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High-throughput (HTP) technologies offer the capability to evaluate the genome, proteome, and metabolome of an organism at a global scale. This opens up new opportunities to define complex signatures of disease that involve signals from multiple types of biomolecules. However, integrating these data types is difficult due to the heterogeneity of the data. We present a Bayesian approach to integration that uses posterior probabilities to assign class memberships to samples using individual and multiple data sources; these probabilities are based on lower-level likelihood functions derived from standard statistical learning algorithms. We demonstrate this approach on microbial infections of mice, where the bronchial alveolar lavage fluid was analyzed by three HTP technologies, two proteomic and one metabolomic. We demonstrate that integration of the three datasets improves classification accuracy to ~89% from the best individual dataset at ~83%. In addition, we present a new visualization tool called Visual Integration for Bayesian Evaluation (VIBE) that allows the user to observe classification accuracies at the class level and evaluate classification accuracies on any subset of available data types based on the posterior probability models defined for the individual and integrated data.
PMCID: PMC4137860  PMID: 19209722
2.  A mutli-omic systems approach to elucidating Yersinia virulence mechanisms 
Molecular bioSystems  2012;9(1):44-54.
The underlying mechanisms that lead to dramatic differences between closely related pathogens are not always readily apparent. For example, the genomes of Yersinia pestis (YP) the causative agent of plague with a high mortality rate and Yersinia pseudotuberculosis (YPT) an enteric pathogen with a modest mortality rate are highly similar with some species specific differences; however the molecular causes of their distinct clinical outcomes remain poorly understood. In this study, a temporal multi-omic analysis of YP and YPT at physiologically relevant temperatures was performed to gain insights into how an acute and highly lethal bacterial pathogen, YP, differs from its less virulent progenitor, YPT. This analysis revealed higher gene and protein expression levels of conserved major virulence factors in YP relative to YPT, including the Yop virulon and the pH6 antigen. This suggests that adaptation in the regulatory architecture, in addition to the presence of unique genetic material, may contribute to the increased pathogenenicity of YP relative to YPT. Additionally, global transcriptome and proteome responses of YP and YPT revealed conserved post-transcriptional control of metabolism and the translational machinery including the modulation of glutamate levels in Yersiniae. Finally, the omics data was coupled with a computational network analysis, allowing an efficient prediction of novel Yersinia virulence factors based on gene and protein expression patterns.
PMCID: PMC3518462  PMID: 23147219
3.  Identifying Low pH Active and Lactate-Utilizing Taxa within Oral Microbiome Communities from Healthy Children Using Stable Isotope Probing Techniques 
PLoS ONE  2012;7(3):e32219.
Many human microbial infectious diseases including dental caries are polymicrobial in nature. How these complex multi-species communities evolve from a healthy to a diseased state is not well understood. Although many health- or disease-associated oral bacteria have been characterized in vitro, their physiology within the complex oral microbiome is difficult to determine with current approaches. In addition, about half of these species remain uncultivated to date with little known besides their 16S rRNA sequence. Lacking culture-based physiological analyses, the functional roles of uncultivated species will remain enigmatic despite their apparent disease correlation. To start addressing these knowledge gaps, we applied a combination of Magnetic Resonance Spectroscopy (MRS) with RNA and DNA based Stable Isotope Probing (SIP) to oral plaque communities from healthy children for in vitro temporal monitoring of metabolites and identification of metabolically active and inactive bacterial species.
Methodology/Principal Findings
Supragingival plaque samples from caries-free children incubated with 13C-substrates under imposed healthy (buffered, pH 7) and diseased states (pH 5.5 and pH 4.5) produced lactate as the dominant organic acid from glucose metabolism. Rapid lactate utilization upon glucose depletion was observed under pH 7 conditions. SIP analyses revealed a number of genera containing cultured and uncultivated taxa with metabolic capabilities at pH 5.5. The diversity of active species decreased significantly at pH 4.5 and was dominated by Lactobacillus and Propionibacterium species, both of which have been previously found within carious lesions from children.
Our approach allowed for identification of species that metabolize carbohydrates under different pH conditions and supports the importance of Lactobacilli and Propionibacterium in the development of childhood caries. Identification of species within healthy subjects that are active at low pH can lead to a better understanding of oral caries onset and generate appropriate targets for preventative measures in the early stages.
PMCID: PMC3293899  PMID: 22403637
4.  An experimentally-supported genome-scale metabolic network reconstruction for Yersinia pestis CO92 
BMC Systems Biology  2011;5:163.
Yersinia pestis is a gram-negative bacterium that causes plague, a disease linked historically to the Black Death in Europe during the Middle Ages and to several outbreaks during the modern era. Metabolism in Y. pestis displays remarkable flexibility and robustness, allowing the bacterium to proliferate in both warm-blooded mammalian hosts and cold-blooded insect vectors such as fleas.
Here we report a genome-scale reconstruction and mathematical model of metabolism for Y. pestis CO92 and supporting experimental growth and metabolite measurements. The model contains 815 genes, 678 proteins, 963 unique metabolites and 1678 reactions, accurately simulates growth on a range of carbon sources both qualitatively and quantitatively, and identifies gaps in several key biosynthetic pathways and suggests how those gaps might be filled. Furthermore, our model presents hypotheses to explain certain known nutritional requirements characteristic of this strain.
Y. pestis continues to be a dangerous threat to human health during modern times. The Y. pestis genome-scale metabolic reconstruction presented here, which has been benchmarked against experimental data and correctly reproduces known phenotypes, provides an in silico platform with which to investigate the metabolism of this important human pathogen.
PMCID: PMC3220653  PMID: 21995956
5.  DNA–XPA interactions: a 31P NMR and molecular modeling study of dCCAATAACC association with the minimal DNA-binding domain (M98–F219) of the nucleotide excision repair protein XPA 
Nucleic Acids Research  2001;29(12):2635-2643.
Recent NMR-based, chemical shift mapping experiments with the minimal DNA-binding domain of XPA (XPA-MBD: M98–F219) suggest that a basic cleft located in the loop-rich subdomain plays a role in DNA-binding. Here, XPA–DNA interactions are further characterized by NMR spectroscopy from the vantage point of the DNA using a single-stranded DNA nonamer, dCCAATAACC (d9). Up to 2.5 molar equivalents of XPA-MBD was titrated into a solution of d9. A subset of 31P resonances of d9 were observed to broaden and/or shift providing direct evidence that XPA-MBD binds d9 by a mechanism that perturbs the phosphodiester backbone of d9. The interior five residues of d9 broadened and/or shifted before 31P resonances of phosphate groups at the termini, suggesting that when d9 is bound to XPA-MBD the internal residues assume a correlation time that is characteristic of the molecular weight of the complex while the residues at the termini undergo a fraying motion away from the surface of the protein on a timescale such that the line widths are more characteristic of the molecular weight of ssDNA. A molecular model of the XPA-MBD complex with d9 was calculated based on the 15N (XPA-MBD) and 31P (d9) chemical shift mapping studies and on the assumption that electrostatic interactions drive the complex formation. The model shows that a nine residue DNA oligomer fully covers the DNA-binding surface of XPA and that there may be an energetic advantage to binding DNA in the 3′→5′ direction rather than in the 5′→3′ direction (relative to XPA-MBD α-helix-3).
PMCID: PMC55733  PMID: 11410673

Results 1-5 (5)