Hypoxia treatment and resistance training alters microRNA profiling in rats skeletal muscle.

MicroRNAs (miRNAs) may play a crucial regulatory role in the process of muscle atrophy induced by high-altitude hypoxia and its amelioration through resistance training. However, research in this aspect is still lacking. Therefore, this study aimed to employ miRNA microarray analysis to investigate the expression profile of miRNAs in skeletal muscle from an animal model of hypoxia-induced muscle atrophy and resistance training aimed at mitigating muscle atrophy. The study utilized a simulated hypoxic environment (oxygen concentration at 11.2%) to induce muscle atrophy and established a rat model of resistance training using ladder climbing, with a total intervention period of 4 weeks. The miRNA expression profile revealed 9 differentially expressed miRNAs influenced by hypoxia (e.g., miR-341, miR-32-5p, miR-465-5p) and 14 differentially expressed miRNAs influenced by resistance training under hypoxic conditions (e.g., miR-338-5p, miR-203a-3p, miR-92b-3p) (∣log2(FC)∣ ≥ 1.5, p < 0.05). The differentially expressed miRNAs were found to target genes involved in muscle protein synthesis and degradation (such as Utrn, mdm2, eIF4E), biological processes (such as negative regulation of transcription from RNA polymerase II promoter, regulation of transcription, DNA-dependent), and signaling pathways (such as Wnt signaling pathway, MAPK signaling pathway, ubiquitin-mediated proteolysis, mTOR signaling pathway). This study provides a foundation for understanding and further exploring the molecular mechanisms underlying hypoxia-induced rats muscle atrophy and the mitigation of atrophy through resistance training.

Targeting PCSK9 to upregulate MHC-II on the surface of tumor cells in tumor immunotherapy.

BACKGROUND: Proprotein convertase subtilisin/kexin type 9 (PCSK9), the last member of the proprotein convertase family, functions as a classic regulator of low-density lipoprotein (LDL) by interacting with low-density lipoprotein receptor (LDLR). Recent studies have shown that PCSK9 can affect the occurrence and development of tumors and can be used as a novel therapeutic target. However, a comprehensive pan-cancer analysis of PCSK9 has yet to be conducted. METHODS: The potential oncogenic effects of PCSK9 in 33 types of tumors were explored based on the datasets of The Cancer Genome Atlas (TCGA) dataset. In addition, the immune regulatory role of PCSK9 inhibition was evaluated via in vitro cell coculture and the tumor-bearing mouse model. Finally, the antitumor efficacy of targeted PCSK9 combined with OVA-II vaccines was verified. RESULTS: Our results indicated that PCSK9 was highly expressed in most tumor types and was significantly correlated with late disease stage and poor prognosis. Additionally, PCSK9 may regulate the tumor immune matrix score, immune cell infiltration, immune checkpoint expression, and major histocompatibility complex expression. Notably, we first found that dendritic cell (DC) infiltration and major histocompatibility complex-II (MHC-II) expression could be upregulated by PCSK9 inhibition and improve CD8+ T cell activation in the tumor immune microenvironment, thereby achieving potent tumor control. Combining PCSK9 inhibitors could enhance the efficacies of OVA-II tumor vaccine monotherapy. CONCLUSIONS: Conclusively, our pan-cancer analysis provided a more comprehensive understanding of the oncogenic and immunoregulatory roles of PCSK9 and demonstrated that targeting PCSK9 could increase the efficacy of long peptide vaccines by upregulating DC infiltration and MHC-II expression on the surface of tumor cells. This study reveals the critical oncogenic and immunoregulatory roles of PCSK9 in various tumors and shows the promise of PCSK9 as a potent immunotherapy target.

Exploring Pyroptosis-related Signature Genes and Potential Drugs in Ulcerative Colitis by Transcriptome Data and Animal Experimental Validation.

