Adaptor protein Abelson interactor 1 in homeostasis and disease.

Dysregulation of Abelson interactor 1 (ABI1) is associated with various states of disease including developmental defects, pathogen infections, and cancer. ABI1 is an adaptor protein predominantly known to regulate actin cytoskeleton organization processes such as those involved in cell adhesion, migration, and shape determination. Linked to cytoskeleton via vasodilator-stimulated phosphoprotein (VASP), Wiskott-Aldrich syndrome protein family (WAVE), and neural-Wiskott-Aldrich syndrome protein (N-WASP)-associated protein complexes, ABI1 coordinates regulation of various cytoplasmic protein signaling complexes dysregulated in disease states. The roles of ABI1 beyond actin cytoskeleton regulation are much less understood. This comprehensive, protein-centric review describes molecular roles of ABI1 as an adaptor molecule in the context of its dysregulation and associated disease outcomes to better understand disease state-specific protein signaling and affected interconnected biological processes.

Prayers for Sickness: What do people pray for and how do they deal with unanswered prayer?

Background: This paper focuses upon prayer for sickness. What do individuals suffering from illness, their families and the wider community pray for? How do they deal with unanswered prayer? Do they pray for cure, to guide medical professionals or to cope with their sickness? What rationalisations do they proffer for unanswered prayer? Methods: Based on a critical literature review and deploying secondary data from the Twenty First Century Evangelical research programme, the data suggest that prayers for guiding medical professionals and coping are more common than for cure, at least in Global North countries such as the UK and US. But why do those who believe in miracles not ask God for divine healing? Furthermore, unanswered prayer can conflict with Christian views of God as omnipotent and all loving. Results: Respondents use a number of theodical rationalisations to resolve this conflict. Conclusions: The results are discussed in relation to cognitive dissonance theory, learned helplessness, the need to conserve a relationship with the Divine, and desire to manage risk of disappointment and reduce consequent emotional pain.

PI3K/AKT/mTOR Signaling Network in Human Health and Diseases.

Transduction of molecular signaling is a fundamental mechanism that allows a living cell to communicate internally with other cells and its environment through chemical or physical signals, thereby maintaining its structural integrity and triggering physiological responses [...].

CBGDA: a manually curated resource for gene-disease associations based on genome-wide CRISPR.

The field of understanding the association between genes and diseases is rapidly expanding, making it challenging for researchers to keep up with the influx of new publications and genetic datasets. Fortunately, there are now several regularly updated databases available that focus on cataloging gene-disease relationships. The development of the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-Cas9 system has revolutionized the field of gene editing, providing a highly efficient, accurate, and reliable method for exploring gene-disease associations. However, currently, there is no resource specifically dedicated to collecting and integrating the latest experimentally supported gene-disease association data derived from genome-wide CRISPR screening. To address this gap, we have developed the CRISPR-Based Gene-Disease Associations (CBGDA) database, which includes over 200 manually curated gene-disease association data derived from genome-wide CRISPR screening studies. Through CBGDA, users can explore gene-disease association data derived from genome-wide CRISPR screening, gaining insights into the expression patterns of genes in different diseases, associated chemical data, and variant information. This provides a novel perspective on understanding the associations between genes and diseases. What is more, CBGDA integrates data from several other databases and resources, enhancing its comprehensiveness and utility. In summary, CBGDA offers a fresh perspective and comprehensive insights into the research on gene-disease associations. It fills the gap by providing a dedicated resource for accessing up-to-date, experimentally supported gene-disease association data derived from genome-wide CRISPR screening. Database URL: http://cbgda.zhounan.org/main.

SSGU-CD: A combined semantic and structural information graph U-shaped network for document-level Chemical-Disease interaction extraction.

Document-level interaction extraction for Chemical-Disease is aimed at inferring the interaction relations between chemical entities and disease entities across multiple sentences. Compared with sentence-level relation extraction, document-level relation extraction can capture the associations between different entities throughout the entire document, which is found to be more practical for biomedical text information. However, current biomedical extraction methods mainly concentrate on sentence-level relation extraction, making it difficult to access the rich structural information contained in documents in practical application scenarios. We put forward SSGU-CD, a combined Semantic and Structural information Graph U-shaped network for document-level Chemical-Disease interaction extraction. This framework effectively stores document semantic and structure information as graphs and can fuse the original context information of documents. Using the framework, we propose a balanced combination of cross-entropy loss function to facilitate collaborative optimization among models with the aim of enhancing the ability to extract Chemical-Disease interaction relations. We evaluated SSGU-CD on the document-level relation extraction dataset CDR and BioRED, and the results demonstrate that the framework can significantly improve the extraction performance.

