Mechanisms Of Human Diseases Research Articles

Emerging Bioengineering Approaches for Cancer and Tumor Therapy

Cancer has become a significant socioeconomic burden globally, with millions of new cases and deaths each year. The promising field of bioengineering has recently undergone significant advancements, providing new methods for combating cancer. Increasing attention has been directed toward understanding the molecular mechanisms of human diseases, supported by the availability of various genetic tools and rapid technological advancements. These developments have enabled the use of the latest gene therapy techniques for cancer treatment, including gene editing, gene deletion, and correcting defective genes through methods such as TALENs, Zinc fingers, RNAi, CRISPR, site-directed mutagenesis (SDM), and enzyme therapy to modulate catalytic activity. In addition, bioengineering vaccines like mRNA vaccines, bioinformatics, computational tools, artificial intelligence (AI), nanotechnology, and chemotherapy are emerging as significant cancer treatment strategies. Among these, gene editing and gene therapy have gained particular attention in recent years and are often used in combination with other therapeutic approaches. The engineering of enzymes and advancements in nanotechnology have also significantly progressed. AI and bioinformatics have contributed to more precise diagnosis, prediction, and prognosis, enabling tailored treatment of cancer and tumors. Imaging and radiotherapy, enhanced by AI, have improved surgical outcomes, even from remote locations. Precision oncology has emerged, using bacteria and viruses to target tumors directly. In this review, we discuss recent advancements and challenges in various cancer therapies.

Characteristic disease defects in circulating endothelial cells isolated from patients with pulmonary arterial hypertension.

Pulmonary arterial hypertension (PAH) is a progressive disease characterized by elevated pulmonary arterial pressures that can lead to right heart failure and death. No cure exists for this disease, but therapeutic advancements have extended its median survival from 2 to 7 years. Mechanistic research in PAH has been limited by factors including that a) animal models do not fully recapitulate the disease or provide insights into its pathogenesis, and b) cellular material from PAH patients is primarily obtained from donor lungs during autopsy or transplantation, which reflect end-stage disease. Therefore, there is a need to identify tools that can elucidate the specific mechanisms of human disease in individual patients, a critical step to guide treatment decisions based on specific pathway abnormalities. Here we demonstrate a simple method to isolate and culture circulating endothelial cells (CECs) obtained at the time of right heart catheterization in PAH patients. We tested these CECs using transcriptomics and found that they have typical traits of PAH, including those involving key treatment pathways, i.e. nitric oxide, endothelin, prostacyclin and BMP/activin pathways. CECs show important gene expression changes in other central PAH disease pathways. In summary, we present a new cellular model for the ex-vivo mechanistic evaluation of critical PAH pathways that participate in the pathogenesis of the disease and may help personalized therapeutic decisions.

Using human disease mutations to understand de novo DNA methyltransferase function.

DNA methylation is a repressive epigenetic mark that is pervasive in mammalian genomes. It is deposited by DNA methyltransferase enzymes (DNMTs) that are canonically classified as having de novo (DNMT3A and DNMT3B) or maintenance (DNMT1) function. Mutations in DNMT3A and DNMT3B cause rare Mendelian diseases in humans and are cancer drivers. Mammalian DNMT3 methyltransferase activity is regulated by the non-catalytic region of the proteins which contain multiple chromatin reading domains responsible for DNMT3A and DNMT3B recruitment to the genome. Characterising disease-causing missense mutations has been central in dissecting the function and regulation of DNMT3A and DNMT3B. These observations have also motivated biochemical studies that provide the molecular details as to how human DNMT3A and DNMT3B mutations drive disorders. Here, we review progress in this area highlighting recent work that has begun dissecting the function of the disordered N-terminal regions of DNMT3A and DNMT3B. These studies have elucidated that the N-terminal regions of both proteins mediate novel chromatin recruitment pathways that are central in our understanding of human disease mechanisms. We also discuss how disease mutations affect DNMT3A and DNMT3B oligomerisation, a process that is poorly understood in the context of whole proteins in cells. This dissection of de novo DNMT function using disease-causing mutations provides a paradigm of how genetics and biochemistry can synergise to drive our understanding of the mechanisms through which chromatin misregulation causes human disease.