Ulcerative colitis (UC) is an idiopathic, relapsing inflammatory disorder of the colonic mucosa. Pyroptosis contributes significantly to UC. However, the molecular mechanisms of UC remain unexplained. Herein, using transcriptome data and animal experimental validation, we sought to explore pyroptosis-related molecular mechanisms, signature genes, and potential drugs in UC. Gene profiles (GSE48959, GSE59071, GSE53306, and GSE94648) were selected from the Gene Expression Omnibus (GEO) database, which contained samples derived from patients with active and inactive UC, as well as health controls. Gene Set Enrichment Analysis (GSEA), Weighted Gene Co-expression Network Analysis (WGCNA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on microarrays to unravel the association between UC and pyroptosis. Then, differential expressed genes (DEGs) and pyroptosis-related DEGs were obtained by differential expression analyses and the public database. Subsequently, pyroptosis-related DEGs and their association with the immune infiltration landscape were analyzed using the CIBERSORT method. Besides, potential signature genes were selected by machine learning (ML) algorithms, and then validated by testing datasets which included samples of colonic mucosal tissue and peripheral blood. More importantly, the potential drug was screened based on this. And these signature genes and the drug effect were finally observed in the animal experiment. GSEA and KEGG enrichment analyses on key module genes derived from WGCNA revealed a close association between UC and pyroptosis. Then, a total of 20 pyroptosis-related DEGs of UC and 27 pyroptosis-related DEGs of active UC were screened. Next, 6 candidate genes (ZBP1, AIM2, IL1ß, CASP1, TLR4, CASP11) in UC and 2 candidate genes (TLR4, CASP11) in active UC were respectively identified using the binary logistic regression (BLR), least absolute shrinkage and selection operator (LASSO), random forest (RF) analysis and artificial neural network (ANN), and these genes also showed high diagnostic specificity for UC in testing sets. Specially, TLR4 was elevated in UC and further elevated in active UC. The results of the drug screen revealed that six compounds (quercetin, cyclosporine, resveratrol, cisplatin, paclitaxel, rosiglitazone) could target TLR4, among which the effect of quercetin on intestinal pathology, pyroptosis and the expression of TLR4 in UC and active UC was further determined by the murine model. These findings demonstrated that pyroptosis may promote UC, and especially contributes to the activation of UC. Pyroptosis-related DEGs offer new ideas for the diagnosis of UC. Besides, quercetin was verified as an effective treatment for pyroptosis and intestinal inflammation. This study might enhance our comprehension on the pathogenic mechanism and diagnosis of UC and offer a treatment option for UC.

In Silico Design of gRNA for CRISPR/Cas9-Mediated Gene Knockout.

CRISPR/Cas9 stands as a revolutionary and versatile gene editing technology. At its core, the Cas9 DNA endonuclease is guided with precision by a specifically designed single-guide RNA (gRNA). This guidance system facilitates the introduction of double-stranded breaks (DSBs) within the DNA. Subsequent imprecise repairs, mainly through the non-homologous end-joining (NHEJ) pathway, yield insertions or deletions, resulting in frameshift mutations. These mutations are instrumental in achieving the successful knockout of the target gene. In this chapter, we describe all necessary steps to create and design a gRNA for a gene knockout to a target gene before to transfer it to a target plant.

From GPUs to AI and quantum: three waves of acceleration in bioinformatics.

The enormous growth in the amount of data generated by the life sciences is continuously shifting the field from model-driven science towards data-driven science. The need for efficient processing has led to the adoption of massively parallel accelerators such as graphics processing units (GPUs). Consequently, the development of bioinformatics methods nowadays often heavily depends on the effective use of these powerful technologies. Furthermore, progress in computational techniques and architectures continues to be highly dynamic, involving novel deep neural network models and artificial intelligence (AI) accelerators, and potentially quantum processing units in the future. These are expected to be disruptive for the life sciences as a whole and for drug discovery in particular. Here, we identify three waves of acceleration and their applications in a bioinformatics context: (i) GPU computing, (ii) AI and (iii) next-generation quantum computers.

Obesity and the cerebral cortex: Underlying neurobiology in mice and humans.

