[Progress of research on circular RNA encoding proteins in disease].

With the advance of research, non-coding RNA has been found to surpass the traditional definition to directly code functional proteins by coding sequence elements and binding with ribosomes. Among the non-coding RNAs, the function of circRNA encoded proteins has been most extensively studied. This study has used "circRNA", "encoded", and "translation" as the key words to search the PubMed and Web of Science databases. The retrieved literature was screened and traced, with the translation mechanism, related research methods, and correlation with diseases of circRNA reviewed. CircRNA can translate proteins through a non-cap-dependent pathway. Multiple molecular techniques, in particular mass spectrometry analysis, have important value in identifying unique peptide segments of circRNA encoded proteins for confirming their existence. The proteins encoded by the circRNA are involved in the pathogenesis of diseases of the digestive, neurological, urinary systems and the breast, and have the potential to serve as novel targets for disease diagnosis and treatment. This article has provided a comprehensive review for the basic theory, experimental methods, and disease-related research in the field of circRNA translation, which may provide clues for the identification of new diagnostic and therapeutic targets.

High-throughput assays to assess variant effects on disease.

Interpreting the wealth of rare genetic variants discovered in population-scale sequencing efforts and deciphering their associations with human health and disease present a critical challenge due to the lack of sufficient clinical case reports. One promising avenue to overcome this problem is deep mutational scanning (DMS), a method of introducing and evaluating large-scale genetic variants in model cell lines. DMS allows unbiased investigation of variants, including those that are not found in clinical reports, thus improving rare disease diagnostics. Currently, the main obstacle limiting the full potential of DMS is the availability of functional assays that are specific to disease mechanisms. Thus, we explore high-throughput functional methodologies suitable to examine broad disease mechanisms. We specifically focus on methods that do not require robotics or automation but instead use well-designed molecular tools to transform biological mechanisms into easily detectable signals, such as cell survival rate, fluorescence or drug resistance. Here, we aim to bridge the gap between disease-relevant assays and their integration into the DMS framework.

An overview of CCN4 (WISP1) role in human diseases.

CCN4 (cellular communication network factor 4), a highly conserved, secreted cysteine-rich matricellular protein is emerging as a key player in the development and progression of numerous disease pathologies, including cancer, fibrosis, metabolic and inflammatory disorders. Over the past two decades, extensive research on CCN4 and its family members uncovered their diverse cellular mechanisms and biological functions, including but not limited to cell proliferation, migration, invasion, angiogenesis, wound healing, repair, and apoptosis. Recent studies have demonstrated that aberrant CCN4 expression and/or associated downstream signaling is key to a vast array of pathophysiological etiology, suggesting that CCN4 could be utilized not only as a non-invasive diagnostic or prognostic marker, but also as a promising therapeutic target. The cognate receptor of CCN4 remains elusive till date, which limits understanding of the mechanistic insights on CCN4 driven disease pathologies. However, as therapeutic agents directed against CCN4 begin to make their way into the clinic, that may start to change. Also, the pathophysiological significance of CCN4 remains underexplored, hence further research is needed to shed more light on its disease and/or tissue specific functions to better understand its clinical translational benefit. This review highlights the compelling evidence of overlapping and/or diverse functional and mechanisms regulated by CCN4, in addition to addressing the challenges, study limitations and knowledge gaps on CCN4 biology and its therapeutic potential.

Epigenetic modulators provide a path to understanding disease and therapeutic opportunity.

The discovery of epigenetic modulators (writers, erasers, readers, and remodelers) has shed light on previously underappreciated biological mechanisms that promote diseases. With these insights, novel biomarkers and innovative combination therapies can be used to address challenging and difficult to treat disease states. This review highlights key mechanisms that epigenetic writers, erasers, readers, and remodelers control, as well as their connection with disease states and recent advances in associated epigenetic therapies.

