Advanced biology最新文献

Inflammation contributes to the pathophysiology of diabetes. Identifying signaling pathways involved in pancreatic β-cell failure and identity loss can give insight into novel potential treatment strategies to prevent the loss of functional β-cell mass in diabetes. It is reported earlier that the immunosuppressive drug tacrolimus has a detrimental effect on human β-cell identity and function by activating bone morphogenetic protein (BMP) signaling. Here it is hypothesized that enhanced BMP signaling plays a role in inflammation-induced β-cell failure. Single-cell transcriptomics analyses of primary human islets reveal that IL-1β+IFNγ and IFNα treatment activated BMP signaling in β-cells. These findings are validated by qPCR. Furthermore, enhanced BMP signaling with recombinant BMP2 or 4 triggers a reduced expression of key β-cell maturity genes, associated with increased ER stress, and impaired β-cell function. Altogether, these results indicate that inflammation-activated BMP signaling is detrimental to pancreatic β-cells and that BMP-signaling can be a target to preserve β-cell identity and function in a pro-inflammatory environment.

Chemotherapy-induced nausea and vomiting (CINV) represents the common gastrointestinal side effect for cancer patients. Xiao-Ban-Xia decoction (XBXD), a classical anti-emetic traditional Chinese medicine formula, is frequently used for the clinical treatment of CINV. This study used a cisplatin-induced rat pica model to explore whether the anti-emetic mechanism of XBXD in treating CINV is related to ferroptosis. The inflammatory damage of the gastrointestinal tract is evaluated by HE staining and ELISA. The degree of ferroptosis are validated by the iron deposition, the levels of ROS, MDA, and GSH, and the ultrastructure of mitochondria in the gastric antrum and ileum. The potential ferroptosis-related targets of XBXD against CINV are screened by network pharmacology and further assessed by Western blot. XBXD significantly decreased the kaolin consumption in rats, and improved the inflammatory pathological damage, with decreased levels of HMGB1, IL-1β, and TNF-α. Furthermore, XBXD significantly suppressed ferroptosis, as indicated by the improvement of iron deposition, mitochondrial abnormalities, and oxidative stress. The network pharmacology and Western blot results indicated that XBXD activated the Nrf2/SLC7A11/GPX4 signaling pathway. This study proved that XBXD activates the Nrf2/SLC7A11/GPX4 signaling pathway, thereby inhibiting ferroptosis, which represents a critical anti-emetic mechanism of XBXD in combatting CINV.

A wide range of cells respond to fibroblast growth factor 2 (FGF2) by proliferation via activation of the Ras/ERK1/2 pathway. In this study, the potential involvement of salt inducible kinase SIK2) in this cascade within retinal Müller glia is explored. It is found that SIK2 phosphorylation status and activity are modulated in an FGF2-dependent manner, possibly via ERK1/2. With SIK2 downregulation, enhanced ERK1/2 activation with delayed attenuation and increased cell proliferation is observed, while SIK2 overexpression hampers FGF2-dependent ERK1/2 activation. In vitro kinase and site-directed mutagenesis studies indicate that SIK2 targets the pathway element GRB2-associated-binding protein 1 (Gab1) on Ser266. This phosphorylation event weakens Gab1 interactions with its partners growth factor receptor-bound protein 2 (Grb2) and Src homology region 2 domain containing phosphatase 2 (Shp2). Collectively, these results suggest that during FGF2-dependent proliferation process ERK1/2-mediated activation of SIK2 targets Gab1, resulting in downregulation of the Ras/ERK1/2 cascade in a feedback loop.