Obesity is a major modifiable risk factor for Alzheimer's disease (AD), characterized by progressive atrophy of the cerebral cortex. The neurobiology of obesity contributions to AD is poorly understood. Here we show with in vivo MRI that diet-induced obesity decreases cortical volume in mice, and that higher body adiposity associates with lower cortical volume in humans. Single-nuclei transcriptomics of the mouse cortex reveals that dietary obesity promotes an array of neuron-adverse transcriptional dysregulations, which are mediated by an interplay of excitatory neurons and glial cells, and which involve microglial activation and lowered neuronal capacity for neuritogenesis and maintenance of membrane potential. The transcriptional dysregulations of microglia more than of other cell types are like those in AD, as assessed with single-nuclei cortical transcriptomics in a mouse model of AD and two sets of human donors with the disease. Serial two-photon tomography of microglia demonstrates microgliosis throughout the mouse cortex. The spatial pattern of adiposity-cortical volume associations in human cohorts interrogated together with in silico bulk and single-nucleus transcriptomic data from the human cortex implicated microglia (along with other glial cells and subtypes of excitatory neurons), and it correlated positively with the spatial profile of cortical atrophy in patients with mild cognitive impairment and AD. Thus, multi-cell neuron-adverse dysregulations likely contribute to the loss of cortical tissue in obesity. The dysregulations of microglia may be pivotal to the obesity-related risk of AD at a population level.

Comprehensive bioinformatics analysis of NOX4 as a biomarker for pan-cancer prognosis and immune infiltration.

BACKGROUND: NADPH oxidase 4 (NOX4) has been proven to be associated with the prognosis of tumors in multiple cancers and can serve as a potential immunotherapy target to provide new treatment options for various tumors. In this study, our aim is to conduct an in-depth investigation of NOX4 across a range of cancer types to determine the relationship between NOX4 and tumors. METHODS: Utilizing large-scale transcriptomic and clinical data from public databases, a systematic examination of NOX4 expression patterns was performed in pan-cancer cohorts. Survival analysis, methylation analysis, and correlation studies were employed to assess the diagnostic and prognostic significance of NOX4 in diverse cancer types. Additionally, an exploration of the relationship between NOX4 expression and immune infiltration across various tumors was conducted. RESULTS: The analyses unveiled a consistent upregulation of NOX4 expression in multiple cancer types relative to normal tissues, indicating its potential as a universal cancer biomarker. Elevated NOX4 expression significantly correlated with poor overall survival in several cancers. Furthermore, the study demonstrated a robust correlation between NOX4 expression and immune cell infiltration, signifying its involvement in the modulation of the tumor microenvironment. CONCLUSIONS: This study imparts valuable insights into the potential applications of NOX4 in cancer research, highlighting its significance as a multifaceted biomarker with diagnostic, prognostic, and immunomodulatory implications across diverse malignancies.

Divergent proteomic profiles of opium poppy cultivars.

We examined the proteomic profiles of three registered opium poppy cultivars (Papaver somniferum L.) with varying alkaloid contents. The study was conducted on both the stem and capsule organs. A high number of differentially expressed proteins (DEPs) were identified between the cultivars and the organs. We analyzed DEPs for their contribution in GO terms and KEGG pathways. The upregulated DEPs were significantly enriched in photosynthesis and translation for morphine-rich and noscapine-rich cultivars, respectively. The data indicated that photosynthesis is crucial for benzylisoquinoline alkaloid (BIA) biosynthesis, but different processes are also effective in morphine and noscapine biosynthesis, which occur at different branches in the biosynthetic pathway. The proteomics profiles revealed that energy demand is more effective in morphine biosynthesis, while translational control plays a leading role in noscapine biosynthesis. This study represents the first report demonstrating organ-based and cultivar-based protein expression differences in mature poppy plants.

Evolutionary analysis reveals the role of a non-catalytic domain of peptidyl arginine deiminase 2 in transcriptional regulation.

Peptidyl arginine deiminases (PADIs) catalyze protein citrullination, a post-translational conversion of arginine to citrulline. The most widely expressed member of this family, PADI2, regulates cellular processes that impact several diseases. We hypothesized that we could gain new insights into PADI2 function through a systematic evolutionary and structural analysis. Here, we identify 20 positively selected PADI2 residues, 16 of which are structurally exposed and maintain PADI2 interactions with cognate proteins. Many of these selected residues reside in non-catalytic regions of PADI2. We validate the importance of a prominent loop in the middle domain that encompasses PADI2 L162, a residue under positive selection. This site is essential for interaction with the transcription elongation factor (P-TEFb) and mediates the active transcription of the oncogenes c-MYC, and CCNB1, as well as impacting cellular proliferation. These insights could be key to understanding and addressing the role of the PADI2 c-MYC axis in cancer progression.

