The Role of Metabolomics and Microbiology in Urinary Tract Infection.

One of the common illnesses that affect women's physical and mental health is urinary tract infection (UTI). The disappointing results of empirical anti-infective treatment and the lengthy time required for urine bacterial culture are two issues. Antibiotic misuse is common, especially in females who experience recurrent UTI (rUTI). This leads to a higher prevalence of antibiotic resistance in the microorganisms that cause the infection. Antibiotic therapy will face major challenges in the future, prompting clinicians to update their practices. New testing techniques are making the potential association between the urogenital microbiota and UTIs increasingly apparent. Monitoring changes in female urinary tract (UT) microbiota, as well as metabolites, may be useful in exploring newer preventive treatments for UTIs. This review focuses on advances in urogenital microbiology and organismal metabolites relevant to the identification and handling of UTIs in an attempt to provide novel methods for the identification and management of infections of the UT. Particular attention is paid to the microbiota and metabolites in the patient's urine in relation to their role in supporting host health.

Electrical stimulation induced structural 3D human engineered neural tissue with well-developed neuronal network and functional connectivity.

Objective.Three-dimensional (3D) neural tissue engineering is expected to provide new stride in developing neural disease models and functional substitutes to aid in the treatment of central nervous system injury. We have previously detailed an electrical stimulation (ES) system to generate 3D mouse engineered neural tissue (mENT)in vitro. However, ES-induced human ENT (hENT) has not previously been either investigated or identified in structural and functional manner. Here, we applied ES as a stimulator to regulate human neural stem cells in 3D Matrigel, explored the components and functional properties of hENTs.Approach.By immunofluorescence chemical staining and electron microscope imaging, we evaluated the effects of ES on (1) neuronal differentiation and maturation, (2) neurites outgrowth and alignment in hENT, (3) formation of synapses and myelin sheaths in hENT. We further investigated the formation of synaptic connections betweenex-vivo-fused mouse and human tissue. We used calcium imaging to detect activities of neurons in hENT culture.Results.ES could induce neuronal differentiation, the orderly growth of neurites and the maturation of neuron subtypes to construct a well-developed neuronal network with synapses and myelin sheaths. Most importantly, we discovered that raising extracellular K+concentration resulted the increasing neuronal excitability in the hENT, indicating electrical activities in neuronal cells.Significance.We applied ES to generate the organised 3D hENTs and identified them in both structural and functional manner.

Integrating microbiome and metabolome revealed microbe-metabolism interactions in the stomach of patients with different severity of peptic ulcer disease.

Background: Peptic ulcer disease (PUD) is a multi-cause illness with an unknown role for gastric flora and metabolism in its pathogenesis. In order to further understand the pathogenesis of gastric flora and metabolism in PUD, this study used histological techniques to analyze the microbiome and metabolome of gastric biopsy tissue. In this paper, our work described the complex interactions of phenotype-microbial-metabolite-metabolic pathways in PUD patients at different pathological stages. Methods: Gastric biopsy tissue samples from 32 patients with chronic non-atrophic gastritis, 24 patients with mucosal erosions, and 8 patients with ulcers were collected for the microbiome. UPLC-MS metabolomics was also used to detect gastric tissue samples. These datasets were analyzed individually and integrated using various bioinformatics methods. Results: Our work found reduced diversity of gastric flora in patients with PUD. PUD patients at different pathological stages presented their own unique flora, and there were significant differences in flora phenotypes. Coprococcus_2, Phenylobacterium, Candidatus_Hepatoplasma, and other bacteria were found in the flora of people with chronic non-atrophic gastritis (HC). The representative flora of mucosal erosion (ME) had uncultured_bacterium_c_Subgroup_6, Sphingomonadaceae, Xanthobacteraceae, and uncultured_bacterium_f_Xanthobacteraceae. In comparison, the characteristic flora of the PUD group was the most numerous and complex, including Ruminococcus_2, Agathobacter, Alistipes, Helicobacter, Bacteroides and Faecalibacterium. Metabolomics identified and annotated 66 differential metabolites and 12 significantly different metabolic pathways. The comprehensive analysis correlated microorganisms with metabolites at different pathological stages and initially explored the complex interactions of phenotype-microbial-metabolite-metabolic pathways in PUD patients at different pathological stages. Conclusion: Our research results provided substantial evidence to support some data on the analysis of the microbial community and its metabolism in the stomach, and they demonstrated many specific interactions between the gastric microbiome and the metabolome. Our study can help reveal the pathogenesis of PUD and indicate plausible disease-specific mechanisms for future studies from a new perspective.

