Infectious viruses and neurodegenerative diseases: The mitochondrial defect hypothesis.

Global attention is riveted on neurodegenerative diseases due to their unresolved aetiologies and lack of efficacious therapies. Two key factors implicated include mitochondrial impairment and microglial ageing. Several viral infections, including Herpes simplex virus-1 (HSV-1), human immunodeficiency virus (HIV) and Epstein-Barr virus, are linked to heightened risk of these disorders. Surprisingly, numerous studies indicate viruses induce these aforementioned precipitating events. Epstein-Barr virus, Hepatitis C Virus, HIV, respiratory syncytial virus, HSV-1, Japanese Encephalitis Virus, Zika virus and Enterovirus 71 specifically impact mitochondrial function, leading to mitochondrial malfunction. These vital organelles govern various cell activities and, under specific circumstances, trigger microglial ageing. This article explores the role of viral infections in elucidating the pathogenesis of neurodegenerative ailments. Various viruses instigate microglial ageing via mitochondrial destruction, causing senescent microglia to exhibit activated behaviour, thereby inducing neuroinflammation and contributing to neurodegeneration.

Custom scoring based on ecological topology of gut microbiota associated with cancer immunotherapy outcome.

The gut microbiota influences the clinical responses of cancer patients to immunecheckpoint inhibitors (ICIs). However, there is no consensus definition of detrimental dysbiosis. Based on metagenomics (MG) sequencing of 245 non-small cell lung cancer (NSCLC) patient feces, we constructed species-level co-abundance networks that were clustered into species-interacting groups (SIGs) correlating with overall survival. Thirty-seven and forty-five MG species (MGSs) were associated with resistance (SIG1) and response (SIG2) to ICIs, respectively. When combined with the quantification of Akkermansia species, this procedure allowed a person-based calculation of a topological score (TOPOSCORE) that was validated in an additional 254 NSCLC patients and in 216 genitourinary cancer patients. Finally, this TOPOSCORE was translated into a 21-bacterial probe set-based qPCR scoring that was validated in a prospective cohort of NSCLC patients as well as in colorectal and melanoma patients. This approach could represent a dynamic diagnosis tool for intestinal dysbiosis to guide personalized microbiota-centered interventions.

Guild-level signature of gut microbiome for diabetic kidney disease.

Current microbiome signatures for chronic diseases such as diabetic kidney disease (DKD) are mainly based on low-resolution taxa such as genus or phyla and are often inconsistent among studies. In microbial ecosystems, bacterial functions are strain specific, and taxonomically different bacteria tend to form co-abundance functional groups called guilds. Here, we identified guild-level signatures for DKD by performing in-depth metagenomic sequencing and conducting genome-centric and guild-based analysis on fecal samples from 116 DKD patients and 91 healthy subjects. Redundancy analysis on 1,543 high-quality metagenome-assembled genomes (HQMAGs) identified 54 HQMAGs that were differentially distributed among the young healthy control group, elderly healthy control group, early-stage DKD patients (EDG), and late-stage DKD patients (LDG). Co-abundance network analysis classified the 54 HQMAGs into two guilds. Compared to guild 2, guild 1 contained more short-chain fatty acid biosynthesis genes and fewer genes encoding uremic toxin indole biosynthesis, antibiotic resistance, and virulence factors. Guild indices, derived from the total abundance of guild members and their diversity, delineated DKD patients from healthy subjects and between different severities of DKD. Age-adjusted partial Spearman correlation analysis showed that the guild indices were correlated with DKD disease progression and with risk indicators of poor prognosis. We further validated that the random forest classification model established with the 54 HQMAGs was also applicable for classifying patients with end-stage renal disease and healthy subjects in an independent data set. Therefore, this genome-level, guild-based microbial analysis strategy may identify DKD patients with different severity at an earlier stage to guide clinical interventions. IMPORTANCE: Traditionally, microbiome research has been constrained by the reliance on taxonomic classifications that may not reflect the functional dynamics or the ecological interactions within microbial communities. By transcending these limitations with a genome-centric and guild-based analysis, our study sheds light on the intricate and specific interactions between microbial strains and diabetic kidney disease (DKD). We have unveiled two distinct microbial guilds with opposite influences on host health, which may redefine our understanding of microbial contributions to disease progression. The implications of our findings extend beyond mere association, providing potential pathways for intervention and opening new avenues for patient stratification in clinical settings. This work paves the way for a paradigm shift in microbiome research in DKD and potentially other chronic kidney diseases, from a focus on taxonomy to a more nuanced view of microbial ecology and function that is more closely aligned with clinical outcomes.

