Experimental Biology and Medicine最新文献

Human cardiac fibroblasts (HCFs) have mRNA transcripts that encode different mechanosensitive ion channels and channel regulatory proteins whose functions are not known yet. The primary goal of this work was to define the mechanosensitive ion channelome of HCFs. The most common type of cationic channel is the transient receptor potential (TRP) family, which is followed by the TWIK-related K⁺ channel (TREK), transmembrane protein 63 (TMEM63), and PIEZO channel (PIEZO) families. In the sodium-dependent NON-voltage-gated channel (SCNN) subfamily, only SCNN1D was shown to be highly expressed. Particular members of the acid-sensing ion channel (ASIC) (ASIC1 and ASIC3) subfamilies were also significantly expressed. The transcripts per kilobase million (TPMs) for Piezo 2 were almost 100 times less abundant than those for Piezo 1. The tandem of P domains in a weak inward rectifying K⁺ channel (TWIK)-2 channel, TWIK-related acid-sensitive K⁺ channel (TASK)-5, TASK-1, and the TWIK-related K1 (TREK-1) channel were the four most prevalent types in the K2P subfamily. The highest expression in the TRPP subfamily was found for PKD2 and PKD1, while in the TRPM subfamily, it was found for TRPM4, TRPM7, and TRPM3. TRPV2, TRPV4, TRPV3, and TRPV6 (all members of the TRPV subfamily) were also substantially expressed. A strong expression of the TRPC1, TRPC4, TRPC6, and TRPC2 channels and all members of the TRPML subfamily (MCOLN1, MCOLN2, and MCOLN3) was also shown. In terms of the transmembrane protein 16 (TMEM16) family, the HCFs demonstrated significant expression of the TMEM16H, TMEM16F, TMEM16J, TMEM16A, and TMEM16G channels. TMC3 is the most expressed channel in HCFs of all known members of the transmembrane channel-like protein (TMC) family. This analysis of the mechanosensitive ionic channel transcriptome in HCFs: (1) agrees with previously documented findings that all currently identified mechanosensitive channels play a significant and well recognized physiological function in elucidating the mechanosensitive characteristics of HCFs; (2) supports earlier preliminary reports that point to the most common expression of the TRP mechanosensitive family in HCFs; and (3) points to other new mechanosensitive channels (TRPC1, TRPC2, TWIK-2, TMEM16A, ASIC1, and ASIC3).

Data imbalance is a challenging problem in classification tasks, and when combined with class overlapping, it further deteriorates classification performance. However, existing studies have rarely addressed both issues simultaneously. In this article, we propose a novel quantum-based oversampling method (QOSM) to effectively tackle data imbalance and class overlapping, thereby improving classification performance. QOSM utilizes the quantum potential theory to calculate the potential energy of each sample and selects the sample with the lowest potential as the center of each cover generated by a constructive covering algorithm. This approach optimizes cover center selection and better captures the distribution of the original samples, particularly in the overlapping regions. In addition, oversampling is performed on the samples of the minority class covers to mitigate the imbalance ratio (IR). We evaluated QOSM using three traditional classifiers (support vector machines [SVM], k-nearest neighbor [KNN], and naive Bayes [NB] classifier) on 10 publicly available KEEL data sets characterized by high IRs and varying degrees of overlap. Experimental results demonstrate that QOSM significantly improves classification accuracy compared to approaches that do not address class imbalance and overlapping. Moreover, QOSM consistently outperforms existing oversampling methods tested. With its compatibility with different classifiers, QOSM exhibits promising potential to improve the classification performance of highly imbalanced and overlapped data.

Ovarian cancer (OC) is a fatal gynecologic disease. The most common treatment for OC patients is surgery combined with chemotherapy but most patients at advanced stages eventually develop relapse due to chemoresistance. This study examined the role and function of insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2) in OC. We observed that the expression of IGF2BP2 mRNA and protein was up-regulated in OC cells and tissues using quantitative real-time polymerase chain reaction (qRT-PCR) and western blot, respectively. An increase in IGF2BP2 expression at mRNA and protein levels was verified by the analyses of The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), respectively. Gene Expression Omnibus (GEO) and Cancer Cell Line Encyclopedia (CCLE) databases were applied to analyze the expression and clinical value of IGF2BP2. Gene set enrichment analysis (GSEA), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO) analyses explored biological functions and the involvement of IGF2BP2 in cell growth. Indeed, the knockdown of IGF2BP2 resulted in the inhibition of OC cell proliferation evaluated by the Cell Counting Kit-8 assay. Genomic amplification of IGF2BP2 partly accounted for its overexpression. High expression of IGF2BP2 was associated with signal transducer and activator of transcription 1 (STAT1) and drug sensitivity and was correlated with an unfavorable survival outcome in OC patients. Furthermore, the responsiveness of chemotherapy and immunotherapy were analyzed using the "pRRophetic" R package and The Cancer Immune Atlas (TCIA) database, respectively. The low expression of IGF2BP2 was associated with chemoresistance but with high tumor microenvironment scores and tumor-infiltrating immune cells, suggesting that immunotherapy may apply in chemoresistant patients. The alteration of IGF2BP2 expression may respond to chemotherapy and immunotherapy. Thus, IGF2BP2 shows potential as a therapeutic target and diagnostic biomarker for OC.

