Metabolic engineering of Escherichia coli for de novo production of 5-hydroxyvalerate via L-lysine α-oxidase pathway.

5-hydroxyvalerate (5-HV) is a crucial C5 platform chemical with versatile applications, yet its efficient production remains a challenge. The Raip, gabT, and yahK genes were integrated into the E. coli LE genome, deleted gabD, and enhanced gabP expression, resulting in the QluMG strain. Additionally, the impact of ethanol and H2O2 on 5-HV production was investigated. Further enhancement was achieved by incorporating an NADPH supplementation system, resulting in the QluMG strain. In the 5 L fermenter, the QluMGD strain produced 21.7 g/L of 5-HV from 50 g/L glucose, with a conversion rate of 43.4 %. The successful integration of the RaiP pathway into the E. coli genome significantly enhanced 5-HV production. The QluMG strain achieved the highest reported yield from glucose in engineered E. coli to date. This study provides a new strategy for the efficient production of 5-HV and other chemicals using 5-HV as a precursor, demonstrating potential for industrial application.

Metabolic Engineering of High L-Lysine-Producing Escherichia coli for de Novo Production of L-Lysine-Derived Compounds.

5-Aminovalerate (5-AVA), 5-hydroxyvalerate (5-HV), and 1,5-pentanediol (1,5-PDO) are l-lysine derivatives with extensive applications in the production of materials such as polyesters, polyurethane, plasticizers, inks, and coatings. However, their large-scale production is limited by the lack of efficient synthetic pathways. Here, we aimed to construct multiple synthetic pathways by screening the key enzymes involved in the synthesis of these compounds in Escherichia coli. The engineered pathway utilizing RaiP demonstrated a superior catalytic efficiency. The LER strain that overexpressed only raiP successfully synthesized 9.70 g/L 5-HV and 8.31 g/L 5-AVA, whereas the strain LERGY that overexpressed raiP, gabT, and yahK accumulated 9.72 g/L 5-HV and 7.95 g/L 5-AVA from 20 g/L glucose. The introduction of exogenous transaminases and dehydrogenases enhanced cell growth and fermentation efficiency with respect to 5-HV synthesis, albeit without significantly impacting the yield. Strain LE05, incorporating only two exogenous enzymes, RaiP and CaR, produced 1.87 g/L 1,5-PDO, 3.85 g/L 5-HV, and 4.78 g/L 5-hydroxyglutaraldehyde from 20 g/L glucose after 6 days. The strain LE02G, fortified with transaminase, dehydrogenase, and NADPH regeneration system, accumulated 7.82 g/L 1,5-PDO, whereas the aldp-knock out LE02G2 synthesized 10.98 g/L 1,5-PDO from 50 g/L glucose in fed-batch fermentation after 6 days, yielding 0.22 g/g glucose (0.37 mol/mol). Introducing the NADPH regeneration pathway and deleting the NADPH-consuming pathways increased the 1,5-PDO yield and decreased the precursor concentration. The proposed pathways and engineering strategies presented in this study can prove instrumental in developing biological routes for the practical production of 5-AVA, 5-HV, and 1,5-PDO.

Efficacy of a mouthwash containing ε-poly-L-lysine, funme peptides and domiphen in reducing halitosis and supragingival plaque: a randomized clinical trial.

