Artificial intelligence and omics in malignant gliomas.

Glioblastoma multiforme (GBM) is one of the most common and aggressive type of malignant glioma with an average survival time of 12-18 mo. Despite the utilization of extensive surgical resections using cutting-edge neuroimaging, and advanced chemotherapy and radiotherapy, the prognosis remains unfavorable. The heterogeneity of GBM and the presence of the blood-brain barrier further complicate the therapeutic process.

Integrative analysis of long isoform sequencing and functional data identifies distinct cortical layer neuronal subtypes derived from human iPSCs.

Generation of human induced pluripotent stem cells (iPSCs) through reprogramming was a transformational change in the field of regenerative medicine that led to new possibilities for drug discovery and cell replacement therapy. Several protocols have been established to differentiate hiPSCs into neuronal lineages. However, low differentiation efficiency is one of the major drawbacks of these approaches.

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.

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.

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.

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.

Single-cell transcriptome identifies 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.

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.

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.

Cryptic intermediates and metastable states of proteins as predicted by OneG computational method.

Understanding structural excursions of proteins under folding conditions is crucial to map energy landscapes of proteins. In the present study, OneG computational tool has been used for analyzing possible existence of cryptic intermediates and metastable states of 26 proteins for which three prerequisite inputs of the OneG such as atomic coordinates of proteins, free energy of unfolding (ΔG) and free energy of exchange (ΔG) determined in the absence of denaturant were available during the course of the study. The veraciousness of the tool on predicting the partially folded states of the proteins has been comprehensively described using experimental data available for 15 of the 26 proteins.

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.

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).

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 β-structures.

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.

Fast H-DROP: A thirty times accelerated version of H-DROP for interactive SVM-based prediction of helical domain linkers.

Efficient and rapid prediction of domain regions from amino acid sequence information alone is often required for swift structural and functional characterization of large multi-domain proteins. Here we introduce Fast H-DROP, a thirty times accelerated version of our previously reported H-DROP (Helical Domain linker pRediction using OPtimal features), which is unique in specifically predicting helical domain linkers (boundaries). Fast H-DROP, analogously to H-DROP, uses optimum features selected from a set of 3000 ones by combining a random forest and a stepwise feature selection protocol.

In silico methods for designing antagonists to anti-apoptotic members of Bcl-2 family proteins.

Designing antagonists to anti-apoptotic proteins of Bcl-2 family has become an important strategy in cancer chemotherapy. Using experimental techniques and computational methods, a few numbers of lead inhibitors to the antiapoptotic proteins have been reported in the literature and a few of them are under clinical trials. In this review, the lead inhibitors designed using in silico methodologies are exclusively covered, systematically organized and critically evaluated.

OneG: a computational tool for predicting cryptic intermediates in the unfolding kinetics of proteins under native conditions.

Understanding the relationships between conformations of proteins and their stabilities is one key to address the protein folding paradigm. The free energy change (ΔG) of unfolding reactions of proteins is measured by traditional denaturation methods and native hydrogen-deuterium (H/D) exchange methods. However, the free energy of unfolding (ΔG(U)) and the free energy of exchange (ΔG(HX)) of proteins are not in good agreement, though the experimental conditions of both methods are well matching to each other.

In silico analysis reveals 75 members of mitogen-activated protein kinase kinase kinase gene family in rice.

Mitogen-Activated Protein Kinase Kinase Kinases (MAPKKKs) are important components of MAPK cascades, which are universal signal transduction modules and play important role in plant growth and development. In the sequenced Arabidopsis genome 80 MAPKKKs were identified and currently being analysed for its role in different stress. In rice, economically important monocot cereal crop only five MAPKKKs were identified so far.