Oral microbiota is related to the severity and recovery of SARS-CoV-2 infection. This study aims to predict clinical classification after SARS-CoV-2 infection using oral microbiota before infection. Herein, we collected tongue-coating samples before infection and then monitored clinical information after infection. Oral microbiota was detected by MiSeq sequencing. We randomly assigned participants from Zhengzhou into discovery and validation cohorts to develop a predictive model and conducted cross-region verification using Xinyang and Hangzhou cohorts. Sixteen asymptomatic patients (AP), 257 mild patients (MP), 106 common patients (CP), and 7 severe patients (SP) were enrolled. Oral microbiota diversity was decreased in CP versus MP. At genus level, 11 microorganisms, including Rothia and Gemella, were increased, while 5 microorganisms, including Selenomonas and Lachnoanaerobaculum, were decreased in CP versus MP. Moreover, the classifier based on 15 optimal markers showed high prediction efficiency in discovery cohort (area under the curve [AUC]: 98.35%), validation cohort (AUC: 81.91%), Xinyang cohort (AUC: 74.34%), and Hangzhou cohort (AUC: 94.44%). Interestingly, a higher abundance of Selenomonas was associated with milder clinical symptoms. In conclusion, our study established a good model to predict clinical classification after SARS-CoV-2 infection using oral microbiota before infection, providing a novel strategy for precise prevention and treatment.

Background: Dietary intake has been suggested to be associated with the oral microbiome, but no study has examined the association between overall diet quality and the oral microbiome. Objectives: This study aimed to investigate the cross-sectional association between the Healthy Eating Index-2020 (HEI-2020) and the diversity and composition of the oral microbiome among participants in the Buffalo Osteoporosis and Periodontal Disease (OsteoPerio) Study. Methods: In 1175 postmenopausal women (mean age: 67 +/- 7.0 y), we estimated the HEI-2020 scores for each woman from a food frequency questionnaire administered from 1997 to 2000. Bacterial DNA was extracted from subgingival plaque samples and analyzed using 16S ribosomal RNA sequencing. The alpha-diversity (within-sample diversity) and beta-diversity (between-sample diversity) across HEI-2020 quartiles were examined using analysis of covariance and permutational multivariate analysis of variance, respectively. The associations between the HEI-2020 score and the relative abundance of microbial taxa were examined by linear regression models. The analyses were further conducted for individual components of the HEI-2020. Results: No statistically significant associations were observed between the HEI-2020 scores and alpha- or beta-diversity. However, greater consumption of seafood, plant proteins, and total protein and lower consumption of added sugars were positively associated with alpha- diversity. After we applied a false detection rate (FDR) correction, higher HEI-2020 scores were significantly associated with decreased abundance of Lautropia, Streptococcus gordonii, Cardiobacterium valvarum, and Cardiobacterium hominis, and increased abundance of Selenomonas sp. oral taxon 133 and Selenomonas dianae (FDR-adjusted P values < 0.10). Additionally, 28 other taxa were identified as being associated with HEI-2020 components. Conclusions: Although the HEI-2020 was associated with the composition, but not the diversity, of the oral microbiome, individual HEI2020 components were associated with both its diversity and composition. Specific dietary components may have more impact on the diversity and composition of oral microbiome than overall diet quality assessed by the HEI-2020.

TCGA disease-related genes, states and paths.

