Frontiers in Molecular Biosciences最新文献

Background: the gold-standard diagnostic protocol (GSDP) for COVID-19 consists of a nasopharyngeal swab (NPS) sample processed through traditional RNA extraction (TRE) and amplified with retrotranscription quantitative polymerase chain reaction (RT-qPCR). Multiple alternatives were developed to decrease time/cost of GSDP, including alternative clinical samples, RNA extraction methods and nucleic acid amplification. Thus, we carried out a cross comparison of various alternatives methods against GSDP and each other.

Methods: we tested alternative diagnostic methods using saliva, heat-induced RNA release (HIRR) and a colorimetric retrotranscription loop-mediated isothermal amplification (RT-LAMP) as substitutions to the GSDP.

Results: RT-LAMP using NPS processed by TRE showed high sensitivity (96%) and specificity (97%), closely matching GSDP. When saliva was processed by TRE and amplified with both RT-LAMP and RT-qPCR, RT-LAMP yielded high diagnostic parameters (88%-96% sensitivity and 95%-100% specificity) compared to RT-qPCR. Nonetheless, when saliva processed by TRE and detected by RT-LAMP was compared against the GSDP, the resulting diagnostic values for sensitivity (78%) and specificity (87%) were somewhat high but still short of those of the GSDP. Finally, saliva processed with HIRR and detected via RT-LAMP was the simplest and fastest method, but its sensitivity against GSDP was too low (56%) for any clinical application. Also, in this last method, the acidity of a large percentage of saliva samples (9%-22%) affected the pH-sensitive colorimetric indicator used in the test, requiring the exclusion of these acidic samples or an extra step for pH correction.

Discussion: our comparison shows that RT-LAMP technology has diagnostic performance on par with RT-qPCR; likewise, saliva offers the same diagnostic functionality as NPS when subjected to a TRE method. Nonetheless, use of direct saliva after a HIRR and detected with RT-LAMP does not produce an acceptable diagnostic performance.

Background: The functions and related signal pathways of the IFIT3 gene in the skin lesions of patients with psoriasis were explored through bioinformatics methods to determine the potential specific molecular markers of psoriasis.

Methods: The "limma" R package was used to analyze three datasets from the Gene Expression Omnibus database (GSE13355, GSE30999 and GSE106992), and the differential genes were screened. The STRING database was used for gene ontology (GO) enrichment analysis, Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis, and protein-protein interaction network integration. Then, the IFIT3 subnetwork was extracted and analyzed by gene set enrichment analysis (GSEA) using the Metascape database to verify the effectiveness of gene differentiation and disease tissue identification.

Results: In this study, 426 differential genes were obtained, of which 322 were significantly upregulated and 104 were significantly downregulated. GO enrichment analysis showed that the differential genes were mainly involved in immunity and metabolism; the KEGG pathway enrichment analysis mainly included the chemokine signal pathway, PPAR signal pathway, and IL-17 signal pathway, among others. Based on the IFIT3 subnetwork analysis, it was found that IFIT3 was mainly involved in the biological processes of viruses, bacteria, and other microorganisms. The pathways obtained by GSEA were mainly related to immunity, metabolism, and antiviral activities. IFIT3 was highly expressed in psoriatic lesions and may thus be helpful in the diagnosis of psoriasis.

Conclusion: The differential genes, biological processes, and signal pathways of psoriasis, especially information related to and diagnostic efficiency of the IFIT3 gene, were obtained by bioinformatics analysis. These results are expected to provide the theoretical basis and new directions for exploring the pathogenesis of psoriasis, in addition to helping with finding diagnostic markers and developing drug treatment targets.

Molecular and cellular characterization of tumors is essential due to the complex and heterogeneous nature of cancer. In recent decades, many bioinformatic tools and experimental techniques have been developed to achieve personalized characterization of tumors. However, sample handling continues to be a major challenge as limitations such as prior treatments before sample acquisition, the amount of tissue obtained, transportation, or the inability to process fresh samples pose a hurdle for experimental strategies that require viable cell suspensions. Here, we present an optimized protocol that allows the recovery of highly viable cell suspensions from breast cancer primary tumor biopsies. Using these cell suspensions we have successfully characterized genome architecture through Hi-C. Also, we have evaluated single-cell gene expression and the tumor cellular microenvironment through single-cell RNAseq. Both technologies are key in the detailed and personalized molecular characterization of tumor samples. The protocol described here is a cost-effective alternative to obtain viable cell suspensions from biopsies simply and efficiently.