Revealing Prdx4 as a potential diagnostic and therapeutic target for acute pancreatitis based on machine learning analysis.

Acute pancreatitis (AP) is a common systemic inflammatory disease resulting from the activation of trypsinogen by various incentives in ICU. The annual incidence rate is approximately 30 out of 100,000. Some patients may progress to severe acute pancreatitis, with a mortality rate of up to 40%. Therefore, the goal of this article is to explore the key genes for effective diagnosis and treatment of AP. The analysis data for this study were merged from two GEO datasets. 1357 DEGs were used for functional enrichment and cMAP analysis, aiming to reveal the pathogenic genes and potential mechanisms of AP, as well as potential drugs for treating AP. Importantly, the study used LASSO and SVM-RFE machine learning to screen the most likely AP occurrence biomarker for Prdx4 among numerous candidate genes. A receiver operating characteristic of Prdx4 was used to estimate the incidence of AP. The ssGSEA algorithm was employed to investigate immune cell infiltration in AP. The biomarker Prdx4 gene exhibited significant associations with a majority of immune cells and was identified as being expressed in NKT cells, macrophages, granulocytes, and B cells based on single-cell transcriptome data. Finally, we found an increase in Prdx4 expression in the pancreatic tissue of AP mice through immunohistochemistry. After treatment with recombinant Prdx4, the pathological damage to the pancreatic tissue of AP mice was relieved. In conclusion, our study identified Prdx4 as a potential AP hub gene, providing a new target for treatment.

Identification and validation of biomarkers related to lipid metabolism in osteoarthritis based on machine learning algorithms.

BACKGROUND: Osteoarthritis and lipid metabolism are strongly associated, although the precise targets and regulatory mechanisms are unknown. METHODS: Osteoarthritis gene expression profiles were acquired from the GEO database, while lipid metabolism-related genes (LMRGs) were sourced from the MigSB database. An intersection was conducted between these datasets to extract gene expression for subsequent differential analysis. Following this, functional analyses were performed on the differentially expressed genes (DEGs). Subsequently, machine learning was applied to identify hub genes associated with lipid metabolism in osteoarthritis. Immune-infiltration analysis was performed using CIBERSORT, and external datasets were employed to validate the expression of these hub genes. RESULTS: Nine DEGs associated with lipid metabolism in osteoarthritis were identified. UGCG and ESYT1, which are hub genes involved in lipid metabolism in osteoarthritis, were identified through the utilization of three machine learning algorithms. Analysis of the validation dataset revealed downregulation of UGCG in the experimental group compared to the normal group and upregulation of ESYT1 in the experimental group compared to the normal group. CONCLUSIONS: UGCG and ESYT1 were considered as hub LMRGs in the development of osteoarthritis, which were regarded as candidate diagnostic markers. The effects are worth expected in the early diagnosis and treatment of osteoarthritis.

Bioinformatics-driven identification of prognostic biomarkers in kidney renal clear cell carcinoma.

Renal cell carcinoma (RCC), particularly the clear cell subtype (ccRCC), poses a significant global health concern due to its increasing prevalence and resistance to conventional therapies. Early detection of ccRCC remains challenging, resulting in poor patient survival rates. In this study, we employed a bioinformatic approach to identify potential prognostic biomarkers for kidney renal clear cell carcinoma (KIRC). By analyzing RNA sequencing data from the TCGA-KIRC project, differentially expressed genes (DEGs) associated with ccRCC were identified. Pathway analysis utilizing the Qiagen Ingenuity Pathway Analysis (IPA) tool elucidated key pathways and genes involved in ccRCC dysregulation. Prognostic value assessment was conducted through survival analysis, including Cox univariate proportional hazards (PH) modeling and Kaplan-Meier plotting. This analysis unveiled several promising biomarkers, such as MMP9, PIK3R6, IFNG, and PGF, exhibiting significant associations with overall survival and relapse-free survival in ccRCC patients. Cox multivariate PH analysis, considering gene expression and age at diagnosis, further confirmed the prognostic potential of MMP9, IFNG, and PGF genes. These findings enhance our understanding of ccRCC and provide valuable insights into potential prognostic biomarkers that can aid healthcare professionals in risk stratification and treatment decision-making. The study also establishes a foundation for future research, validation, and clinical translation of the identified prognostic biomarkers, paving the way for personalized approaches in the management of KIRC.