FLI-1-driven regulation of endothelial cells in human diseases.

Endothelial cells (ECs) are widely distributed in the human body and play crucial roles in the circulatory and immune systems. ECs dysfunction contributes to the progression of various chronic cardiovascular, renal, and metabolic diseases. As a key transcription factor in ECs, FLI-1 is involved in the differentiation, migration, proliferation, angiogenesis and blood coagulation of ECs. Imbalanced FLI-1 expression in ECs can lead to various diseases. Low FLI-1 expression leads to systemic sclerosis by promoting fibrosis and vascular lesions, to pulmonary arterial hypertension by promoting a local inflammatory state and vascular lesions, and to tumour metastasis by promoting the EndMT process. High FLI-1 expression leads to lupus nephritis by promoting a local inflammatory state. Therefore, FLI-1 in ECs may be a good target for the treatment of the abovementioned diseases. This comprehensive review provides the first overview of FLI-1-mediated regulation of ECs processes, with a focus on its influence on the abovementioned diseases and existing FLI-1-targeted drugs. A better understanding of the role of FLI-1 in ECs may facilitate the design of more effective targeted therapies for clinical applications, particularly for tumour treatment.

Rare coding variant analysis for human diseases across biobanks and ancestries.

Large-scale sequencing has enabled unparalleled opportunities to investigate the role of rare coding variation in human phenotypic variability. Here, we present a pan-ancestry analysis of sequencing data from three large biobanks, including the All of Us research program. Using mixed-effects models, we performed gene-based rare variant testing for 601 diseases across 748,879 individuals, including 155,236 with ancestry dissimilar to European. We identified 363 significant associations, which highlighted core genes for the human disease phenome and identified potential novel associations, including UBR3 for cardiometabolic disease and YLPM1 for psychiatric disease. Pan-ancestry burden testing represented an inclusive and useful approach for discovery in diverse datasets, although we also highlight the importance of ancestry-specific sensitivity analyses in this setting. Finally, we found that effect sizes for rare protein-disrupting variants were concordant between samples similar to European ancestry and other genetic ancestries (ßDeming = 0.7-1.0). Our results have implications for multi-ancestry and cross-biobank approaches in sequencing association studies for human disease.

Identifying diseases symptoms and general rules using supervised and unsupervised machine learning.

The symptoms of diseases can vary among individuals and may remain undetected in the early stages. Detecting these symptoms is crucial in the initial stage to effectively manage and treat cases of varying severity. Machine learning has made major advances in recent years, proving its effectiveness in various healthcare applications. This study aims to identify patterns of symptoms and general rules regarding symptoms among patients using supervised and unsupervised machine learning. The integration of a rule-based machine learning technique and classification methods is utilized to extend a prediction model. This study analyzes patient data that was available online through the Kaggle repository. After preprocessing the data and exploring descriptive statistics, the Apriori algorithm was applied to identify frequent symptoms and patterns in the discovered rules. Additionally, the study applied several machine learning models for predicting diseases, including stepwise regression, support vector machine, bootstrap forest, boosted trees, and neural-boosted methods. Several predictive machine learning models were applied to the dataset to predict diseases. It was discovered that the stepwise method for fitting outperformed all competitors in this study, as determined through cross-validation conducted for each model based on established criteria. Moreover, numerous significant decision rules were extracted in the study, which can streamline clinical applications without the need for additional expertise. These rules enable the prediction of relationships between symptoms and diseases, as well as between different diseases. Therefore, the results obtained in this study have the potential to improve the performance of prediction models. We can discover diseases symptoms and general rules using supervised and unsupervised machine learning for the dataset. Overall, the proposed algorithm can support not only healthcare professionals but also patients who face cost and time constraints in diagnosing and treating these diseases.

Network medicine-based epistasis detection in complex diseases: ready for quantum computing.

Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs) (1-3). Existing statistical computational methods for EI detection are mostly limited to pairs of SNPs due to the combinatorial explosion of higher-order EIs. With NeEDL (network-based epistasis detection via local search), we leverage network medicine to inform the selection of EIs that are an order of magnitude more statistically significant compared to existing tools and consist, on average, of five SNPs. We further show that this computationally demanding task can be substantially accelerated once quantum computing hardware becomes available. We apply NeEDL to eight different diseases and discover genes (affected by EIs of SNPs) that are partly known to affect the disease, additionally, these results are reproducible across independent cohorts. EIs for these eight diseases can be interactively explored in the Epistasis Disease Atlas (https://epistasis-disease-atlas.com). In summary, NeEDL demonstrates the potential of seamlessly integrated quantum computing techniques to accelerate biomedical research. Our network medicine approach detects higher-order EIs with unprecedented statistical and biological evidence, yielding unique insights into polygenic diseases and providing a basis for the development of improved risk scores and combination therapies.

Identification of microbe-disease signed associations via multi-scale variational graph autoencoder based on signed message propagation.

BACKGROUND: Plenty of clinical and biomedical research has unequivocally highlighted the tremendous significance of the human microbiome in relation to human health. Identifying microbes associated with diseases is crucial for early disease diagnosis and advancing precision medicine. RESULTS: Considering that the information about changes in microbial quantities under fine-grained disease states helps to enhance a comprehensive understanding of the overall data distribution, this study introduces MSignVGAE, a framework for predicting microbe-disease sign associations using signed message propagation. MSignVGAE employs a graph variational autoencoder to model noisy signed association data and extends the multi-scale concept to enhance representation capabilities. A novel strategy for propagating signed message in signed networks addresses heterogeneity and consistency among nodes connected by signed edges. Additionally, we utilize the idea of denoising autoencoder to handle the noise in similarity feature information, which helps overcome biases in the fused similarity data. MSignVGAE represents microbe-disease associations as a heterogeneous graph using similarity information as node features. The multi-class classifier XGBoost is utilized to predict sign associations between diseases and microbes. CONCLUSIONS: MSignVGAE achieves AUROC and AUPR values of 0.9742 and 0.9601, respectively. Case studies on three diseases demonstrate that MSignVGAE can effectively capture a comprehensive distribution of associations by leveraging signed information.

Fine-tuning AMPK in physiology and disease using point-mutant mouse models.

AMP-activated protein kinase (AMPK) is an evolutionarily conserved serine/threonine kinase that monitors the cellular energy status to adapt it to the fluctuating nutritional and environmental conditions in an organism. AMPK plays an integral part in a wide array of physiological processes, such as cell growth, autophagy and mitochondrial function, and is implicated in diverse diseases, including cancer, metabolic disorders, cardiovascular diseases and neurodegenerative diseases. AMPK orchestrates many different physiological outcomes by phosphorylating a broad range of downstream substrates. However, the importance of AMPK-mediated regulation of these substrates in vivo remains an ongoing area of investigation to better understand its precise role in cellular and metabolic homeostasis. Here, we provide a comprehensive overview of our understanding of the kinase function of AMPK in vivo, as uncovered from mouse models that harbor phosphorylation mutations in AMPK substrates. We discuss some of the inherent limitations of these mouse models, highlight the broader implications of these studies for understanding human health and disease, and explore the valuable insights gained that could inform future therapeutic strategies for the treatment of metabolic and non-metabolic disorders.

Predicting potential microbe-disease associations based on dual branch graph convolutional network.

Studying the association between microbes and diseases not only aids in the prevention and diagnosis of diseases, but also provides crucial theoretical support for new drug development and personalized treatment. Due to the time-consuming and costly nature of laboratory-based biological tests to confirm the relationship between microbes and diseases, there is an urgent need for innovative computational frameworks to anticipate new associations between microbes and diseases. Here, we propose a novel computational approach based on a dual branch graph convolutional network (GCN) module, abbreviated as DBGCNMDA, for identifying microbe-disease associations. First, DBGCNMDA calculates the similarity matrix of diseases and microbes by integrating functional similarity and Gaussian association spectrum kernel (GAPK) similarity. Then, semantic information from different biological networks is extracted by two GCN modules from different perspectives. Finally, the scores of microbe-disease associations are predicted based on the extracted features. The main innovation of this method lies in the use of two types of information for microbe/disease similarity assessment. Additionally, we extend the disease nodes to address the issue of insufficient features due to low data dimensionality. We optimize the connectivity between the homogeneous entities using random walk with restart (RWR), and then use the optimized similarity matrix as the initial feature matrix. In terms of network understanding, we design a dual branch GCN module, namely GlobalGCN and LocalGCN, to fine-tune node representations by introducing side information, including homologous neighbour nodes. We evaluate the accuracy of the DBGCNMDA model using five-fold cross-validation (5-fold-CV) technique. The results show that the area under the receiver operating characteristic curve (AUC) and area under the precision versus recall curve (AUPR) of the DBGCNMDA model in the 5-fold-CV are 0.9559 and 0.9630, respectively. The results from the case studies using published experimental data confirm a significant number of predicted associations, indicating that DBGCNMDA is an effective tool for predicting potential microbe-disease associations.

