Molecular Basis Of Human Disease Research Articles

Geneapp: A web application for visualizing alternative splicing for biomedicine

Alternative Splicing (AS) is an essential mechanism for eukaryotes. However, the consequences of deleting a single exon can be dramatic for the organism and can lead to cancer in humans. Additionally, alternative 5′ and 3′ splice sites, which define the boundaries of exons, also play key roles to human disorders. Therefore, Investigating AS events is crucial for understanding the molecular basis of human diseases and developing therapeutic strategies. Workflow for AS event analysis can be sampling followed by data analysis with bioinformatics to identify the different AS events in the control and case samples, data visualization for curation, and selection of relevant targets for experimental validation. The raw output of the analysis software does not favor the inspection of events by bioinformaticians requiring custom scripts for data visualization. In this work, we propose the Geneapp application with three modules: GeneappScript, GeneappServer, and GeneappExplorer. GeneappScript is a wrapper that assists in identifying AS in samples compared in two different approaches, while GeneappServer integrates data from AS analysis already performed by the user. In GeneappExplorer, the user visualizes the previous dataset by exploring AS events in genes with functional annotation. This targeted screens that Geneapp allows to perform helps in the identification of targets for experimental validation to confirm the hypotheses under study. The Geneapp is freely available for non-commercial use at https://geneapp.net to advance research on AS for bioinformatics.

Regional analysis to delineate intrasample heterogeneity with RegionalST.

Spatial transcriptomics has greatly contributed to our understanding of spatial and intra-sample heterogeneity, which could be crucial for deciphering the molecular basis of human diseases. Intra-tumor heterogeneity, e.g. may be associated with cancer treatment responses. However, the lack of computational tools for exploiting cross-regional information and the limited spatial resolution of current technologies present major obstacles to elucidating tissue heterogeneity. To address these challenges, we introduce RegionalST, an efficient computational method that enables users to quantify cell type mixture and interactions, identify sub-regions of interest, and perform cross-region cell type-specific differential analysis for the first time. Our simulations and real data applications demonstrate that RegionalST is an efficient tool for visualizing and analyzing diverse spatial transcriptomics data, thereby enabling accurate and flexible exploration of tissue heterogeneity. Overall, RegionalST provides a one-stop destination for researchers seeking to delve deeper into the intricacies of spatial transcriptomics data. The implementation of our method is available as an open-source R/Bioconductor package with a user-friendly manual available at https://bioconductor.org/packages/release/bioc/html/RegionalST.html.

Caenorhabditis elegans: A transgenic model for studying age-associated neurodegenerative diseases.

Neurodegenerative diseases (NDs) are a heterogeneous group of aging-associated ailments characterized by interrupting cellular proteostasic machinery and the misfolding of distinct proteins to form toxic aggregates in neurons. Neurodegenerative diseases, which include Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), and others, are becoming an increasing threat to human health worldwide. The degeneration and death of certain specific groups of neurons are the hallmarks of these diseases. Over the past decades, Caenorhabditis eleganshas beenwidely used as a transgenic model to investigate biological processes related to health and disease. The nematode Caenorhabditis elegans (C. elegans) has developed as a powerful tool for studying disease mechanisms due to its ease of genetic handling and instant cultivation while providing a whole-animal system amendable to several molecular and biochemical techniques. In this review, we elucidate the potential of C. elegans as a versatile platform for systematic dissection of the molecular basis of human disease, focusing on neurodegenerative disorders, and may help better our understanding of the disease mechanisms and search for new therapeutics for these devastating diseases.

Caenorhabditis elegans as a Model System to Study Human Neurodegenerative Disorders.