Murine Extraparenchymal Neurocysticercosis: Appropriate Model for Evaluating Anthelminthic and Anti-Inflammatory Treatment Schedules.

Experimental models of neurocysticercosis (NCC) are helpful for an improved understanding of the pathophysiological mechanisms of human diseases and for testing novel therapeutic approaches. Controlling inflammation without reducing the effectiveness of anthelmintics is an important challenge in treating neurocysticercosis. This study investigates the effects of currently used drugs (Albendazole and Dexamethasone) in treating murine extraparenchymal NCC. Twenty-two rats were inoculated with Taenia crassiceps in the subarachnoid space. The animals underwent magnetic resonance imaging to ascertain the success of infection 3 months after inoculation. The infected animals were randomly assigned to one of the three groups (five rats each): control (no treatment), Albendazole (ABZ), or Albendazole + Dexamethasone (ABZ + DXM) for 14 days. The animals were subsequently euthanised for morphological assessment 2 weeks after the end of treatment. Macroscopically integrated cysts were found in all animals. The ABZ + DXM animals demonstrated lower ventricular sizes, lymphocyte infiltration rates, and immunopositivity for IL-6, with statistical differences in lymphocytes within the arachnoid region. This experimental model, which has previously shown similarities to human infections, is also helpful in reproducing the morphological changes upon treatment with Albendazole and Dexamethasone.

ZG16 impacts gut microbiota-associated intestinal inflammation and pulmonary mucosal function through bacterial metabolites

Zymogen granule 16 (ZG16) is a secretory glycoprotein found in zymogen granules, which also plays an important role in colorectal inflammation and cancer. Herein, a ZG16 gene knock-out (ZG16−/−) mouse line was established and we found that ZG16 deletion damaged the intestinal mucosal barrier and gut microbiota, which resulted in low-level inflammation and further promoted the development of ulcerative colitis and inflammation-related colorectal cancer. Meanwhile, a metabolomics analysis on mouse feces showed that the metabolites significantly differed between ZG16−/− and WT mice, which were important mediators of host-microbiota communication and may impact the pulmonary inflammation of mice. Indeed, ZG16−/− mice showed more severe inflammation in a bronchial asthma model. Taken together, the results demonstrate that ZG16 plays a pivotal role in inhibiting inflammation and regulating immune responses in colorectum and lung of experimental animals, which may provide a better understanding of the underlying mechanism of human inflammatory diseases associated with ZG16.

DRpred: A Novel Deep Learning-Based Predictor for Multi-Label mRNA Subcellular Localization Prediction by Incorporating Bayesian Inferred Prior Label Relationships.

The subcellular localization of messenger RNA (mRNA) not only helps us to understand the localization regulation of gene expression but also helps to understand the relationship between RNA localization pattern and human disease mechanism, which has profound biological and medical significance. Several predictors have been proposed for predicting the subcellular localization of mRNA. However, there is still considerable room for improvement in their predictive performance, especially regarding multi-label prediction. This study proposes a novel multi-label predictor, DRpred, for mRNA subcellular localization prediction. This predictor first utilizes Bayesian networks to capture the dependencies among labels. Subsequently, it combines these dependencies with features extracted from mRNA sequences using Word2vec, forming the input for the predictor. Finally, it employs a neural network combining BiLSTM and an attention mechanism to capture the internal relationships of the input features for mRNA subcellular localization. The experimental validation on an independent test set demonstrated that DRpred obtained a competitive predictive performance in multi-label prediction and outperformed state-of-the-art predictors in predicting single subcellular localizations, obtaining accuracies of 82.14%, 93.02%, 80.37%, 94.00%, 90.58%, 84.53%, 82.01%, 79.71%, and 85.67% for the chromatin, cytoplasm, cytosol, exosome, membrane, nucleolus, nucleoplasm, nucleus, and ribosome, respectively. It is anticipated to offer profound insights for biological and medical research.

Multimodal functional deep learning for multiomics data.

