Differential Analysis Research Articles

Evaluating the fairness of a high-stakes college entrance exam in Kuwait

The use of college entrance exams for facilitating admission decisions become controversial, and the central argument is around the fairness of test scores. The Kuwait University English Aptitude Test (KUEAT) is a high-stakes test, but very few studies have examined the psychometric quality of the scores for this national-level assessment. This study illustrates how measurement approaches can be used to examine the fairness issues in educational testing. Through a modern view of fairness, we assess the internal and external bias of KUEAT scores using differential item functioning analysis and differential prediction analysis, respectively, and provide a comprehensive fairness argument for KUEAT scores. The analysis for examining the internal evidence of bias was based on 1790 examinees’ KUEAT scores in November 2018. KUEAT scores and first-year college GPAs of 4033 students enrolled in KU were used for assessing the external evidence of bias. Results revealed many items showing differential item functioning across student subpopulation groups (i.e., nationality, gender, high school majors, and high school types). Meanwhile, KUEAT scores also predicted college performance differentially by different student subgroups (i.e., nationality, high school majors, and high school types). Discussion and implications on the fairness issues of college entrance tests in Kuwait are provided.

Gut microbiome in endometriosis: a cohort study on 1000 individuals

BackgroundEndometriosis, defined as the presence of endometrial-like tissue outside of the uterus, is one of the most prevalent gynecological disorders. Although different theories have been proposed, its pathogenesis is not clear. Novel studies indicate that the gut microbiome may be involved in the etiology of endometriosis; nevertheless, the connection between microbes, their dysbiosis, and the development of endometriosis is understudied. This case–control study analyzed the gut microbiome in women with and without endometriosis to identify microbial targets involved in the disease.MethodsA subsample of 1000 women from the Estonian Microbiome cohort, including 136 women with endometriosis and 864 control women, was analyzed. Microbial composition was determined by shotgun metagenomics and microbial functional pathways were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Partitioning Around Medoids (PAM) algorithm was performed to cluster the microbial profile of the Estonian population. The alpha- and beta-diversity and differential abundance analyses were performed to assess the gut microbiome (species and KEGG orthologies (KO)) in both groups. Metagenomic reads were mapped to estrobolome-related enzymes’ sequences to study potential microbiome-estrogen metabolism axis alterations in endometriosis.ResultsDiversity analyses did not detect significant differences between women with and without endometriosis (alpha-diversity: all p-values > 0.05; beta-diversity: PERMANOVA, both R2 < 0.0007, p-values > 0.05). No differential species or pathways were detected after multiple testing adjustment (all FDR p-values > 0.05). Sensitivity analysis excluding women at menopause (> 50 years) confirmed our results. Estrobolome-associated enzymes’ sequence reads were not significantly different between groups (all FDR p-values > 0.05).ConclusionsOur findings do not provide enough evidence to support the existence of a gut microbiome-dependent mechanism directly implicated in the pathogenesis of endometriosis. To the best of our knowledge, this is the largest metagenome study on endometriosis conducted to date.

Small RNA and Degradome Deep Sequencing Reveal Regulatory Roles of MicroRNAs in Response to Sugarcane Mosaic Virus Infection on Two Contrasting Sugarcane Cultivars.

MicroRNAs (miRNAs) play an essential regulatory role in plant-virus interaction. However, few studies have focused on the roles of miRNAs and their targets after sugarcane mosaic virus (SCMV) infection in sugarcane. To address this issue, we conducted small RNA (sRNA) and degradome sequencing on two contrasting sugarcanes (SCMV-resistant 'Fuoguo1' [FG1] and susceptible 'Badila') infected by SCMV at five time points. A total of 1,578 miRNAs were profiled from 30 sRNA libraries, comprising 660 known miRNAs and 380 novel miRNAs. Differential expression analysis of miRNAs revealed that most were highly expressed during the SCMV exponential phase in Badila at 18 h postinfection, with expression profiles positively correlated with virus replication dynamics as observed through clustering. Analysis of degradome data indicated a higher number of differential miRNA targets in Badila compared to FG1 at 18 h postinfection. Gene ontology (GO) enrichment analysis significantly enriched the stimulus-response pathway, suggesting negative regulatory roles to SCMV resistance. Specifically, miR160 upregulated expression patterns and validated in Badila through quantitative real-time PCR (qRT-PCR) in the early stages of SCMV multiplication. Our research provides new insights into the dynamic response of plant miRNA and virus replication and contributes valuable information on the intricate interplay between miRNAs and SCMV infection dynamics. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Full Native timsTOF PASEF-Enabled Quantitative Proteomics with the i2MassChroQ Software Package.