The Abundance and Function of Neutrophils in the Endometriosis Systemic and Pelvic Microenvironment.

Endometriosis is a common inflammatory illness in which endometrial tissue grows outside the uterine cavity. Immune dysfunction is now widely acknowledged as the primary cause of endometriosis. The immune cell population represented by neutrophils is thought to play an essential role in the etiology, pathophysiology, and associated clinical outcome. There is growing evidence that neutrophils have a role in chronic and aseptic inflammatory diseases, and endometriosis patients have increased levels of neutrophils in plasma, peritoneal fluid, and ectopic endometrium. Here, we sought to review the function of neutrophils in the pathogenesis of endometriosis, with an emphasis on the role of neutrophils in regulating endometrial angiogenesis and the local inflammatory microenvironment.

From 2D to 3D Co-Culture Systems: A Review of Co-Culture Models to Study the Neural Cells Interaction.

The central nervous system (CNS) controls and regulates the functional activities of the organ systems and maintains the unity between the body and the external environment. The advent of co-culture systems has made it possible to elucidate the interactions between neural cells in vitro and to reproduce complex neural circuits. Here, we classified the co-culture system as a two-dimensional (2D) co-culture system, a cell-based three-dimensional (3D) co-culture system, a tissue slice-based 3D co-culture system, an organoid-based 3D co-culture system, and a microfluidic platform-based 3D co-culture system. We provide an overview of these different co-culture models and their applications in the study of neural cell interaction. The application of co-culture systems in virus-infected CNS disease models is also discussed here. Finally, the direction of the co-culture system in future research is prospected.

Laminar shear stress inhibits inflammation by activating autophagy in human aortic endothelial cells through HMGB1 nuclear translocation.

Prevention and treatment of atherosclerosis (AS) by targeting the inflammatory response in vascular endothelial cells has attracted much attention in recent years. Laminar shear stress (LSS) has well-recognized anti-AS properties, however, the exact molecular mechanism remains unclear. In this study, we found that LSS could inhibit the increased expression of intercellular adhesion molecule-1 (ICAM-1), vascular cell adhesion molecule-1 (VCAM-1), cyclooxygenase-2 (COX-2), and matrix metallopeptidase-9 (MMP-9) caused by TNF-α in an autophagy-dependent pathway in human aortic endothelial cells (HAECs) and human umbilical vein endothelial cells (HUVECs). Whole-transcriptome sequencing analysis revealed that erythropoietin-producing hepatocyte receptor B2 (EPHB2) was a key gene in response to LSS. Moreover, co-immunoprecipitation assay indicated that LSS could enhance the EPHB2-mediated nuclear translocation of high mobility group box-1 (HMGB1), which interacts with Beclin-1 (BECN1) and finally leads to autophagy. Simultaneously, we identified an LSS-sensitive long non-coding RNA (lncRNA), LOC10798635, and constructed an LSS-related LOC107986345/miR-128-3p/EPHB2 regulatory axis. Further research revealed the anti-inflammatory effect of LSS depends on autophagy activation resulting from the nuclear translocation of HMGB1 via the LOC107986345/miR-128-3p/EPHB2 axis. Our study demonstrates that LSS could regulate the expression of EPHB2 in HAECs, and the LOC107986345/miR-128-3p/EPHB2 axis plays a vital role in AS development.

Physiological Electric Field: A Potential Construction Regulator of Human Brain Organoids.

Brain organoids can reproduce the regional three-dimensional (3D) tissue structure of human brains, following the in vivo developmental trajectory at the cellular level; therefore, they are considered to present one of the best brain simulation model systems. By briefly summarizing the latest research concerning brain organoid construction methods, the basic principles, and challenges, this review intends to identify the potential role of the physiological electric field (EF) in the construction of brain organoids because of its important regulatory function in neurogenesis. EFs could initiate neural tissue formation, inducing the neuronal differentiation of NSCs, both of which capabilities make it an important element of the in vitro construction of brain organoids. More importantly, by adjusting the stimulation protocol and special/temporal distributions of EFs, neural organoids might be created following a predesigned 3D framework, particularly a specific neural network, because this promotes the orderly growth of neural processes, coordinate neuronal migration and maturation, and stimulate synapse and myelin sheath formation. Thus, the application of EF for constructing brain organoids in a3D matrix could be a promising future direction in neural tissue engineering.