Oral fungal microbiota plays an important role in many diseases, however, the role of oral fungal microorganisms in the development of patients infected with Omicron has not been reported. A total of 963 tongue coating samples were prospectively included in this study, and finally 336 samples from patients infected Omicron variant (PIOV), 234 samples from recovered patients infected with Omicron (RP), 71 samples from patients infected original strain of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) (PIOS), 299 samples from healthy controls (HC) completed internal transcribed spacer (ITS) sequencing after screening and quality control. By comparing the difference of oral fungal microorganisms between PIOV, RP and HC, we found that with the recovery of PIOV, their oral fungal microecological diversity increased gradually. Besides, at the species level, there were 24 oral fungal species such as Zanclospora_jonesii increased gradually, while there were 24 oral fungal species such as Saccharomyces_cerevisiae decreased gradually. In addition, by comparing PIOS and PIOV, we found that the alpha diversity of oral fungal microorganisms in PIOV was significantly lower than PIOS and the main species of the two groups were different. At the same time, we randomly divided PIOV and HC into training and validation set. Based on random forest model and five-fold cross-validation, we identified three optimal microbial markers of oral fungi and constructed a diagnostic model of PIOV. The area under the curve (AUC) value of PIOV group was 99.01% in discovery phase and 97.84% in verification phase. In summary, based on large-scale samples, this study is the first to elucidate the characteristics of oral fungal microbiota changes during PIOV recovery and establish a supplemental non-invasive diagnostic model for PIOV based on the oral fungal microbiome.

Purpose:Rheumatoid arthritis (RA) is a persistent inflammatory condition, and electroacupuncture (EA) has been demonstrated to effectively reduce the symptoms associated with RA. However, the molecular mechanisms underlying the effects of EA in RA remained poorly understood. This study aimed to identify potential diagnostic biomarkers for RA and elucidated the molecular targets of EA by using bioinformatics analysis and machine learning algorithms in peripheral blood samples. Methods:We obtained datasets from the Gene Expression Omnibus(GEO) database containing samples from RA patients (GSE15573) and from RA patients after EA treatment (GSE59526) for bioinformatics analysis. Diagnostic biomarkers were identified using three distinct machine learning algorithms (LASSO, Random Forest and SVM-REF). A rat model of RA was established using Complete Freund's Adjuvant (CFA), and quantitative real-time PCR was performed to confirm the differential expression of identified diagnostic biomarkers and assess the modulatory impact of EA on these genes. Results:Twenty-six genes were identified as differentially expressed following EA treatment. Three machine learning algorithms converged on ARHGAP17 and VEGFB as potential diagnostic biomarkers for RA, exhibiting robust diagnostic performance (AUC > 0.75) and consistent expression patterns across multiple RA cohorts (GSE17755, GSE205962 and GSE93272). Besides, EA treatment significantly increased the paw withdrawal threshold (PWT) and the peripheral blood expression of both ARHGAP17 and VEGFB in CFA rats. Conclusion:This study employed three machine learning algorithms to identify potential diagnostic biomarkers for the alleviation of RA by EA. The biomarkers demonstrated robust diagnostic performance across multiple validation datasets. Furthermore, animal experiments confirmed that EA exerted a favorable regulatory effect on these diagnostic biomarkers. The findings of this study provided novel therapeutic targets for the EA treatment of RA.

Significantly varied genes, BEAM results, enriched pathways.

Putative oncogenetic events associated with transition between states on specific progression pathways. Black, somatic mutation; red, copy number amplification; and blue, copy number deletion. Within listed regions of CNV, genes previously identified to be associated with cancer were indicated in parentheses.

Porphyromonas gingivalis, a Gram-negative anaerobic bacterium commonly found in human subgingival plaque, is a major etiologic agent for periodontitis and has been associated with multiple systemic pathologies. Many P. gingivalis strains have been identified and different strains possess different virulence factors. Current oral microbiome approaches (16S or shotgun) have been unable to differentiate P. gingivalis strains. This study presents a new approach that aims to improve the accuracy of strain identification, using a detection method based on sequencing of the intergenic spacer region (ISR) which is variable between P. gingivalis strains. Our approach uses two-step PCR to amplify only the P. gingivalis ISR region. Samples are then sequenced with an Illumina sequencer and mapped to specific strains. Our approach was validated by examining subgingival plaque from 153 participants with and without periodontal disease. We identified the avirulent strain ATCC33277/381 as the most abundant strain across all sample types. The W83/W50 strain was significantly enriched in periodontitis, with 13% of participants harboring that strain. Overall, this approach can have significant implications not only for the diagnosis and treatment of periodontal disease but also for other diseases where P. gingivalis or its toxins have been implicated, such as Alzheimer's disease.