Ion mobility mass spectrometry has become popular in proteomics lately, in particular because the Bruker timsTOF instruments have found significant adoption in proteomics facilities. The Bruker's implementation of the ion mobility dimension generates massive amounts of mass spectrometric data that require carefully designed software both to extract meaningful information and to perform processing tasks at reasonable speed. In a historical move, the Bruker company decided to harness the skills of the scientific software development community by releasing to the public the timsTOF data file format specification. As a proteomics facility that has been developing Free Open Source Software (FOSS) solutions since decades, we took advantage of this opportunity to implement the very first FOSS proteomics complete solution to natively read the timsTOF data, low-level process them, and explore them in an integrated quantitative proteomics software environment. We dubbed our software i2MassChroQ because it implements a (peptide)identification-(protein)inference-mass-chromatogram-quantification processing workflow. The software benchmarking results reported in this paper show that i2MassChroQ performed better than competing software on two critical characteristics: (1) feature extraction capability and (2) protein quantitative dynamic range. Altogether, i2MassChroQ yielded better quantified protein numbers, both in a technical replicate MS runs setting and in a differential protein abundance analysis setting.

Q2-metnet: QIIME2 package to analyze 16S rRNA data via high-quality metabolic reconstructions of the human gut microbiota.

16S rRNA gene sequencing is the most frequent approach for the characterization of the human gut microbiota. Despite different efforts in the literature, the inference of functional and metabolic interpretations from 16S rRNA gene sequencing data is still a challenging task. High-quality metabolic reconstructions of the human gut microbiota, such as AGORA and AGREDA, constitute a curated resource to improve functional inference from 16S rRNA data, but they are not typically integrated into standard bioinformatics tools. Here, we present q2-metnet, a QIIME2 plugin that enables the contextualization of 16S rRNA gene sequencing data into AGORA and AGREDA. In particular, based on relative abundances of taxa, q2-metnet determines normalized activity scores for the reactions and subsystems involved in the selected metabolic reconstruction. Using these scores, q2-metnet allows the user to conduct differential activity analysis for reactions and subsystems, as well as exploratory analysis using PCA and hierarchical clustering. We apply q2-metnet to a dataset from our group that involves 16S rRNA data from stool samples from lean, allergic to cow's milk, obese and celiac children, and the Belgian Flemish Gut Flora Project cohort, which includes faecal 16S rRNA data from obese and normal-weight adult individuals. In the first case, q2-metnet outperforms existing algorithms in separating different clinical conditions based on predicted pathway abundances and subsystem scores. In the second case, q2-metnet complements competing approaches in predicting functional alterations in the gut microbiota of obese individuals. Overall, q2-metnet constitutes a powerful bioinformatics tool to provide metabolic context to 16S rRNA data from the human gut microbiota. Python code of q2-metnet is available in https://github.com/PlanesLab/q2-metnet and https://figshare.com/articles/dataset/q2-metnet_package/26180446. Supplementary data are available at Bioinformatics online.

Targeting AKR1B10 by drug repurposing with epalrestat overcomes chemoresistance in non-small cell lung cancer patient-derived tumor organoids.

Systemic treatments given to non-small cell lung cancer (NSCLC) patients are often ineffective due to drug resistance. In the present study, we investigated patient-derived tumor organoids (PDTOs) and matched tumor tissues from surgically treated NSCLC patients to identify drug repurposing targets to overcome resistance towards standard-of-care platinum-based doublet chemotherapy. PDTOs were established from ten prospectively enrolled non-metastatic NSCLC patients from resected tumors. PDTOs were compared with matched tumor tissues by histopathology/immunohistochemistry, whole exome and transcriptome sequencing. PDTO growths and drug responses were determined by measuring 3D tumoroid volumes, cell viability, and proliferation/apoptosis. Differential gene expression analysis identified drug-repurposing targets. Validations were performed with internal/external NSCLC patient data sets. NSCLC cell lines were used for aldo-keto reductase 1B10 (AKR1B10) knockdown studies and xenograft models to determine the intratumoral bioavailability of epalrestat. PDTOs retained histomorphology and pathological biomarker expression, mutational/transcriptomic signatures, and cellular heterogeneity of the matched tumor tissues. Five (50%) PDTOs were chemoresistant towards carboplatin/paclitaxel. Chemoresistant PDTOs and matched tumor tissues demonstrated overexpression of AKR1B10. Epalrestat, an orally available AKR1B10 inhibitor in clinical use for diabetic polyneuropathy, was repurposed to overcome chemoresistance of PDTOs. In vivo efficacy of epalrestat to overcome drug resistance corresponded to intratumoral epalrestat levels. PDTOs are efficient preclinical models recapitulating the tumor characteristics and are suitable for drug testing. AKR1B10 can be targeted by repurposing epalrestat to overcome chemoresistance in NSCLC. Epalrestat has the potential to advance to clinical trials in drug-resistant NSCLC patients due to favorable toxicity, pharmacological profile, and bioavailability.