Cellular protein homeostasis (proteostasis) plays an essential role in regulating the folding, sequestration, and turnover of misfolded proteins via a network of chaperones and clearance factors. Previous work has shown that misfolded proteins are spatially sequestered into membrane-less compartments in the cell as part of the proteostasis process. Soluble misfolded proteins in the cytoplasm are trafficked into the juxtanuclear quality control compartment (JUNQ), and nuclear proteins are sequestered into the intranuclear quality control compartment (INQ). However, the mechanisms that control the formation, localization, and degradation of these compartments are unknown. Previously, we showed that the JUNQ migrates to the nuclear membrane adjacent to the INQ at nucleus-vacuole junctions (NVJ), and the INQ moves through the NVJ into the vacuole for clearance in an ESCRT-mediated process. Here we have investigated what mechanisms are involved in the formation, migration, and clearance of the JUNQ. We find Hsp70s Ssa1 and Ssa2 are required for JUNQ localization to the NVJ and degradation of cytoplasmic misfolded proteins. We also confirm that sequestrases Btn2 and Hsp42 sort misfolded proteins to the JUNQ or IPOD, respectively. Interestingly, proteins required for piecemeal microautophagy of the nucleus (PMN) (i.e., Nvj1, Vac8, Atg1, and Atg8) drive the formation and clearance of the JUNQ. This suggests that the JUNQ migrates to the NVJ to be cleared via microautophagy.

Background: Hypoxia has been found to cause cellular dysfunction and cell death, which are essential mechanisms in the development of acute myocardial infarction (AMI). However, the impact of hypoxia-related genes (HRGs) on AMI remains uncertain.

Methods: The training dataset GSE66360, validation dataset GSE48060, and scRNA dataset GSE163956 were downloaded from the GEO database. We identified hub HRGs in AMI using machine learning methods. A prediction model for AMI occurrence was constructed and validated based on the identified hub HRGs. Correlations between hub HRGs and immune cells were explored using ssGSEA analysis. Unsupervised consensus clustering analysis was used to identify robust molecular clusters associated with hypoxia. Single-cell analysis was used to determine the distribution of hub HRGs in cell populations. RT-qPCR verified the expression levels of hub HRGs in the human cardiomyocyte model of AMI by oxygen-glucose deprivation (OGD) treatment in AC16 cells.

Results: Fourteen candidate HRGs were identified by differential analysis, and the RF model and the nomogram based on 8 hub HRGs (IRS2, ZFP36, NFIL3, TNFAIP3, SLC2A3, IER3, MAFF, and PLAUR) were constructed, and the ROC curves verified its good prediction effect in training and validation datasets (AUC = 0.9339 and 0.8141, respectively). In addition, the interaction between hub HRGs and smooth muscle cells, immune cells was elucidated by scRNA analysis. Subsequently, the HRG pattern was constructed by consensus clustering, and the HRG gene pattern verified the accuracy of its grouping. Patients with AMI could be categorized into three HRG subclusters, and cluster A was significantly associated with immune infiltration. The RT-qPCR results showed that the hub HRGs in the OGD group were significantly overexpressed.

Conclusion: A predictive model of AMI based on HRGs was developed and strongly associated with immune cell infiltration. Characterizing patients for hypoxia could help identify populations with specific molecular profiles and provide precise treatment.

Background: Cytokine network disturbances in primary Sjögren's syndrome (pSS) have been reported in many studies. However, their functions in patients with primary Sjögren's syndrome and interstitial lung disease (pSS-ILD) is controversial. In this study, we aim to investigate the associations of immunological characteristics and cytokine profiles with pSS-ILD pathogenesis and explore their predictive values for pSS progression.