Percepción del cuidado centrado en la persona y autonomía en la atención clínica de usuarios que viven con multimorbilidad / Perception of Person-Centered Care and Autonomy in Clinical Care in Patients living with Multimorbidity / Percepção do cuidado centrado na pessoa e autonomia no cuidado clínico de usuários que convivem com multimorbidade

Introducción: promover autonomía significa transitar desde un modelo paternalista hacia uno que posi-cione en un rol activo a las personas durante el encuentro clínico, con participación en la toma de deci-siones relativas al cuidado de su salud. Este artículo describe la percepción de usuarios que viven con multimorbilidad respecto del ejercicio de su autonomía durante la atención clínica. Método: estudio de caso cualitativo en usuarios con multimorbilidad atendidos en un centro de salud familiar de Santiago (Chile).Se realizó análisis de contenido según Krippendorf. Resultados: la muestra quedó conformada por 19 participantes adultos con un promedio de 2.7 condiciones crónicas de salud. Del análisis de contenido de las entrevistas emergieron tres categorías: a) significado atribuido por los usuarios a la autonomía en la atención de salud, b) elementos que debe considerar una atención en salud que respete la autonomía del usuario y c) participación durante la atención clínica. Conclusiones: frente al aumento de las condicio-nes crónicas de salud es imperativo repensar la forma de brindar atención de salud, relevando el valor de la participación usuaria a través de la toma de decisiones compartida como expresión de respeto de su autonomía y una forma de fomentar el cuidado centrado en las personas

Aim: Promoting autonomy means changing from a paternalistic model to one in which individuals play an active role in their healthcare, which their participation in medical decision-making will reflect. This issue needs to be sufficiently explored in Chile, so this article aims to describe the perception of users liv-ing with multimorbidity regarding their ability to exercise autonomy in clinical care. Method: Qualitative case study conducted in a sample of patients with multimorbility from a family health center in Santiago de Chile. Content analysis was performed according to the Krippendorf method. Results: The sample com-prised 19 adult participants with an average of 2.7 chronic health conditions. Three categories emerged from the content analysis of the interviews: (a) Meaning attributed by users to autonomy in health care, (b) Elements that health care respecting user autonomy should consider, and (c) Participation during clinical care. Conclusions: Considering the sustained increase in chronic health conditions, it is impera-tive to rethink how health care is provided, highlighting the value of user participation through shared decision-making as an expression of respect for individuals' autonomy and the promotion of patient-cen-tered care

Objetivo: promover a autonomia significa passar de um modelo paternalista para um que posicione as pessoas num papel ativo durante o encontro clínico, com participação na tomada de decisões relaciona-das com os seus cuidados de saúde. Este manuscrito descreve a percepção de usuários que convivem com multimorbidade quanto ao exercício de sua autonomia durante o atendimento clínico. Método: estudo de caso qualitativo em usuários com multimorbidade atendidos em um Centro de Saúde da Família de Santiago, no Chile. A análise de conteúdo foi realizada segundo Krippendorf. Resultados: a amostra foi composta por 19 participantes adultos com média de 2.7 condições crônicas de saúde. Da análise de conteúdo das entrevistas emergem três categorias: a) Significado atribuído pelos usuários à autonomia no cuidado em saúde, b) Elementos que um cuidado de saúde que respeite a autonomia do usuário deve considerar, e c) Participação durante o atendimento clínico. Conclusões: face ao aumento das condições crónicas de saúde, é imperativo repensar a forma de prestar cuidados de saúde, destacando o valor da participação dos pacientes através da tomada de decisão partilhada como expressão de respeito pela sua autonomia e forma de promover o cuidado centrado nas pessoas

Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes.