Multi-omics machine learning to study host-microbiome interactions in early-onset colorectal cancer

The incidence of early-onset colorectal cancer (eoCRC) is rising, and its pathogenesis is not completely understood. We hypothesized that machine learning utilizing paired tissue microbiome and plasma metabolome features could uncover distinct host-microbiome associations between eoCRC and average-onset CRC (aoCRC). Individuals with stages I–IV CRC (n = 64) were categorized as eoCRC (age ≤ 50, n = 20) or aoCRC (age ≥ 60, n = 44). Untargeted plasma metabolomics and 16S rRNA amplicon sequencing (microbiome analysis) of tumor tissue were performed. We fit DIABLO (Data Integration Analysis for Biomarker Discovery using Latent variable approaches for Omics studies) to construct a supervised machine-learning classifier using paired multi-omics (microbiome and metabolomics) data and identify associations unique to eoCRC. A differential association network analysis was also performed. Distinct clustering patterns emerged in multi-omic dimension reduction analysis. The metabolomics classifier achieved an AUC of 0.98, compared to AUC 0.61 for microbiome-based classifier. Circular correlation technique highlighted several key associations. Metabolites glycerol and pseudouridine (higher abundance in individuals with aoCRC) had negative correlations with Parasutterella, and Ruminococcaceae (higher abundance in individuals with eoCRC). Cholesterol and xylitol correlated negatively with Erysipelatoclostridium and Eubacterium, and showed a positive correlation with Acidovorax with higher abundance in individuals with eoCRC. Network analysis revealed different clustering patterns and associations for several metabolites e.g.: urea cycle metabolites and microbes such as Akkermansia. We show that multi-omics analysis can be utilized to study host-microbiome correlations in eoCRC and demonstrates promising biomarker potential of a metabolomics classifier. The distinct host-microbiome correlations for urea cycle in eoCRC may offer opportunities for therapeutic interventions.

Analysis of the Skin and Brain Transcriptome of Normally Pigmented and Pseudo-Albino Southern Flounder (Paralichthys lethostigma) Juveniles to Study the Molecular Mechanisms of Hypopigmentation and Its Implications for Species Survival in the Natural Environment

Southern flounder skin pigmentation is a critical phenotypic characteristic for this species’ survival in the natural environment. Normal pigmentation allows rapid changes of color for concealment to capture prey and UV light protection. In contrast, highly visible hypopigmented pseudo-albinos exhibit a compromised immune system and are vulnerable to predation, sensitive to UV exposure, and likely have poor survival in the wild. Skin and brain tissue samples from normally pigmented and hypopigmented individuals were analyzed with next-generation RNA sequencing. A total of 1,589,613 transcripts were used to identify 952,825 genes to assemble a de novo transcriptome, with 99.43% of genes mapped to the assembly. Differential gene expression and gene enrichment analysis of contrasting tissues and phenotypes revealed that pseudo-albino individuals appeared more susceptible to environmental stress, UV light exposure, hypoxia, and osmotic stress. The pseudo-albinos’ restricted immune response showed upregulated genes linked to cancer development, signaling and response, skin tissue formation, regeneration, and healing. The data indicate that a modified skin collagen structure likely affects melanocyte differentiation and distribution, generating the pseudo-albino phenotype. In addition, the comparison of the brain transcriptome revealed changes in myelination and melanocyte stem cell activity, which may indicate modified brain function, reduced melanocyte migration, and impaired vision.

CXCL9, IL2RB, and SPP1, potential diagnostic biomarkers in the co-morbidity pattern of atherosclerosis and non-alcoholic steatohepatitis

Non-alcoholic steatohepatitis (NASH) is a hepatocyte inflammation based on hepatocellular steatosis, yet there is no effective drug treatment. Atherosclerosis (AS) is caused by lipid deposition in the endothelium, which can lead to various cardiovascular diseases. NASH and AS share common risk factors, and NASH can also elevate the risk of AS, causing a higher morbidity and mortality rate for atherosclerotic heart disease. Therefore, timely detection and diagnosis of NASH and AS are particularly important. In this study, differential gene expression analysis and weighted gene co-expression network analysis were performed on the AS (GSE100927) and NASH (GSE89632) datasets to obtain common crosstalk genes, respectively. Then, candidate Hub genes were screened using four topological algorithms and externally validated in the GSE43292 and GSE63067 datasets to obtain Hub genes. Furthermore, immune infiltration analysis and gene set variation analysis were performed on the Hub genes to explore the underlying mechanisms. The DGIbd database was used to screen candidate drugs for AS and NASH. Finally, a NASH model was constructed using free fatty acid-induced human L02 cells, an AS model was constructed using lipopolysaccharide-induced HUVECs, and a co-morbidity model was constructed using L02 cells and HUVECs to verify Hub gene expression. The result showed that a total of 113 genes common to both AS and NASH were identified as crosstalk genes, and enrichment analysis indicated that these genes were mainly involved in the regulation of immune and metabolism-related pathways. 28 candidate Hub genes were screened according to four topological algorithms, and CXCL9, IL2RB, and SPP1 were identified as Hub genes after in vitro experiments and external dataset validation. The ROC curves and SVM modeling demonstrated the good diagnostic efficacy of these three Hub genes. In addition, the Hub genes are strongly associated with immune cell infiltration, especially macrophages and γ–δ T cell infiltration. Finally, five potential therapeutic drugs were identified. has-miR-185 and hsa-miR-335 were closely related to AS and NASH. This study demonstrates that CXCL9, IL2RB, and SPP1 may serve as potential biomarkers for the diagnosis of the co-morbidity patterns of AS and NASH and as potential targets for drug therapy.