Proteostasis in health and disease: a conversation with Professor Rick Morimoto.

Professor Richard (Rick) Morimoto is the Bill and Gayle Cook Professor of Biology and Director of the Rice Institute for Biomedical Research at Northwestern University. He has made foundational contributions to our understanding of how cells respond to various stresses, and the role played in those responses by chaperones. Working across a variety of experimental models, from C. elegans to human neuronal cells, he has identified a number of important molecular components that sense and respond to stress, and he has dissected how stress alters cellular and organismal physiology. Together with colleagues, Professor Morimoto has coined the term "proteostasis" to signify the homeostatic control of protein expression and function, and in recent years he has been one of the leaders of a consortium trying to understand proteostasis in healthy and disease states. I took the opportunity to talk with Professor Morimoto about proteostasis in general, the aims of the consortium, and how autophagy is playing an important role in their research effort.

Biological resilience in health and disease.

All living organisms - from single-celled prokaryotes through to invertebrates and humans - are frequently exposed to numerous challenges during their lifetime, which could damage their molecular and cellular contents and threaten their survival. Nevertheless, these diverse organisms are, on the whole, remarkably resilient to potential threats. Recent years have seen rapid advances in our mechanistic understanding of this emerging phenomenon of biological resilience, which enables cells, tissues and whole organisms to bounce back from challenges or stress. In this At a Glance article, I discuss current knowledge on the diverse molecular mechanisms driving biological resilience across scales, with particular focus on its dynamic and adaptive nature. I highlight emerging evidence that loss of biological resilience could underly numerous pathologies, including age-related frailty and degenerative disease. Finally, I present the multi-disciplinary experimental approaches that are helping to unravel the causal mechanisms of resilience and how this emerging knowledge could be harnessed therapeutically in the clinic.

[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.

Learning from disability studies to introduce the role of the individual to naturalistic accounts of disease.

Disability studies have been successfully focusing on individuals' lived experiences, the personalization of goals, and the constitution of the individual in defining disease and restructuring public understandings of disability. Although they had a strong influence in the policy making and medical modeling of disease, their framework has not been translated to traditional naturalistic accounts of disease. I will argue that, using new developments in evolutionary biology (Extended Evolutionary Synthesis [EES] about questions of proper function) and behavioral ecology (Niche conformance and construction about the questions of reference classes in biostatistics accounts), the main elements of the framework of disability studies can be used to represent life histories at the conceptual level of the two main "non-normative" accounts of disease. I chose these accounts since they are related to medicine in a more descriptive way. The success of the practical aspects of disability studies this way will be communicated without causing injustice to the individual since they will represent the individuality of the patient in two main naturalistic accounts of disease: the biostatistical account and the evolutionary functional account. Although most accounts criticizing the concept of disease as value-laden do not supply a positive element, disability studies can supply a good point for descriptive extension of the concept through inclusion of epistemic agency.

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.

A Multi-Dimensional Approach to Map Disease Relationships Challenges Classical Disease Views.

The categorization of human diseases is mainly based on the affected organ system and phenotypic characteristics. This is limiting the view to the pathological manifestations, while it neglects mechanistic relationships that are crucial to develop therapeutic strategies. This work aims to advance the understanding of diseases and their relatedness beyond traditional phenotypic views. Hence, the similarity among 502 diseases is mapped using six different data dimensions encompassing molecular, clinical, and pharmacological information retrieved from public sources. Multiple distance measures and multi-view clustering are used to assess the patterns of disease relatedness. The integration of all six dimensions into a consensus map of disease relationships reveals a divergent disease view from the International Classification of Diseases (ICD), emphasizing novel insights offered by a multi-view disease map. Disease features such as genes, pathways, and chemicals that are enriched in distinct disease groups are identified. Finally, an evaluation of the top similar diseases of three candidate diseases common in the Western population shows concordance with known epidemiological associations and reveals rare features shared between Type 2 diabetes (T2D) and Alzheimer's disease. A revision of disease relationships holds promise for facilitating the reconstruction of comorbidity patterns, repurposing drugs, and advancing drug discovery in the future.