OBJECTIVE: To evaluate the antibacterial effectiveness of a combination of ε-poly-L-lysine (ε-PL), funme peptide (FP) as well as domiphen against oral pathogens, and assess the efficacy of a BOP® mouthwash supplemented with this combination in reducing halitosis and supragingival plaque in a clinical trial. MATERIALS AND METHODS: The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of the compound against Fusobacterium nucleatum, Porphyromonas gingivalis, Streptococcus mutans, and Aggregatibacter actinomycetemcomitans were determined by the gradient dilution method. Subsequently, the CCK-8 assay was used to detect the toxicity of mouthwash on human gingival fibroblastst, and the effectiveness in reducing halitosis and supragingival plaque of the mouthwash supplemented with the combination was analyzed by a randomized, double-blind, parallel-controlled clinical trial. RESULTS: The combination exhibited significant inhibitory effects on tested oral pathogens with the MIC < 1.56% (v/v) and the MBC < 3.13% (v/v), and the mouthwash containing this combination did not inhibit the viability of human gingival fibroblasts at the test concentrations. The clinical trial showed that the test group displayed notably lower volatile sulfur compounds (VSCs) at 0, 10, 24 h, and 7 d post-mouthwash (P < 0.05), compared with the baseline. After 7 days, the VSC levels of the and control groups were reduced by 50.27% and 32.12%, respectively, and notably cutting severe halitosis by 57.03% in the test group. Additionally, the Plaque Index (PLI) of the test and control group decreased by 54.55% and 8.38%, respectively, and there was a significant difference in PLI between the two groups after 7 days (P < 0.01). CONCLUSIONS: The combination of ε-PL, FP and domiphen demonstrated potent inhibitory and bactericidal effects against the tested oral pathogens, and the newly formulated mouthwash added with the combination exhibited anti-dental plaque and anti-halitosis properties in a clinical trial and was safe. TRIAL REGISTRATION: The randomized controlled clinical trial was registered on Chinese Clinical Trial Registry (No. ChiCTR2300073816, Date: 21/07/2023).

Predicting the potential distribution of 12 threatened medicinal plants on the Qinghai-Tibet Plateau, with a maximum entropy model.

Climate change is a vital driver of biodiversity patterns and species distributions, understanding how organisms respond to climate change will shed light on the conservation of endangered species. In this study, the MaxEnt model was used to predict the potential suitable area of 12 threatened medicinal plants in the QTP (Qinghai-Tibet Plateau) under the current and future (2050s, 2070s) three climate scenarios (RCP2.6, RCP4.5, RCP8.5). The results showed that the climatically suitable habitats for the threatened medicinal plants were primarily found in the eastern, southeast, southern, and some parts of the central regions on the QTP. Moreover, 25% of the threatened medicinal plants would have reduced suitable habitat areas within the next 30-50 years in the different future global warming scenarios. Among these medicinal plants, RT (Rheum tanguticum) would miss the most habitat (98.97%), while the RAN (Rhododendron anthopogonoides) would miss the least habitat (10.15%). Nevertheless, 33.3% of the threatened medicinal plants showed an increase in their future habitat area because of their physiological characteristics which are more adaptable to a wide range of climates. The climatic suitable habitat for 50% of the threatened medicinal plants would migrate to higher altitudes or higher latitudes regions. This study provides a data foundation for the conservation of biodiversity and wild medicinal plants on the QTP.

Using Bayesian networks with tabu algorithm to explore factors related to chronic kidney disease with mental illness: A cross-sectional study.

While Bayesian networks (BNs) offer a promising approach to discussing factors related to many diseases, little attention has been poured into chronic kidney disease with mental illness (KDMI) using BNs. This study aimed to explore the complex network relationships between KDMI and its related factors and to apply Bayesian reasoning for KDMI, providing a scientific reference for its prevention and treatment. Data was downloaded from the online open database of CHARLS 2018, a population-based longitudinal survey. Missing values were first imputed using Random Forest, followed by propensity score matching (PSM) for class balancing regarding KDMI. Elastic Net was then employed for variable selection from 18 variables. Afterwards, the remaining variables were included in BNs model construction. Structural learning of BNs was achieved using tabu algorithm and the parameter learning was conducted using maximum likelihood estimation. After PSM, 427 non-KDMI cases and 427 KDMI cases were included in this study. Elastic Net identified 11 variables significantly associated with KDMI. The BNs model comprised 12 nodes and 24 directed edges. The results suggested that diabetes, physical activity, education levels, sleep duration, social activity, self-report on health and asset were directly related factors for KDMI, whereas sex, age, residence and Internet access represented indirect factors for KDMI. BN model not only allows for the exploration of complex network relationships between related factors and KDMI, but also could enable KDMI risk prediction through Bayesian reasoning. This study suggests that BNs model holds great prospects in risk factor detection for KDMI.

