EMNPD: a comprehensive endophytic microorganism natural products database for prompt the discovery of new bioactive substances.

The discovery and utilization of natural products derived from endophytic microorganisms have garnered significant attention in pharmaceutical research. While remarkable progress has been made in this field each year, the absence of dedicated open-access databases for endophytic microorganism natural products research is evident. To address the increasing demand for mining and sharing of data resources related to endophytic microorganism natural products, this study introduces EMNPD, a comprehensive endophytic microorganism natural products database comprising manually curated data. Currently, EMNPD offers 6632 natural products from 1017 endophytic microorganisms, targeting 1286 entities (including 94 proteins, 282 cell lines, and 910 species) with 91 diverse bioactivities. It encompasses the physico-chemical properties of natural products, ADMET information, quantitative activity data with their potency, natural products contents with diverse fermentation conditions, systematic taxonomy, and links to various well-established databases. EMNPD aims to function as an open-access knowledge repository for the study of endophytic microorganisms and their natural products, thereby facilitating drug discovery research and exploration of bioactive substances. The database can be accessed at http://emnpd.idrblab.cn/ without the need for registration, enabling researchers to freely download the data. EMNPD is expected to become a valuable resource in the field of endophytic microorganism natural products and contribute to future drug development endeavors.

Association of tea and coffee consumption with the risk of all-cause and cause-specific mortality among individuals with metabolic syndrome: a prospective cohort study.

BACKGROUND: The relationship between tea and coffee consumption and mortality among patients with metabolic syndrome (MetS) remains barely explored. Herein, this study aimed to examine the association between tea and coffee consumption and the likelihood of all-cause and cause-specific mortality in patients with MetS. METHODS: A total of 118,872 participants with MetS at baseline from the UK Biobank cohort were included. Information on tea and coffee consumption was obtained during recruitment using a touchscreen questionnaire. Hazard ratios (HRs) and 95% confidence intervals (CIs) for mortality were determined using Cox proportional hazards models. RESULTS: During a median follow-up of 13.87 years, 13,666 deaths were recorded, with 5913, 3362, and 994 deaths from cancer, cardiovascular diseases (CVD), and respiratory disease (RD), respectively. This research showed a significant inverse association between tea intake and the risk of all-cause and cancer mortality, the respective HRs (95% CI) for consuming tea 2 vs. 0 cup/day were 0.89 (0.84-0.95), and 0.91 (0.83-0.99), and tea intake ≥ 4 cups/day could reduce CVD mortality by 11% (HR 0.89; 95% CI 0.81-0.98). The U-shaped nonlinear association between coffee intake and all-cause/CVD mortality was examined (all p-nonlinear < 0.001). The HRs (95% CI) for coffee consumption 1 vs. 0 cup/day were 0.93 (0.89-0.98) and 0.89 (0.80-0.99), and for ≥ 4 vs. 0 cup/day were 1.05 (1.01-1.11) and 1.13 (1.03-1.25), respectively. Notably, the combined intake of tea and coffee presented a protective effect against all-cause mortality (HR < 1). CONCLUSIONS: The importance of daily tea and moderate coffee consumption in individuals with MetS to optimise health benefits are highlighted.

Association of serum 25-hydroxyvitamin D with the incidence of 16 cancers, cancer mortality, and all-cause mortality among individuals with metabolic syndrome: a prospective cohort study.

PURPOSE: The relationship between vitamin D levels and cancer incidence and mortality in individuals with metabolic syndrome (MetS) remains poorly explored. Herein, we aimed to determine the association between 25-hydroxyvitamin D [25(OH)D] concentrations and the risk of 16 cancer incidence types and cancer/all-cause mortality in patients with MetS. METHODS: We enrolled 97,621 participants with MetS at recruitment from the UK Biobank cohort. The exposure factor was baseline serum 25(OH)D concentrations. The associations were examined using Cox proportional hazards models, which were displayed as hazard ratios (HRs) with 95% confidence intervals (CIs). RESULTS: Over a median follow-up period of 10.92 years for cancer incidence outcomes, 12,137 new cancer cases were recorded. We observed that 25(OH)D concentrations were inversely related to the risk of colon, lung, and kidney cancer, and HRs (95% CI) for 25(OH)D ≥ 75.0 vs. < 25.0 nmol/L were 0.67 (0.45-0.98), 0.64 (0.45-0.91), and 0.54 (0.31-0.95), respectively. The fully adjusted model revealed a null correlation between 25(OH)D and the incidence of stomach, rectum, liver, pancreas, breast, ovary, bladder, brain, multiple myeloma, leukemia, non-Hodgkin lymphoma, esophagus, and corpus uteri cancer. Over a median follow-up period of 12.72 years for mortality outcomes, 8286 fatalities (including 3210 cancer mortalities) were documented. An "L-shaped" nonlinear dose-response correlation was detected between 25(OH)D and cancer/all-cause mortality; the respective HRs (95% CI) were 0.75 (0.64-0.89) and 0.65 (0.58-0.72). CONCLUSION: These findings emphasize the importance of 25(OH)D in cancer prevention and longevity promotion among patients with MetS.

A systematic review of state-of-the-art strategies for machine learning-based protein function prediction.