FAM136A promotes the progression and metastasis of various tumors. However, there are few studies on the role of FAM136A in esophageal cancer (ESCA). The TCGA, GTEx, and GEO databases are employed to analyze the expression of FAM136A in ESCA, and qPCR and TMA experiments are performed for validation. Enrichment analyzes are performed to investigate the association of FAM136A expression with immune features, m6A modification, alternative splicing, cuproptosis, and the ceRNA network via bioinformatics analysis. FAM136A is highly expressed in ESCA and correlated with lymph node metastasis and overall survival (OS). Bioinformatics analysis suggested that FAM136A may participate in the following processes to promote ESCA development and progression: 1) Promotion of mast cells infiltration to influence the ESCA immune microenvironment, 2) HNRNPC upregulation to regulate m6A modification, 3) ALYREF upregulation to increase the occurrence of retained intron (RI) events, 4) CDK5RAP1 upregulation to achieve inhibition of tumor cell apoptosis, and 5) promotion of ESCA progression through the lncRNA SNHG15/hsa-miR-29c-3p/FAM136A ceRNA network. FAM136A is a potential biomarker for ESCA diagnosis and treatment response evaluation, and the underlying mechanisms may be associated with immune infiltration, m6A modification, alternative splicing, cuproptosis, and the ceRNA regulatory network.

Premature Aging (PA) diseases are rare genetic disorders that mimic some aspects of physiological aging at an early age. Various causative genes of PA diseases have been identified in recent years, providing insights into some dysfunctional cellular processes. However, the identification of PA genes also revealed significant genetic heterogeneity and highlighted the gaps in this understanding of PA-associated molecular mechanisms. Furthermore, many patients remain undiagnosed. Overall, the current lack of knowledge about PA diseases hinders the development of effective diagnosis and therapies and poses significant challenges to improving patient care. Here, a network-based approach to systematically unravel the cellular functions disrupted in PA diseases is presented. Leveraging a network community identification algorithm, it is delved into a vast multilayer network of biological interactions to extract the communities of 67 PA diseases from their 132 associated genes. It is found that these communities can be grouped into six distinct clusters, each reflecting specific cellular functions: DNA repair, cell cycle, transcription regulation, inflammation, cell communication, and vesicle-mediated transport. That these clusters collectively represent the landscape of the molecular mechanisms that are perturbed in PA diseases, providing a framework for better understanding their pathogenesis is proposed. Intriguingly, most clusters also exhibited a significant enrichment in genes associated with physiological aging, suggesting a potential overlap between the molecular underpinnings of PA diseases and natural aging.

Xinglou Chengqi decoction (XLCQD) is a Chinese formula that offers benefits in ischemic stroke. However, the underlying mechanism of the effects of XLCQD-mediated anti-ischemic stroke effects remains obscure. This study investigates the ferroptosis mechanism of XLCQD against cerebral ischemia/reperfusion (I/R) injury using rat models of middle cerebral artery occlusion/reperfusion (MCAO/R). Ferroptosis differs from traditional cell death pathways and is linked to oxidative stress-induced lipid peroxidation and glutathione (GSH) depletion, which is essential to the development of ischemic stroke. In this study, it is shown that XLCQD improves brain infarction, neurological dysfunction, and histopathological changes caused by MCAO/R exposure, and improving I/R-induced oxidative damage through inhibition of ferroptosis via (Solute Carrier Family 7 Member 11) SLC7A11/ (glutathione peroxidase 4) GPX4 pathway. Interestingly, it is found that XLCQD-mediated protection in I/R is reversed by the silence of SLC7A11. XLCQD intervention significantly promotes GSH content and suppresses Reactive Oxygen Species(ROS), iron accumulation, as well as Malondialdehyde (MDA) generation, are markedly abrogated when SLC7A11 is knockdown by SLC7A11-shRNA transfection, indicating that SLC7A11 is the main target of XLCQD to further trigger intracellular events. In conclusion, XLCQD attenuates in vivo cerebral I/R injury by reducing ferroptosis via the SLC7A11/GPX4 pathway.

