Investigating Genetic Determinants of Plasma Inositol Status in Adult Humans

BACKGROUND: Myo-inositol (MI) is incorporated into numerous biomolecules, including phosphoinositides and inositol phosphates. Disturbance of inositol availability or metabolism is associated with various disorders, including neurological conditions and cancers, while supplemental MI has therapeutic potential in conditions such as depression, polycystic ovary syndrome and congenital anomalies. Inositol status may be influenced by diet, synthesis, transport, utilisation and catabolism. OBJECTIVES: We aimed to investigate potential genetic regulation of circulating MI status and to evaluate correlation of MI concentration with other metabolites. METHODS: Gas chromatography mass spectrometry was used to determine plasma MI concentration of more than 2,000 healthy, young adults (aged 18-28 years) from the Trinity Student Study. Genotyping data was used to test association of plasma MI with SNPs in candidate genes, encoding inositol transporters and synthesising enzymes, and test for genome-wide association. We evaluated potential correlation of plasma MI with D-chiro inositol, glucose and other metabolites by Spearman's rank correlation. RESULTS: Mean plasma MI showed a small but significant difference between males and females (28.5 and 26.9 µM, respectively). Candidate gene analysis revealed several nominally significant associations with plasma MI, most notably for SLC5A11, encoding a sodium-coupled inositol transporter, also known as SMIT2 (sodium-dependent myo-inositol transporter 2). However, these did not survive correction for multiple testing. Subsequent testing for genome-wide association with plasma MI did not identify associations of genome-wide significance (p < 5 × 10-8). However, 8 SNPs exceeded the threshold for suggestive significant association with plasma MI concentration (p < 1 × 10-5), 3 of which were located within or close to genes: MTDH, LAPTM4B and ZP2. We found significant positive correlation of plasma MI concentration with concentration of D-chiro-inositol and several other biochemicals including glucose, methionine, betaine, sarcosine and tryptophan. CONCLUSION: Our findings suggest potential for modulation of plasma MI in young adults by variation in SLC5A11 which is worthy of further investigation

Tyrosine Phosphorylation of Tau by the Src Family Kinases Lck and Fyn

<p>Abstract</p> <p>Background</p> <p>Tau protein is the principal component of the neurofibrillary tangles found in Alzheimer's disease, where it is hyperphosphorylated on serine and threonine residues, and recently phosphotyrosine has been demonstrated. The Src-family kinase Fyn has been linked circumstantially to the pathology of Alzheimer's disease, and shown to phosphorylate Tyr18. Recently another Src-family kinase, Lck, has been identified as a genetic risk factor for this disease.</p> <p>Results</p> <p>In this study we show that Lck is a tau kinase. <it>In vitro</it>, comparison of Lck and Fyn showed that while both kinases phosphorylated Tyr18 preferentially, Lck phosphorylated other tyrosines somewhat better than Fyn. In co-transfected COS-7 cells, mutating any one of the five tyrosines in tau to phenylalanine reduced the apparent level of tau tyrosine phosphorylation to 25-40% of that given by wild-type tau. Consistent with this, tau mutants with only one remaining tyrosine gave poor phosphorylation; however, Tyr18 was phosphorylated better than the others.</p> <p>Conclusions</p> <p>Fyn and Lck have subtle differences in their properties as tau kinases, and the phosphorylation of tau is one mechanism by which the genetic risk associated with Lck might be expressed pathogenically.</p

Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong

Recent studies have reported numerous predictors for adverse outcomes in COVID-19 disease. However, there have been few simple clinical risk scores available for prompt risk stratification. The objective is to develop a simple risk score for predicting severe COVID-19 disease using territory-wide data based on simple clinical and laboratory variables. Consecutive patients admitted to Hong Kong’s public hospitals between 1 January and 22 August 2020 and diagnosed with COVID-19, as confirmed by RT-PCR, were included. The primary outcome was composite intensive care unit admission, need for intubation or death with follow-up until 8 September 2020. An external independent cohort from Wuhan was used for model validation. COVID-19 testing was performed in 237,493 patients and 4442 patients (median age 44.8 years old, 95% confidence interval (CI): [28.9, 60.8]); 50% males) were tested positive. Of these, 209 patients (4.8%) met the primary outcome. A risk score including the following components was derived from Cox regression: gender, age, diabetes mellitus, hypertension, atrial fibrillation, heart failure, ischemic heart disease, peripheral vascular disease, stroke, dementia, liver diseases, gastrointestinal bleeding, cancer, increases in neutrophil count, potassium, urea, creatinine, aspartate transaminase, alanine transaminase, bilirubin, D-dimer, high sensitive troponin-I, lactate dehydrogenase, activated partial thromboplastin time, prothrombin time, and C-reactive protein, as well as decreases in lymphocyte count, platelet, hematocrit, albumin, sodium, low-density lipoprotein, high-density lipoprotein, cholesterol, glucose, and base excess. The model based on test results taken on the day of admission demonstrated an excellent predictive value. Incorporation of test results on successive time points did not further improve risk prediction. The derived score system was evaluated with out-of-sample five-cross-validation (AUC: 0.86, 95% CI: 0.82–0.91) and external validation (N = 202, AUC: 0.89, 95% CI: 0.85–0.93). A simple clinical score accurately predicted severe COVID-19 disease, even without including symptoms, blood pressure or oxygen status on presentation, or chest radiograph results

Host-Microbe Co-metabolism Dictates Cancer Drug Efficacy in C. elegans

Fluoropyrimidines are the first-line treatment for colorectal cancer, but their efficacy is highly variable between patients. We queried whether gut microbes, a known source of inter-individual variability, impacted drug efficacy. Combining two tractable genetic models, the bacterium E. coli and the nematode C. elegans, we performed three-way high-throughput screens that unraveled the complexity underlying host-microbe-drug interactions. We report that microbes can bolster or suppress the effects of fluoropyrimidines through metabolic drug interconversion involving bacterial vitamin B-6, B-9, and ribonucleotide metabolism. Also, disturbances in bacterial deoxynucleotide pools amplify 5-FU-induced autophagy and cell death in host cells, an effect regulated by the nucleoside diphosphate kinase ndk-1. Our data suggest a two-way bacterial mediation of fluoropyrimidine effects on host metabolism, which contributes to drug efficacy. These findings highlight the potential therapeutic power of manipulating intestinal microbiota to ensure host metabolic health and treat disease.Peer reviewe

Host-Microbe Co-metabolism Dictates Cancer Drug Efficacy in C. elegans.

Fluoropyrimidines are the first-line treatment for colorectal cancer, but their efficacy is highly variable between patients. We queried whether gut microbes, a known source of inter-individual variability, impacted drug efficacy. Combining two tractable genetic models, the bacterium E. coli and the nematode C. elegans, we performed three-way high-throughput screens that unraveled the complexity underlying host-microbe-drug interactions. We report that microbes can bolster or suppress the effects of fluoropyrimidines through metabolic drug interconversion involving bacterial vitamin B6, B9, and ribonucleotide metabolism. Also, disturbances in bacterial deoxynucleotide pools amplify 5-FU-induced autophagy and cell death in host cells, an effect regulated by the nucleoside diphosphate kinase ndk-1. Our data suggest a two-way bacterial mediation of fluoropyrimidine effects on host metabolism, which contributes to drug efficacy. These findings highlight the potential therapeutic power of manipulating intestinal microbiota to ensure host metabolic health and treat disease

Identification of microbial community in the urban environment: The concordance between conventional culture and nanopore 16S rRNA sequencing