Diabetes mellitus early warning and factor analysis using ensemble Bayesian networks with SMOTE-ENN and Boruta.

Diabetes mellitus (DM) has become the third chronic non-infectious disease affecting patients after tumor, cardiovascular and cerebrovascular diseases, becoming one of the major public health issues worldwide. Detection of early warning risk factors for DM is key to the prevention of DM, which has been the focus of some previous studies. Therefore, from the perspective of residents' self-management and prevention, this study constructed Bayesian networks (BNs) combining feature screening and multiple resampling techniques for DM monitoring data with a class imbalance in Shanxi Province, China, to detect risk factors in chronic disease monitoring programs and predict the risk of DM. First, univariate analysis and Boruta feature selection algorithm were employed to conduct the preliminary screening of all included risk factors. Then, three resampling techniques, SMOTE, Borderline-SMOTE (BL-SMOTE) and SMOTE-ENN, were adopted to deal with data imbalance. Finally, BNs developed by three algorithms (Tabu, Hill-climbing and MMHC) were constructed using the processed data to find the warning factors that strongly correlate with DM. The results showed that the accuracy of DM classification is significantly improved by the BNs constructed by processed data. In particular, the BNs combined with the SMOTE-ENN resampling improved the most, and the BNs constructed by the Tabu algorithm obtained the best classification performance compared with the hill-climbing and MMHC algorithms. The best-performing joint Boruta-SMOTE-ENN-Tabu model showed that the risk factors of DM included family history, age, central obesity, hyperlipidemia, salt reduction, occupation, heart rate, and BMI.

Nicotine promotes renal interstitial fibrosis via upregulation of XIAP in an alpha7-nAChR-dependent manner.

Renal fibrosis, characterized by excessive accumulation of the extracellular matrix in the renal tubulointerstitium, can lead to chronic kidney disease (CKD), resulting in a heavy burden on families and society. Clinical studies have shown that smoking is closely associated with CKD deterioration in patients with diabetes, hypertension, polycystic kidney disease, and kidney transplantation. However, the mechanism of action of nicotine in renal fibrosis pathogenesis remains largely unknown. X-linked inhibitor of apoptosis (XIAP), a member of the inhibitor of apoptosis protein (IAP) family, is involved in apoptosis, necroptosis, autophagy, and immune response. Here, the upregulated expression of XIAP and α7 nicotine acetylcholine receptor (α7-nAChR) was determined in the kidneys of the CKD smoking group in human and animal studies. A significant positive correlation between XIAP and cotinine was observed. In addition, the nuclear translocation and transcriptional activity of SP1 were promoted when nicotine bound to α7-nAChR, resulting in XIAP overexpression and renal interstitial fibrosis progression. This phenotype can be reversed by the nicotine receptor subtype α7-nAChR antagonists methyllycaconitine. Our results revealed the complex underlying mechanism of nicotine in promoting renal fibrosis by altering SP1 nucleocytoplasmic translocation and regulating XIAP expression. These results provide novel insights into the pathogenesis and treatment of CKD.

The prognostic index PRIMA-PI combined with Ki67 as a better predictor of progression of disease within 24 months in follicular lymphoma.