Germline and somatic mutations can give rise to proteins with altered activity, including both gain and loss-of-function. The effects of these variants can be captured in disease-specific reactions and pathways that highlight the resulting changes to normal biology. A disease reaction is defined as an aberrant reaction in which a variant protein participates. A disease pathway is defined as a pathway that contains a disease reaction. Annotation of disease variants as participants of disease reactions and disease pathways can provide a standardized overview of molecular phenotypes of pathogenic variants that is amenable to computational mining and mathematical modeling. Reactome (https://reactome.org/), an open source, manually curated, peer-reviewed database of human biological pathways, in addition to providing annotations for >11 000 unique human proteins in the context of ∼15 000 wild-type reactions within more than 2000 wild-type pathways, also provides annotations for >4000 disease variants of close to 400 genes as participants of ∼800 disease reactions in the context of ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, described in wild-type reactions and pathways, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Reactome's data model enables mapping of disease variant datasets to specific disease reactions within disease pathways, providing a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity. Database URL: https://reactome.org/.

mi-Mic: a novel multi-layer statistical test for microbiota-disease associations.

mi-Mic, a novel approach for microbiome differential abundance analysis, tackles the key challenges of such statistical tests: a large number of tests, sparsity, varying abundance scales, and taxonomic relationships. mi-Mic first converts microbial counts to a cladogram of means. It then applies a priori tests on the upper levels of the cladogram to detect overall relationships. Finally, it performs a Mann-Whitney test on paths that are consistently significant along the cladogram or on the leaves. mi-Mic has much higher true to false positives ratios than existing tests, as measured by a new real-to-shuffle positive score.

Comparing natural language processing representations of coded disease sequences for prediction in electronic health records.

OBJECTIVE: Natural language processing (NLP) algorithms are increasingly being applied to obtain unsupervised representations of electronic health record (EHR) data, but their comparative performance at predicting clinical endpoints remains unclear. Our objective was to compare the performance of unsupervised representations of sequences of disease codes generated by bag-of-words versus sequence-based NLP algorithms at predicting clinically relevant outcomes. MATERIALS AND METHODS: This cohort study used primary care EHRs from 6 286 233 people with Multiple Long-Term Conditions in England. For each patient, an unsupervised vector representation of their time-ordered sequences of diseases was generated using 2 input strategies (212 disease categories versus 9462 diagnostic codes) and different NLP algorithms (Latent Dirichlet Allocation, doc2vec, and 2 transformer models designed for EHRs). We also developed a transformer architecture, named EHR-BERT, incorporating sociodemographic information. We compared the performance of each of these representations (without fine-tuning) as inputs into a logistic classifier to predict 1-year mortality, healthcare use, and new disease diagnosis. RESULTS: Patient representations generated by sequence-based algorithms performed consistently better than bag-of-words methods in predicting clinical endpoints, with the highest performance for EHR-BERT across all tasks, although the absolute improvement was small. Representations generated using disease categories perform similarly to those using diagnostic codes as inputs, suggesting models can equally manage smaller or larger vocabularies for prediction of these outcomes. DISCUSSION AND CONCLUSION: Patient representations produced by sequence-based NLP algorithms from sequences of disease codes demonstrate improved predictive content for patient outcomes compared with representations generated by co-occurrence-based algorithms. This suggests transformer models may be useful for generating multi-purpose representations, even without fine-tuning.

RDscan: Extracting RNA-disease relationship from the literature based on pre-training model.

With the rapid advancements in molecular biology and genomics, a multitude of connections between RNA and diseases has been unveiled, making the efficient and accurate extraction of RNA-disease (RD) relationships from extensive biomedical literature crucial for advancing research in this field. This study introduces RDscan, a novel text mining method developed based on the pre-training and fine-tuning strategy, aimed at automatically extracting RD-related information from a vast corpus of literature using pre-trained biomedical large language models (LLM). Initially, we constructed a dedicated RD corpus by manually curating from literature, comprising 2,082 positive and 2,000 negative sentences, alongside an independent test dataset (comprising 500 positive and 500 negative sentences) for training and evaluating RDscan. Subsequently, by fine-tuning the Bioformer and BioBERT pre-trained models, RDscan demonstrated exceptional performance in text classification and named entity recognition (NER) tasks. In 5-fold cross-validation, RDscan significantly outperformed traditional machine learning methods (Support Vector Machine, Logistic Regression and Random Forest). In addition, we have developed an accessible webserver that assists users in extracting RD relationships from text. In summary, RDscan represents the first text mining tool specifically designed for RD relationship extraction, and is poised to emerge as an invaluable tool for researchers dedicated to exploring the intricate interactions between RNA and diseases. Webserver of RDscan is free available at https://cellknowledge.com.cn/RDscan/.