With rapidly evolving high-throughput technologies and consistently decreasing costs, collecting multimodal omics data in large-scale studies has become feasible. Although studying multiomics provides a new comprehensive approach in understanding the complex biological mechanisms of human diseases, the high dimensionality of omics data and the complexity of the interactions among various omics levels in contributing to disease phenotypes present tremendous analytical challenges. There is a great need of novel analytical methods to address these challenges and to facilitate multiomics analyses. In this paper, we propose a multimodal functional deep learning (MFDL) method for the analysis of high-dimensional multiomics data. The MFDL method models the complex relationships between multiomics variants and disease phenotypes through the hierarchical structure of deep neural networks and handles high-dimensional omics data using the functional data analysis technique. Furthermore, MFDL leverages the structure of the multimodal model to capture interactions between different types of omics data. Through simulation studies and real-data applications, we demonstrate the advantages of MFDL in terms of prediction accuracy and its robustness to the high dimensionality and noise within the data.

Nematode Extracellular Protein Interactome Expands Connections between Signaling Pathways.

Multicellularity was accompanied by the emergence of new classes of cell surface and secreted proteins. The nematode C. elegans is a favorable model to study cell surface interactomes, given its well-defined and stereotyped cell types and intercellular contacts. Here we report our C. elegans extracellular interactome dataset, the largest yet for an invertebrate. Most of these interactions were unknown, despite recent datasets for flies and humans, as our collection contains a larger selection of protein families. We uncover new interactions for all four major axon guidance pathways, including ectodomain interactions between three of the pathways. We demonstrate that a protein family known to maintain axon locations are secreted receptors for insulins. We reveal novel interactions of cystine-knot proteins with putative signaling receptors, which may extend the study of neurotrophins and growth-factor-mediated functions to nematodes. Finally, our dataset provides insights into human disease mechanisms and how extracellular interactions may help establish connectomes.

Short Tandem Repeats in the era of next-generation sequencing: from historical loci to population databases.

In this study, we explore the landscape of short tandem repeats (STRs) within the human genome through the lens of evolving technologies to detect genomic variations. STRs, which encompass approximately 3% of our genomic DNA, are crucial for understanding human genetic diversity, disease mechanisms, and evolutionary biology. The advent of high-throughput sequencing methods has revolutionized our ability to accurately map and analyze STRs, highlighting their significance in genetic disorders, forensic science, and population genetics. We review the current available methodologies for STR analysis, the challenges in interpreting STR variations across different populations, and the implications of STRs in medical genetics. Our findings underscore the urgent need for comprehensive STR databases that reflect the genetic diversity of global populations, facilitating the interpretation of STR data in clinical diagnostics, genetic research, and forensic applications. This work sets the stage for future studies aimed at harnessing STR variations to elucidate complex genetic traits and diseases, reinforcing the importance of integrating STRs into genetic research and clinical practice.

Inonotus obliquus aqueous extract inhibits intestinal inflammation and insulin metabolism defects in Drosophila

In biomedical research, the fruit fly (Drosophila melanogaster) is among the most effective and flexible model organisms. Through the use of the Drosophila model, molecular mechanisms of human diseases can be investigated and candidate pharmaceuticals can be screened. White rot fungus Inonotus obliquus is a member of the family Hymenochaetaceae. Due to its multifaceted pharmacological effects, this fungus has been the subject of scientific investigation. Nevertheless, the precise mechanisms by which Inonotus obliquus treats diseases remain unclear. In this study, we prepared an aqueous extract derived from Inonotus obliquus and demonstrated that it effectively prevented the negative impacts of inflammatory agents on flies, including overproliferation and overdifferentiation of intestinal progenitor cells and decreased survival rate. Furthermore, elevated reactive oxygen species levels and cell death were alleviated by Inonotus obliquus aqueous extract, suggesting that this extract inhibited intestinal inflammation. Additionally, Inonotus obliquus aqueous extract had an impact on the insulin pathway, as it alleviated growth defects in flies that were fed a high-sugar diet and in chico mutants. In addition, we determined the composition of Inonotus obliquus aqueous extract and conducted a network pharmacology analysis in order to identify prospective key compounds and targets. In brief, Inonotus obliquus aqueous extract exhibited considerable potential as a therapeutic intervention for human diseases. Our research has established a foundational framework that supports the potential clinical implementation of Inonotus obliquus.