Background: Progression of disease within 24 months (POD24) is a risk factor for poor survival in follicular lymphoma (FL), and there is currently no optimal prognostic model to accurately predict patients with early disease progression. How to combine traditional prognostic models with new indicators to establish a new prediction system, to predict the early progression of FL patients more accurately is a future research direction. Methods: This study retrospectively analyzed patients with newly diagnosed FL patients in Shanxi Provincial Cancer Hospital from January 2015 to December 2020. Data from patients undergoing immunohistochemical detection (IHC) were analyzed using χ2 test and multivariate Logistic regression. Also, we built a nomogram model based on the results of LASSO regression analysis of POD24, which was validated in both the training set and validation set, and additional external validation was performed using a dataset (n = 74) from another center, Tianjin Cancer Hospital. Results: The multivariate Logistic regression results suggest that high-risk PRIMA-PI group, Ki-67 high expression represent risk factors for POD24 (P<0.05). Next, PRIMA-PI and Ki67 were combined to build a new model, namely, PRIMA-PIC to reclassify high and low-risk groups. The result showed that the new clinical prediction model constructed by PRIMA-PI with ki67 has a high sensitivity to the prediction of POD24. Compared to PRIMA-PI, PRIMA-PIC also has better discrimination in predicting patient's progression-free survival (PFS) and overall survival (OS). In addition, we built nomogram models based on the results of LASSO regression (histological grading, NK cell percentage, PRIMA-PIC risk group) in the training set, which were validated using internal validation set and external validation set, we found that C-index and calibration curve showed good performance. Conclusion: As such, the new predictive model-based nomogram established by PRIMA-PI and Ki67 could well predict the risk of POD24 in FL patients, which boasts clinical practical value.

Specific discrimination and efficient elimination of gram-positive bacteria by an aggregation-induced emission-active ruthenium (II) photosensitizer.

The infections caused by Gram-positive bacteria (G+) have seriously endangered public heath due to their high morbidity and mortality. Therefore, it is urgent to develop a multifunctional system for selective recognition, imaging and efficient eradication of G+. Aggregation-induced emission materials have shown great promise for microbial detection and antimicrobial therapy. In this paper, a multifunctional ruthenium (II) polypyridine complex Ru2 with aggregation-induced emission (AIE) characteristic, was developed and used for selective discrimination and efficient extermination of G+ from other bacteria with unique selectivity. The selective G+ recognition benefited from the interaction between lipoteichoic acids (LTA) and Ru2. Accumulation of Ru2 on the G+ membrane turned on its AIE luminescence and allowed specific G+ staining. Meanwhile, Ru2 under light irradiation also possessed robust antibacterial activity for G+in vitro and in vivo antibacterial experiments. To the best of our knowledge, Ru2 is the first Ru-based AIEgen photosensitizer for simultaneous dual applications of G+ detection and treatment, and inspires the development of promising antibacterial agents in the future.

Study on the prediction effect of a combined model of SARIMA and LSTM based on SSA for influenza in Shanxi Province, China.

BACKGROUND: Influenza is an acute respiratory infectious disease that is highly infectious and seriously damages human health. Reasonable prediction is of great significance to control the epidemic of influenza. METHODS: Our Influenza data were extracted from Shanxi Provincial Center for Disease Control and Prevention. Seasonal-trend decomposition using Loess (STL) was adopted to analyze the season characteristics of the influenza in Shanxi Province, China, from the 1st week in 2010 to the 52nd week in 2019. To handle the insufficient prediction performance of the seasonal autoregressive integrated moving average (SARIMA) model in predicting the nonlinear parts and the poor accuracy of directly predicting the original sequence, this study established the SARIMA model, the combination model of SARIMA and Long-Short Term Memory neural network (SARIMA-LSTM) and the combination model of SARIMA-LSTM based on Singular spectrum analysis (SSA-SARIMA-LSTM) to make predictions and identify the best model. Additionally, the Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used to evaluate the performance of the models. RESULTS: The influenza time series in Shanxi Province from the 1st week in 2010 to the 52nd week in 2019 showed a year-by-year decrease with obvious seasonal characteristics. The peak period of the disease mainly concentrated from the end of the year to the beginning of the next year. The best fitting and prediction performance was the SSA-SARIMA-LSTM model. Compared with the SARIMA model, the MSE, MAE and RMSE of the SSA-SARIMA-LSTM model decreased by 38.12, 17.39 and 21.34%, respectively, in fitting performance; the MSE, MAE and RMSE decreased by 42.41, 18.69 and 24.11%, respectively, in prediction performances. Furthermore, compared with the SARIMA-LSTM model, the MSE, MAE and RMSE of the SSA-SARIMA-LSTM model decreased by 28.26, 14.61 and 15.30%, respectively, in fitting performance; the MSE, MAE and RMSE decreased by 36.99, 7.22 and 20.62%, respectively, in prediction performances. CONCLUSIONS: The fitting and prediction performances of the SSA-SARIMA-LSTM model were better than those of the SARIMA and the SARIMA-LSTM models. Generally speaking, we can apply the SSA-SARIMA-LSTM model to the prediction of influenza, and offer a leg-up for public policy.

