Experimental Biology and Medicine最新文献

This review summarizes the current knowledge of fibroblast growth factor 23 signaling in bone and its role in the disease pathology of X-linked hypophosphatemia. Craniosynostosis is an under-recognized complication of X-linked hypophosphatemia. The clinical implications and potential cellular mechanisms invoked by increased fibroblast growth factor 23 signaling causing craniosynostosis are reviewed. Knowledge gaps are identified and provide direction for future clinical and basic science studies.

Arthritogenic alphaviruses are mosquito-borne viruses that cause a debilitating rheumatic disease characterized by fever, headache, rash, myalgia, and polyarthralgia with the potential to evolve into a severe and very prolonged illness. Although these viruses have been geographically restricted by vector hosts and reservoirs, recent epidemics have revealed the risks of their spread worldwide. In this review, we aim to discuss the protective and pathological roles of macrophages during the development of arthritis caused by alphaviruses. The progression to the chronic phase of the disease is related to the extension of viral replication and the maintenance of articular inflammation, in which the cellular infiltrate is predominantly composed of macrophages. We explore the possible implications of macrophage polarization to M1/M2 activation phenotypes, drawing a parallel between alphavirus arthritis and rheumatoid arthritis (RA), a chronic inflammatory disease that also affects articular tissues. In RA, it is well established that M1 macrophages contribute to tissue damage and inflammation, while M2 macrophages have a role in cartilage repair, so modulating the M1/M2 macrophage ratio is being considered as a strategy in the treatment of this disease. In the case of alphavirus-induced arthritis, the picture is more complex, as proinflammatory factors derived from M1 macrophages contribute to the antiviral response but cause tissue damage, while M2 macrophages may contribute to tissue repair but impair viral clearance.

Rett syndrome is a neurodevelopmental disorder caused by loss-of-function mutations in the methyl-CpG binding protein-2 (MeCP2) gene that is characterized by epilepsy, intellectual disability, autistic features, speech deficits, and sleep and breathing abnormalities. Neurologically, patients with all three disorders display microcephaly, aberrant dendritic morphology, reduced spine density, and an imbalance of excitatory/inhibitory signaling. Loss-of-function mutations in the cyclin-dependent kinase-like 5 (CDKL5) and FOXG1 genes also cause similar behavioral and neurobiological defects and were referred to as congenital or variant Rett syndrome. The relatively recent realization that CDKL5 deficiency disorder (CDD), FOXG1 syndrome, and Rett syndrome are distinct neurodevelopmental disorders with some distinctive features have resulted in separate focus being placed on each disorder with the assumption that distinct molecular mechanisms underlie their pathogenesis. However, given that many of the core symptoms and neurological features are shared, it is likely that the disorders share some critical molecular underpinnings. This review discusses the possibility that deregulation of common molecules in neurons and astrocytes plays a central role in key behavioral and neurological abnormalities in all three disorders. These include KCC2, a chloride transporter, vGlut1, a vesicular glutamate transporter, GluD1, an orphan-glutamate receptor subunit, and PSD-95, a postsynaptic scaffolding protein. We propose that reduced expression or activity of KCC2, vGlut1, PSD-95, and AKT, along with increased expression of GluD1, is involved in the excitatory/inhibitory that represents a key aspect in all three disorders. In addition, astrocyte-derived brain-derived neurotrophic factor (BDNF), insulin-like growth factor 1 (IGF-1), and inflammatory cytokines likely affect the expression and functioning of these molecules resulting in disease-associated abnormalities.

Arboviral diseases comprise a group of important infectious diseases imposing a heavy burden to public health in many locations of the world. Orthoflaviviruses are viruses belonging to the genus Orthoflavivirus; this genus includes some of the most relevant arboviruses to human health. Orthoflaviviruses can infect several different hosts, with some species being transmitted in cycles involving birds and anthropophilic mosquitoes and others transmitted between mammals and mostly Aedes sp. mosquitoes. Some of the most important sylvatic reservoirs of orthoflaviviruses are non-human primates (NHPs). Many flaviviruses that infect NHPs in nature have the potential to cause epidemics in humans, as has been observed in the cases of Orthoflavivirus denguei (dengue virus - DENV), Orthoflavivirus flavi (yellow fever virus - YFV), and Orthoflavivirus zikaense (Zika virus - ZIKV). In this minireview, we discuss important aspects regarding history, ecology involving NHP, distribution, disease outcome, and pathogenesis of these three major orthoflaviviruses that affect humans and NHP and relate this information to the potential of using NHP as experimental models. In addition, we suggest some orthoflaviviruses that could be better investigated, both in nature and in experimental studies, in light of the recent revolution in molecular biology.