Prostate cancer is a significant health concern and the most commonly diagnosed cancer in men worldwide. Understanding the complex process of prostate tumor evolution and progression is crucial for improved diagnosis, treatments, and patient outcomes. Previous studies have focused on unraveling the dynamics of prostate cancer evolution using phylogenetic or lineage analysis approaches. However, those approaches have limitations in capturing the complete disease process or incorporating genomic and transcriptomic variations comprehensively. In this study, we applied a novel computational approach to derive a prostate cancer progression model using multidimensional data from 497 prostate tumor samples and 52 tumor-adjacent normal samples obtained from The Cancer Genome Atlas study. The model was validated using data from an independent cohort of 545 primary tumor samples. By integrating transcriptomic and genomic data, our model provides a comprehensive view of prostate tumor progression, identifies crucial signaling pathways and genetic events, and uncovers distinct transcription signatures associated with disease progression. Our findings have significant implications for cancer research and hold promise for guiding personalized treatment strategies in prostate cancer.

Network-based methods utilize protein-protein interaction information to identify significantly perturbed subnetworks in cancer and to propose key molecular pathways. Numerous methods have been developed, but to date, a rigorous benchmark analysis to compare the performance of existing approaches is lacking. In this paper, we proposed a novel benchmarking framework using synthetic data and conducted a comprehensive analysis to investigate the ability of existing methods to detect target genes and subnetworks and to control false positives, and how they perform in the presence of topological biases at both gene and subnetwork levels. Our analysis revealed insights into algorithmic performance that were previously unattainable. Based on the results of the benchmark study, we presented a practical guide for users on how to select appropriate detection methods and protein-protein interaction networks for cancer pathway identification, and provided suggestions for future algorithm development.

BACKGROUND:The identification of cancer driver genes and key molecular pathways has been the focus of large-scale cancer genome studies. Network-based methods detect significantly perturbed subnetworks as putative cancer pathways by incorporating genomics data with the topological information of PPI networks. However, commonly used PPI networks have distinct topological structures, making the results of the same method vary widely when applied to different networks. Furthermore, emerging context-specific PPI networks often have incomplete topological structures, which pose serious challenges for existing subnetwork detection algorithms.METHODS:In this paper, we propose a novel method, referred to as MultiFDRnet, to address the above issues. The basic idea is to model a set of PPI networks as a multiplex network to preserve the topological structure of individual networks, while introducing dependencies among them, and, then, to detect significantly perturbed subnetworks on the modeled multiplex network using all the structural information simultaneously.RESULTS:To illustrate the effectiveness of the proposed approach, an extensive benchmark analysis was conducted on both simulated and real cancer data. The experimental results showed that the proposed method is able to detect significantly perturbed subnetworks jointly supported by multiple PPI networks and to identify novel modular structures in context-specific PPI networks.

PDF file - 44K, Transcriptional profiling of urothelia from 52 bladder cancer patiens and 40 patients with no evidence of bladder cancer. Differentiallyexpressedgenes (p value ≤0.001 ) 223816_

Intestinal colonization of the oral bacterium Haemophilus parainfluenzae has been associated with Crohn's disease (CD) severity and progression. This study examines the role of periodontal disease (PD) as a modifier for colonization of H. parainfluenzae in patients with CD and explores the mechanisms behind H. parainfluenzae-mediated intestinal inflammation. Fifty subjects with and without CD were evaluated for the presence of PD, and their oral and fecal microbiomes were characterized. PD is associated with increased levels of H. parainfluenzae strains in subjects with CD. Oral inoculation of H. parainfluenzae elicits strain-dependent intestinal inflammation in murine models of inflammatory bowel disease, which is associated with increased intestinal interferon-γ (IFN-γ)+ CD4+ T cells and disruption of the host hypusination pathway. In summary, this study establishes a strain-specific pathogenic role of H. parainfluenzae in intestinal inflammation and highlights the potential effect of PD on intestinal colonization by pathogenic H. parainfluenzae strains in patients with CD.