In recent years, advances in science and technology have improved our quality of life, enabling us to tackle diseases and increase human life expectancy. However, longevity is accompanied by an accretion in the frequency of age-related neurodegenerative diseases, creating a growing burden, with pervasive social impact for human societies. The cost of managing such chronic disorders and the lack of effective treatments highlight the need to decipher their molecular and genetic underpinnings, in order to discover new therapeutic targets. In this effort, the nematode Caenorhabditis elegans serves as a powerful tool to recapitulate several disease-related phenotypes and provides a highly malleable genetic model that allows the implementation of multidisciplinary approaches, in addition to large-scale genetic and pharmacological screens. Its anatomical transparency allows the use of co-expressed fluorescent proteins to track the progress of neurodegeneration. Moreover, the functional conservation of neuronal processes, along with the high homology between nematode and human genomes, render C. elegans extremely suitable for the study of human neurodegenerative disorders. This review describes nematode models used to study neurodegeneration and underscores their contribution in the effort to dissect the molecular basis of human diseases and identify novel gene targets with therapeutic potential.

The Neurogenetics Collection: emerging themes and future considerations for the field in Brain.

Genomics has emerged over the last two decades as a fundamental approach to understanding the molecular basis of human diseases. This Collection brings together some recent articles published in Brain, selected to illustrate the impact of genomics on neurology and to highlight emerging themes in the neurogenetics space.

Mitochondrial Carriers and Substrates Transport Network: A Lesson from Saccharomyces cerevisiae.

The yeast Saccharomyces cerevisiae is one of the most widely used model organisms for investigating various aspects of basic cellular functions that are conserved in human cells. This organism, as well as human cells, can modulate its metabolism in response to specific growth conditions, different environmental changes, and nutrient depletion. This adaptation results in a metabolic reprogramming of specific metabolic pathways. Mitochondrial carriers play a fundamental role in cellular metabolism, connecting mitochondrial with cytosolic reactions. By transporting substrates across the inner membrane of mitochondria, they contribute to many processes that are central to cellular function. The genome of Saccharomyces cerevisiae encodes 35 members of the mitochondrial carrier family, most of which have been functionally characterized. The aim of this review is to describe the role of the so far identified yeast mitochondrial carriers in cell metabolism, attempting to show the functional connections between substrates transport and specific metabolic pathways, such as oxidative phosphorylation, lipid metabolism, gluconeogenesis, and amino acids synthesis. Analysis of the literature reveals that these proteins transport substrates involved in the same metabolic pathway with a high degree of flexibility and coordination. The understanding of the role of mitochondrial carriers in yeast biology and metabolism could be useful for clarifying unexplored aspects related to the mitochondrial carrier network. Such knowledge will hopefully help in obtaining more insight into the molecular basis of human diseases.

LZTR1: A promising adaptor of the CUL3 family.

The study of the disorders of ubiquitin-mediated proteasomal degradation may unravel the molecular basis of human diseases, such as cancer (prostate cancer, lung cancer and liver cancer, etc.) and nervous system disease (Parkinson's disease, Alzheimer's disease and Huntington's disease, etc.) and help in the design of new therapeutic methods. Leucine zipper-like transcription regulator 1 (LZTR1) is an important substrate recognition subunit of cullin-RING E3 ligase that plays an important role in the regulation of cellular functions. Mutations in LZTR1 and dysregulation of associated downstream signaling pathways contribute to the pathogenesis of Noonan syndrome (NS), glioblastoma and chronic myeloid leukemia. Understanding the molecular mechanism of the normal function of LZTR1 is thus critical for its eventual therapeutic targeting. In the present review, the structure and function of LZTR1 are described. Moreover, recent advances in the current knowledge of the functions of LZTR1 in NS, glioblastoma (GBM), chronic myeloid leukemia (CML) and schwannomatosis and the influence of LZTR1 mutations are also discussed, providing insight into how LZTR1 may be targeted for therapeutic purposes.