An integrated bioinformatics analysis to identify the shared biomarkers in patients with obstructive sleep apnea syndrome and nonalcoholic fatty liver disease

BackgroundObstructive sleep apnea (OSA) syndrome and nonalcoholic fatty liver disease (NAFLD) have been shown to have a close association in previous studies, but their pathogeneses are unclear. This study explores the molecular mechanisms associated with the pathogenesis of OSA and NAFLD and identifies key predictive genes.MethodsUsing the Gene Expression Omnibus (GEO) database, we obtained gene expression profiles GSE38792 for OSA and GSE89632 for NAFLD and related clinical characteristics. Mitochondrial unfolded protein response-related genes (UPRmtRGs) were acquired by collating and collecting UPRmtRGs from the GeneCards database and relevant literature from PubMed. The differentially expressed genes (DEGs) associated with OSA and NAFLD were identified using differential expression analysis. Gene Set Enrichment Analysis (GSEA) was conducted for signaling pathway enrichment analysis of related disease genes. Based on the STRING database, protein–protein interaction (PPI) analysis was performed on differentially co-expressed genes (Co-DEGs), and the Cytoscape software (version 3.9.1) was used to visualize the PPI network model. In addition, the GeneMANIA website was used to predict and construct the functional similar genes of the selected Co-DEGs. Key predictor genes were analyzed using the receiver operating characteristic (ROC) curve.ResultsThe intersection of differentially expressed genes shared between OSA and NAFLD-related gene expression profiles with UPRmtRGs yielded four Co-DEGs: ASS1, HDAC2, SIRT3, and VEGFA. GSEA obtained the relevant enrichment signaling pathways for OSA and NAFLD. PPI network results showed that all four Co-DEGs interacted (except for ASS1 and HDAC2). Ultimately, key predictor genes were selected in the ROC curve, including HDAC2 (OSA: AUC = 0.812; NAFLD: AUC = 0.729), SIRT3 (OSA: AUC = 0.775; NAFLD: AUC = 0.750), and VEGFA (OSA: AUC = 0.812; NAFLD: AUC = 0.861) (they have a high degree of accuracy in predicting whether a subject will develop two diseases).ConclusionIn this study, four co-expression differential genes for OSA and NAFLD were obtained, and they can predict the occurrence of both diseases. Transcriptional mechanisms involved in OSA and NAFLD interactions may be better understood by exploring these key genes. Simultaneously, this study provides potential diagnostic and therapeutic markers for patients with OSA and NAFLD.

Identification of the CDH18 gene associated with age-related macular degeneration using weighted gene co-expression network analysis

Purpose: Age-related macular degeneration (AMD) is a chronic and progressive macular degenerative disease that culminates in a gradual deterioration of central vision. Despite its prevalence, the key biomarkers for AMD have not yet been fully elucidated. In this study, we aimed to efficiently identify biomarkers crucial for diagnosing AMD.Methods: Three datasets pertaining to retinal pigment epithelium (RPE)/choroid tissues associated with AMD were selected from the GEO database. The GSE50195 dataset was utilized to conduct weighted gene co-expression network analysis (WGCNA) for identifying module genes linked to AMD. KEGG and GO enrichment analyses were subsequently conducted on these module genes. GSE29801 and GSE135092 datasets were subjected to differential expression analysis to pinpoint the DEGs intersecting with the module genes. Subsequently, wet AMD (wAMD) and dry AMD (dAMD) mouse models were developed, from which RPE/choroid tissues were harvested to validate the hub genes via RT-qPCR and Western blot.Results: Using the WGCNA, we selected the “antiquewhite4” module (r = 0.91 and p = 7e-07), which contains a total of 325 genes. Through the intersection of module genes with DEGs, nine hub genes were identified. Pathways involved in complement and coagulation cascades, ECM–receptor interactions, unsaturated fatty acid biosynthesis, and fatty acid elongation play important roles in AMD. Notably, CDH18 demonstrated notable variance across all three datasets. Post validation using RT-qPCR experiments revealed a significant downregulation of CDH18 in both dAMD and wAMD. EGLN3 was expressed at low levels in wAMD. In dAMD, EYA2, LTB, and PODXL were significantly downregulated, whereas APOC1 was notably upregulated. Western blot confirmed that CDH18 was lowly expressed in dAMD and wAMD mouse models.Conclusion: CDH18 was identified as the key gene involved in the pathogenesis of AMD. An imbalance of the complement and coagulation cascades is a potential mechanism of AMD. This study provides a novel idea for diagnosing and treating AMD in the future.

Genetic diversity and population genetics of Schistosoma haematobium isolated from children in Lusaka and Siavonga districts, Zambia.

