. For complete details on the use and execution of this protocol, please refer to Nassir et al. (2021b).

Single-cell transcriptome identifies FCGR3B upregulated subtype of alveolar macrophages in patients with critical COVID-19.

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.

Association of cystic fibrosis transmembrane conductance regulator with epithelial sodium channel subunits carrying Liddle's syndrome mutations.

The association of the cystic fibrosis transmembrane conductance regulator (CFTR) and epithelial sodium channel (ENaC) in the pathophysiology of cystic fibrosis (CF) is controversial. Previously, we demonstrated a close physical association between wild-type (WT) CFTR and WT ENaC. We have also shown that the F508del CFTR fails to associate with ENaC unless the mutant protein is rescued pharmacologically or by low temperature. In this study, we present the evidence for a direct physical association between WT CFTR and ENaC subunits carrying Liddle's syndrome mutations. We show that all three ENaC subunits bearing Liddle's syndrome mutations (both point mutations and the complete truncation of the carboxy terminus), could be coimmunoprecipitated with WT CFTR. The biochemical studies were complemented by fluorescence lifetime imaging microscopy (FLIM), a distance-dependent approach that monitors protein-protein interactions between fluorescently labeled molecules. Our measurements revealed significantly increased fluorescence resonance energy transfer between CFTR and all tested ENaC combinations as compared with controls (ECFP and EYFP cotransfected cells). Our findings are consistent with the notion that CFTR and ENaC are within reach of each other even in the setting of Liddle's syndrome mutations, suggestive of a direct intermolecular interaction between these two proteins.

Single-cell transcriptomics trajectory and molecular convergence of clinically relevant mutations in Brugada syndrome.

Brugada syndrome (BrS) is a rare, inherited arrhythmia with high risk of sudden cardiac death. To evaluate the molecular convergence of clinically relevant mutations and to identify developmental cardiac cell types that are associated with BrS etiology, we collected 733 mutations represented by 16 sodium, calcium, potassium channels, and regulatory and structural genes related to BrS. Among the clinically relevant mutations, 266 are unique singletons and 88 mutations are recurrent. We observed an over-representation of clinically relevant mutations (∼80%) in SCN5A gene and also identified several candidate genes, including GPD1L, TRPM4, and SCN10A. Furthermore, protein domain enrichment analysis revealed that a large proportion of the mutations impacted ion transport domains in multiple genes, including SCN5A, TRPM4, and SCN10A. A comparative protein domain analysis of SCN5A further established a significant (P = 0.04) enrichment of clinically relevant mutations within ion transport domain, including a significant (P = 0.02) mutation hotspot within 1321-1380 residue. The enrichment of clinically relevant mutations within SCN5A ion transport domain is stronger (P = 0.00003) among early onset of BrS. Our spatiotemporal cellular heart developmental (prenatal to adult) trajectory analysis applying single-cell transcriptome identified the most frequently BrS-mutated genes (SCN5A and GPD1L) are significantly upregulated in the prenatal cardiomyocytes. A more restrictive cellular expression trajectory is prominent in the adult heart ventricular cardiomyocytes compared to prenatal. Our study suggests that genomic and proteomic hotspots in BrS converge into ion transport pathway and cardiomyocyte as a major BrS-associated cell type that provides insight into the complex genetic etiology of BrS.NEW & NOTEWORTHY Brugada syndrome is a rare inherited arrhythmia with high risk of sudden cardiac death. We present the findings for a molecular convergence of clinically relevant mutations and identification of a single-cell transcriptome-derived cardiac cell types that are associated with the etiology of BrS. Our study suggests that genomic and proteomic hotspots in BrS converge into ion transport pathway and cardiomyocyte as a major BrS-associated cell type that provides insight into the complex genetic etiology of BrS.

Long-Read Sequencing Improves the Detection of Structural Variations Impacting Complex Non-Coding Elements of the Genome.

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.

Augmenting Flexnerism Via Twitterism: Need for Integrating Social Media Application in Blueprinting Pedagogical Strategies for Undergraduate Medical Education.