Horizontal mitochondrial transfer as a novel bioenergetic tool for mesenchymal stromal/stem cells: molecular mechanisms and therapeutic potential in a variety of diseases.

Intercellular mitochondrial transfer (MT) is a newly discovered form of cell-to-cell signalling involving the active incorporation of healthy mitochondria into stressed/injured recipient cells, contributing to the restoration of bioenergetic profile and cell viability, reduction of inflammatory processes and normalisation of calcium dynamics. Recent evidence has shown that MT can occur through multiple cellular structures and mechanisms: tunneling nanotubes (TNTs), via gap junctions (GJs), mediated by extracellular vesicles (EVs) and other mechanisms (cell fusion, mitochondrial extrusion and migrasome-mediated mitocytosis) and in different contexts, such as under physiological (tissue homeostasis and stemness maintenance) and pathological conditions (hypoxia, inflammation and cancer). As Mesenchimal Stromal/ Stem Cells (MSC)-mediated MT has emerged as a critical regulatory and restorative mechanism for cell and tissue regeneration and damage repair in recent years, its potential in stem cell therapy has received increasing attention. In particular, the potential therapeutic role of MSCs has been reported in several articles, suggesting that MSCs can enhance tissue repair after injury via MT and membrane vesicle release. For these reasons, in this review, we will discuss the different mechanisms of MSCs-mediated MT and therapeutic effects on different diseases such as neuronal, ischaemic, vascular and pulmonary diseases. Therefore, understanding the molecular and cellular mechanisms of MT and demonstrating its efficacy could be an important milestone that lays the foundation for future clinical trials.

Integrating population genetics, stem cell biology and cellular genomics to study complex human diseases.

Human pluripotent stem (hPS) cells can, in theory, be differentiated into any cell type, making them a powerful in vitro model for human biology. Recent technological advances have facilitated large-scale hPS cell studies that allow investigation of the genetic regulation of molecular phenotypes and their contribution to high-order phenotypes such as human disease. Integrating hPS cells with single-cell sequencing makes identifying context-dependent genetic effects during cell development or upon experimental manipulation possible. Here we discuss how the intersection of stem cell biology, population genetics and cellular genomics can help resolve the functional consequences of human genetic variation. We examine the critical challenges of integrating these fields and approaches to scaling them cost-effectively and practically. We highlight two areas of human biology that can particularly benefit from population-scale hPS cell studies, elucidating mechanisms underlying complex disease risk loci and evaluating relationships between common genetic variation and pharmacotherapeutic phenotypes.

DiSMVC: a multi-view graph collaborative learning framework for measuring disease similarity.

MOTIVATION: Exploring potential associations between diseases can help in understanding pathological mechanisms of diseases and facilitating the discovery of candidate biomarkers and drug targets, thereby promoting disease diagnosis and treatment. Some computational methods have been proposed for measuring disease similarity. However, these methods describe diseases without considering their latent multi-molecule regulation and valuable supervision signal, resulting in limited biological interpretability and efficiency to capture association patterns. RESULTS: In this study, we propose a new computational method named DiSMVC. Different from existing predictors, DiSMVC designs a supervised graph collaborative framework to measure disease similarity. Multiple bio-entity associations related to genes and miRNAs are integrated via cross-view graph contrastive learning to extract informative disease representation, and then association pattern joint learning is implemented to compute disease similarity by incorporating phenotype-annotated disease associations. The experimental results show that DiSMVC can draw discriminative characteristics for disease pairs, and outperform other state-of-the-art methods. As a result, DiSMVC is a promising method for predicting disease associations with molecular interpretability. AVAILABILITY AND IMPLEMENTATION: Datasets and source codes are available at https://github.com/Biohang/DiSMVC.