Exploration of the Changes in Facial Microbiota of Maskne Patients and Healthy Controls Before and After Wearing Masks Using 16 S rRNA Analysis.

Whether in the field of medical care, or in people's daily life and health protection, the importance of masks has been paid more and more attention. Acne, the most common complication after wearing masks, which is also called maskne, has been successfully introduced into the common language as a common topic of dermatologist consultations. This study aims to study the changes of microflora in maskne patients and healthy controls before and after wearing masks. In the summer of 2023, we collected a total of 50 samples from 15 maskne patients and 10 healthy controls before and after wearing surgical masks for a long time. 16 S ribosomal DNA sequencing and identification technology with V3-V4 variable region were adopted to explore the microbiome changes caused by mask wearing, analyze the changes in microbial diversity, and make interaction network. LDA effect size analysis was used to identify which bacteria showed significant changes in their relative abundance from phylum to genus. After wearing a mask, the microbiome of the maskne patients changed significantly more than that of the healthy controls, with both α diversity and ß diversity lower than those of maskne patients before wearing masks and those of healthy controls after wearing masks. Co-occurrence network analysis showed that compared with other groups, the network of maskne patients after wearing masks for a long time had the lowest connectivity and complexity, but the highest clustering property, while the opposite was true for healthy controls. Many microbes that are potentially beneficial to the skin decreased significantly after wearing a mask. There was almost no difference in healthy controls before and after wearing a mask.

Drugs targeting CMPK2 inhibit pyroptosis to alleviate severe pneumonia caused by multiple respiratory viruses.

Severe pneumonia caused by respiratory viruses has become a major threat to humans, especially with the SARS-CoV-2 outbreak and epidemic. The aim of this study was to investigate the universal molecular mechanism of severe pneumonia induced by multiple respiratory viruses and to search for therapeutic strategies targeting this universal molecular mechanism. The common differential genes of four respiratory viruses, including respiratory syncytial virus (RSV), rhinovirus, influenza, and SARS-CoV-2, were screened by GEO database, and the hub gene was obtained by Sytohubba in Cytoscape. Then, the effect of hub genes on inflammasome and pyrodeath was investigated in the model of RSV infection in vitro and in vivo. Finally, through virtual screening, drugs targeting the hub gene were obtained, which could alleviate severe viral pneumonia in vitro and in vivo. The results showed that CMPK2 is one of the hub genes after infection by four respiratory viruses. CMPK2 activates the inflammasome by activating NLRP3, and promotes the releases of inflammatory factors interleukin (IL)-1ß and IL-18 to induce severe viral pneumonia. Z25 and Z08 can reduce the expression level of CMPK2 mRNA and protein, thereby inhibiting NLRP3 and alleviating the development of severe viral pneumonia. In conclusion, the inflammatory response mediated by CMPK2 is the common molecular mechanism of severe pneumonia induced by viral infection, and Z25 and Z08 can effectively alleviate viral infection and severe pneumonia through this mechanism.

A bioinformatics approach to systematically analyze the molecular patterns of monkeypox virus-host cell interactions.

Monkeypox has been spreading worldwide since May 2022, when the World Health Organization (WHO) declared the outbreak a "public health emergency of international concern." The spread of monkeypox has posed a serious threat to the health of people around the world, but few studies have been conducted, and the molecular mechanism of monkeypox after infection remains unclear. We therefore implemented a transcriptome analysis to identify signaling pathways and biomarkers in monkeypox-infected cells to help understand monkeypox-host cell interactions. In this study, datasets GSE36854 and GSE11234 were obtained from GEO. Of these, 84 significantly different genes were identified in the dataset GSE36854, followed by KEGG, GO analysis protein-protein interaction (PPI) construction, and Hub gene extraction. We also analyzed the expression regulation of hub genes and screened for drugs targeting hub genes. The results showed that monkeypox-infected cells significantly activated the cellular immune response. The top 10 hub genes are IER3, IFIT2, IL11, ZC3H12A, EREG, IER2, NFKBIE, FST, IFIT1 and AREG. AP-26113 and itraconazole can be used to counteract the inhibitory effect of monkeypox on IFIT1 and IFIT2 and serve as candidate drugs for the treatment of monkeypox virus infection. IRF1 may also be a transcription factor of IFIT. Our results provide a new entry point for understanding how monkeypox virus interacts with its host.