Construction of a diagnostic model based on random forest and artificial neural network for peri-implantitis.

OBJECTIVES: This study aimed to reveal critical genes regulating peri-implantitis during its development and construct a diagnostic model by using random forest (RF) and artificial neural network (ANN). METHODS: GSE-33774, GSE106090, and GSE57631 datasets were obtained from the GEO database. The GSE33774 and GSE106090 datasets were analyzed for differential expression and functional enrichment. The protein-protein interaction networks (PPI) and RF screened vital genes. A diagnostic model for peri-implantitis was established using ANN and validated on the GSE33774 and GSE57631 datasets. A transcription factor-gene interaction network and a transcription factor-micro-RNA (miRNA) regulatory network were also established. RESULTS: A total of 124 differentially expressed genes (DEGs) involved in the regulation of peri-implantitis were screened. Enrichment analysis showed that DEGs were mainly associated with immune receptor activity and cytokine receptor activity and were mainly involved in processes such as leukocyte and neutrophil migration. The PPI and RF screened six essential genes, namely, CD38, CYBB, FCGR2A, SELL, TLR4, and CXCL8. The receiver operating characteristic curve (ROC) indicated that the ANN model had an excellent diagnostic performance. FOXC1, GATA2, and NF-κB1 may be essential transcription factors in peri-implantitis, and hsa-miR-204 may be a key miRNA. CONCLUSIONS: The diagnostic model of peri-implantitis constructed by RF and ANN has high confidence, and CD38, CYBB, FCGR2A, SELL, TLR4, and CXCL8 are potential diagnostic markers. FOXC1, GATA2, and NF-κB1 may be essential transcription factors in peri-implantitis, and hsa-miR-204 plays a vital role as a critical miRNA.

An energy-conserving reaction in amino acid metabolism catalyzed by arginine synthetase.

All forms of life are presumed to synthesize arginine from citrulline via a two-step pathway consisting of argininosuccinate synthetase and argininosuccinate lyase using citrulline, adenosine 5'-triphosphate (ATP), and aspartate as substrates. Conversion of arginine to citrulline predominantly proceeds via hydrolysis. Here, from the hyperthermophilic archaeon Thermococcus kodakarensis, we identified an enzyme which we designate "arginine synthetase". In arginine synthesis, the enzyme converts citrulline, ATP, and free ammonia to arginine, adenosine 5'-diphosphate (ADP), and phosphate. In the reverse direction, arginine synthetase conserves the energy of arginine deimination and generates ATP from ADP and phosphate while releasing ammonia. The equilibrium constant of this reaction at pH 7.0 is [Cit][ATP][NH3]/[Arg][ADP][Pi] = 10.1 ± 0.7 at 80 °C, corresponding to a ΔG°' of -6.8 ± 0.2 kJ mol-1. Growth of the gene disruption strain was compared to the host strain in medium composed of amino acids. The results suggested that arginine synthetase is necessary in providing ornithine, the precursor for proline biosynthesis, as well as in generating ATP. Growth in medium supplemented with citrulline indicated that arginine synthetase can function in the direction of arginine synthesis. The enzyme is widespread in nature, including bacteria and eukaryotes, and catalyzes a long-overlooked energy-conserving reaction in microbial amino acid metabolism. Along with ornithine transcarbamoylase and carbamate kinase, the pathway identified here is designated the arginine synthetase pathway.

NXPH4 can be used as a biomarker for pan-cancer and promotes colon cancer progression.