Molecular functions of ANKLE2 and its implications in human disease.

Ankyrin repeat and LEM domain-containing 2 (ANKLE2) is a scaffolding protein with established roles in cell division and development, the dysfunction of which is increasingly implicated in human disease. ANKLE2 regulates nuclear envelope disassembly at the onset of mitosis and its reassembly after chromosome segregation. ANKLE2 dysfunction is associated with abnormal nuclear morphology and cell division. It regulates the nuclear envelope by mediating protein-protein interactions with barrier to autointegration factor (BANF1; also known as BAF) and with the kinase and phosphatase that modulate the phosphorylation state of BAF. In brain development, ANKLE2 is crucial for proper asymmetric division of neural progenitor cells. In humans, pathogenic loss-of-function mutations in ANKLE2 are associated with primary congenital microcephaly, a condition in which the brain is not properly developed at birth. ANKLE2 is also linked to other disease pathologies, including congenital Zika syndrome, cancer and tauopathy. Here, we review the molecular roles of ANKLE2 and the recent literature on human diseases caused by its dysfunction.

The biological function of the N6-Methyladenosine reader YTHDC2 and its role in diseases.

N6-methyladenosine (m6A) stands as the most prevalent modified form of RNA in eukaryotes, pivotal in various biological processes such as regulating RNA stability, translation, and transcription. All members within the YT521-B homology (YTH) gene family are categorized as m6A reading proteins, capable of identifying and binding m6A modifications on RNA, thereby regulating RNA metabolism and functioning across diverse physiological processes. YTH domain-containing 2 (YTHDC2), identified as the latest member of the YTH family, has only recently started to emerge for its biological function. Numerous studies have underscored the significance of YTHDC2 in human physiology, highlighting its involvement in both tumor progression and non-tumor diseases. Consequently, this review aims to further elucidate the pathological mechanisms of YTHDC2 by summarizing its functions and roles in tumors and other diseases, with a particular focus on its downstream molecular targets and signaling pathways.

Automated annotation of disease subtypes.

BACKGROUND: Distinguishing diseases into distinct subtypes is crucial for study and effective treatment strategies. The Open Targets Platform (OT) integrates biomedical, genetic, and biochemical datasets to empower disease ontologies, classifications, and potential gene targets. Nevertheless, many disease annotations are incomplete, requiring laborious expert medical input. This challenge is especially pronounced for rare and orphan diseases, where resources are scarce. METHODS: We present a machine learning approach to identifying diseases with potential subtypes, using the approximately 23,000 diseases documented in OT. We derive novel features for predicting diseases with subtypes using direct evidence. Machine learning models were applied to analyze feature importance and evaluate predictive performance for discovering both known and novel disease subtypes. RESULTS: Our model achieves a high (89.4%) ROC AUC (Area Under the Receiver Operating Characteristic Curve) in identifying known disease subtypes. We integrated pre-trained deep-learning language models and showed their benefits. Moreover, we identify 515 disease candidates predicted to possess previously unannotated subtypes. CONCLUSIONS: Our models can partition diseases into distinct subtypes. This methodology enables a robust, scalable approach for improving knowledge-based annotations and a comprehensive assessment of disease ontology tiers. Our candidates are attractive targets for further study and personalized medicine, potentially aiding in the unveiling of new therapeutic indications for sought-after targets.

Special Issue: "Genes and Human Diseases".

Studying mechanisms of development and the causes of various human diseases continues to be the focus of attention of various researchers [...].