Urogenital schistosomiasis remains a pervasive health challenge in rural Zambian communities. This study explores the molecular epidemiology and genetic diversity of Schistosoma haematobium using mitochondrial genes (cox1 and nadh1). Urine samples from 421 children in Siavonga and Lusaka districts, Zambia, were collected between December 2020 and February 2022. Microscopy and DNA extraction facilitated the identification of S. haematobium, followed by amplification, sequencing, and phylogenetic analysis of cox1 and nadh1 genes. Phylogenetic analysis revealed clustering with samples from mainland African countries, emphasizing shared haplotypes. Both mitochondrial genes exhibited substantial diversity, with 5 haplotypes from 37 cox1 sequences and 12 haplotypes from 23 nadh1 sequences. High haplotype diversity (0.621-0.808) and low nucleotide diversity (0.00181-0.03288) were observed. Siavonga and Lusaka districts shared the majority of S. haematobium haplotypes. Molecular variance and genetic differentiation analysis indicated variations within populations rather than between populations (cox1: -0.025, nadh1: 0.01646). These findings suggest a limited differentiation between S. haematobium populations in Siavonga and Lusaka, potentially indicating gene flow. Tajima's test revealed negative values, indicating a departure from neutrality, introduction of rare alleles, and recent population expansion. This study contributes essential insights into S. haematobium population genetics, crucial for effective urogenital schistosomiasis control in Zambia.

Machine Learning for Early Discrimination Between Lung Cancer and Benign Nodules Using Routine Clinical and Laboratory Data.

Lung cancer poses a global health threat necessitating early detection and precise staging for improved patient outcomes. This study focuses on developing and validating a machine learning-based risk model for early lung cancer screening and staging, using routine clinical data. Two medical center, observational, retrospective studies were conducted, involving 2312 lung cancer patients and 653 patients with benign nodules. Machine learning techniques, including differential analysis and feature selection, were employed to identify key factors for modeling. The study focused on variables such as nodule density, carcinoembryonic antigen (CEA), age, and lifestyle habits. The Logistic Regression model was utilized for early diagnoses, and the XGBoost model was utilized for staging based on selected features. For early diagnoses, the Logistic Regression model achieved an area under the curve (AUC) of 0.716 (95% confidence interval [CI] 0.607-0.826), with 0.703 sensitivity and 0.654 specificity. The XGBoost model excelled in distinguishing late-stage from early-stage lung cancer, exhibiting an AUC of 0.913 (95% CI 0.862-0.963), with 0.909 sensitivity and 0.814 specificity. These findings highlight the model's potential for enhancing diagnostic accuracy and staging in lung cancer. This study introduces a novel machine learning-based risk model for early lung cancer screening and staging, leveraging routine clinical information and laboratory data. The model shows promise in enhancing accuracy, mitigating overdiagnosis, and improving patient outcomes.

Investigation into the role of H2-Ab1 in vascular remodeling in pulmonary arterial hypertension via Bioinformatics

BackgroundPulmonary arterial hypertension (PAH) is a progressive disease of vascular remodeling characterized by persistent pulmonary arterial pressure elevation, which can lead to right heart failure and premature death. Given the complex pathogenesis and poor prognosis of PAH, the identification and investigation of biomarkers become increasingly critical for advancing further understanding of the disease.MethodsPAH-related datasets, GSE49114, GSE180169 and GSE154959, were downloaded from the publicly available GEO database. By performing WGCNA on the GSE49114 dataset, a total of 906 PAH-related key module genes were screened out. By carrying out differential analysis on the GSE180169 dataset, a total of 576 differentially expressed genes were identified. Additionally, the GSE154959 single-cell sequencing dataset was also subjected to differential analysis, leading to the identification of 34 DEGs within endothelial cells. By taking intersection of the above three groups of DEGs, five PAH-related hub genes were screened out, namely Plvap, Cyp4b1, Foxf1, H2-Ab1, and H2-Eb1, among which H2-Ab1 was selected for subsequent experiments.ResultsA SuHx mouse model was prepared using the SU5416/hypoxia method, and the successful construction of the model was evaluated through Hematoxylin-Eosin staining, hemodynamic detection, fulton index, and Western Blot (WB). The results of WB and qRT-PCR demonstrated a significant upregulation of H2-Ab1 expression in SuHx mice. Consistent with the results of bioinformatics analysis, a time-dependent increase was observed in H2-Ab1 expression in hypoxia-treated mouse pulmonary artery endothelial cells (PAECs). To investigate whether H2-Ab1 affects the development and progression of PAH, we knocked down H2-Ab1 expression in PAECs, and found that its knockdown inhibited the viability, adhesion, migration, and angiogenesis, while concurrently promoted the apoptosis of PAECs.ConclusionH2-Ab1 could regulate the proliferation, apoptosis, adhesion, migration, and angiogenesis of PAECs.

