Deep sequencing reveals as-yet-undiscovered small RNAs in Escherichia coli

<p>Abstract</p> <p>Background</p> <p>In <it>Escherichia coli</it>, approximately 100 regulatory small RNAs (sRNAs) have been identified experimentally and many more have been predicted by various methods. To provide a comprehensive overview of sRNAs, we analysed the low-molecular-weight RNAs (< 200 nt) of <it>E. coli </it>with deep sequencing, because the regulatory RNAs in bacteria are usually 50-200 nt in length.</p> <p>Results</p> <p>We discovered 229 novel candidate sRNAs (≥ 50 nt) with computational or experimental evidence of transcription initiation. Among them, the expression of seven intergenic sRNAs and three <it>cis</it>-antisense sRNAs was detected by northern blot analysis. Interestingly, five novel sRNAs are expressed from prophage regions and we note that these sRNAs have several specific characteristics. Furthermore, we conducted an evolutionary conservation analysis of the candidate sRNAs and summarised the data among closely related bacterial strains.</p> <p>Conclusions</p> <p>This comprehensive screen for <it>E. coli </it>sRNAs using a deep sequencing approach has shown that many as-yet-undiscovered sRNAs are potentially encoded in the <it>E. coli </it>genome. We constructed the <it>Escherichia coli </it>Small RNA Browser (ECSBrowser; <url>http://rna.iab.keio.ac.jp/</url>), which integrates the data for previously identified sRNAs and the novel sRNAs found in this study.</p

Deep sequencing reveals as-yet-undiscovered small RNAs in Escherichia coli

In Escherichia coli, approximately 100 regulatory small RNAs (sRNAs) have been identified experimentally and many more have been predicted by various methods. To provide a comprehensive overview of sRNAs, we analysed the low-molecular-weight RNAs (< 200 nt) of E. coli with deep sequencing, because the regulatory RNAs in bacteria are usually 50-200 nt in length.journal articl

Predictive Properties of Plasma Amino Acid Profile for Cardiovascular Disease in Patients with Type 2 Diabetes

<div><p>Prevention of cardiovascular disease (CVD) is an important therapeutic object of diabetes care. This study assessed whether an index based on plasma free amino acid (PFAA) profiles could predict the onset of CVD in diabetic patients. The baseline concentrations of 31 PFAAs were measured with high-performance liquid chromatography-electrospray ionization-mass spectrometry in 385 Japanese patients with type 2 diabetes registered in 2001 for our prospective observational follow-up study. During 10 years of follow-up, 63 patients developed cardiovascular composite endpoints (myocardial infarction, angina pectoris, worsening of heart failure and stroke). Using the PFAA profiles and clinical information, an index (CVD-AI) consisting of six amino acids to predict the onset of any endpoints was retrospectively constructed. CVD-AI levels were significantly higher in patients who did than did not develop CVD. The area under the receiver-operator characteristic curve of CVD-AI (0.72 [95% confidence interval (CI): 0.64–0.79]) showed equal or slightly better discriminatory capacity than urinary albumin excretion rate (0.69 [95% CI: 0.62–0.77]) on predicting endpoints. A multivariate Cox proportional hazards regression analysis showed that the high level of CVD-AI was identified as an independent risk factor for CVD (adjusted hazard ratio: 2.86 [95% CI: 1.57–5.19]). This predictive effect of CVD-AI was observed even in patients with normoalbuminuria, as well as those with albuminuria. In conclusion, these results suggest that CVD-AI based on PFAA profiles is useful for identifying diabetic patients at risk for CVD regardless of the degree of albuminuria, or for improving the discriminative capability by combining it with albuminuria.</p></div

Crude and multivariate-adjusted hazard ratios for the cardiovascular composite endpoint in patient subgroups stratified according to urinary albumin excretion rate and the CVD-AI.

<p>Subjects were categorized as being above or below a UAER of 20 µg/min and above or below the CVD-AI cut-off value of −1.662. Crude (unadjusted) and adjusted hazard ratios were calculated using Cox proportional hazards regression models.</p>a<p>Estimates were adjusted for the conventional risk factors of cardiovascular disease, including age, sex, HbA1c, total cholesterol, triglyceride, high density lipoprotein cholesterol, estimated glomerular filtration rate, body mass index and hypertension.</p><p><i>Abbreviations:</i> CVD-AI, cardiovascular disease-amino acid based index; UAER, urinary albumin excretion rate.</p

Baseline characteristics of patients who did (cases) and did not (controls) experience cardiovascular events during follow-up.

<p>Data are expressed as mean ± SD for normally distributed continuous variables or median (interquartile range) for skewed continuous variables.</p><p><i>Abbreviations:</i> GFR, glomerular filtration rate; HDL, high density lipoprotein; baPWV, brachial-ankle pulse wave velocity.</p