BACKGROUND: Flexnerism, or "competency-based medical education," advocates that formal analytic reasoning, the kind of rational thinking fundamental to the basic sciences, especially the natural sciences, should be the foundation of physicians' intellectual training. The complexity of 21st century health care requires rethinking of current (medical) educational paradigms. In this "Millennial Era," promulgation of the tenets of Flexnerism in undergraduate medical education requires a design and blueprint of innovative pedagogical strategies, as the targeted learners are millennials (designated as generation-Y medical students). OBJECTIVE: The aim of this proof-of-concept study was to identify the specific social media app platforms that are selectively preferred by generation-Y medical students in undergraduate medical education. In addition, we aimed to explore if these preferred social media apps can be used to design an effective pedagogical strategy in order to disseminate course learning objectives in the preclinical phase of a spiral curriculum. METHODS: A cross-sectional survey was conducted by distributing a 17-item questionnaire among the first- and second-year medical students in the preclinical phase at the Mohammed Bin Rashid University of Medicine and Health Science. RESULTS: The study identified YouTube and WhatsApp as the social media app platforms preferred by generation-Y medical students in undergraduate medical education. This study also identified the differences between female and male generation-Y medical students in terms of the use of social media apps in medical education, which we believe will assist instructors in designing pedagogical strategies to integrate social media apps. In addition, we determined the perceptions of generation-Y medical students on the implementation of social media apps in medical education. The pedagogical strategy designed using social media apps and implemented in the Biochemistry course was well accepted by generation-Y medical students and can be translated to any course in the preclinical phase of the medical curriculum. Moreover, the identified limitations of this study provide an understanding of the gaps in research in the integration of social media apps in a medical curriculum catering to generation-Y medical students. CONCLUSIONS: 21st century medical education requires effective use of social media app platforms to augment competency-based medical education: Augmentation of Flexnerism in the current scenario is possible only by the adaptation of Twitterism.

Large-scale all-atom molecular dynamics alanine-scanning of IAPP octapeptides provides insights into the molecular determinants of amyloidogenicity.

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 ß-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 ß-rich clusters. In the oligomeric state, the wild type and the S7A sequences had the highest ß-structure content (~14%), as calculated by DSSP, in line with experimental observations, whereas I5A and G3A had the highest helical content (~20%). Importantly, the ß-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.

Blending Gagne's Instructional Model with Peyton's Approach to Design an Introductory Bioinformatics Lesson Plan for Medical Students: Proof-of-Concept Study.

BACKGROUND: With the rapid integration of genetics into medicine, it has become evident that practicing physicians as well as medical students and clinical researchers need to be updated on the fundamentals of bioinformatics. To achieve this, the following gaps need to be addressed: a lack of defined learning objectives for "Bioinformatics for Medical Practitioner" courses, an absence of a structured lesson plan to disseminate the learning objectives, and no defined step-by-step strategy to teach the essentials of bioinformatics in the medical curriculum. OBJECTIVE: The objective of this study was to address these gaps to design a streamlined pedagogical strategy for teaching basics of bioinformatics in the undergraduate medical curriculum. METHODS: The established instructional design strategies employed in medical education-Gagne's 9 events of instruction-were followed with further contributions from Peyton's four-step approach to design a structured lesson plan in bioinformatics. RESULTS: First, we defined the specifics of bioinformatics that a medical student or health care professional should be introduced to use this knowledge in a clinical context. Second, we designed a structured lesson plan using a blended approach from both Gagne's and Peyton's instructional models. Lastly, we delineated a step-by-step strategy employing free Web-based bioinformatics module, combining it with a clinical scenario of familial hypercholesterolemia to disseminate the defined specifics of bioinformatics. Implementation of Schon's reflective practice model indicated that the activity was stimulating for the students with favorable outcomes regarding their basic training in bioinformatics. CONCLUSIONS: To the best of our knowledge, the present lesson plan is the first that outlines an effective dissemination strategy for integrating introductory bioinformatics into a medical curriculum. Further, the lesson plan blueprint can be used to develop similar skills in workshops, continuing professional development, or continuing medical education events to introduce bioinformatics to practicing physicians.