ANT2 functions as a translocon for mitochondrial cross-membrane translocation of RNAs.

Bidirectional transcription of mammalian mitochondrial DNA generates overlapping transcripts that are capable of forming double-stranded RNA (dsRNA) structures. Release of mitochondrial dsRNA into the cytosol activates the dsRNA-sensing immune signaling, which is a defense mechanism against microbial and viral attack and possibly cancer, but could cause autoimmune diseases when unchecked. A better understanding of the process is vital in therapeutic application of this defense mechanism and treatment of cognate human diseases. In addition to exporting dsRNAs, mitochondria also export and import a variety of non-coding RNAs. However, little is known about how these RNAs are transported across mitochondrial membranes. Here we provide direct evidence showing that adenine nucleotide translocase-2 (ANT2) functions as a mammalian RNA translocon in the mitochondrial inner membrane, independent of its ADP/ATP translocase activity. We also show that mitochondrial dsRNA efflux through ANT2 triggers innate immunity. Inhibiting this process alleviates inflammation in vivo, providing a potential therapeutic approach for treating autoimmune diseases.

TUFM in health and disease: exploring its multifaceted roles.

The nuclear-encoded mitochondrial protein Tu translation elongation factor, mitochondrial (TUFM) is well-known for its role in mitochondrial protein translation. Originally discovered in yeast, TUFM demonstrates significant evolutionary conservation from prokaryotes to eukaryotes. Dysregulation of TUFM has been associated with mitochondrial disorders. Although early hypothesis suggests that TUFM is localized within mitochondria, recent studies identify its presence in the cytoplasm, with this subcellular distribution being linked to distinct functions of TUFM. Significantly, in addition to its established function in mitochondrial protein quality control, recent research indicates a broader involvement of TUFM in the regulation of programmed cell death processes (e.g., autophagy, apoptosis, necroptosis, and pyroptosis) and its diverse roles in viral infection, cancer, and other disease conditions. This review seeks to offer a current summary of TUFM's biological functions and its complex regulatory mechanisms in human health and disease. Insight into these intricate pathways controlled by TUFM may lead to the potential development of targeted therapies for a range of human diseases.

Challenges in IBD Research 2024: Preclinical Human IBD Mechanisms.

Preclinical human inflammatory bowel disease (IBD) mechanisms is one of 5 focus areas of the Challenges in IBD Research 2024 document, which also includes environmental triggers, novel technologies, precision medicine, and pragmatic clinical research. Herein, we provide a comprehensive overview of current gaps in inflammatory bowel diseases research that relate to preclinical research and deliver actionable approaches to address them with a focus on how these gaps can lead to advancements in IBD interception, remission, and restoration. The document is the result of multidisciplinary input from scientists, clinicians, patients, and funders and represents a valuable resource for patient-centric research prioritization. This preclinical human IBD mechanisms section identifies major research gaps whose investigation will elucidate pathways and mechanisms that can be targeted to address unmet medical needs in IBD. Research gaps were identified in the following areas: genetics, risk alleles, and epigenetics; the microbiome; cell states and interactions; barrier function; IBD complications (specifically fibrosis and stricturing); and extraintestinal manifestations. To address these gaps, we share specific opportunities for investigation for basic and translational scientists and identify priority actions.