Machine learning-enabled risk prediction of chronic obstructive pulmonary disease with unbalanced data.

BACKGROUND AND OBJECTIVE: Since the early symptoms of chronic obstructive pulmonary disease (COPD) are not obvious, patients are not easily identified, causing improper time for prevention and treatment. In present study, machine learning (ML) methods were employed to construct a risk prediction model for COPD to improve its prediction efficiency. METHODS: We collected data from a sample of 5807 cases with a complete COPD diagnosis from the 2019 COPD Surveillance Program in Shanxi Province and extracted 34 potentially relevant variables from the dataset. Firstly, we used feature selection methods (i.e., Generalized elastic net, Lasso and Adaptive lasso) to select ten variables. Afterwards, we employed supervised classifiers for class imbalanced data by combining the cost-sensitive learning and SMOTE resampling methods with the ML methods (Logistic Regression, SVM, Random Forest, XGBoost, LightGBM, NGBoost and Stacking), respectively. Last, we assessed their performance. RESULTS: The cough frequently at age 14 and before and other 9 variables are significant parameters for COPD. The Stacking heterogeneous ensemble model showed relatively good performance in the unbalanced datasets. The Logistic Regression with class weighting enjoyed the best classification performance in the balancing data when these composite indicators (AUC, F1-Score and G-mean) were used as criteria for model comparison. The values of F1-Score and G-mean for the top three ML models were 0.290/0.660 for Logistic Regression with class weighting, 0.288/0.649 for Stacking with synthetic minority oversampling technique (SMOTE), and 0.285/0.648 for LightGBM with SMOTE. CONCLUSIONS: This paper combining feature selection methods, unbalanced data processing methods and machine learning methods with data from disease surveillance questionnaires and physical measurements to identify people at risk of COPD, concluded that machine learning models based on survey questionnaires could provide an automated identification for patients at risk of COPD, and provide a simple and scientific aid for early identification of COPD.

Using Bayesian networks with Tabu-search algorithm to explore risk factors for hyperhomocysteinemia.

Hyperhomocysteinemia (HHcy) is a condition closely associated with cardiovascular and cerebrovascular diseases. Detecting its risk factors and taking some relevant interventions still represent the top priority to lower its prevalence. Yet, in discussing risk factors, Logistic regression model is usually adopted but accompanied by some defects. In this study, a Tabu Search-based BNs was first constructed for HHcy and its risk factors, and the conditional probability between nodes was calculated using Maximum Likelihood Estimation. Besides, we tried to compare its performance with Hill Climbing-based BNs and Logistic regression model in risk factor detection and discuss its prospect in clinical practice. Our study found that Age, sex, α1-microgloblobumin to creatinine ratio, fasting plasma glucose, diet and systolic blood pressure represent direct risk factors for HHcy, and smoking, glycosylated hemoglobin and BMI constitute indirect risk factors for HHcy. Besides, the performance of Tabu Search-based BNs is better than Hill Climbing-based BNs. Accordingly, BNs with Tabu Search algorithm could be a supplement for Logistic regression, allowing for exploring the complex network relationship and the overall linkage between HHcy and its risk factors. Besides, Bayesian reasoning allows for risk prediction of HHcy, which is more reasonable in clinical practice and thus should be promoted.

Gut microbiota landscape and potential biomarker identification in female patients with systemic lupus erythematosus using machine learning.

