WIREs Mechanisms of Disease最新文献

Adult form and function are dependent upon the activity of specialized signaling centers that act early in development at the embryonic midline. These centers instruct the surrounding cells to adopt a positional fate and to form the patterned structures of the phylotypic embryo. Abnormalities in these processes have devastating consequences for the individual, as exemplified by holoprosencephaly in which anterior midline development fails, leading to structural defects of the brain and/or face. In the 25 years since the first association between human holoprosencephaly and the sonic hedgehog gene, a combination of human and animal genetic studies have enhanced our understanding of the genetic and embryonic causation of this congenital defect. Comparative biology has extended the holoprosencephaly network via the inclusion of gene mutations from multiple signaling pathways known to be required for anterior midline formation. It has also clarified aspects of holoprosencephaly causation, showing that it arises when a deleterious variant is present within a permissive genome, and that environmental factors, as well as embryonic stochasticity, influence the phenotypic outcome of the variant. More than two decades of research can now be distilled into a framework of embryonic and genetic causation. This framework means we are poised to move beyond our current understanding of variants in signaling pathway molecules. The challenges now at the forefront of holoprosencephaly research include deciphering how the mutation of genes involved in basic cell processes can also cause holoprosencephaly, determining the important constituents of the holoprosencephaly permissive genome, and identifying environmental compounds that promote holoprosencephaly. This article is categorized under: Congenital Diseases > Stem Cells and Development Congenital Diseases > Genetics/Genomics/Epigenetics Congenital Diseases > Molecular and Cellular Physiology Congenital Diseases > Environmental Factors.

The intestine, the largest immune organ in the human body, harbors approximately 10¹³ microorganisms, including bacteria, fungi, viruses, and other unknown microbes. The intestine is a most important crosstalk anatomic structure between the first (the host) and second (the microorganisms) genomes. The imbalance of the intestinal microecology, especially dysbiosis of the composition, structure, and function of gut microbiota, is linked to human diseases. In this review, we investigated the roles and underlying mechanisms of gut microecology in the development, progression, and prognosis of infectious diseases. Furthermore, we discussed potential new strategies of prevention and treatment for infectious diseases based on manipulating the composition, structure, and function of intestinal microorganisms in the future. This article is categorized under: Infectious Diseases > Molecular and Cellular Physiology.

Arterial collateralization, as determined by leptomeningeal anastomoses or pial collateral vessels, is a well-established vital player in cerebral blood flow restoration and neurological recovery from ischemic stroke. A secondary network of cerebral collateral circulation apart from the Circle of Willis, exist as remnants of arteriole development that connect the distal arteries in the pia mater. Recent interest lies in understanding the cellular and molecular adaptations that control the growth and remodeling, or arteriogenesis, of these pre-existing collateral vessels. New findings from both animal models and human studies of ischemic stroke suggest a multi-factorial and complex, temporospatial interplay of endothelium, immune and vessel-associated cell interactions may work in concert to facilitate or thwart arteriogenesis. These valuable reports may provide critical insight into potential predictors of the pial collateral response in patients with large vessel occlusion and may aid in therapeutics to enhance collateral function and improve recovery from stroke. This article is categorized under: Neurological Diseases > Molecular and Cellular Physiology.

Synapse elimination, also known as synaptic pruning, is a critical step in the maturation of neural circuits during brain development. Mounting evidence indicates that the complement cascade of the innate immune system plays an important role in synapse elimination. Studies indicate that excess synapses during development are opsonized by complement proteins and subsequently phagocytosed by microglia which expresses complement receptors. The process is regulated by diverse molecular signals, including complement inhibitors that affect the activation of complement, as well as signals that affect microglial recruitment and activation. These signals may promote or inhibit the removal of specific sets of synapses during development. The complement-microglia system has also been implicated in the pathogenesis of several developmental brain disorders, suggesting that the dysregulation of mechanisms of synapse pruning may underlie the specific circuitry defects in these diseases. Here, we review the latest evidence on the molecular and cellular mechanisms of complement-dependent and microglia-dependent synapse elimination during brain development, and highlight the potential of this system as a therapeutic target for developmental brain disorders. This article is categorized under: Neurological Diseases > Molecular and Cellular Physiology Neurological Diseases > Stem Cells and Development Immune System Diseases > Molecular and Cellular Physiology.

Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of morbidity and mortality among Western populations. Many risk factors have been identified for ASCVD; however, elevated low-density lipoprotein cholesterol (LDL-C) remains the gold standard. Cholesterol metabolism at the cellular and whole-body level is maintained by an array of interacting components. These regulatory mechanisms have complex behavior. Likewise, the mechanisms which underpin atherogenesis are nontrivial and multifaceted. To help overcome the challenge of investigating these processes mathematical modeling, which is a core constituent of the systems biology paradigm has played a pivotal role in deciphering their dynamics. In so doing models have revealed new insights about the key drivers of ASCVD. The aim of this review is fourfold; to provide an overview of cholesterol metabolism and atherosclerosis, to briefly introduce mathematical approaches used in this field, to critically discuss models of cholesterol metabolism and atherosclerosis, and to highlight areas where mathematical modeling could help to investigate in the future. This article is categorized under: Cardiovascular Diseases > Computational Models.

The increasing availability of large collections of electronic health record (EHR) data and unprecedented technical advances in deep learning (DL) have sparked a surge of research interest in developing DL based clinical decision support systems for diagnosis, prognosis, and treatment. Despite the recognition of the value of deep learning in healthcare, impediments to further adoption in real healthcare settings remain due to the black-box nature of DL. Therefore, there is an emerging need for interpretable DL, which allows end users to evaluate the model decision making to know whether to accept or reject predictions and recommendations before an action is taken. In this review, we focus on the interpretability of the DL models in healthcare. We start by introducing the methods for interpretability in depth and comprehensively as a methodological reference for future researchers or clinical practitioners in this field. Besides the methods' details, we also include a discussion of advantages and disadvantages of these methods and which scenarios each of them is suitable for, so that interested readers can know how to compare and choose among them for use. Moreover, we discuss how these methods, originally developed for solving general-domain problems, have been adapted and applied to healthcare problems and how they can help physicians better understand these data-driven technologies. Overall, we hope this survey can help researchers and practitioners in both artificial intelligence and clinical fields understand what methods we have for enhancing the interpretability of their DL models and choose the optimal one accordingly. This article is categorized under: Cancer > Computational Models.