Introduction and Objective: Recently, we reported that OncuriaTM, a multiplex urinalysis test, could be predictive of BCG treatment response. Furthermore, high-grade BCG unresponsive bladder cancer have limited treatment options. Such patients are offered either radical cystectomy or systemic therapy. One of the systemic therapies include immune-oncology agents, e.g., PD-1 or PD-L1 inhibitors. In this study, we tested the performance of OncuriaTM in a BCG unresponsive cohort to determine if it could predict response to a PDL1 inhibitor. Methods: OncuriaTM data was evaluated in voided urine samples obtained from a prospectively collected cohort of 18 subjects who have BCG-unresponsive NMIBC with treatment of atezolizumab monotherapy or atezolizumab BCG combination (provided by Genentech, NCT02792192). The urine samples were collected prior to treatment in both arms. The OncuriaTM test, which measures 10 cancer-associated biomarkers was performed in an independent clinical laboratory. Predictive models were previously developed using supervised learning and cross-validation analyses. Model performance was validated using ROC curves. Results: Pre-treatment urinary concentrations of MMP9, VEGFA, CA9, SDC1, PAI1, APOE, A1AT, ANG and MMP10 were increased in patients who developed disease recurrence. A combinatorial predictive model of treatment outcome achieved sensitivity and specificity of >90%. Conclusion: Previous pilot study found that monitoring the urinary levels of a cancer-associated biomarker panel enabled the discrimination of patients who did not respond to intravesical BCG therapy. In this study, we noted the performance of OncuriaTM for the prediction of systemic PDL1 inhibitor treatment response. A limitation of this study includes its small sample size. With further study, the multiplex OncuriaTM test may be applicable for the clinical evaluation of bladder cancer patients who have not previously responded to intravesical BCG treatment and is considering systemic immune-oncology options. Citation Format: Hideki Furuya, Kaoru Murakami, Ian Pagano, Runpu Chen, Yijun Sun, Nari Kim, Edward E. Kadel, Cheryl V. Wong, Nicole Davarpanah, Charles J. Rosser. Application of a multiplex urinalysis test for predicting treatment response in patients with BCG unresponsive bladder cancer: a pilot study. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5456.

Climate change impacts global ecosystems at the interface of infectious disease agents and hosts and vectors for animals, humans, and plants. The climate is changing, and the impacts are complex, with multifaceted effects. In addition to connecting climate change and infectious diseases, we aim to draw attention to the challenges of working across multiple disciplines. Doing this requires concentrated efforts in a variety of areas to advance the technological state of the art and at the same time implement ideas and explain to the everyday citizen what is happening. The world's experience with COVID-19 has revealed many gaps in our past approaches to anticipating emerging infectious diseases. Most approaches to predicting outbreaks and identifying emerging microbes of major consequence have been with those causing high morbidity and mortality in humans and animals. These lagging indicators offer limited ability to prevent disease spillover and amplifications in new hosts. Leading indicators and novel approaches are more valuable and now feasible, with multidisciplinary approaches also within our grasp to provide links to disease predictions through holistic monitoring of micro and macro ecological changes. In this commentary, we describe niches for climate change and infectious diseases as well as overarching themes for the important role of collaborative team science, predictive analytics, and biosecurity. With a multidisciplinary cooperative "all call," we can enhance our ability to engage and resolve current and emerging problems.

Supplementary data: Detailed descriptions of bioinformatics methods used in the study

<p>Table S1: Patient clinical information</p>

PDF file - 79K, Quantitative PCR analysis

PDF file - 84K, Molecular Biomarker Signature for the Non-Invasive Detection of Bladder Cancer