IntroductionMicrobes in the built environment have been implicated as a source of infectious diseases. Bacterial culture is the standard method for assessing the risk of exposure to pathogens in urban environments, but this method only accounts for &lt;1% of the diversity of bacteria. Recently, full-length 16S rRNA gene analysis using nanopore sequencing has been applied for microbial evaluations, resulting in a rise in the development of long-read taxonomic tools for species-level classification. Regarding their comparative performance, there is, however, a lack of information.MethodsHere, we aim to analyze the concordance of the microbial community in the urban environment inferred by multiple taxonomic classifiers, including ARGpore2, Emu, Kraken2/Bracken and NanoCLUST, using our 16S-nanopore dataset generated by MegaBLAST, as well as assess their abilities to identify culturable species based on the conventional culture results.ResultsAccording to our results, NanoCLUST was preferred for 16S microbial profiling because it had a high concordance of dominant species and a similar microbial profile to MegaBLAST, whereas Kraken2/Bracken, which had similar clustering results as NanoCLUST, was also desirable. Second, for culturable species identification, Emu with the highest accuracy (81.2%) and F1 score (29%) for the detection of culturable species was suggested.DiscussionIn addition to generating datasets in complex communities for future benchmarking studies, our comprehensive evaluation of the taxonomic classifiers offers recommendations for ongoing microbial community research, particularly for complex communities using nanopore 16S rRNA sequencing

Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong

Recent studies have reported numerous predictors for adverse outcomes in COVID-19 disease. However, there have been few simple clinical risk scores available for prompt risk stratification. The objective is to develop a simple risk score for predicting severe COVID-19 disease using territory-wide data based on simple clinical and laboratory variables. Consecutive patients admitted to Hong Kong’s public hospitals between 1 January and 22 August 2020 and diagnosed with COVID-19, as confirmed by RT-PCR, were included. The primary outcome was composite intensive care unit admission, need for intubation or death with follow-up until 8 September 2020. An external independent cohort from Wuhan was used for model validation. COVID-19 testing was performed in 237,493 patients and 4442 patients (median age 44.8 years old, 95% confidence interval (CI): [28.9, 60.8]); 50% males) were tested positive. Of these, 209 patients (4.8%) met the primary outcome. A risk score including the following components was derived from Cox regression: gender, age, diabetes mellitus, hypertension, atrial fibrillation, heart failure, ischemic heart disease, peripheral vascular disease, stroke, dementia, liver diseases, gastrointestinal bleeding, cancer, increases in neutrophil count, potassium, urea, creatinine, aspartate transaminase, alanine transaminase, bilirubin, D-dimer, high sensitive troponin-I, lactate dehydrogenase, activated partial thromboplastin time, prothrombin time, and C-reactive protein, as well as decreases in lymphocyte count, platelet, hematocrit, albumin, sodium, low-density lipoprotein, high-density lipoprotein, cholesterol, glucose, and base excess. The model based on test results taken on the day of admission demonstrated an excellent predictive value. Incorporation of test results on successive time points did not further improve risk prediction. The derived score system was evaluated with out-of-sample five-cross-validation (AUC: 0.86, 95% CI: 0.82–0.91) and external validation (N = 202, AUC: 0.89, 95% CI: 0.85–0.93). A simple clinical score accurately predicted severe COVID-19 disease, even without including symptoms, blood pressure or oxygen status on presentation, or chest radiograph results

The differential translation capabilities of the human DHFR2 gene indicates a developmental and tissue specific endogenous protein of low abundance.

A functional role has been ascribed to the human Dihydrofolate reductase 2 (DHFR2) gene based on the enzymatic activity of recombinant versions of the predicted translated protein. However, the in vivo function is still unclear. The high amino acid sequence identity (92%) between DHFR2 and its parental homologue, DHFR, makes analysis of the endogenous protein challenging. This paper describes a targeted mass spectrometry proteomics approach in several human cell lines and tissue types to identify DHFR2 specific peptides as evidence of its translation. We show definitive evidence that the dihydrofolate reductase activity in the mitochondria is in fact mediated by DHFR, and not DHFR2. Analysis of Ribo-seq data and an experimental assessment of ribosome association using a sucrose cushion, showed that the two main Ensembl annotated mRNA isoforms of DHFR2, 201 and 202, show differential association with the ribosome. This indicates a functional role at both the RNA and protein level. However, we were unable to detect DHFR2 protein at a detectable level in most cell types examined despite various RNA isoforms of DHFR2 being relatively abundant. We did detect a DHFR2 specific peptide in embryonic heart, indicating that the protein may have a specific role during embryogenesis. We propose that the main functionality of the DHFR2 gene in adult cells is likely to arise at the RNA level