Predictive properties of plasma amino acid profile for cardiovascular disease in patients with type 2 diabetes.

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.

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

BACKGROUND: 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. RESULTS: 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 cis-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. CONCLUSIONS: This comprehensive screen for E. coli sRNAs using a deep sequencing approach has shown that many as-yet-undiscovered sRNAs are potentially encoded in the E. coli genome. We constructed the Escherichia coli Small RNA Browser (ECSBrowser; http://rna.iab.keio.ac.jp/), which integrates the data for previously identified sRNAs and the novel sRNAs found in this study.

A screening system for artificial small RNAs that inhibit the growth of Escherichia coli.

We have developed a screening system for artificial small RNAs (sRNAs) that inhibit the growth of Escherichia coli. In this system, we used a plasmid library to express artificial sRNAs (approximately 200 bases long) containing 60 bases of random nucleotide sequence. The induced expression of the known rydB sRNA in the system reduced the amount of its possible target mRNA, rpoS, supporting the reliability of the method. To isolate clones of sRNAs that inhibited the growth of E. coli, we used two successive screening steps: (i) colony size selection on plates and (ii) monitoring E. coli growth in a 96-well plate format. As a result, 83 artificial sRNAs were identified that showed a range of inhibitory effects on bacterial growth. We also introduced nucleotide replacements into one of the highly inhibitory sRNA clones, H12, which partially abolished the inhibition of bacterial growth, suggesting that bacterial growth was inhibited in a sequence-specific manner.