Objectives: Systemic Lupus Erythematosus (SLE) is a complex autoimmune disease that disproportionately affects women. Early diagnosis and prevention are crucial for women's health, and the gut microbiota has been found to be strongly associated with SLE. This study aimed to identify potential biomarkers for SLE by characterizing the gut microbiota landscape using feature selection and exploring the use of machine learning (ML) algorithms with significantly dysregulated microbiotas (SDMs) for early identification of SLE patients. Additionally, we used the SHapley Additive exPlanations (SHAP) interpretability framework to visualize the impact of SDMs on the risk of developing SLE in females. Methods: Stool samples were collected from 54 SLE patients and 55 Negative Controls (NC) for microbiota analysis using 16S rRNA sequencing. Feature selection was performed using Elastic Net and Boruta on species-level taxonomy. Subsequently, four ML algorithms, namely logistic regression (LR), Adaptive Boosting (AdaBoost), Random Forest (RF), and eXtreme gradient boosting (XGBoost), were used to achieve early identification of SLE with SDMs. Finally, the best-performing algorithm was combined with SHAP to explore how SDMs affect the risk of developing SLE in females. Results: Both alpha and beta diversity were found to be different in SLE group. Following feature selection, 68 and 21 microbiota were retained in Elastic Net and Boruta, respectively, with 16 microbiota overlapping between the two, i.e., SDMs for SLE. The four ML algorithms with SDMs could effectively identify SLE patients, with XGBoost performing the best, achieving Accuracy, Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value, and AUC values of 0.844, 0.750, 0.938, 0.923, 0.790, and 0.930, respectively. The SHAP interpretability framework showed a complex non-linear relationship between the relative abundance of SDMs and the risk of SLE, with Escherichia_fergusonii having the largest SHAP value. Conclusions: This study revealed dysbiosis in the gut microbiota of female SLE patients. ML classifiers combined with SDMs can facilitate early identification of female patients with SLE, particularly XGBoost. The SHAP interpretability framework provides insight into the impact of SDMs on the risk of SLE and may inform future scientific treatment for SLE.

Prevalence of Non-SARS-CoV-2 Respiratory Pathogens and Co-Infection with SARS-CoV-2 in the Early Stage of COVID-19 Epidemic.

BACKGROUND: This study aims to reflect the prevalence of non-SARS-CoV-2 respiratory pathogens and co-infection with SARS-CoV-2 in the early stage of the COVID-19 epidemic, considering SARS-CoV-2 broke out during influenza season and its symptoms resemble those of influenza. METHODS: A total of 685 nucleic acid samples of respiratory pathogens were collected from 1 November 2019 to 20 January 2020 and were detected by the 13 Respiratory Pathogen Multiplex Detection Kit and Novel Coronavirus (2019-nCoV) Nucleic Acid Diagnostic Kit. RESULTS: In Wuhan, human rhinovirus was the most frequent infectious pathogen in November (31.5%) and human respiratory syncytial virus appeared the most in December and January (37.1%, 8.6%, respectively). Detection of SARS-CoV-2 first appeared from January 1 to January 10. Generally, 115 patients of 616 patients (18.7%) from Wuhan were infected with SARS-CoV-2, and only two children were co-infected with other respiratory pathogens. In Taiyuan, influenza A virus was detected most frequently in December and January (30.3%, 12%, respectively) without infection of SARS-CoV-2. CONCLUSIONS: Some cases diagnosed with influenza before routine nucleic acid testing of SARS-CoV-2 were attributed to COVID-19. Co-infection between SARS-CoV-2 and other non-SARS-CoV-2 respiratory pathogens existed in the early stage of COVID-19 epidemic.

Fecal Microbiota Transplantation May Represent a Good Approach for Patients with Focal Segmental Glomerulosclerosis: A Brief Report.

