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.

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/.

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.

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.

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 [...].

Learning Association Characteristics by Dynamic Hypergraph and Gated Convolution Enhanced Pairwise Attributes for Prediction of Disease-Related lncRNAs.

As the long non-coding RNAs (lncRNAs) play important roles during the incurrence and development of various human diseases, identifying disease-related lncRNAs can contribute to clarifying the pathogenesis of diseases. Most of the recent lncRNA-disease association prediction methods utilized the multi-source data about the lncRNAs and diseases. A single lncRNA may participate in multiple disease processes, and multiple lncRNAs usually are involved in the same disease process synergistically. However, the previous methods did not completely exploit the biological characteristics to construct the informative prediction models. We construct a prediction model based on adaptive hypergraph and gated convolution for lncRNA-disease association prediction (AGLDA), to embed and encode the biological characteristics about lncRNA-disease associations, the topological features from the entire heterogeneous graph perspective, and the gated enhanced pairwise features. First, the strategy for constructing hyperedges is designed to reflect the biological characteristic that multiple lncRNAs are involved in multiple disease processes. Furthermore, each hyperedge has its own biological perspective, and multiple hyperedges are beneficial for revealing the diverse relationships among multiple lncRNAs and diseases. Second, we encode the biological features of each lncRNA (disease) node using a strategy based on dynamic hypergraph convolutional networks. The strategy may adaptively learn the features of the hyperedges and formulate the dynamically evolved hypergraph topological structure. Third, a group convolutional network is established to integrate the entire heterogeneous topological structure and multiple types of node attributes within an lncRNA-disease-miRNA graph. Finally, a gated convolutional strategy is proposed to enhance the informative features of the lncRNA-disease node pairs. The comparison experiments indicate that AGLDA outperforms seven advanced prediction methods. The ablation studies confirm the effectiveness of major innovations, and the case studies validate AGLDA's ability in application for discovering potential disease-related lncRNA candidates.

QNetDiff: a quantitative measurement of network rewiring.

Bacteria in the human body, particularly in the large intestine, are known to be associated with various diseases. To identify disease-associated bacteria (markers), a typical method is to statistically compare the relative abundance of bacteria between healthy subjects and diseased patients. However, since bacteria do not necessarily cause diseases in isolation, it is also important to focus on the interactions and relationships among bacteria when examining their association with diseases. In fact, although there are common approaches to represent and analyze bacterial interaction relationships as networks, there are limited methods to find bacteria associated with diseases through network-driven analysis. In this paper, we focus on rewiring of the bacterial network and propose a new method for quantifying the rewiring. We then apply the proposed method to a group of colorectal cancer patients. We show that it can identify and detect bacteria that cannot be detected by conventional methods such as abundance comparison. Furthermore, the proposed method is implemented as a general-purpose tool and made available to the general public.

Massive RNA sequencing effort proposed.

U.S. plan would harness the "RNome" for medicine and more-but funding is uncertain.

Age-related disgust responses to signs of disease.

Previous studies found similarities in adults' disgust responses to benign (e.g. obesity) and actual disease signs (e.g. influenza). However, limited research has compared visual (i.e. benign and actual) to cognitive (i.e. disease label) disease cues in different age groups. The current study investigated disgust responses across middle childhood (7-9 years), late childhood (10-12 years), adolescence (13-17 years), and adulthood (18+ years). Participants viewed individuals representing a benign visual disease (obese), sick-looking (staphylococcus), sick-label (cold/flu), and healthy condition. Disgust-related outcomes were: (1) avoidance, or contact level with apparel the individual was said to have worn, (2) disgust facial reactions, and (3) a combination of (1) and (2). Avoidance was greater for the sick-looking and sick-label than the healthy and obese conditions. For facial reaction and combination outcomes, middle childhood participants responded with greater disgust to the sick-looking than the healthy condition, while late childhood participants expressed stronger disgust towards the sick-looking and obese conditions than the healthy condition. Adolescents and adults exhibited stronger disgust towards sick-label and sick-looking than obese and healthy conditions. Results suggest visual cues are central to children's disgust responses whereas adolescents and adult responses considered cognitive cues.

Phosphoinositide switches in cell physiology - From molecular mechanisms to disease.

Phosphoinositides are amphipathic lipid molecules derived from phosphatidylinositol that represent low abundance components of biological membranes. Rather than serving as mere structural elements of lipid bilayers, they represent molecular switches for a broad range of biological processes, including cell signaling, membrane dynamics and remodeling, and many other functions. Here, we focus on the molecular mechanisms that turn phosphoinositides into molecular switches and how the dysregulation of these processes can lead to disease.

Reference-Class Problems Are Real: Health-Adjusted Reference Classes and Low Bone Mineral Density.

Elselijn Kingma argues that Christopher Boorse's biostatistical theory (the BST) does not show how the reference classes it uses are objective and naturalistic. Recently, philosophers of medicine have attempted to rebut Kingma's concerns. I argue that these rebuttals are theoretically unconvincing, and that there are clear examples of physicians adjusting their reference classes according to their prior knowledge of health and disease. I focus on the use of age-adjusted reference classes to diagnose low bone mineral density in children. In addition to using the BST's age, sex, and species, physicians also choose to use other factors to define reference classes, such as pubertal status, bone age, body size, and muscle mass. I show that physicians calibrate the reference classes they use according to their prior knowledge of health and disease. Reference classes are also chosen for pragmatic reasons, such as to predict fragility fractures.

NIH megastudy analyzes first 250,000 genomes.

All of Us finds new DNA variants and refines genetic risk scores in diverse groups.

Hierarchical Negative Sampling Based Graph Contrastive Learning Approach for Drug-Disease Association Prediction.

Predicting potential drug-disease associations (RDAs) plays a pivotal role in elucidating therapeutic strategies for diseases and facilitating drug repositioning, making it of paramount importance. However, existing methods are constrained and rely heavily on limited domain-specific knowledge, impeding their ability to effectively predict candidate associations between drugs and diseases. Moreover, the simplistic definition of unknown information pertaining to drug-disease relationships as negative samples presents inherent limitations. To overcome these challenges, we introduce a novel hierarchical negative sampling-based graph contrastive model, termed HSGCLRDA, which aims to forecast latent associations between drugs and diseases. In this study, HSGCLRDA integrates the association information as well as similarity between drugs, diseases and proteins. Meanwhile, the model constructs a drug-disease-protein heterogeneous network. Subsequently, employing a hierarchical structural sampling technique, we establish reliable negative drug-disease samples utilizing PageRank algorithms. Utilizing meta-path aggregation within the heterogeneous network, we derive low-dimensional representations for drugs and diseases, thereby constructing global and local feature graphs that capture their interactions comprehensively. To obtain representation information, we adopt a self-supervised graph contrastive approach that leverages graph convolutional networks (GCNs) and second-order GCNs to extract feature graph information. Furthermore, we integrate a contrastive cost function derived from the cross-entropy cost function, facilitating holistic model optimization. Experimental results obtained from benchmark datasets not only showcase the superior performance of HSGCLRDA compared to various baseline methods in predicting RDAs but also emphasize its practical utility in identifying novel potential diseases associated with existing drugs through meticulous case studies.