Influential factors on urine EV DNA methylation detection and its diagnostic potential in prostate cancer.

The value of Extracellular vesicles (EVs) diagnostic markers is widely recognized. However, current research on EV DNA remains limited. This study investigates the biological properties, preprocessing factors, and diagnostic potential of EV DNA. We found that DNA positive vesicles account for 23.3% ± 6.7% of the urine total EV, with a large amount of DNA attached to the outside. EV DNA fragments are large, there is no significant effect on uEV DNA when store urine less than 6 h at 4°C. In addition, the influence of different EV extraction methods on methylation detection is also minor. More importantly, RASSF1A methylation in urine total EV DNA can distinguish between PCa and BPH, with an AUC of 0.874. Our results suggest the potential of urine EV DNA as a novel marker for PCa diagnosis. This provides a new idea for the study of urinary tumor markers.

Analysis of risk factors leading to anxiety and depression in patients with prostate cancer after castration and the construction of a risk prediction model.

BACKGROUND: Cancer patients often suffer from severe stress reactions psychologically, such as anxiety and depression. Prostate cancer (PC) is one of the common cancer types, with most patients diagnosed at advanced stages that cannot be treated by radical surgery and which are accompanied by complications such as bodily pain and bone metastasis. Therefore, attention should be given to the mental health status of PC patients as well as physical adverse events in the course of clinical treatment. AIM: To analyze the risk factors leading to anxiety and depression in PC patients after castration and build a risk prediction model. METHODS: A retrospective analysis was performed on the data of 120 PC cases treated in Xi'an People's Hospital between January 2019 and January 2022. The patient cohort was divided into a training group (n = 84) and a validation group (n = 36) at a ratio of 7:3. The patients' anxiety symptoms and depression levels were assessed 2 wk after surgery with the Self-Rating Anxiety Scale (SAS) and the Self-rating Depression Scale (SDS), respectively. Logistic regression was used to analyze the risk factors affecting negative mood, and a risk prediction model was constructed. RESULTS: In the training group, 35 patients and 37 patients had an SAS score and an SDS score greater than or equal to 50, respectively. Based on the scores, we further subclassified patients into two groups: a bad mood group (n = 35) and an emotional stability group (n = 49). Multivariate logistic regression analysis showed that marital status, castration scheme, and postoperative Visual Analogue Scale (VAS) score were independent risk factors affecting a patient's bad mood (P < 0.05). In the training and validation groups, patients with adverse emotions exhibited significantly higher risk scores than emotionally stable patients (P < 0.0001). The area under the curve (AUC) of the risk prediction model for predicting bad mood in the training group was 0.743, the specificity was 70.96%, and the sensitivity was 66.03%, while in the validation group, the AUC, specificity, and sensitivity were 0.755, 66.67%, and 76.19%, respectively. The Hosmer-Lemeshow test showed a χ2 of 4.2856, a P value of 0.830, and a C-index of 0.773 (0.692-0.854). The calibration curve revealed that the predicted curve was basically consistent with the actual curve, and the calibration curve showed that the prediction model had good discrimination and accuracy. Decision curve analysis showed that the model had a high net profit. CONCLUSION: In PC patients, marital status, castration scheme, and postoperative pain (VAS) score are important factors affecting postoperative anxiety and depression. The logistic regression model can be used to successfully predict the risk of adverse psychological emotions.

Multiple marine algae identification based on three-dimensional fluorescence spectroscopy and multi-label convolutional neural network.

Accurate identification of algal populations plays a pivotal role in monitoring seawater quality. Fluorescence-based techniques are effective tools for quickly identifying different algae. However, multiple coexisting algae and their similar photosynthetic pigments can constrain the efficacy of fluorescence methods. This study introduces a multi-label classification model that combines a specific Excitation-Emission matric convolutional neural network (EEM-CNN) with three-dimensional (3D) fluorescence spectroscopy to detect single and mixed algal samples. Spectral data can be input directly into the model without transforming into images. Rectangular convolutional kernels and double convolutional layers are applied to enhance the extraction of balanced and comprehensive spectral features for accurate classification. A dataset comprising 3D fluorescence spectra from eight distinct algae species representing six different algal classes was obtained, preprocessed, and augmented to create input data for the classification model. The classification model was trained and validated using 4448 sets of test samples and 60 sets of test samples, resulting in an accuracy of 0.883 and an F1 score of 0.925. This model exhibited the highest recognition accuracy in both single and mixed algae samples, outperforming comparative methods such as ML-kNN and N-PLS-DA. Furthermore, the classification results were extended to three different algae species and mixed samples of skeletonema costatum to assess the impact of spectral similarity on multi-label classification performance. The developed classification models demonstrated robust performance across samples with varying concentrations and growth stages, highlighting CNN's potential as a promising tool for the precise identification of marine algae.