Construction and Investigation of Competing Endogenous RNA Networks and Candidate Genes Involved in SARS-CoV-2 Infection.

INTRODUCTION: The current COVID-19 pandemic caused by a novel coronavirus SARS-CoV-2 is a quickly developing global health crisis, yet the mechanisms of pathogenesis in COVID-19 are not fully understood. METHODS: The RNA sequencing data of SARS-CoV-2-infected cells was obtained from the Gene Expression Omnibus (GEO). The differentially expressed mRNAs (DEmRNAs), long non-coding RNAs (DElncRNAs), and microRNAs (DEmiRNAs) were identified by edgeR, and the SARS-CoV-2-associated competing endogenous RNA (ceRNA) network was constructed based on the prediction of bioinformatic databases. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted with the SARS-CoV-2-related DEmRNAs, and the protein-protein interaction network was also built basing on STRING database. The ROC analysis was performed for assessing the diagnostic efficiency of hub genes. RESULTS: The results indicated that SARS-CoV-2-related DEmRNAs were associated with the interferon signaling pathway and other antiviral processes, such as IFNL3, IFNL1 and CH25H. Our analysis suggested that lncRNA NEAT1 might regulate the host immune response through two miRNAs, hsa-miR-374-5p and hsa-miR-155-5p, which control the expression of SOCS1, IL6, IL1B, CSF1R, CD274, TLR6, and TNF. Additionally, IFI6, HRASLS2, IGFBP4 and PTN may be potential targets based on an analysis comparing the transcriptional responses of SARS-CoV-2 infection with that of other respiratory viruses. DISCUSSION: The unique ceRNA network identified potential non-coding RNAs and their possible targets as well as a new perspective to understand the molecular mechanisms of the host immune response to SARS-CoV-2. This study may also aid in the development of innovative diagnostic and therapeutic strategies.

Airway Microbiome and Serum Metabolomics Analysis Identify Differential Candidate Biomarkers in Allergic Rhinitis.

Allergic rhinitis (AR) is a common heterogeneous chronic disease with a high prevalence and a complex pathogenesis influenced by numerous factors, involving a combination of genetic and environmental factors. To gain insight into the pathogenesis of AR and to identity diagnostic biomarkers, we combined systems biology approach to analyze microbiome and serum composition. We collected inferior turbinate swabs and serum samples to study the microbiome and serum metabolome of 28 patients with allergic rhinitis and 15 healthy individuals. We sequenced the V3 and V4 regions of the 16S rDNA gene from the upper respiratory samples. Metabolomics was used to examine serum samples. Finally, we combined differential microbiota and differential metabolites to find potential biomarkers. We found no significant differences in diversity between the disease and control groups, but changes in the structure of the microbiota. Compared to the HC group, the AR group showed a significantly higher abundance of 1 phylum (Actinobacteria) and 7 genera (Klebsiella, Prevotella and Staphylococcus, etc.) and a significantly lower abundance of 1 genus (Pelomonas). Serum metabolomics revealed 26 different metabolites (Prostaglandin D2, 20-Hydroxy-leukotriene B4 and Linoleic acid, etc.) and 16 disrupted metabolic pathways (Linoleic acid metabolism, Arachidonic acid metabolism and Tryptophan metabolism, etc.). The combined respiratory microbiome and serum metabolomics datasets showed a degree of correlation reflecting the influence of the microbiome on metabolic activity. Our results show that microbiome and metabolomics analyses provide important candidate biomarkers, and in particular, differential genera in the microbiome have also been validated by random forest prediction models. Differential microbes and differential metabolites have the potential to be used as biomarkers for the diagnosis of allergic rhinitis.

Integrated analysis of a ceRNA network reveals potential prognostic lncRNAs in gastric cancer.

