HIV Drug Resistant Prediction and Featured Mutants Selection using Machine Learning Approaches

HIV/AIDS is widely spread and ranks as the sixth biggest killer all over the world. Moreover, due to the rapid replication rate and the lack of proofreading mechanism of HIV virus, drug resistance is commonly found and is one of the reasons causing the failure of the treatment. Even though the drug resistance tests are provided to the patients and help choose more efficient drugs, such experiments may take up to two weeks to finish and are expensive. Because of the fast development of the computer, drug resistance prediction using machine learning is feasible. In order to accurately predict the HIV drug resistance, two main tasks need to be solved: how to encode the protein structure, extracting the more useful information and feeding it into the machine learning tools; and which kinds of machine learning tools to choose. In our research, we first proposed a new protein encoding algorithm, which could convert various sizes of proteins into a fixed size vector. This algorithm enables feeding the protein structure information to most state of the art machine learning algorithms. In the next step, we also proposed a new classification algorithm based on sparse representation. Following that, mean shift and quantile regression were included to help extract the feature information from the data. Our results show that encoding protein structure using our newly proposed method is very efficient, and has consistently higher accuracy regardless of type of machine learning tools. Furthermore, our new classification algorithm based on sparse representation is the first application of sparse representation performed on biological data, and the result is comparable to other state of the art classification algorithms, for example ANN, SVM and multiple regression. Following that, the mean shift and quantile regression provided us with the potentially most important drug resistant mutants, and such results might help biologists/chemists to determine which mutants are the most representative candidates for further research

Health effects of high serum calcium levels:Updated phenome-wide Mendelian randomisation investigation and review of Mendelian randomisation studies

BACKGROUND: Calcium plays a role in a wide range of biological functions. Here we conducted a phenome-wide Mendelian randomisation (MR-PheWAS) analysis and a systematic review for MR studies to comprehensively investigate the health effects of serum calcium. METHODS: One-hundred and thirty genetic variants strongly associated with serum calcium levels were used as instrumental variables. A phenome-wide association analysis (PheWAS) was conducted to examine the associations of genetically predicted serum calcium with 1473 distinct phenotypes in the UK Biobank including 339,197 individuals. Observed associations in PheWAS were further tested for replication in two-sample MR replication analysis. A systematic review for MR studies on serum calcium was performed to synthesize the published evidence and compare with the current MR-PheWAS findings. FINDINGS: Higher genetically predicted calcium levels were associated with decreased risk of 5 diseases in dermatologic and musculoskeletal systems and increased risk of 17 diseases in circulatory, digestive, endocrine, genitourinary and immune systems. Eight associations were replicated in two-sample MR analysis. These included decreased risk of osteoarthritis and increased risk of coronary artery disease, myocardial infarction, coronary atherosclerosis, hyperparathyroidism, disorder of parathyroid gland, gout, and calculus of kidney and ureter with increased serum calcium. Systematic review of 25 MR studies provided supporting evidence on five out of the eight disease outcomes, while the increased risk of gout, hyperparathyroidism and disorder of parathyroid gland were novel findings. INTERPRETATION: This study found wide-ranged health effects of high serum calcium, which suggests that the benefits and adversities of strategies promoting calcium intake should be assessed. FUNDING: ET is supported by a CRUK Career Development Fellowship (C31250/A22804). XL is supported by the Natural Science Fund for Distinguished Young Scholars of Zhejiang Province. SCL acknowledges research funding from the Swedish Heart Lung Foundation (Hjärt-Lungfonden, 20210351), the Swedish Research Council (Vetenskapsrådet, 2019-00977), and the Swedish Cancer Society (Cancerfonden)

Antifungal effects and biocontrol potential of lipopeptide-producing Streptomyces against banana Fusarium wilt fungus Fusarium oxysporum f. sp. cubense

Fusarium wilt of banana (FWB), caused by Fusarium oxysporum f. sp. cubense (Foc), especially tropical race 4 (TR4), presents the foremost menace to the global banana production. Extensive efforts have been made to search for efficient biological control agents for disease management. Our previous study showed that Streptomyces sp. XY006 exhibited a strong inhibitory activity against several phytopathogenic fungi, including F. oxysporum. Here, the corresponding antifungal metabolites were purified and determined to be two cyclic lipopeptide homologs, lipopeptin A and lipopeptin B. Combined treatment with lipopeptin complex antagonized Foc TR4 by inhibiting mycelial growth and conidial sporulation, suppressing the synthesis of ergosterol and fatty acids and lowering the production of fusaric acid. Electron microscopy observation showed that lipopeptide treatment induced a severe disruption of the plasma membrane, leading to cell leakage. Lipopeptin A displayed a more pronounced antifungal activity against Foc TR4 than lipopeptin B. In pot experiments, strain XY006 successfully colonized banana plantlets and suppressed the incidence of FWB, with a biocontrol efficacy of up to 87.7%. Additionally, XY006 fermentation culture application improved plant growth parameters and induced peroxidase activity in treated plantlets, suggesting a possible role in induced resistance. Our findings highlight the potential of strain XY006 as a biological agent for FWB, and further research is needed to enhance its efficacy and mode of action in planta

