Profiles of Volatile Biomarkers Detect Tuberculosis from Skin

Tuberculosis (TB) is an infectious disease that threatens >10 million people annually. Despite advances in TB diagnostics, patients continue to receive an insufficient diagnosis as TB symptoms are not specific. Many existing biodiagnostic tests are slow, have low clinical performance, and can be unsuitable for resource-limited settings. According to the World Health Organization (WHO), a rapid, sputum-free, and cost-effective triage test for real-time detection of TB is urgently needed. This article reports on a new diagnostic pathway enabling a noninvasive, fast, and highly accurate way of detecting TB. The approach relies on TB-specific volatile organic compounds (VOCs) that are detected and quantified from the skin headspace. A specifically designed nanomaterial-based sensors array translates these findings into a point-of-care diagnosis by discriminating between active pulmonary TB patients and controls with sensitivity above 90%. This fulfills the WHO's triage test requirements and poses the potential to become a TB triage test

Integrating multiple analytical platforms and chemometrics for comprehensive metabolic profiling: application to meat spoilage detection

Untargeted metabolic profiling has become a common approach to attempt to understand biological systems. However, due to the large chemical diversity in the metabolites it is generally necessary to employ multiple analytical platforms so as to encompass a wide range of metabolites. Thus it is beneficial to find chemometrics approaches which can effectively integrate data generated from multiple platforms and ideally combine the strength of each platform and overcome their inherent weaknesses; most pertinent is with respect to limited chemistries. We have reported a few studies using untargeted metabolic profiling techniques to monitor the natural spoilage process in pork and also to detect specific metabolites associated with contaminations with the pathogen Salmonella typhimurium. One method used was to analyse the volatile organic compounds (VoCs) generated throughout the spoilage process while the other was to analyse the soluble small molecule metabolites (SMM) extracted from the microbial community, as well as from the surface of the spoiled/contaminated meat. In this study, we exploit multi-block principal component analysis (MB-PCA) and multi-block partial least squares (MB-PLS) to combine the VoCs and SMM data together and compare the results obtained by analysing each data set individually. We show that by combining the two data sets and applying appropriate chemometrics, a model with much better prediction and importantly with improved interpretability was obtained. The MB-PCA model was able to combine the strength of both platforms together and generated a model with high consistency with the biological expectations, despite its unsupervised nature. MB-PLS models also achieved the best over-all performance in modelling the spoilage progression and discriminating the naturally spoiled samples and the pathogen contaminated samples. Correlation analysis and Bayesian network analysis were also performed to elucidate which metabolites were correlated strongly in the two data sets and such information could add additional information in understanding the meat spoilage process

Erratum: Genetic and physiological data implicating the new human gene G72 and the gene for D-amino acid oxidase in schizophrenia (Proceedings of the National Academy of Sciences of the United States of America (October 15, 2002) 99:21 (13675-13680))

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Polymorphisms in the 5 '-untranslated region of the human serotonin receptor 1B (HTR1B) gene affect gene expression

We present evidence of complex balancing regulation of HTR1B transcription by common polymorphisms in its promoter. Computational analysis of the HTR1B gene predicted that a 50 segment, spanning common DNA sequence variations, T-261G, A-161T, and -182INS/DEL-181, contained a putative functional promoter. Using a secreted alkaline phosphatase (SEAP) reporter gene system, we found that the haplotype -261G_-182INS-181_A-161 enhanced transcriptional activity 2.3-fold compared with the haplotype T-261_-182INS-181_A-161. Conversely, -161T reversed this, and the net effect when -261G and -161T were in the same haplotype (-261G_-182INS-181_-161T) was equivalent to the major haplotype (T-261_-182INS-181_A-161). Electrophoretic mobility shift experiments showed that -261G and -161T modify the binding of transcription factors (TFs): -261G generates a new AP2 binding site, while alleles A-161 and -161T exhibit different binding characteristics to AP1. T-261G and A-161T were found to be in linkage disequilibrium (LD) with G861C in a European ancestry population. Interestingly, G861C has been reported to be associated with several psychiatric disorders. Our results indicate that HTR1B is the target of substantial transcriptional genetic regulation by common haplotypes, which are in LD with the HTR1B single-nucleotide polymorphism (SNP) most commonly used in association studies