Gut microbiota profiling reflects the renal dysfunction and psychological distress in patients with diabetic kidney disease

BackgroundThe gut microbiota plays a pivotal role in the development of diabetes and kidney disease. However, it is not clear how the intestinal microecological imbalance is involved in the context of diabetic kidney disease (DKD), the leading cause of renal failure.ObjectivesTo elucidate the gut microbial signatures associated with DKD progression towards end-stage renal disease (ESRD) and explore whether they could reflect renal dysfunction and psychological distress.MethodsA cross-sectional study was conducted to explore the gut microbial signatures of 29 DKD non-ESRD patients and 19 DKD ESRD patients compared to 20 healthy controls. Differential analysis was performed to detect distinct gut microbial alterations in diversities and taxon abundance of DKD with and without ESRD. Renal dysfunction was estimated by urea, creatinine, and estimated glomerular filtration rate. Psychological distress was assessed using the Self-Rating Anxiety Scale, Self-Rating Depression Scale, Hamilton Anxiety Rating Scale, and Hamilton Depression Rating Scale.ResultsAlpha diversity indexes were reduced in DKD patients, particularly those with ESRD. Beta diversity analysis revealed that the gut microbial compositions of DKD patients were different with healthy individuals whereas similar compositions were observed in DKD patients. Taxon differential analysis showed that when compared with the controls, DKD patients exhibit distinct microbial profiles including reduced abundances of butyrate-produced, anti-inflammatory bacteria Faecalibacterium, Lachnospira, Roseburia Lachnoclostridium, and increased abundances of pro-inflammatory bacteria Collinsella, Streptococcus etc. These distinctive genera presented consistent associations with renal dysfunction, as well as psychological distress, especially in DKD patients.ConclusionsDKD patients, especially those who have progressed to ESRD, exhibit unique characteristics in their gut microbiota that are associated with both renal dysfunction and psychological distress. The gut microbiota may be a significant factor in the deterioration of DKD and its eventual progression to ESRD.

Molecular analyses indicate profuse bacterial diversity in primary and post- treatment endodontic infections within a cohort from the United Arab Emirates-A preliminary study.

Endodontic microbiota appears to undergo evolutionary changes during disease progression from inflammation to necrosis and post-treatment. The aim of this study was to compare microbiome composition and diversity in primary and post-treatment endodontic infections from a cohort of patients from the UAE. Intracanal samples were collected from primarily infected (n = 10) and post-treatment infected (n = 10) root canals of human teeth using sterile paper points. Bacterial DNA was amplified from seven hypervariable regions (V2-V4 and V6-V9) of the 16S rRNA gene, then sequenced using next-generation sequencing technology. The data was analyzed using appropriate bioinformatic tools. Analyses of all the samples revealed eight major bacterial phyla, 112 genera and 260 species. Firmicutes was the most representative phylum in both groups and was significantly more abundant in the post-treatment (54.4%) than in primary (32.2%) infections (p>0.05). A total of 260 operational taxonomic units (OTUs) were identified, of which 126 (48.5%) were shared between the groups, while 83 (31.9%) and 51 (19.6%) disparate species were isolated from primary and post-treatment infections, respectively. A significant difference in beta, but not alpha diversity was noted using several different indices (p< 0.05). Differential abundance analysis indicated that, Prevotella maculosa, Streptococcus constellatus, Novosphigobium sediminicola and Anaerococcus octavius were more abundant in primary infections while Enterrococcus faecalis, Bifidobacterium dentium, Olsenella profusa and Actinomyces dentalis were more abundant in post-treatment infections (p <0.05). Significant differences in the microbiome composition and diversity in primary and post-treatment endodontic infections were noted in our UAE cohort. Such compositional differences of microbiota at various stages of infection could be due to both intrinsic and extrinsic factors impacting the root canal ecosystem during disease progression, as well as during their therapeutic management. Identification of the key microbiota in primarily and secondarily infected root canals can guide in the management of these infections.

Early changes of microRNAs in blood one month after bariatric surgery

BackgroundChanges in microRNAs (miRNAs) are relevant to bariatric surgery and its comorbidities. The characteristics of changes in miRNAs of the early postoperative period following both bariatric procedures, sleeve gastrectomy (SG) and Roux-en-Y gastric bypass (RYGB), as well as the factors that related to the effectiveness of early weight loss remain unclear.MethodsWe recruited 18 patients who performed SG and 15 patients who performed RYGB. Their preoperative and 1-month postoperative clinical data and fasting serum samples were collected, and the latter were analyzed by RNA-sequencing. Differential expression analysis of miRNAs was performed by the R-tool. Functional classification annotation and pathway enrichment analysis of targeted genes were analyzed by KOBAS software. The change profiles of miRNAs for both surgeries and their correlation with clinical characteristics and weight loss effectiveness were further analyzed.ResultsA total of 85 differentially expressed miRNAs were identified before and after SG, while a total of 76 were found before and after RYGB. The target genes of these miRNAs were similar in the Gene Ontology enrichment analysis in SG and RYGB, and the enrichment analysis in the Kyoto Encyclopedia of Genes and Genomes was mainly related to metabolic pathways. Hsa-miR-493-5p, hsa-miR-184, and hsa-miR-3199 exhibited similar changes in SG and RYGB, and the former two were correlated with clinical characteristics. Hsa-miR-6729-5p, hsa-miR-4659b-5p, and hsa-miR-2277-5p were correlated with the weight loss effectiveness of SG, while hsa-miR-4662a-5p was correlated with the weight loss effectiveness of RYGB.ConclusionsShort-term metabolic improvement and weight loss occurring after SG and RYGB surgery might be related to changes in miRNAs, which act on multiple biological pathways by regulating genes. In addition, some clinical characteristics and miRNAs were related to the effectiveness of early weight loss after SG and RYGB surgery.Clinical Trial RegistrationChiCTR2200058333.