Chagas disease (CD), caused by the protozoan parasite Trypanosoma cruzi, is a neglected disease affecting around 6 million people. About 30% of CD patients develop chronic Chagas disease cardiomyopathy (CCC), an inflammatory cardiomyopathy that occurs decades after the initial infection, while most infected patients (60%) remain asymptomatic in the so-called indeterminate form (IF). Death results from heart failure or arrhythmia in a subset of CCC patients. Myocardial fibrosis, inflammation, and mitochondrial dysfunction are involved in the arrhythmia substrate and triggering events. Survival in CCC is worse than in other cardiomyopathies, which may be linked to a Th1-T cell rich myocarditis with abundant interferon (IFN)-γ and tumor necrosis factor (TNF)-α, selectively lower levels of mitochondrial energy metabolism enzymes in the heart, and reduced levels of high-energy phosphate, indicating poor adenosine triphosphate (ATP) production. IFN-γ and TNF-α signaling, which are constitutively upregulated in CD patients, negatively affect mitochondrial function in cardiomyocytes, recapitulating findings in CCC heart tissue. Genetic studies such as whole-exome sequencing (WES) in nuclear families with multiple CCC/IF cases has disclosed rare heterozygous pathogenic variants in mitochondrial and inflammatory genes segregating in CCC cases. In this minireview, we summarized studies showing how IFN-γ and TNF-α affect cell energy generation, mitochondrial health, and redox homeostasis in cardiomyocytes, in addition to human CD and mitochondria. We hypothesize that cytokine-induced mitochondrial dysfunction in genetically predisposed patients may be the underlying cause of CCC severity and we believe this mechanism may have a bearing on other inflammatory cardiomyopathies.

The gut microbiota sit at an important interface between the host and the environment, and are exposed to a multitude of nutritive and non-nutritive substances. These microbiota are critical to maintaining host health, but their supportive roles may be compromised in response to endogenous compounds. Numerous non-nutritive substances are introduced through contaminated foods, with three common groups of contaminants being bisphenols, phthalates, and mycotoxins. The former contaminants are commonly introduced through food and/or beverages packaged in plastic, while mycotoxins contaminate various crops used to feed livestock and humans alike. Each group of contaminants have been shown to shift microbial communities following exposure; however, specific patterns in microbial responses have yet to be identified, and little is known about the capacity of the microbiota to metabolize these contaminants. This review characterizes the state of existing research related to gut microbial responses to and biotransformation of bisphenols, phthalates, and mycotoxins. Collectively, we highlight the need to identify consistent, contaminant-specific responses in microbial shifts, whether these community alterations are a result of contaminant effects on the host or microbiota directly, and to identify the extent of contaminant biotransformation by microbiota, including if these transformations occur in physiologically relevant contexts.

The ever-increasing number of chemicals has raised public concerns due to their adverse effects on human health and the environment. To protect public health and the environment, it is critical to assess the toxicity of these chemicals. Traditional in vitro and in vivo toxicity assays are complicated, costly, and time-consuming and may face ethical issues. These constraints raise the need for alternative methods for assessing the toxicity of chemicals. Recently, due to the advancement of machine learning algorithms and the increase in computational power, many toxicity prediction models have been developed using various machine learning and deep learning algorithms such as support vector machine, random forest, k-nearest neighbors, ensemble learning, and deep neural network. This review summarizes the machine learning- and deep learning-based toxicity prediction models developed in recent years. Support vector machine and random forest are the most popular machine learning algorithms, and hepatotoxicity, cardiotoxicity, and carcinogenicity are the frequently modeled toxicity endpoints in predictive toxicology. It is known that datasets impact model performance. The quality of datasets used in the development of toxicity prediction models using machine learning and deep learning is vital to the performance of the developed models. The different toxicity assignments for the same chemicals among different datasets of the same type of toxicity have been observed, indicating benchmarking datasets is needed for developing reliable toxicity prediction models using machine learning and deep learning algorithms. This review provides insights into current machine learning models in predictive toxicology, which are expected to promote the development and application of toxicity prediction models in the future.

S100B is a 21-kDa protein that is produced and secreted by astrocytes and widely used as a marker of brain injury in clinical and experimental studies. The majority of these studies are based on measurements in blood serum, assuming an associated increase in cerebrospinal fluid and a rupture of the blood-brain barrier (BBB). Moreover, extracerebral sources of S100B are often underestimated. Herein, we will review these interpretations and discuss the routes by which S100B, produced by astrocytes, reaches the circulatory system. We discuss the concept of S100B as an alarmin and its dual activity as an inflammatory and neurotrophic molecule. Furthermore, we emphasize the lack of data supporting the idea that S100B acts as a marker of BBB rupture, and the need to include the glymphatic system in the interpretations of serum changes of S100B. The review is also dedicated to valorizing extracerebral sources of S100B, particularly adipocytes. Furthermore, S100B per se may have direct and indirect modulating roles in brain barriers: on the tight junctions that regulate paracellular transport; on the expression of its receptor, RAGE, which is involved in transcellular protein transport; and on aquaporin-4, a key protein in the glymphatic system that is responsible for the clearance of extracellular proteins from the central nervous system. We hope that the data on S100B, discussed here, will be useful and that it will translate into further health benefits in medical practice.

Brain tumors are often fatal. Therefore, accurate brain tumor image segmentation is critical for the diagnosis, treatment, and monitoring of patients with these tumors. Magnetic resonance imaging (MRI) is a commonly used imaging technique for capturing brain images. Both machine learning and deep learning techniques are popular in analyzing MRI images. This article reviews some commonly used machine learning and deep learning techniques for brain tumor MRI image segmentation. The limitations and advantages of the reviewed machine learning and deep learning methods are discussed. Even though each of these methods has a well-established status in their individual domains, the combination of two or more techniques is currently an emerging trend.