Blimp-1 and c-Maf regulate immune gene networks to protect against distinct pathways of pathobiont-induced colitis

Intestinal immune responses to microbes are controlled by the cytokine IL-10 to avoid immune pathology. Here, we use single-cell RNA sequencing of colon lamina propria leukocytes (LPLs) along with RNA-seq and ATAC-seq of purified CD4+ T cells to show that the transcription factors Blimp-1 (encoded by Prdm1) and c-Maf co-dominantly regulate Il10 while negatively regulating proinflammatory cytokines in effector T cells. Double-deficient Prdm1fl/flMaffl/flCd4Cre mice infected with Helicobacter hepaticus developed severe colitis with an increase in TH1/NK/ILC1 effector genes in LPLs, while Prdm1fl/flCd4Cre and Maffl/flCd4Cre mice exhibited moderate pathology and a less-marked type 1 effector response. LPLs from infected Maffl/flCd4Cre mice had increased type 17 responses with increased Il17a and Il22 expression and an increase in granulocytes and myeloid cell numbers, resulting in increased T cell–myeloid–neutrophil interactions. Genes over-expressed in human inflammatory bowel disease showed differential expression in LPLs from infected mice in the absence of Prdm1 or Maf, revealing potential mechanisms of human disease.

Development and Implementation of an Integrated Preclinical Atherosclerosis Database.

Basic scientists have used preclinical animal models to explore mechanisms driving human diseases for decades, resulting in thousands of publications, each supporting causative inferences. Despite substantial advances in the mechanistic construct of disease, there has been limited translation from individual studies to advances in clinical care. An integrated approach to these individual studies has the potential to improve translational success. Using atherosclerosis as a test case, we extracted data from the 2 most common mouse models of atherosclerosis (ApoE [apolipoprotein E]-knockout and LDLR [low-density lipoprotein receptor]-knockout). We restricted analyses to manuscripts published in 2 well-established journals, Arteriosclerosis, Thrombosis, and Vascular Biology and Circulation, as of query in 2021. Predefined variables including experimental conditions, intervention, and outcomes were extracted from each publication to produce a preclinical atherosclerosis database. Extracted data include animal sex, diet, intervention type, and distinct plaque pathologies (size, inflammation, and lipid content). Procedures are provided to standardize data extraction, attribute interventions to specific genes, and transform the database for use with available transcriptomics software. The database integrates hundreds of genes, each directly tested in vivo for causation in a murine atherosclerosis model. The database is provided to allow the research community to perform integrated analyses that reflect the global impact of decades of atherosclerosis investigation. This database is provided as a resource for future interrogation of sub-data sets associated with distinct plaque pathologies, cell type, or sex. We also provide the methods and software needed to expand this data set and apply this approach to the extensive repository of peer-reviewed data utilizing preclinical models to interrogate mechanisms of diverse human diseases.

A Genetic Analysis of Current Medication Use in the UK Biobank.

Genomics has been forecasted to revolutionise human health by improving medical treatment through a better understanding of the molecular mechanisms of human diseases. Despite great successes of the last decade's genome-wide association studies (GWAS), the results have been translated to genomic medicine to a limited extent. One route to get closer to improved medical treatment could be by understanding the genetics of medication use. Current medication profiles from 335,744 individuals from the UK Biobank were obtained, and a GWAS was conducted to identify common genetic variants associated with current medication use. In total, 59 independent loci were identified for medication use, and approximately 18% of the total variation was attributable to common genetic variation. The largest fraction of genetic variance for current medication use was captured by variants with low-to-medium minor allele frequency, with coding, conserved genomic regions and transcription start sites being enriched for associated variants. The average correlation (R) between medication use and the polygenic score was 0.14. The results further demonstrated that individuals with higher polygenic burden for medication use were, on average, sicker and had a higher risk for adverse drug reactions. These results provide an insight into the genetic contribution of medication use and pave the way for developments of novel multiple trait polygenic scores, which include the genetically informed medication use.

One-step in vivo gene knock-out in porcine embryos using recombinant adeno-associated viruses.