NXPH4 promotes cancer proliferation and invasion. However, its specific role and mechanism in cancer remain unclear. Transcriptome and clinical data for pan-cancer were derived from the TCGA database. K-M survival curve and univariate Cox were used for prognostic analysis. CIBERSORT and TIMER algorithms were employed to calculate immune cell infiltration. Gene set enrichment analysis (GSEA) was employed for investigating the function of NXPH4. Western blot verified differential expression of NXPH4 in colon cancer. Functional assays (CCK-8, plate clonogenicity assay, wound healing assay, and Transwell assay) confirmed the impact of NXPH4 on proliferation, invasion, and migration of colon cancer cells. Dysregulation of NXPH4 in pan-cancer suggests its potential as a diagnostic and prognostic marker for certain cancers, including colon and liver cancer. High expression of NXPH4 in pan-cancer might be associated with the increase in copy number and hypomethylation. NXPH4 expression in pan-cancer is substantially linked to immune cell infiltration in the immune microenvironment. NXPH4 expression is associated with the susceptibility to immunotherapy and chemotherapy. Western blot further confirmed the higher expression of NXPH4 in colon cancer. Knockdown of NXPH4 significantly suppresses proliferation, invasion, and migration of colon cancer cell lines HT-29 and HCT116, as validated by functional assays.

Tongue-Coating Microbial and Metabolic Characteristics in Halitosis.

Halitosis is a common oral condition, which leads to social embarrassment and affects quality of life. Cumulative evidence has suggested the association of tongue-coating microbiome with the development of intraoral halitosis. The dynamic variations of tongue-coating microbiota and metabolites in halitosis have not been fully elucidated. Therefore, the present study aimed to determine the tongue-coating microbial and metabolic characteristics in halitosis subjects without other oral diseases using metagenomics and metabolomics analysis. The participants underwent oral examination, halitosis assessment, and tongue-coating sample collection for the microbiome and metabolome analysis. It was found that the microbiota richness and diversity were significantly elevated in the halitosis group. Furthermore, species from Actinomyces, Prevotella, Veillonella, and Solobacterium were significantly more abundant in the halitosis group. However, the Rothia and Streptococcus species exhibited opposite tendencies. Eleven Kyoto Encyclopedia of Genes and Genomes pathways were significantly enriched in the halitosis tongue coatings, including cysteine and methionine metabolism. Functional genes related to sulfur, indole, skatole, and cadaverine metabolic processes (such as serA, metH, metK and dsrAB) were identified to be more abundant in the halitosis samples. The metabolome analysis revealed that indole-3-acetic, ornithine, and L-tryptophan were significantly elevated in the halitosis samples. Furthermore, it was observed that the values of volatile sulfur compounds and indole-3-acetic abundances were positively correlated. The multiomics analysis identified the metagenomic and metabolomic characteristics to differentiate halitosis from healthy individuals using the least absolute shrinkage and selection operator logistic regression and random forest classifier. A total of 19 species and 39 metabolites were identified as features in halitosis patients, which included indole-3-acetic acid, Bacillus altitudinis, Candidatus Saccharibacteria, and Actinomyces species. In conclusion, an evident shift in microbiome and metabolome characteristics was observed in the halitosis tongue coating, which may have a potential etiological significance and provide novel insights into the mechanism for halitosis.

Protocol for constructing glycan biosynthetic networks using glycowork.

Glycans, present across all domains of life, comprise a wide range of monosaccharides assembled into complex, branching structures. Here, we present an in silico protocol to construct biosynthetic networks from a list of observed glycans using the Python package glycowork. We describe steps for data preparation, network construction, feature analysis, and data export. This protocol is implemented in Python using example data and can be adapted for use with customized datasets. For complete details on the use and execution of this protocol, please refer to Thomès et al.1.

Identification of the succinate-CoA ligase protein gene family reveals that TaSUCL1-1 positively regulate cadmium resistance in wheat.

The Succinate-CoA ligase (SUCL1) gene family is involved in energy metabolism, phytohormone signaling, and plant growth, development, and tolerance to stress. This is the first study to analyze the SUCL1 gene family in wheat (Triticum aestivum). 17 TaSUCL1 genes were identified in the complete genome sequence and classified into five subfamilies based on related genes found in three other species. The 17 TaSUCL1 genes were unevenly distributed across 11 chromosomes, and the collinearity of these genes was further investigated. Through using real-time qPCR (RT-qPCR) analysis, we identified the expression patterns of the TaSUCL1 genes under various tissues and different heavy metal stress conditions. The functions of selected TaSUCL1-1 gene were investigated by RNA interference (RNAi). This study provided a comprehensive analysis of the TaSUCL1 gene family. Within the TaSUCL1 genes, the exon-intron structure and motif composition exhibited significant similarity among members of the same evolutionary branch. Homology analysis and phylogenetic comparison of the SUCL1 genes in different plants offered valuable insights for studying the evolutionary characteristics of the SUCL1 genes. The expression levels of the TaSUCL1 genes in different tissues and under various metal stress conditions reveal its important role in plant growth and development. Gene function analysis demonstrated that TaSUCL1-1 silenced wheat plants exhibited a decrease in the total cadmium (Cd) concentrations and gene expression levels compared to the wild type (WT). Additionally, TaSUCL1-1 belonging to class c physically interacts with the ß-amylase protein TaBMY1 as verified by yeast two-hybridization. This research provides a useful resource for further study of the function and molecular genetic mechanism of the SUCL1 gene family members.

