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Drought stress is a primary abiotic stress that causes significant losses to forestry and agricultural production. Therefore, exploring drought-responsive genes and their regulatory mechanism is crucial for plant molecular breeding for forestry and agriculture production safety. Small auxin-up RNA (SAUR) proteins are essential in plant growth and development but show functional diversity in stress response. In this study, the transcriptome sequencing data of Ammopiptanthus nanus seedlings revealed that the expression of AnSAUR50 was continuously downregulated under drought stress. Hence, the AnSAUR50 gene was cloned and functionally analyzed in drought response. The results showed that the coding sequence of AnSAUR50 was 315 bp in length and encoded 104 amino acids. The AnSAUR50 protein showed high conservation, possessed a SAUR-specific domain, and localized in the nucleus and cell membrane. The heterologous expression of the AnSAUR50 gene enhanced the drought sensitivity of the transgenic Arabidopsis with a lower survival rate, biomass, and higher malondialdehyde content and relative electrolyte leakage. Moreover, transgenic plants showed shorter root lengths and bigger stomatal apertures, resulting in facilitating water loss under drought stress. The study indicates that AnSAUR50 negatively regulates drought tolerance by inhibiting root growth and stomatal closure, which provides insights into the underlying function and regulatory mechanism of SAURs in plant stress response. [ABSTRACT FROM AUTHOR]

Background: Lymphocyte-activation gene 3 (LAG3) is a recently discovered immune checkpoint molecule that has been linked to immunosuppression and the advancement of cancer in different types of solid tumors. This study aimed to evaluate the prognostic importance of LAG3 and its role in the immune system within solid tumors. Methods: Extensive literature searches were conducted using the Pubmed, EMBASE, and Cochrane Library databases to identify relevant studies exploring the effect of LAG3 on survival outcomes. Pooled hazard ratios (HRs) with its 95% confidence intervals (CIs) were calculated to evaluate the prognostic values of LAG3. Afterwards, subgroup analysis and sensitivity analysis were conducted. Pan-cancer analysis investigated the possible relationships between LAG3 expression and genetic alterations, RNA methylation modification-related genes, genomic instability, immune checkpoint genes, and infiltration of immune cells. Results: A total of 43 studies with 7,118 patients were included in this analysis. Higher expression of LAG3 was associated with worse overall survival (HR = 1.10, 95% CI 1.01–1.19, P = 0.023), but not disease-free survival (HR = 1.41, 95% CI 0.96–2.07, P = 0.078), progression-free survival (HR = 1.12, 95% CI 0.90–1.39, P = 0.317) or recurrence-free survival (HR = 0.98, 95% CI 0.81–1.19, P = 0.871). Subgroup analysis showed that LAG3 might play different prognostic roles in different solid tumors. LAG3 expression was positively associated with immune cell infiltration and immune checkpoint genes in all of the cancers included. LAG3 expression was also found to be associated with microsatellite instability (MSI), copy number variation (CNV), simple nucleoside variation (SNV), tumor mutation burden (TMB), and neoantigen in various types of cancers. Conclusions: Elevated expression of LAG3 is linked to poorer prognosis among patients diagnosed with solid cancers. LAG3 might play varying prognostic roles in different types of solid tumors. Given its substantial involvement in cancer immunity and tumorigenesis, LAG3 has garnered attention as a promising prognostic biomarker and a potential target for immunotherapy. [ABSTRACT FROM AUTHOR]