New drug discovery is inseparable from the discovery of drug targets, and the vast majority of the known targets are proteins. At the same time, proteins are essential structural and functional elements of living cells necessary for the maintenance of all forms of life. Therefore, protein functions have become the focus of many pharmacological and biological studies. Traditional experimental techniques are no longer adequate for rapidly growing annotation of protein sequences, and approaches to protein function prediction using computational methods have emerged and flourished. A significant trend has been to use machine learning to achieve this goal. In this review, approaches to protein function prediction based on the sequence, structure, protein-protein interaction (PPI) networks, and fusion of multi-information sources are discussed. The current status of research on protein function prediction using machine learning is considered, and existing challenges and prominent breakthroughs are discussed to provide ideas and methods for future studies.

A systematic review on the state-of-the-art strategies for protein representation.

The study of drug-target protein interaction is a key step in drug research. In recent years, machine learning techniques have become attractive for research, including drug research, due to their automated nature, predictive power, and expected efficiency. Protein representation is a key step in the study of drug-target protein interaction by machine learning, which plays a fundamental role in the ultimate accomplishment of accurate research. With the progress of machine learning, protein representation methods have gradually attracted attention and have consequently developed rapidly. Therefore, in this review, we systematically classify current protein representation methods, comprehensively review them, and discuss the latest advances of interest. According to the information extraction methods and information sources, these representation methods are generally divided into structure and sequence-based representation methods. Each primary class can be further divided into specific subcategories. As for the particular representation methods involve both traditional and the latest approaches. This review contains a comprehensive assessment of the various methods which researchers can use as a reference for their specific protein-related research requirements, including drug research.

Genetic risk, incident colorectal cancer, and the benefits of adhering to a healthy lifestyle: A prospective study using data from UK Biobank and FinnGen.

Background: Genetic factors increase the individual risk of colorectal cancer (CRC); however, the extent to which a healthy lifestyle can offset increased genetic risk is unknown. This study investigated whether a healthy lifestyle is associated with lower CRC risk, regardless of genetic risk. Methods: We recruited 390,365 participants without cancer at baseline (2006-2010) from the UK Biobank. The primary outcome was CRC incidence. A healthy lifestyle score constructed using 16 factors of six dimensions (smoking, drinking, body mass index, diet, exercise, and sleep) was categorized into three risk categories: favorable, intermediate, and unfavorable. To calculate the polygenic risk scores (PRSs) of UK Biobank participants, we extracted 454,678 single nucleotide polymorphisms (SNPs) from the UK Biobank and FinnGen Biobank after quality control. Cox proportional hazards regression was performed to evaluate the associations and was expressed as hazard ratios (HRs) with 95% confidence intervals (CIs). Results: During a median follow-up of 10.90 years, 4,090 new CRC cases were reported in the UK Biobank. The "best-fit" PRSs were constructed using 59 SNPs based on the UK Biobank cohort and FinnGen genome-wide association study summary data (R2 = 0.23%) and were divided into low (lowest quintile), intermediate (including second-fourth quintile), and high (highest quintile) genetic risk categories. The multivariate-adjusted Cox model revealed that participants with favorable lifestyles had HRs of 0.66 (95% CI = 0.60-0.72) for developing CRC vs. those with unfavorable lifestyles; low genetic risk was associated with a decreased risk of CRC (HR = 0.67, 95% CI =0.61-0.74) compared with those with high genetic risk. The HRs for low genetic risk participants with favorable lifestyles were 0.44 (95% CI =0.36-0.55) vs. participants with high genetic risk and unfavorable lifestyles. Among the participants with low, intermediate, or high genetic risk, the HRs of favorable vs. unfavorable lifestyles were 0.74, 0.64, and 0.72 (all p< 0.05). Conclusions: Low genetic risk and a favorable lifestyle were significantly associated with a decreased risk of CRC. A favorable lifestyle was associated with a lower CRC risk, regardless of genetic risk.

Association of a Healthy Lifestyle with All-Cause, Cause-Specific Mortality and Incident Cancer among Individuals with Metabolic Syndrome: A Prospective Cohort Study in UK Biobank.

This study investigated the association between a healthy lifestyle with all-cause, cause-specific mortality, and cancer incidence among individuals with metabolic syndrome (MetS). Healthy lifestyle scores were created based on MetS management guidelines, including never/quitting smoking, moderate drinking, good sleep, healthy diet, sufficient exercise, social support, and less sedentary behaviour. Weighted healthy lifestyle scores were further constructed and classified into three groups: unfavourable (lowest quintile), intermediate (quintiles 2−4), and favourable (highest quintile) lifestyles. We included 87,342 MetS participants from the UK Biobank. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using multivariate-adjusted Cox proportional hazards regression. During a median follow-up of 12.54 years, 6739 deaths were reported; during a median follow-up of 10.69 years, 10,802 new cancer cases were documented. We found a favourable lifestyle was inversely associated with all-cause mortality (HR: 0.57; 95%CI: 0.53−0.62), cause-specific mortality from respiratory disease, cancer, digestive disease, cardiovascular disease (HR < 1; p-trend < 0.001), and overall cancer incidence (HR: 0.84; 95% CI: 0.79−0.90). Our results indicate that adherence to healthy lifestyles is associated with lower overall cancer incidence and all-cause mortality risk among MetS individuals. However, causality cannot be made due to the nature of observational studies.