Although various strategies have been used to prevent and treat SARS-CoV-2, the spread and evolution of SARS-CoV-2 is still progressing rapidly. The emerging variants Omicron and its sublineage have a greater ability to spread and escape nearly all current monoclonal antibodies treatments, highlighting an urgent need to develop therapeutics targeting current and emerging Omicron variants or recombinants with breadth and potency. Here, some small molecule drugs are rapidly identified that could covalently binding to receptor binding domain (RBD) protein of Omicron through the combined application of artificial intelligence (AI) and activity-based protein profiling (ABPP) technology. The surface plasmon resonance (SPR) and pseudo-virus neutralization experiments further reveal that an FDA-approved drug gallic acid has robust neutralization potency against Omicron pseudo-virus with the IC₅₀ values of 23.56 × 10^-6 m. Taken together, a platform combining AI intelligence, biochemical, SPR, molecular docking, and pseudo-virus-based screening for rapid identification and evaluation of potential anti-SARS-CoV-2 small molecule drugs is established and the effectiveness of the platform is validated.

Early allograft dysfunction (EAD) is a frequent phenomenon, leading to increased graft loss and higher mortality after liver transplantation (LT). Despite significant efforts for early diagnosis of EAD, there is no existing approach that can predict EAD on the first post-operative day. The aim is to define a metabolite-based biomarker on the first day after LT complicated with EAD. Ten patients diagnosed with EAD and 26 non-EAD are recruited for the study. A HPLC-MS/MS is used to determine 14 amino acids and 15 bile acids serum concentration. Comparative analyses are conducted between EAD and non-EAD groups. Arginine is identified as the most significant metabolite distinguishing the EAD and non-EAD groups, and therefore, is identified as a potential biomarker of EAD. The optimal cut-off value for arginine is 52.09 µmol L^-1, with an AUROC of 0.804 (95% confidence interval: 0.638-0.917, p < 0.001), yielding a sensitivity of 100%, specificity of 53.8%, and Youden index of 0.54, NPVof 100%, and PPV of 45.45%. In summary, the study indicated that targeted metabolomics analysis would be a promising strategy for discovering novel biomarkers to predict EAD. The identified arginine may be helpful in developing an objective diagnostic method for EAD.

3D co-cultures are key tools for in vitro biomedical research as they recapitulate more closely the in vivo environment while allowing a tighter control on the culture's composition and experimental conditions. The limited technologies available for the analysis of these models, however, hamper their widespread application. The separation of the contribution of the different cell types, in particular, is a fundamental challenge. In this work, ORACLE (OvaRiAn Cancer ceLl rEcognition) is presented, a deep neural network trained to distinguish between ovarian cancer and healthy cells based on the shape of their nucleus. The extensive validation that are conducted includes multiple cell lines and patient-derived cultures to characterize the effect of all the major potential confounding factors. High accuracy and reliability are maintained throughout the analysis (F1_score> 0.9 and Area under the ROC curve -ROC-AUC- score = 0.99) demonstrating ORACLE's effectiveness with this detection and classification task. ORACLE is freely available (https://github.com/MarilisaCortesi/ORACLE/tree/main) and can be used to recognize both ovarian cancer cell lines and primary patient-derived cells. This feature is unique to ORACLE and thus enables for the first time the analysis of in vitro co-cultures comprised solely of patient-derived cells.

The average cost to bring a new drug from its initial discovery to a patient's bedside is estimated to surpass $2 billion and requires over a decade of research and development. There is a need for new drug screening technologies that can parse drug candidates with increased likelihood of clinical utility early in development in order to increase the cost-effectiveness of this pipeline. For example, during the COVID-19 pandemic, resources were rapidly mobilized to identify effective therapeutic treatments but many lead antiviral compounds failed to demonstrate efficacy when progressed to human trials. To address the lack of predictive preclinical drug screening tools, PREDICT96-ALI, a high-throughput (n = 96) microphysiological system (MPS)  that recapitulates primary human tracheobronchial tissue,is adapted for the evaluation of differential antiviral efficacy of native SARS-CoV-2 variants of concern. Here, PREDICT96-ALI resolves both the differential viral kinetics between variants and the efficacy of antiviral compounds over a range of drug doses. PREDICT96-ALI is able to distinguish clinically efficacious antiviral therapies like remdesivir and nirmatrelvir from promising lead compounds that do not show clinical efficacy. Importantly, results from this proof-of-concept study track with known clinical outcomes, demonstrate the feasibility of this technology as a prognostic drug discovery tool.