The in vitro and in vivo antiviral effects of IGF1R inhibitors against respiratory syncytial virus infection.

The insulin-like growth factor 1 receptor (IGF1R) was recognized as a pivotal receptor that facilitated the cellular entry of RSV. Small molecule inhibitors designed to target IGF1R exhibited potential as potent antiviral agents. Through virtual screening, we conducted a screening process involving small molecule compounds derived from natural products, aiming to target the IGF1R protein against respiratory syncytial virus infection. The molecular dynamics simulation analysis showed that tannic acid and daptomycin interacted with the IGF1R. The experimental results in vivo and in vitro showed that tannic acid and daptomycin had anti-RSV infection potential through reducing viral loads, inflammation, airway resistance and protecting alveolar integrity. The CC50 values of tannic acid and daptomycin were 6 nM and 0.45 µM, respectively. Novel small-molecule inhibitors targeting the IGF1R, tannic acid and daptomycin, may be effective anti-RSV therapy agents. This study may in future broaden the arsenal of therapeutics for use against RSV infection and lead to more effective care against the virus.Communicated by Ramaswamy H. Sarma.

Effective correction of dissolved organic carbon interference in nitrate detection using ultraviolet spectroscopy combined with the equivalent concentration offset method.

Nitrate contamination in water sources poses a substantial environmental and health risk. However, accurate detection of nitrate in water, particularly in the presence of dissolved organic carbon (DOC) interference, remains a significant analytical challenge. This study investigates a novel approach for the reliable detection of nitrate in water samples with varying levels of DOC interference based on the equivalent concentration offset method. The characteristic wavelengths of DOC were determined based on the first-order derivatives, and a nitrate concentration prediction model based on partial least squares (PLS) was established using the absorption spectra of nitrate solutions. Subsequently, the absorption spectra of the nitrate solutions were subtracted from that of the nitrate-DOC mixed solutions to obtain the difference spectra. These difference spectra were introduced into the nitrate prediction model to calculate the equivalent concentration offset values caused by DOC. Finally, a DOC interference correction model was established based on a binary linear regression between the absorbances at the DOC characteristic wavelengths and the DOC-induced equivalent concentration offset values of nitrate. Additionally, a modeling wavelength selection algorithm based on a sliding window was proposed to ensure the accuracy of the nitrate concentration prediction model and the equivalent concentration offset model. The experimental results demonstrated that by correcting the DOC-induced offsets, the relative error of nitrate prediction was reduced from 94.44% to 3.36%, and the root mean square error of prediction was reduced from 1.6108 mg L-1 to 0.1037 mg L-1, which is a significant correction effect. The proposed method applied to predict nitrate concentrations in samples from two different water sources shows a certain degree of comparability with the standard method. It proves that this method can effectively correct the deviations in nitrate measurements caused by DOC and improve the accuracy of nitrate measurement.

Oligoribonuclease mediates high adaptability of P. aeruginosa through metabolic conversion.

BACKGROUND: Oligoribonuclease (orn) of P. aeruginosa is a highly conserved exonuclease, which can regulate the global gene expression levels of bacteria through regulation of both the nanoRNA and c-di-GMP. NanoRNA can regulate the expression of the bacterial global genome as a transcription initiator, and c-di-GMP is the most widely second messenger in bacterial cells. OBJECTIVE: This study seeks to elucidate on the regulation by orn on pathogenicity of P. aeruginosa. METHODS: P. aeruginosa with orn deletion was constructed by suicide plasmid homologous recombination method. The possible regulatory process of orn was analyzed by TMT quantitative labeling proteomics. Then experiments were conducted to verify the changes of Δorn on bacterial motility, virulence and biofilm formation. Bacterial pathogenicity was further detected in cell and animal skin trauma models. ELISA detection c-di-GMP concentration and colony aggregation and biofilm formation were observed by scanning electron microscope. RESULTS: orn deletion changed the global metabolism of P. aeruginosa and reduced intracellular energy metabolism. It leads to the disorder of the quorum sensing system, the reduction of bacterial motility and virulence factors pyocyanin and rhamnolipids. But, orn deletion enhanced pathogenicity in vitro and in vivo, a high level of c-di-GMP and biofilm development of P. aeruginosa. CONCLUSION: orn regulates the ability of P. aeruginosa to adapt to the external environment.