Regulatory genomic circuitry of human disease loci by integrative epigenomics

Annotating the molecular basis of human disease remains an unsolved challenge, as 93% of disease loci are non-coding and gene-regulatory annotations are highly incomplete1–3. Here we present EpiMap, a compendium comprising 10,000 epigenomic maps across 800 samples, which we used to define chromatin states, high-resolution enhancers, enhancer modules, upstream regulators and downstream target genes. We used this resource to annotate 30,000 genetic loci that were associated with 540 traits4, predicting trait-relevant tissues, putative causal nucleotide variants in enriched tissue enhancers and candidate tissue-specific target genes for each. We partitioned multifactorial traits into tissue-specific contributing factors with distinct functional enrichments and disease comorbidity patterns, and revealed both single-factor monotropic and multifactor pleiotropic loci. Top-scoring loci frequently had multiple predicted driver variants, converging through multiple enhancers with a common target gene, multiple genes in common tissues, or multiple genes and multiple tissues, indicating extensive pleiotropy. Our results demonstrate the importance of dense, rich, high-resolution epigenomic annotations for the investigation of complex traits.

Clinical Applications of Next-Generation Sequencing

The significant drop in sequencing costs boosted by chemistries optimization and sample multiplexation has resulted in an immense growth within the field of next-generation sequencing (NGS) in the last decade. This has allowed a diversification of techniques, promoting a rapid advance in knowledge on the molecular basis of human disease. Due to the applicability and importance of this technology in basic research, it has quickly migrated to the clinical setting. NGS enables clinicians to make improved diagnostic and treatment decisions, which ultimately may influence precision medicine.

Predicting disease-related genes by path structure and community structure in protein–protein networks

Network-based computational approaches in the prediction of genes that are associated with diseases are of considerable importance in uncovering the molecular basis of human diseases. Here, we proposed a novel disease-gene-prediction method by combining path-based structure with community structure characteristics in human protein–protein networks. A new similarity measure was first proposed that is based on the path and community structures of networks and leverages community structures for disease-gene prediction. Then, the distinguishing capacity of the methods to identify disease genes from non-disease genes was assessed statistically to analyze their ability to predict disease genes. Finally, the new method was applied to disease-gene prediction for several datasets, and the performances of the measures in disease-gene prediction were analyzed, with an emphasis on assessing the effect of community structure on the predictive performance. The results indicated an ability of the new method to predict disease-genes in several networks and within several disease classes. Further, the results reported here confirm that the incorporation of community structures can indeed improve the performance of disease-gene prediction methods.

Meet the First Authors.

Meet the First Authors.

Integrative clustering of multi-level 'omic data based on non-negative matrix factorization algorithm.

Integrative analyses of high-throughput ‘omic data, such as DNA methylation, DNA copy number alteration, mRNA and protein expression levels, have created unprecedented opportunities to understand the molecular basis of human disease. In particular, integrative analyses have been the cornerstone in the study of cancer to determine molecular subtypes within a given cancer. As malignant tumors with similar morphological characteristics have been shown to exhibit entirely different molecular profiles, there has been significant interest in using multiple ‘omic data for the identification of novel molecular subtypes of disease, which could impact treatment decisions. Therefore, we have developed intNMF, an integrative approach for disease subtype classification based on non-negative matrix factorization. The proposed approach carries out integrative clustering of multiple high dimensional molecular data in a single comprehensive analysis utilizing the information across multiple biological levels assessed on the same individual. As intNMF does not assume any distributional form for the data, it has obvious advantages over other model based clustering methods which require specific distributional assumptions. Application of intNMF is illustrated using both simulated and real data from The Cancer Genome Atlas (TCGA).

Molecular Basis of Human Diseases and Targeted Therapy Based on Small-Molecule Inhibitors of ER Stress-Induced Signaling Pathways.