Long noncoding RNAs (lncRNAs) have important biological functions as competing endogenous RNAs (ceRNAs) in tumors, yet the functions and regulatory mechanisms of lncRNA-related ceRNAs in gastric cancer have not been fully elucidated. In this study, we constructed a lncRNA-miRNA-mRNA ceRNA network and identified potential lncRNA biomarkers in gastric cancer. Basing on the RNA profiles downloaded from The Cancer Genome Atlas (TCGA) platform, the gastric cancer-specific differentially expressed lncRNAs, miRNAs, and mRNAs were screened for constructing a ceRNA network using bioinformatic tools. The enrichment analysis of the biological processes in Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes pathways was performed on the ceRNA-related DEmRNAs. According to the modularization of protein-protein interaction (PPI) network, we extracted a ceRNA subnetwork and analyzed the correlation between the expression of the lncRNAs involved and specific clinical features of patients. Next, the expression of highly up-regulated in liver cancer (HULC) and RP11-314B1.2 showed significant changes in several pathological processes involved in gastric cancer, and nine lncRNAs were found to be correlated with the overall survival of patients with gastric cancer. Through the univariate and multivariate Cox regression analyses, two lncRNAs (LINC00106 and RP11-999E24.3) were identified and utilized to establish a risk score model for assessing the prognosis of patients. The analysis results were also partially verified using quantitative real-time PCR. The findings from this study indicate that HULC, RP11-314B1.2, LINC00106, and RP11-999E24.3 could be considered as potential therapeutic targets or prognostic biomarkers in gastric cancer, and provide a new perspective for cancer pathogenesis research.

Shear-Sensitive lncRNA AF131217.1 Inhibits Inflammation in HUVECs via Regulation of KLF4.

Atherosclerosis is one of the most common vascular diseases, and inflammation participates in all stages of its progression. Laminar shear stress protects arteries from atherosclerosis and reduces endothelial inflammation. Long noncoding RNAs have emerged as critical regulators in many diseases, including atherosclerosis. However, the expression and functions of long noncoding RNAs subjected to laminar shear stress in endothelial cells remain unclear. This study aimed to reveal the mechanism by which shear stress-regulated long noncoding RNAs contribute to anti-inflammation. In this study, we identified a novel long noncoding RNA AF131217.1, which was upregulated after laminar shear stress treatment in human umbilical vein endothelial cells. Knockdown of AF131217.1 inhibited flow-mediated reduction of monocyte adhesion VCAM-1 (vascular cell adhesion molecule-1) and ICAM-1 (intercellular adhesion molecule-1) expression and inhibited flow-mediated enhancement of flow-responsive expression of KLF (Kruppel-like factor) 2 and eNOS (endothelial NO synthase). Furthermore, TNF-α (tumor necrosis factor-α) was used to induce an inflammatory response in human umbilical vein endothelial cells. Knockdown of AF131217.1 promoted ICAM-1 and VCAM-1 expression, as well as changes in monocyte adhesion and KLF2 and eNOS expression induced by TNF-α. Mechanistic investigations indicated that AF131217.1 acted as a competing endogenous RNA for miR-128-3p, leading to regulation of its target gene KLF4. In conclusion, our study demonstrates for the first time that laminar shear stress regulates the expression of AF131217.1 in human umbilical vein endothelial cells, and the AF131217.1/miR-128-3p/KLF4 axis plays a vital role in atherosclerosis development.

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances.

Biomarker detection is one of the more important biomedical questions for high-throughput 'omics' researchers, and almost all existing biomarker detection algorithms generate one biomarker subset with the optimized performance measurement for a given dataset. However, a recent study demonstrated the existence of multiple biomarker subsets with similarly effective or even identical classification performances. This protocol presents a simple and straightforward methodology for detecting biomarker subsets with binary classification performances, better than a user-defined cutoff. The protocol consists of data preparation and loading, baseline information summarization, parameter tuning, biomarker screening, result visualization and interpretation, biomarker gene annotations, and result and visualization exportation at publication quality. The proposed biomarker screening strategy is intuitive and demonstrates a general rule for developing biomarker detection algorithms. A user-friendly graphical user interface (GUI) was developed using the programming language Python, allowing biomedical researchers to have direct access to their results. The source code and manual of kSolutionVis can be downloaded from http://www.healthinformaticslab.org/supp/resources.php.