This is the first report of fecal microbiota transplantation (FMT) in patients with chronic kidney disease. The patient was subjected to focal segmental glomerulosclerosis (FSGS), with onset in April 2021. The main manifestation featured abnormal renal function and no proteinuria at the level of nephrotic syndrome. In May 2021, she showed biopsy-proven FSGS and was treated with glucocorticoid. However, after glucocorticoid reduction, the patient's serum creatinine increased again, so she adjusted the dosage and continued use until now. In April 2022, the patient was prescribed the FMT capsules. After FMT, the renal function remained stable, urinary protein decreased, reaching the clinical standard of complete remission, and there was no recurrence after glucocorticoid reduction. Furthermore, the patient showed significantly decreased hyperlipidemia, triglyceride (TG) and cholesterol (CHO) after FMT. During FMT, the level of cytokines fluctuated slightly, but returned to the pre-transplantation level after three months. From this, we conclude that FMT is a potential adjuvant therapy for FSGS, and patients can benefit from improving renal function and dyslipidemia.

VHL syndrome without clear family history: A rare case report and literature review of Chinese patients.

Objective: To analyze the clinical manifestations and imaging features of a hospitalized patient with intermittent headache who was finally diagnosed with von Hippel-Lindau (VHL) syndrome and to perform whole-exon gene detection to improve the understanding of the diagnosis and treatment strategies of the disease. Methods: A case of suspected VHL syndrome in Shanxi Provincial People's Hospital was analyzed. Proband DNA was also extracted for whole exome sequencing and screened for causative mutation sites, which were validated by Sanger sequencing. The literature about VHL gene mutations in Chinese patients in the past 10 years were also reviewed. Results: There is a heterozygous mutation site c.499C > G on the VHL gene on the short arm of chromosome 3 of the patient, which is a missense mutation. The mutation results in the substitution of arginine with glycine at amino acid 167 of the encoded protein, which may be primarily responsible for the disease in the patient with VHL syndrome. However, the mutation did not occur in other family members. Conclusion: Early recognition and treatment of VHL syndrome can be available with genetic testing technology. Strengthening the understanding of this complex genetic disease and improving the diagnostic rate of VHL syndrome are helpful for the precise treatment of patients with this disease, which may help prolong the survival time of patients to a certain extent and improve their quality of life.

Th17 cells in primary Sjögren's syndrome negatively correlate with increased Roseburia and Coprococcus.

Background: Dysbiosis of the gut microbiota is closely related to chronic systemic inflammation and autoimmunity, playing an essential role in the pathogenesis of primary Sjögren's syndrome (pSS). Abnormalities in the proportions of blood T lymphocyte subtype, that is Th17/Treg, were detected in pSS patients. We aimed to determine the associations between gut microbiota and Th17/Treg in pSS. Method: 98 pSS patients and 105 healthy controls (NC) were enrolled between Dec 1, 2018, and Aug 31, 2019. The baseline information and clinical parameters on pSS patients and healthy controls were collected. 16S rRNA sequencing was performed to characterize the gut microbiome and identify gut microbes that are differentially abundant between patients and healthy controls. Lastly, associations between relative abundances of specific bacterial taxa in the gut and clinical outcome parameters were evaluated. Results: Patients with pSS show decreased gut microbial diversity and richness, decreased abundance of butyrate producing bacteria, such as Roseburia and Coprococcus, and increased abundance of other taxa, such as Eubacterium rectale and Roseburia inulinivorans. These bacteria are enriched with functions related to glycolytic and lipogenic, energy, substance, galactose, pentose metabolism pathways and glucuronate interconversions, decreased with functions related to peptidoglycan biosynthesis, pyrimidine metabolism pathways. An integrative analysis identified pSS-related specific bacterial taxa in the gut, for which the abundance of Eubacterium rectale is negatively correlated with Th17/Treg. Furthermore, the pathways of biosynthesis of secondary metabolites, biosynthesis of amino acids, peptidoglycan biosynthesis and pyrimidine, galactose, pentose, microbial metabolism in diverse environments, glyoxylate and dicarboxylate metabolism are associated with Treg or Th17/Treg. Conclusions: Primary Sjögren's syndrome could lead to decreased gut microbial diversity and richness of intestinal flora in patients. The proportions of Th17 and Treg cells induced by microbiota were predictive pSS manifestations and accounted for the pSS severity.