Observational Study: Clinical characteristics and Survival Rates in Patients with Prostate Cancer with Normal Prostate-Specific Antigen Concentrations.

Objectives: This study aimed to explore the clinical characteristics of patients with prostate cancer with prostate-specific antigen (PSA) concentrations of less than 4 ng/mL (normal PSA) to provide clinical insights regarding diagnosis and treatment. Methods: We recruited 35 patients with prostate cancer with normal PSA who were admitted to Xi'an People's Hospital from January 2013 to January 2018, and further determined their clinical characteristics, serum PSA concentration, prostate volume, tumor pathology, surgical margins, seminal vesicle invasion, lymph node metastasis, Gleason score, TNM staging, risk classification, and survival, and described the patients' interventions and treatments. All patients and their families signed informed consent forms before enrollment. Results: In our study, we observed a 3-year survival rate of 77.14% for patients with prostate cancer and normal PSA concentrations. This outcome can be attributed to several clinical characteristics, including the absence of obvious clinical presentation, a high detection rate of seminal vesicle invasion, as well as high Gleason scores and risk levels. The primary outcome, 3-year survival rate, reflects the long-term prognosis of this specific patient subgroup. We also conducted correlation analyses to better understand the relationships between these clinical characteristics and patient survival.

Microbial-host isozyme: A novel target in "drug the bug" strategies for diabetes.

The role of the gut microbiome in metabolic diseases, such as diabetes, has emerged as a pivotal area of medical research. Wang et al.'s recent work reported that a gut bacteria-derived microbial-host isozyme, mimicking a human enzyme responsible for blood glucose regulation, can significantly impact the efficacy of diabetes medications.

Gut microbiota-derived tryptamine and phenethylamine impair insulin sensitivity in metabolic syndrome and irritable bowel syndrome.

The incidence of metabolic syndrome is significantly higher in patients with irritable bowel syndrome (IBS), but the mechanisms involved remain unclear. Gut microbiota is causatively linked with the development of both metabolic dysfunctions and gastrointestinal disorders, thus gut dysbiosis in IBS may contribute to the development of metabolic syndrome. Here, we show that human gut bacterium Ruminococcus gnavus-derived tryptamine and phenethylamine play a pathogenic role in gut dysbiosis-induced insulin resistance in type 2 diabetes (T2D) and IBS. We show levels of R. gnavus, tryptamine, and phenethylamine are positively associated with insulin resistance in T2D patients and IBS patients. Monoassociation of R. gnavus impairs insulin sensitivity and glucose control in germ-free mice. Mechanistically, treatment of R. gnavus-derived metabolites tryptamine and phenethylamine directly impair insulin signaling in major metabolic tissues of healthy mice and monkeys and this effect is mediated by the trace amine-associated receptor 1 (TAAR1)-extracellular signal-regulated kinase (ERK) signaling axis. Our findings suggest a causal role for tryptamine/phenethylamine-producers in the development of insulin resistance, provide molecular mechanisms for the increased prevalence of metabolic syndrome in IBS, and highlight the TAAR1 signaling axis as a potential therapeutic target for the management of metabolic syndrome induced by gut dysbiosis.

Gut microbiota modulate distal symmetric polyneuropathy in patients with diabetes.

The pathogenic mechanisms underlying distal symmetric polyneuropathy (DSPN), a common neuropathy in patients with diabetes mellitus (DM), are not fully understood. Here, we discover that the gut microbiota from patients with DSPN can induce a phenotype exhibiting more severe peripheral neuropathy in db/db mice. In a randomized, double-blind, and placebo-controlled trial (ChiCTR1800017257), compared to 10 patients who received placebo, DSPN was significantly alleviated in the 22 patients who received fecal microbiota transplants from healthy donors, independent of glycemic control. The gut bacterial genomes that correlated with the Toronto Clinical Scoring System (TCSS) score were organized in two competing guilds. Increased guild 1, which had higher capacity in butyrate production, and decreased guild 2, which harbored more genes in synthetic pathway of endotoxin, were associated with improved gut barrier integrity and decreased proinflammatory cytokine levels. Moreover, matched enterotype between transplants and recipients showed better therapeutic efficacy with more enriched guild 1 and suppressed guild 2. Thus, changes in these two competing guilds may play a causative role in DSPN and have the potential for therapeutic targeting.