The Endoplasmic Reticulum (ER) provides a conserved protein quality control system and plays a fundamental role in cell growth and homeostasis. Disturbances in the ER homeostasis may originate especially from hypoxia, glucose deficiency, presence of mutant proteins, that directly impair protein folding capacity and after deposition of unfolded and misfolded proteins within ER lumen trigger ER stress conditions. This subsequently activates the Unfolded Protein Response (UPR) branches, which have a dual pro-adaptive or pro-apoptotic role depending on the severity and time of duration of ER stress conditions. This review is the first to offer a detailed overview on molecular mechanisms of all major ER stress-dependent signaling branches, that are activated through three specific ER transmembrane receptors of impaired protein folding: Protein kinase RNA (PKR)-like ER kinase (PERK), Inositol-requiring enzyme-1 (IRE1) and Activating transcription factor 6 (ATF6). Molecular crosstalk among ER transmembrane receptors-dependent pathways determines a final UPR response, but the recent data reported that especially PERK over-activation has a significant impact on the development and progression of a wide spectrum of disease entities. Based on these findings, small-molecules, highly specific PERK inhibitors may provide effective, groundbreaking treatment strategy against human diseases. However, after foregoing in vitro cellular and in vivo animal models conducted examination, supplementary investigations of PERK inhibitors are required for their further clinical use. Future research may answer the question of how to minimize toxicity and side effects of characterized small-molecule PERK inhibitors, that may be used, as breakthrough drugs, alone or in combination with currently known models of therapy.

Structural basis for regulation of human calcium-sensing receptor by magnesium ions and an unexpected tryptophan derivative co-agonist.

Ca(2+)-sensing receptors (CaSRs) modulate calcium and magnesium homeostasis and many (patho)physiological processes by responding to extracellular stimuli, including divalent cations and amino acids. We report the first crystal structure of the extracellular domain (ECD) of human CaSR bound with Mg(2+) and a tryptophan derivative ligand at 2.1 Å. The structure reveals key determinants for cooperative activation by metal ions and aromatic amino acids. The unexpected tryptophan derivative was bound in the hinge region between two globular ECD subdomains, and represents a novel high-affinity co-agonist of CaSR. The dissection of structure-function relations by mutagenesis, biochemical, and functional studies provides insights into the molecular basis of human diseases arising from CaSR mutations. The data also provide a novel paradigm for understanding the mechanism of CaSR-mediated signaling that is likely shared by the other family C GPCR [G protein (heterotrimeric guanine nucleotide-binding protein)-coupled receptor] members and can facilitate the development of novel CaSR-based therapeutics.

Integrative analysis of 111 reference human epigenomes.

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but a similar reference has lacked for epigenomic studies. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection to-date of human epigenomes for primary cells and tissues. Here, we describe the integrative analysis of 111 reference human epigenomes generated as part of the program, profiled for histone modification patterns, DNA accessibility, DNA methylation, and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically-relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation, and human disease.

Cracking the Code of Human Diseases Using Next-Generation Sequencing: Applications, Challenges, and Perspectives.

Next-generation sequencing (NGS) technologies have greatly impacted on every field of molecular research mainly because they reduce costs and increase throughput of DNA sequencing. These features, together with the technology's flexibility, have opened the way to a variety of applications including the study of the molecular basis of human diseases. Several analytical approaches have been developed to selectively enrich regions of interest from the whole genome in order to identify germinal and/or somatic sequence variants and to study DNA methylation. These approaches are now widely used in research, and they are already being used in routine molecular diagnostics. However, some issues are still controversial, namely, standardization of methods, data analysis and storage, and ethical aspects. Besides providing an overview of the NGS-based approaches most frequently used to study the molecular basis of human diseases at DNA level, we discuss the principal challenges and applications of NGS in the field of human genomics.