Bioinformatics analysis and experimental verification of NLRX1 as a prognostic factor for esophageal squamous cell carcinoma.

Nucleotide binding and oligomeric domain-like receptor X1 (NLRX1), a member of the NLR family, is associated with the physiological and pathological processes of inflammation, autophagy, immunity, metabolism and mitochondrial regulation, and has been demonstrated to have pro- or antitumor effects in various tumor types. However, the biological function of NLRX1 in esophageal squamous cell carcinoma (ESCC) has remained elusive. In the present study, by using bioinformatics methods, the differential expression of NLRX1 at the mRNA level was examined. Overall survival, clinical correlation, receiver operating characteristic curve, Cox regression, co-expression, enrichment, immune infiltration and drug sensitivity analyses were carried out. A nomogram and a calibration curve were constructed. Changes in protein expression levels were investigated by immunohistochemistry and western blotting. The impact of NLRX1 on i) cell proliferation was evaluated by Cell Counting Kit-8 assays; ii) migration was examined by wound-healing assays; iii) migration and invasion were evaluated by Transwell assays; and iv) apoptosis was assessed by Annexin V/PI staining and flow cytometry. The results revealed that, compared to normal adjacent tissue, NLRX1 was lowly expressed in ESCC, and patients with low NLRX1 expression had a shorter survival time. NLRX1 was an independent prognostic factor for ESCC and was associated with tumor grading. Patients in the low-NLRX1 group showed a decrease in the infiltration of activated natural killer cells, monocytes and M0 macrophages, and these immune-cell infiltration levels were positively correlated with NLRX1 expression. Knocking down NLRX1 promoted the proliferation of KYSE450 cells, while overexpression of NLRX1 inhibited the proliferation of ECA109 cells. NLRX1 negatively regulated the PI3K/AKT signaling pathway in ESCC. These findings indicate that, through several mechanisms, NLRX1 suppresses tumor growth in ESCC, which offers new insight for investigating the causes and progression of ESCC, as well as for identifying more efficient therapeutic approaches.

Towards facilitated interpretation of shotgun metagenomics long-read sequencing data analyzed with KMA for the detection of bacterial pathogens and their antimicrobial resistance genes.

Metagenomic sequencing is a promising method that has the potential to revolutionize the world of pathogen detection and antimicrobial resistance (AMR) surveillance in food-producing environments. However, the analysis of the huge amount of data obtained requires performant bioinformatics tools and databases, with intuitive and straightforward interpretation. In this study, based on long-read metagenomics data of chicken fecal samples with a spike-in mock community, we proposed confidence levels for taxonomic identification and AMR gene detection, with interpretation guidelines, to help with the analysis of the output data generated by KMA, a popular k-mer read alignment tool. Additionally, we demonstrated that the completeness and diversity of the genomes present in the reference databases are key parameters for accurate and easy interpretation of the sequencing data. Finally, we explored whether KMA, in a two-step procedure, can be used to link the detected AMR genes to their bacterial host chromosome, both detected within the same long-reads. The confidence levels were successfully tested on 28 metagenomics datasets which were obtained with sequencing of real and spiked samples from fecal (chicken, pig, and buffalo) or food (minced beef and food enzyme products) origin. The methodology proposed in this study will facilitate the analysis of metagenomics sequencing datasets for KMA users. Ultimately, this will contribute to improvements in the rapid diagnosis and surveillance of pathogens and AMR genes in food-producing environments, as prioritized by the EU.