Introduction: Gene-edited pigs have become prominent models for studying human disease mechanisms, gene therapy, and xenotransplantation. CRISPR (clustered regularly interspaced short palindromic repeats)/CRISPR-associated 9 (CRISPR/Cas9) technology is a widely employed tool for generating gene-edited pigs. Nevertheless, delivering CRISPR/Cas9 to pre-implantation embryos has traditionally posed challenges due to its reliance on intricate micromanipulation equipment and specialized techniques, resulting in high costs and time-consuming procedures. This study aims to introduce a novel one-step approach for generating genetically modified pigs by transducing CRISPR/Cas9 components into pre-implantation porcine embryos through oviductal injection of recombinant adeno-associated viruses (rAAV). Methods: We first used rAAV-1, rAAV-6, rAAV-8, rAAV-9 expressing EGFP to screen for rAAV serotypes that efficiently target porcine embryos, and then, to achieve efficient expression of CRISPR/Cas9 in vivo for a short period, we packaged sgRNAs targeting the GHR genes to self-complementary adeno-associated virus (scAAV), and Cas9 proteins to single-stranded adeno-associated virus (ssAAV). The efficiency of porcine embryos -based editing was then validated in vitro. The feasibility of this one-step method to produce gene-edited pigs using rAAV-CRISPR/Cas9 oviductal injection into sows within 24h of conception was then validated. Results: Our research firstly establishes the efficient delivery of CRISPR/Cas9 to pig zygotes, both in vivo and in vitro, using rAAV6. Successful gene editing in pigs was achieved through oviductal injection of rAAV-CRISPR/Cas9. Conclusion: This method circumvents the intricate procedures involved in in vitro embryo manipulation and embryo transfers, providing a straightforward and cost-effective approach for the production of gene-edited pigs.

Analyzing Biomedical Datasets with Symbolic Tree Adaptive Resonance Theory

Biomedical datasets distill many mechanisms of human diseases, linking diseases to genes and phenotypes (signs and symptoms of disease), genetic mutations to altered protein structures, and altered proteins to changes in molecular functions and biological processes. It is desirable to gain new insights from these data, especially with regard to the uncovering of hierarchical structures relating disease variants. However, analysis to this end has proven difficult due to the complexity of the connections between multi-categorical symbolic data. This article proposes symbolic tree adaptive resonance theory (START), with additional supervised, dual-vigilance (DV-START), and distributed dual-vigilance (DDV-START) formulations, for the clustering of multi-categorical symbolic data from biomedical datasets by demonstrating its utility in clustering variants of Charcot–Marie–Tooth disease using genomic, phenotypic, and proteomic data.

RNA structure: implications in viral infections and neurodegenerative diseases

AbstractRNA is an intermediary between DNA and protein, a catalyzer of biochemical reactions, and a regulator of genes and transcripts. RNA structures are essential for complicated functions. Recent years have witnessed rapid advancements in RNA secondary structure probing techniques. These technological strides provided comprehensive insights into RNA structures, which significantly contributed to our understanding of diverse cellular regulatory processes, including gene regulation, epigenetic regulation, and post-transactional regulation. Meanwhile, they have facilitated the creation of therapeutic tools for tackling human diseases. Despite their therapeutic applications, RNA structure probing methods also offer a promising avenue for exploring the mechanisms of human diseases, potentially providing the key to overcoming existing research constraints and obtaining the in-depth information necessary for a deeper understanding of disease mechanisms.

Complex In Vitro Model Characterization for Context of Use in Toxicologic Pathology: Use Cases by Collaborative Teams of Biologists, Bioengineers, and Pathologists.

Complex in vitro models (CIVMs) offer the potential to increase the clinical relevance of preclinical efficacy and toxicity assessments and reduce the reliance on animals in drug development. The European Society of Toxicologic Pathology (ESTP) and Society for Toxicologic Pathology (STP) are collaborating to highlight the role of pathologists in the development and use of CIVM. Pathologists are trained in comparative animal medicine which enhances their understanding of mechanisms of human and animal diseases, thus allowing them to bridge between animal models and humans. This skill set is important for CIVM development, validation, and data interpretation. Ideally, diverse teams of scientists, including engineers, biologists, pathologists, and others, should collaboratively develop and characterize novel CIVM, and collectively assess their precise use cases (context of use). Implementing a morphological CIVM evaluation should be essential in this process. This requires robust histological technique workflows, image analysis techniques, and needs correlation with translational biomarkers. In this review, we demonstrate how such tissue technologies and analytics support the development and use of CIVM for drug efficacy and safety evaluations. We encourage the scientific community to explore similar options for their projects and to engage with health authorities on the use of CIVM in benefit-risk assessment.