Two-hybrid-based systems: Powerful tools for investigation of membrane traffic machineries

Protein–protein interactions regulate biological processes and are fundamental for cell functions. Recently, efforts have been made to define interactomes, which are maps of protein–protein interactions that are useful for understanding biological pathways and networks and for investigating how perturbations of these networks lead to diseases. Therefore, interactomes are becoming fundamental for establishing the molecular basis of human diseases and contributing to the discovery of effective therapies. Interactomes are constructed based on experimental data present in the literature and computational predictions of interactions. Several biochemical, genetic and biotechnological techniques have been used in the past to identify protein–protein interactions. The yeast two-hybrid system has beyond doubt represented a revolution in the field, being a versatile tool and allowing the immediate identification of the interacting proteins and isolation of the cDNA coding for the interacting peptide after in vivo screening. Recently, variants of the yeast two-hybrid assay have been developed, including high-throughput systems that promote the rapidly growing field of proteomics. In this review we will focus on the role of this technique in the discovery of Rab interacting proteins, highlighting the importance of high-throughput two-hybrid screening as a tool to study the complexity of membrane traffic machineries.

Exploring mechanisms of human disease through structurally resolved protein interactome networks

The study of the molecular basis of human disease has gained increasing attention over the past decade. With significant improvements in sequencing efficiency and throughput, a wealth of genotypic data has become available. However the translation of this information into concrete advances in diagnostic and clinical setups has proved far more challenging. Two major reasons for this are the lack of functional annotation for genomic variants and the complex nature of genotype-to-phenotype relationships. One fundamental approach to bypass these issues is to examine the effects of genetic variation at the level of proteins as they are directly involved in carrying out biological functions. Within the cell, proteins function by interacting with other proteins as a part of an underlying interactome network. This network can be determined using interactome mapping - a combination of high-throughput experimental toolkits and curation from small-scale studies. Integrating structural information from co-crystals with the network allows generation of a structurally resolved network. Within the context of this network, the structural principles of disease mutations can be examined and used to generate reliable mechanistic hypotheses regarding disease pathogenesis.

Advanced Systems Biology Methods in Drug Discovery and Translational Biomedicine

Systems biology is in an exponential development stage in recent years and has been widely utilized in biomedicine to better understand the molecular basis of human disease and the mechanism of drug action. Here, we discuss the fundamental concept of systems biology and its two computational methods that have been commonly used, that is, network analysis and dynamical modeling. The applications of systems biology in elucidating human disease are highlighted, consisting of human disease networks, treatment response prediction, investigation of disease mechanisms, and disease-associated gene prediction. In addition, important advances in drug discovery, to which systems biology makes significant contributions, are discussed, including drug-target networks, prediction of drug-target interactions, investigation of drug adverse effects, drug repositioning, and drug combination prediction. The systems biology methods and applications covered in this review provide a framework for addressing disease mechanism and approaching drug discovery, which will facilitate the translation of research findings into clinical benefits such as novel biomarkers and promising therapies.

A STATus report on DC development

DCs have a vital role in the immune system by recognizing exogenous or self-antigens and eliciting appropriate stimulatory or tolerogenic adaptive immune responses. DCs also contribute to human autoimmune disease and, when depleted, to immunodeficiency. Moreover, DCs are being explored for potential use in clinical therapies including cancer treatment. Thus, understanding the molecular mechanisms that regulate DCs is crucial to improving treatments for human immune disease and cancer. DCs constitute a heterogeneous population including plasmacytoid (pDC) and classic (cDC) subsets; however, the majority of DCs residing in lymphoid organs and peripheral tissues in steady state share common progenitor populations, originating with hematopoietic stem cells. Like other hematopoietic lineages, DCs require extracellular factors including cytokines, as well as intrinsic transcription factors, to control lineage specification, commitment, and maturation. Here, we review recent findings on the roles for cytokines and cytokine-activated STAT transcription factors in DC subset development. We also discuss how cytokines and STATs intersect with lineage-regulatory transcription factors and how insight into the molecular basis of human disease has revealed transcriptional regulators of DCs. Whereas this is an emerging area with much work remaining, we anticipate that knowledge gained by delineating cytokine and transcription factor mechanisms will enable a better understanding of DC subset diversity, and the potential to manipulate these important immune cells for human benefit.