Gut microbiota-derived metabolite Trimethylamine-N-oxide (TMAO) and multiple health outcomes:an umbrella review and updated meta-analysis

BACKGROUND: Trimethylamine-N-oxide (TMAO) is a gut microbiota-derived metabolite produced from dietary nutrients. Many studies have discovered that circulating TMAO levels are linked to a wide range of health outcomes. OBJECTIVES: This study aimed to summarize health outcomes related to circulating TMAO levels. METHODS: We searched Embase, Medline, Web of Science and Scopus databases from inception to 15 February 2022 to identify and update meta-analyses examining the associations between TMAO and multiple health outcomes. For each health outcome, we estimated the summary effect size, 95% prediction confidence interval (CI), between-study heterogeneity, evidence of small-study effects, and evidence of excess-significance bias. These metrics were used to evaluate the evidence credibility of the identified associations. RESULTS: This umbrella review identified 24 meta-analyses that investigated the association between circulating TMAO levels and health outcomes including all-cause mortality, cardiovascular diseases, diabetes mellitus, cancer, and renal function. We updated these meta-analyses by including a total of 82 individual studies in 18 unique health outcomes. Among them, 14 associations were nominally significant. After evidence credibility assessment, we found six (33%) associations (i.e., all-cause mortality, cardiovascular disease mortality, major adverse cardiovascular events, hypertension, diabetes mellitus, and glomerular filtration rate) to present highly suggestive evidence. CONCLUSIONS: TMAO might be a novel biomarker related to human health conditions including all-cause mortality, hypertension, cardiovascular disease, diabetes, cancer and kidney function. Further studies are needed to investigate whether circulating TMAO levels could be an intervention target for chronic disease

An Explanation of the Underlying Mechanisms for the In Vitro and In Vivo Antiurolithic Activity of Glechoma longituba

Purpose. To use in vitro and in vivo models to evaluate Glechoma longituba extract to provide scientific evidence for this extract’s antiurolithic activity. Materials and Methods. Potassium citrate was used as a positive control group. Oxidative stress (OS) markers and the expression of osteopontin (OPN) and kidney injury molecule-1 (KIM-1) were measured to assess the protective effects of Glechoma longituba. Multiple urolithiasis-related biochemical parameters were evaluated in urine and serum. Kidneys were harvested for histological examination and the assessment of crystal deposits. Results. In vitro and in vivo experiments demonstrated that treatment with Glechoma longituba extract significantly decreased calcium oxalate- (CaOx-) induced OPN expression, KIM-1 expression, and OS compared with the positive control group (P<0.05). Additionally, in vivo rats that received Glechoma longituba extract exhibited significantly decreased CaOx deposits and pathological alterations (P<0.05) compared with urolithic rats. Significantly lower levels of oxalate, creatinine, and urea and increased citrate levels were observed among rats that received Glechoma longituba (P<0.05) compared with urolithic rats. Conclusion. Glechoma longituba has antiurolithic effects due to its possible combined effects of increasing antioxidant levels, decreasing urinary stone-forming constituents and urolithiasis-related protein expression, and elevating urinary citrate levels

Electrospun diclofenac sodium loaded Eudragit® L 100-55 nanofibers for colon-targeted drug delivery

Eudragit® L 100-55 nanofibers loaded with diclofenac sodium (DS) were successfully prepared using an electrospinning process, and characterized for structural and pharmacodynamic properties. The influence of solvent and drug content on fiber formation and quality was also investigated. Fiber formation was successful using a solvent mixture 5:1 (v/v) ethanol:DMAc. XRD and DSC analysis of fibers confirm electron microscopic evidence that DS is evenly distributed in the nanofibers in an amorphous state. FTIR analysis indicates hydrogen bonding occurs between the drug and the polymer, which accounts for the molecular integration of the two components. In vitro dissolution tests verified that all the drug-loaded Eudragit® L 100-55 nanofibers had pH-dependent drug release profiles, with limited, less than 3%, release at pH 1.0, but a sustained and complete release at pH 6.8. This profile of properties indicates drug-loaded Eudragit® L 100-55 nanofibers have the potential to be developed as oral colon-targeted drug delivery systems