Using Bayesian networks with Max-Min Hill-Climbing algorithm to detect factors related to multimorbidity.

Objectives: Multimorbidity (MMD) is a medical condition that is linked with high prevalence and closely related to many adverse health outcomes and expensive medical costs. The present study aimed to construct Bayesian networks (BNs) with Max-Min Hill-Climbing algorithm (MMHC) algorithm to explore the network relationship between MMD and its related factors. We also aimed to compare the performance of BNs with traditional multivariate logistic regression model. Methods: The data was downloaded from the Online Open Database of CHARLS 2018, a population-based longitudinal survey. In this study, we included 10 variables from data on demographic background, health status and functioning, and lifestyle. Missing value imputation was first performed using Random Forest. Afterward, the variables were included into logistic regression model construction and BNs model construction. The structural learning of BNs was achieved using MMHC algorithm and the parameter learning was conducted using maximum likelihood estimation. Results: Among 19,752 individuals (9,313 men and 10,439 women) aged 64.73 ± 10.32 years, there are 9,129 ones without MMD (46.2%) and 10,623 ones with MMD (53.8%). Logistic regression model suggests that physical activity, sex, age, sleep duration, nap, smoking, and alcohol consumption are associated with MMD (P < 0.05). BNs, by establishing a complicated network relationship, reveals that age, sleep duration, and physical activity have a direct connection with MMD. It also shows that education levels are indirectly connected to MMD through sleep duration and residence is indirectly linked to MMD through sleep duration. Conclusion: BNs could graphically reveal the complex network relationship between MMD and its related factors, outperforming traditional logistic regression model. Besides, BNs allows for risk reasoning for MMD through Bayesian reasoning, which is more consistent with clinical practice and thus holds some application prospects.

Fucose as a potential therapeutic molecule against the immune-mediated inflammation in IgA nepharopathy: An unrevealed link.

Background: IgA nephropathy (IgAN) is an autoimmune disease that affects people of any age and is an important cause of end-stage renal disease. However, the pathogenesis and pathophysiology of IgAN is not clear. This article aimed to explore the immune-mediated inflammation and genetic mechanisms in IgAN. Methods: The transcriptome sequencing data of IgAN glomeruli in the Gene Expression Omnibus database were downloaded. Single-sample gene set enrichment analysis was used to estimate the immune microenvironment of the merged microarray data and GSE141295. IgAN samples were divided into two clusters by cluster analysis. "limma" and "DEseq2" package in R were used to identify differentially expressed genes (DEGs). The weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression modules related to inflammation in IgAN. R software package "clusterProfiler" was used for enrichment analysis, whereas Short Time-Series Expression Miner (STEM) analysis was used to identify the trend of gene expression. Machine-learn (ML) was performed using the shiny app. Finally, Drug Signatures Database (DSigDB) was used to identify potential molecules for treating IgAN. Results: The infiltration of macrophages in IgAN glomeruli was increased, whereas CD4+ T cells, especially inducedregulatory T cells (iTregs) were decreased. A total of 1,104 common DEGs were identified from the merged data and GSE141295. Brown module was identified to have the highest inflammatory correlation with IgAN using WGCNA, and 15 hub genes were screened from this module. Among these 15 hub genes, 14 increased with the severity of IgAN inflammation based on STEM analysis. Neural network (nnet) is considered as the best model to predict the severity of IgAN. Fucose identified from DSigDB has a potential biological activity to treat IgAN. Conclusion: The increase of macrophages and the decrease of iTregs in glomeruli represent the immune-mediated inflammation of IgAN, and fucose may be a potential therapeutic molecule against IgAN because it affects genes involved in the severe inflammation of IgAN.