Crohn's disease (CD) is a complex autoimmune disorder presumed to be driven by complex interactions of genetic, immune, microbial and even environmental factors. Intrinsic molecular mechanisms in CD, however, remain poorly understood. The identification of novel biomarkers in CD cases based on larger samples through machine learning approaches may inform the diagnosis and treatment of diseases. A comprehensive analysis was conducted on all CD datasets of Gene Expression Omnibus (GEO); our team then used the robust rank aggregation (RRA) method to identify differentially expressed genes (DEGs) between controls and CD patients. PPI (protein‒protein interaction) network and functional enrichment analyses were performed to investigate the potential functions of the DEGs, with molecular complex detection (MCODE) identifying some important functional modules from the PPI network. Three machine learning algorithms, support vector machine-recursive feature elimination (SVM-RFE), random forest (RF), and least absolute shrinkage and selection operator (LASSO), were applied to determine characteristic genes, which were verified by ROC curve analysis and immunohistochemistry (IHC) using clinical samples. Univariable and multivariable logistic regression were used to establish a machine learning score for diagnosis. Single-sample GSEA (ssGSEA) was performed to examine the correlation between immune infiltration and biomarkers. In total, 5 datasets met the inclusion criteria: GSE75214, GSE95095, GSE126124, GSE179285, and GSE186582. Based on RRA integrated analysis, 203 significant DEGs were identified (120 upregulated genes and 83 downregulated genes), and MCODE revealed some important functional modules in the PPI network. Machine learning identified LCN2, REG1A, AQP9, CCL2, GIP, PROK2, DEFA5, CXCL9, and NAMPT; AQP9, PROK2, LCN2, and NAMPT were further verified by ROC curves and IHC in the external cohort. The final machine learning score was defined as [Expression level of AQP9 × (2.644)] + [Expression level of LCN2 × (0.958)] + [Expression level of NAMPT × (1.115)]. ssGSEA showed markedly elevated levels of dendritic cells and innate immune cells, such as macrophages and NK cells, in CD, consistent with the gene enrichment results that the DEGs are mainly involved in the IL-17 signaling pathway and humoral immune response. The selected biomarkers analyzed by the RRA method and machine learning are highly reliable. These findings improve our understanding of the molecular mechanisms of CD pathogenesis. [ABSTRACT FROM AUTHOR]

Sulfide biomineralization is a microorganism-induced process for transforming the environmentally hazardous cadmium into useful resource utilization. This study successfully constructed cadmium sulfide nanoparticles- Rhodopseudomonas palustris (Bio-CdS NPs- R. palustris) hybrids. For the self-assembling hybrids, Bio-CdS NPs were treated as new artificial-antennas to enhance photosynthesis, especially under low light (LL). Bacterial physiological results of hybrids were significantly increased, particularly for cells under LL, with higher enhancement photon harvesting ability. The enhancement included the pigment contents, and the ratio of the peripheral light-harvesting complex Ⅱ (LH2) to light-harvesting Ⅰ (1.33 ± 0.01 under LL), leading to the improvements of light-harvesting, transfer, and antenna conversion efficiencies. Finally, the stimulated electron chain of hybrids improved bacterial metabolism with increased nicotinamide adenine dinucleotide (NADH, 174.5% under LL) and adenosine triphosphate (ATP, 41.1% under LL). Furthermore, the modified photosynthetic units were induced by the up-regulated expression of fixK , which was activated by reduced oxygen tension of the medium for hybrids. fixK up-regulated genes encoding pigments (crt, and bch) and complexes (puf , pucAB , and pucC), leading to improved light-harvesting and transfer, and transform ability. This study provides a comprehensive understanding of the solar energy utilization mechanism of in-situ semiconductor-phototrophic microbe hybrids, contributing to further theoretical insight into their practical application. [Display omitted] • Light appeared obvious effect on the light-harvesting ability of the CdS NPs-bacteria hybrid system. • Bacterial photosynthesis unit structure was readjusted in the hybrids. • Hybrids improved photon absorption and energy efficiency. • Hybrids construction significant increase in photosynthetic gene expression. [ABSTRACT FROM AUTHOR]

Renal dysfunction is a common complication of sepsis. Early diagnosis and prompt treatment of sepsis with renal insufficiency are crucial for improving patient outcomes. Diagnostic markers can help identify patients at risk for sepsis and AKI, allowing for early intervention and potentially preventing the development of severe complications. The aim of the present study was to investigate the expression difference of urinary microRNAs (miRNAs/miRs) in elderly patients with sepsis and secondary renal insufficiency, and to evaluate their diagnostic value in these patients. In the present study, RNA was extracted from urine samples of elderly sepsis-related acute renal damage patients and the expression profiles of several miRNAs were analyzed. In order to evaluate the expression profile of several miRNAs, urine samples from elderly patients with acute renal damage brought on by sepsis were obtained. RNA extraction and sequencing were then performed on the samples. Furthermore, multiple bioinformatics methods were used to analyze miRNA profiles, including differential expression analysis, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis of different miRNA target genes, to further explore miRNAs that are suitable for utilization as biomarkers. A total of four miRNAs, including hsa-miR-31-5p, hsa-miR-151a-3p, hsa-miR-142-5p and hsa-miR-16-5p, were identified as potential biological markers and were further confirmed in sepsis using reverse transcription-quantitative PCR. The results of the present study demonstrated that the four urinary miRNAs were differentially expressed and may serve as specific markers for prediction of secondary acute kidney injury in elderly patients with sepsis. [ABSTRACT FROM AUTHOR]