Disturbance of sleep homeostasis encompasses health issues, including metabolic disorders like obesity, diabetes, and augmented stress vulnerability. Sleep and stress interact bidirectionally to influence the central nervous system and metabolism. Murine models demonstrate that decreased sleep time is associated with an increased systemic stress response, characterized by endocrinal imbalance, including the elevated activity of hypothalamic-pituitary-adrenal axis, augmented insulin, and reduced adiponectin, affecting peripheral organs physiology, mainly the white adipose tissue (WAT). Within peripheral organs, a local stress response can also be activated by promoting the formation of corticosterone. This local amplifying glucocorticoid signaling is favored through the activation of the enzyme 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1). In WAT, 11β-HSD1 activity is upregulated by the sympathetic nervous system, suggesting a link between sleep loss, augmented stress response, and a potential WAT metabolic disturbance. To gain more understanding about this relationship, metabolic and stress responses of WAT-sympathectomized rats were analyzed to identify the contribution of the autonomic nervous system to stress response-related metabolic disorders during chronic sleep restriction. Male Wistar rats under sleep restriction were allowed just 6 h of daily sleep over eight weeks. Results showed that rats under sleep restriction presented higher serum corticosterone, increased adipose tissue 11β-HSD1 activity, weight loss, decreased visceral fat, augmented adiponectin, lower leptin levels, glucose tolerance impairment, and mildly decreased daily body temperature. In contrast, sympathectomized rats under sleep restriction exhibited decreased stress response (lower serum corticosterone and 11β-HSD1 activity). In addition, they maintained weight loss, explained by a reduced visceral fat pad, leptin, and adiponectin, improved glucose management, and persisting decline in body temperature. These results suggest autonomic nervous system is partially responsible for the WAT-exacerbated stress response and its metabolic and physiological disturbances.

The coronavirus disease 2019 (COVID-19) global pandemic resulted in millions of people becoming infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and close to seven million deaths worldwide. It is essential to further explore and design effective COVID-19 treatment drugs that target the main protease of SARS-CoV-2, a major target for COVID-19 drugs. In this study, machine learning was applied for predicting the SARS-CoV-2 main protease binding of Food and Drug Administration (FDA)-approved drugs to assist in the identification of potential repurposing candidates for COVID-19 treatment. Ligands bound to the SARS-CoV-2 main protease in the Protein Data Bank and compounds experimentally tested in SARS-CoV-2 main protease binding assays in the literature were curated. These chemicals were divided into training (516 chemicals) and testing (360 chemicals) data sets. To identify SARS-CoV-2 main protease binders as potential candidates for repurposing to treat COVID-19, 1188 FDA-approved drugs from the Liver Toxicity Knowledge Base were obtained. A random forest algorithm was used for constructing predictive models based on molecular descriptors calculated using Mold2 software. Model performance was evaluated using 100 iterations of fivefold cross-validations which resulted in 78.8% balanced accuracy. The random forest model that was constructed from the whole training dataset was used to predict SARS-CoV-2 main protease binding on the testing set and the FDA-approved drugs. Model applicability domain and prediction confidence on drugs predicted as the main protease binders discovered 10 FDA-approved drugs as potential candidates for repurposing to treat COVID-19. Our results demonstrate that machine learning is an efficient method for drug repurposing and, thus, may accelerate drug development targeting SARS-CoV-2.

Causality assessment is vital in patient safety and pharmacovigilance (PSPV) for safety signal detection, adverse reaction management, and regulatory submission. Large language models (LLMs), especially those designed with transformer architecture, are revolutionizing various fields, including PSPV. While attempts to utilize Bidirectional Encoder Representations from Transformers (BERT)-like LLMs for causal inference in PSPV are underway, a detailed evaluation of "fit-for-purpose" BERT-like model selection to enhance causal inference performance within PSPV applications remains absent. This study conducts an in-depth exploration of BERT-like LLMs, including generic pre-trained BERT LLMs, domain-specific pre-trained LLMs, and domain-specific pre-trained LLMs with safety knowledge-specific fine-tuning, for causal inference in PSPV. Our investigation centers around (1) the influence of data complexity and model architecture, (2) the correlation between the BERT size and its impact, and (3) the role of domain-specific training and fine-tuning on three publicly accessible PSPV data sets. The findings suggest that (1) BERT-like LLMs deliver consistent predictive power across varied data complexity levels, (2) the predictive performance and causal inference results do not directly correspond to the BERT-like model size, and (3) domain-specific pre-trained LLMs, with or without safety knowledge-specific fine-tuning, surpass generic pre-trained BERT models in causal inference. The findings are valuable to guide the future application of LLMs in a broad range of application.

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.