Three HCM-causing tropomyosin (Tm) mutants (V95A, D175N, and E180G) were examined using the thin-filament extraction and reconstitution technique. The effects of Ca(2+), ATP, phosphate, and ADP concentrations on cross-bridge kinetics in myocardium reconstituted with each of these mutants were studied at 25°C, and compared to wild-type (WT) Tm at physiological ionic strength (200 mM). All three mutants showed significantly higher (2-3.5 fold) low Ca(2+) tension (T(LC)) and stiffness than WT at pCa 8.0. High Ca(2+) tension (T(HC)) was significantly higher for E180G than that for WT, whereas T(HC) of V95A and D175N was similar to WT; high Ca(2+) stiffness (Y(HC)) had the same trend. The Ca(2+) sensitivity of isometric force was significantly greater for V95A and E180G than for WT, whereas that of D175N remained the same as for WT; for all mutants, cooperativity was lower than for WT. Nine kinetic constants and the cross-bridge distribution were deduced using sinusoidal analysis. The number of force-generating cross bridges was similar among the D175N, E180G, and WT Tm forms, but it was significantly larger in the case of V95A than WT. We conclude that the increased number of actively cycling cross bridges at pCa 8 is the major cause of Tm mutation-related HCM pathogenesis, which may result in diastolic dysfunction. Decreased contractility (T(act)) in V95A and D175N may further contribute to the severity of myocyte hypertrophy and related prognosis of the disease.

Background 16S rRNA gene amplicon sequencing (16S analysis) is widely used to analyze microbiota with next-generation sequencing technologies. Here, we compared fecal 16S analysis data from 192 Japanese volunteers using the modified V1–V2 (V12) and the standard V3–V4 primer (V34) sets to optimize the gut microbiota analysis protocol. Results QIIME1 and QIIME2 analysis revealed a higher number of unclassified representative sequences in the V34 data than in the V12 data. The comparison of bacterial composition demonstrated that at the phylum level, Actinobacteria and Verrucomicrobia were detected at higher levels with V34 than with V12. Among these phyla, we observed higher relative compositions of Bifidobacterium and Akkermansia with V34. To estimate the actual abundance, we performed quantitative real-time polymerase chain reaction (qPCR) assays for Akkermansia and Bifidobacterium. We found that the abundance of Akkermansia as detected by qPCR was close to that in V12 data, but was markedly lower than that in V34 data. The abundance of Bifidobacterium detected by qPCR was higher than that in V12 and V34 data. Conclusions These results indicate that the bacterial composition derived from the V34 region might differ from the actual abundance for specific gut bacteria. We conclude that the use of the modified V12 primer set is more desirable in the 16S analysis of the Japanese gut microbiota.

Several lines of evidence support possible serotonin transporter (5-HTT) involvement in modulating eating disorders (ED). The 5-HTT gene is a good candidate for genetic studies on the course of ED, despite controversy concerning the association between polymorphism in the 5-HTT gene promoter region (5-HTTLPR) and ED. Comparison of 5-HTTLPR distribution in 195 female Japanese ED patients and 290 age- and gender-matched control subjects facilitated examining the association between the course of the disease and 5-HTTLPR in 138 of 195 ED subjects. The 5-HTTLPR S allele frequency was significantly higher in subjects with anorexia nervosa (AN) than in control subjects. Among subjects observed > or =3 years, the S allele frequency was significantly higher in those diagnosed as AN at ED onset than in those diagnosed as AN in this study. The 5-HTTLPR S allele might play some role in the development of AN with persistent disease.

Irritable bowel syndrome (IBS) is diagnosed by subjective clinical symptoms. We aimed to establish an objective IBS prediction model based on gut microbiome analyses employing machine learning. We collected fecal samples and clinical data from 85 adult patients who met the Rome III criteria for IBS, as well as from 26 healthy controls. The fecal gut microbiome profiles were analyzed by 16S ribosomal RNA sequencing, and the determination of short-chain fatty acids was performed by gas chromatography–mass spectrometry. The IBS prediction model based on gut microbiome data after machine learning was validated for its consistency for clinical diagnosis. The fecal microbiome alpha-diversity indices were significantly smaller in the IBS group than in the healthy controls. The amount of propionic acid and the difference between butyric acid and valerate were significantly higher in the IBS group than in the healthy controls (p < 0.05). Using LASSO logistic regression, we extracted a featured group of bacteria to distinguish IBS patients from healthy controls. Using the data for these featured bacteria, we established a prediction model for identifying IBS patients by machine learning (sensitivity >80%; specificity >90%). Gut microbiome analysis using machine learning is useful for identifying patients with IBS.

Ca2þ signaling in striated muscle cells is critically dependent upon thin filament proteins tropomyosin (Tm) and troponin (Tn) to regulate mechanical output. Using in vitro measurements of contractility, we demonstrate that even in the absence of actin and Tm, human cardiac Tn (cTn) enhances heavy meromyosin MgATPase activity by up to 2.5-fold in solution. In addition, cTn without Tm significantly increases, or superactivates sliding speed of filamentous actin (F-actin) in skeletal motility assays by at least 12%, depending upon [cTn]. cTn alone enhances skeletal heavy meromyosin's MgATPase in a concentration-dependent manner and with submicromolar affinity. cTn-mediated increases in myosin ATPase may be the cause of superactivation of maximum Ca 2þ -activated regulated thin filament sliding speed in motility assays relative to unregulated skeletal F-actin. To specifically relate this classical superactivation to cardiac muscle, we demonstrate the same response using motility assays where only cardiac proteins were used, where regulated cardiac thin filament sliding speeds with cardiac myosin are >50% faster than unregulated cardiac F-actin. We additionally demonstrate that the COOHterminal mobile domain of cTnI is not required for this interaction or functional enhancement of myosin activity. Our results provide strong evidence that the interaction between cTn and myosin is responsible for enhancement of cross-bridge kinetics when myosin binds in the vicinity of Tn on thin filaments. These data imply a novel and functionally significant molecular interaction that may provide new insights into Ca 2þ activation in cardiac muscle cells.