Network pharmacology and molecular dynamics study of the effect of the Astragalus-Coptis drug pair on diabetic kidney disease

BACKGROUND Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease. The Astragalus -Coptis drug pair is frequently employed in the management of DKD. However, the precise molecular mechanism underlying its therapeutic effect remains elusive. AIM To investigate the synergistic effects of multiple active ingredients in the Astragalus-Coptis drug pair on DKD through multiple targets and pathways. METHODS The ingredients of the Astragalus -Coptis drug pair were collected and screened using the TCMSP database and the SwissADME platform. The targets were predicted using the SwissTargetPrediction database, while the DKD differential gene expression analysis was obtained from the Gene Expression Omnibus database. DKD targets were acquired from the GeneCards, Online Mendelian Inheritance in Man database, and DisGeNET databases, with common targets identified through the Venny platform. The protein-protein interaction network and the “disease-active ingredient-target” network of the common targets were constructed utilizing the STRING database and Cytoscape software, followed by the analysis of the interaction relationships and further screening of key targets and core active ingredients. Gene Ontology (GO) function and Kyoto Ency-clopedia of Genes and Genomes (KEGG) pathway enrichments were performed using the DAVID database. The tissue and organ distributions of key targets were evaluated. PyMOL and AutoDock software validate the molecular docking between the core ingredients and key targets. Finally, molecular dynamics (MD) simulations were conducted to simulate the optimal complex formed by interactions between core ingredients and key target proteins. RESULTS A total of 27 active ingredients and 512 potential targets of the Astragalus -Coptis drug pair were identified. There were 273 common targets between DKD and the Astragalus -Coptis drug pair. Through protein-protein interaction network topology analysis, we identified 9 core active ingredients and 10 key targets. GO and KEGG pathway enrichment analyses revealed that Astragalus -Coptis drug pair treatment for DKD involves various biological processes, including protein phosphorylation, negative regulation of apoptosis, inflammatory response, and endoplasmic reticulum unfolded protein response. These pathways are mainly associated with the advanced glycation end products (AGE)-receptor for AGE products signaling pathway in diabetic complications, as well as the Lipid and atherosclerosis. Molecular docking and MD simulations demonstrated high affinity and stability between the core active ingredients and key targets. Notably, the quercetin-AKT serine/threonine kinase 1 (AKT1) and quercetin-tumor necrosis factor (TNF) protein complexes exhibited exceptional stability. CONCLUSION This study demonstrated that DKD treatment with the Astragalus -Coptis drug pair involves multiple ingredients, targets, and signaling pathways. We propose a novel approach for investigating the molecular mechanism underlying the therapeutic effects of the Astragalus -Coptis drug pair on DKD. Furthermore, we suggest that quercetin is the most potent active ingredient and specifically targets AKT1 and TNF, providing a theoretical foundation for further exploration of pharmacologically active ingredients and elucidating their molecular mechanisms in DKD treatment.

Exome sequencing confirms the clinical diagnosis of both joubert syndrome and klinefelter syndrome with keratoconus in a han Chinese family