Methods: A total of 256 patients initially diagnosed with pSS at Henan Provincial People's Hospital were enrolled. After excluding the patients previously diagnosed with various serious acute and chronic respiratory system diseases and cases with other connective tissue diseases or congenital heart diseases, 94 pSS patients were included for further analysis, including 40 patients with ILD (pSS-ILD) and 54 patients without ILD (pSS-N-ILD). For comparison, 41 age- and sex-matched healthy individuals were included as normal controls. Their clinical symptoms and serological data including cyclic citrullinated peptide (CCP) antibody (anti-CCP), antinuclear antibody (ANA), anti-Ro52, anti-SSA, anti-SSB, C-reactive protein, IgG, IgM, IgA, C3, C4, and 10 cytokines and chemokines were obtained. Wilcoxon test, chi-square test, Spearman correlation analysis, and logistics regression analysis were performed.

Results: Higher positive rates of anti-SSB and higher incidence of dry cough, dyspnea, and arthrosis symptoms were shown in pSS-ILD patients than in the pSS-N-ILD cases. Anti-CCP antibodies and cytokines (IL-1β, TNFα, IL-6, IL-5, IL-12p70, and IL-17) were higher, while C3 was lower in pSS-ILD patients than in pSS-N-ILD cases. Significant negative correlations of IgG with C3 and C4 and positive correlations of IL-12p70 and IL-17 with IL-6 were only shown in pSS-ILD patients. The anti-CCP antibody was positively correlated with IL-5 in pSS-ILD patients, but not in pSS-N-ILD cases. Multi-variable logistics regression analysis revealed the combination of anti-CCP, IL-17, IL-12p70, and IL-5 was effective in predicting the status of pSS-ILD in the pSS cases.

Conclusion: There were significant differences in serum marker levels between pSS-ILD and pSS-N-ILD cases. The combination of anti-CCP, IL-17, IL-12p70, and IL-5 might be a potential risk predictor for pSS-ILD occurrence. The cytokines might be involved in the development and progression of pSS-ILD. These results would provide new therapeutic targets for pSS-ILD treatment.

Introduction: Esophageal squamous cell carcinoma (ESCC) accounts for over 90% of all esophageal tumors. However, the molecular mechanism underlying ESCC development and prognosis remains unclear, and there are still no effective molecular biomarkers for diagnosing or predicting the clinical outcome of patients with ESCC. Here, we used bioinformatics analysis to identify potential biomarkers and therapeutic targets for ESCC. Methodology: Differentially expressed genes (DEGs) between ESCC and normal esophageal tissue samples were obtained by comprehensively analyzing publicly available RNA-seq datasets from the TCGA and GTEX. Gene Ontology (GO) annotation and Reactome pathway analysis identified the biological roles of the DEGs. Moreover, the Cytoscape 3.10.1 platform and subsidiary tools such as CytoHubba were used to visualize the DEGs' protein-protein interaction (PPI) network and identify hub genes, Furthermore our results are validated by using Single-cell RNA analysis. Results: Identification of 2524 genes exhibiting altered expression enriched in pathways including keratinization, epidermal cell differentiation, G alpha(s) signaling events, and biological process of cell proliferation and division, extracellular matrix (ECM) disassembly, and muscle function. Moreover, upregulation of hallmarks E2F targets, G2M checkpoints, and TNF signaling. CytoHubba revealed 20 hub genes that had a valuable influence on the progression of ESCC in these patients. Among these, the high expression levels of four genes, CDK1 MAD2L1, PLK1, and TOP2A, were associated with critical dependence for cell survival in ESCC cell lines, as indicated by CRISPR dependency scores, gene expression data, and cell line metadata. We also identify the molecules targeting these essential hub genes, among which GSK461364 is a promising inhibitor of PLK1, BMS265246, and Valrubicin inhibitors of CDK1 and TOP2A, respectively. Moreover, we identified that elevated expression of MMP9 is associated with worse overall survival in ESCC patients, which may serve as potential prognostic biomarker or therapeutic target for ESCC. The single-cell RNA analysis showed MMP9 is highly expressed in myeloid, fibroblast, and epithelial cells, but low in T cells, endothelial cells, and B cells. This suggests MMP9's role in tumor progression and matrix remodeling, highlighting its potential as a prognostic marker and therapeutic target. Discussion: Our study identified key hub genes in ESCC, assessing their potential as therapeutic targets and biomarkers through detailed expression and dependency analyses. Notably, MMP9 emerged as a significant prognostic marker with high expression correlating with poor survival, underscoring its potential for targeted therapy. These findings enhance our understanding of ESCC pathogenesis and highlight promising avenues for treatment.