IntroductionJoubert syndrome a rare genetic disorder, is characterized by abnormalities in the development of the central nervous system with “molar signs” on magnetic resonance imaging of the brain and accompanied by cerebellar vermis hypoplasia, ataxia, hypotonia, and developmental delay. Keratoconus (KC) is a kind of genetically predisposed eye disease that causes blindness characterized by a dilated thinning of the central or paracentral cornea conically projected forward, highly irregular astigmatism, and severe visual impairment. Klinefelter syndrome is caused by an extra X chromosome in the cells of male patients, and the main phenotype is tall stature and dysplasia with secondary sex characteristics. This study was intended to identify the genetic etiology and determine the clinical diagnosis of one Han Chinese family with specific clinical manifestations of keratoconus and multiorgan involvement.MethodsA comprehensive ocular and related general examination was performed on one patient and his asymptomatic parents and brother. Pathogenic genes were tested by exome sequencing. CNV-seq was used to verify the copy number variation, and peripheral blood was cultured for karyotype analysis. The pathogenicity of the identified variant was determined subject to ACMG guidelines. The Gene Expression Omnibus (GEO) dataset of keratoconus-related genes in the NCBI database was obtained to analyze the differentially expressed genes in corneal tissues of the keratoconus group and the normal control group, and analysis of protein-protein interaction networks (PPI) was performed.ResultsProband, a 25-year-old male, had sudden loss of vision in the left eye for 1 week. Best corrected visual acuity (BCVA): 0.5 (−1.00DS/-5.00DC*29°) in the right eye, counting fingers/40 cm in the left eye. Slit-lamp microscopy of the right eye showed mild anterior protrusion of the cornea and thinning of the cone-topped cornea. The left eye showed marked thinning of the central region of the cornea, rounded edema in the form of a cone-like bulge, epithelial bullae, edema and turbidity of the stroma, and bulging of the Descemet’s membrane. Cranial magnetic resonance imaging (MRI) revealed changes in the midbrain and cerebellum, with a “molar sign” and a “bat-winged” ventriculus quartus cerebri. General check-up: 168 cm in height, decreased muscle tone in all four limbs, knee jerk elicited, negative Babinski sign, abdominal reflexes elicited, finger-to-nose test positive, intentional tremor evident in both hands, positive Romberg’s sign, instability of gait, level I intellectual disability, poor adaptive behavior, communication disorders, teeth all dentures, a peculiar face with blepharophimosis, wide inner canthus distance, mild ptosis, severe positive epicanthus, high palatal arches, exotropia, hypotrichosis of beard and face, inconspicuous prominentia laryngea, and short upper and lower limbs. Exome sequencing detected compound heterozygous frameshift variants M1:c.9279dup:p.His3094Thrfs*18 and M2:c.6515_6522del:p.Lys2172Thrfs*37 in the patient’s CPLANE1 gene and the presence of duplication-type CNV on the X chromosome. Sanger sequencing showed that the mother and father carried the M1 and M2 variants, respectively, and the younger brother carried the M2 variant, which was a novel variant. CNV-seq analysis showed the presence of a duplication-type CNV Xp22.33-Xq28 (2757837-156030895) of approximately 155 Mb on the X chromosome of the proband, which was a de novo variant and carried by neither of the parents. The two heterozygous frameshift variants and duplication-type CNV were pathogenic according to the ACMG guidelines. Differential expression analysis of keratoconus-related genes showed that CPLANE1 was upregulated in the corneal tissues of keratoconus patients compared with normal controls, and such a difference was statistically significant (p = 0.000515, &amp;lt;0.05). PPI analysis showed that the CPLANE1-NPHP3 complex protein acted as a bridge between cilia and extracellular matrix tissue. According to the genetic test results and clinical phenotype analysis, the family was finally diagnosed with Joubert syndrome combined with Keratoconus and Klinefelter syndrome.DiscussionIn this study, we report a proband in a Han Chinese family with both Joubert syndrome and X-linked Klinefelter syndrome as well as keratoconus, and the phenotype spectrum of CPLANE1-Joubert syndrome may be expanded accordingly. Meanwhile, the significance of exome sequencing was emphasized in aiding the clinical diagnosis of complex cases, which is difficult to make.

Carnitine O-octanoyltransferase (CROT) deficiency in mice leads to an increase of omega-3 fatty acids

Introduction: Carnitine O-octanoyltransferase (CROT) is a well-established peroxisomal enzyme involved in liver fatty acid oxidation, but less is known about its recently discovered role in promoting vascular calcification, and whether CROT-dependent liver metabolism contributes to the latter. To date, CROT function in the context of calcification potential has been conducted in the dyslipidemic low-density lipoprotein receptor-deficient (Ldlr−/−) mice.Objectives: To differentiate peroxisome and CROT-dependent lipid biology from that of lipoprotein-mediated lipid biology, we therefore conducted a metabolomic analysis of the liver and plasma of normolipidemic CROT-deficient (Crot−/−) mice.Methods: We performed LC-MS-based metabolomics on liver and plasma derived from Crot−/− and Crot +/− mice and sibling Crot+/+ mice, using a dual-phase metabolite extraction protocol, and multiple LC-MS acquisition strategies.Results: We identified between 79 to 453 annotated metabolites from annotated metabolites from liver samples, and 117 to 424 annotated metabolites from plasma samples. Through differential abundance analysis, we determined that omega-3 fatty acids such as EPA, DPA, and DHA were higher in the liver of Crot−/− and Crot +/− mice than Crot+/+ mice. EPA were higher in plasma of Crot−/− mice than Crot+/+ mice. We also determined that the anti-inflammatory dicarboxylic acids, tetradecanedioic acid and azelaic acid, were higher in the plasma of CROT-deficient mice.Conclusion: Our study associated genetic CROT deletion with increased levels of anti-inflammatory molecules in mouse liver and plasma. These results suggest a potential mechanism for anti-calcification effects of CROT suppression and the potential use of omega-3 fatty acids as biomarkers for future CROT inhibition therapies.