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1.  A Comparison of Aggregate P-Value Methods and Multivariate Statistics for Self-Contained Tests of Metabolic Pathway Analysis 
PLoS ONE  2015;10(4):e0125081.
For pathway analysis of genomic data, the most common methods involve combining p-values from individual statistical tests. However, there are several multivariate statistical methods that can be used to test whether a pathway has changed. Because of the large number of variables and pathway sizes in genomics data, some of these statistics cannot be computed. However, in metabolomics data, the number of variables and pathway sizes are typically much smaller, making such computations feasible. Of particular interest is being able to detect changes in pathways that may not be detected for the individual variables. We compare the performance of both the p-value methods and multivariate statistics for self-contained tests with an extensive simulation study and a human metabolomics study. Permutation tests, rather than asymptotic results are used to assess the statistical significance of the pathways. Furthermore, both one and two-sided alternatives hypotheses are examined. From the human metabolomic study, many pathways were statistically significant, although the majority of the individual variables in the pathway were not. Overall, the p-value methods perform at least as well as the multivariate statistics for these scenarios.
PMCID: PMC4415974  PMID: 25927705
2.  Chimpanzee population structure in Cameroon and Nigeria is associated with habitat variation that may be lost under climate change 
BMC Evolutionary Biology  2015;15(1):2.
The Nigeria-Cameroon chimpanzee (Pan troglodytes ellioti) is found in the Gulf of Guinea biodiversity hotspot located in western equatorial Africa. This subspecies is threatened by habitat fragmentation due to logging and agricultural development, hunting for the bushmeat trade, and possibly climate change. Although P. t. ellioti appears to be geographically separated from the neighboring central chimpanzee (P. t. troglodytes) by the Sanaga River, recent population genetics studies of chimpanzees from across this region suggest that additional factors may also be important in their separation. The main aims of this study were: 1) to model the distribution of suitable habitat for P. t. ellioti across Cameroon and Nigeria, and P. t. troglodytes in southern Cameroon, 2) to determine which environmental factors best predict their optimal habitats, and 3) to compare modeled niches and test for their levels of divergence from one another. A final aim of this study was to examine the ways that climate change might impact suitable chimpanzee habitat across the region under various scenarios.
Ecological niche models (ENMs) were created using the software package Maxent for the three populations of chimpanzees that have been inferred to exist in Cameroon and eastern Nigeria: (i) P. t. troglodytes in southern Cameroon, (ii) P. t. ellioti in northwestern Cameroon, and (iii) P. t. ellioti in central Cameroon. ENMs for each population were compared using the niche comparison test in ENMtools, which revealed complete niche divergence with very little geographic overlap of suitable habitat between populations.
These findings suggest that a positive relationship may exist between environmental variation and the partitioning of genetic variation found in chimpanzees across this region. ENMs for each population were also projected under three different climate change scenarios for years 2020, 2050, and 2080. Suitable habitat of P. t. ellioti in northwest Cameroon / eastern Nigeria is expected to remain largely unchanged through 2080 in all considered scenarios. In contrast, P. t. ellioti in central Cameroon, which represents half of the population of this subspecies, is expected to experience drastic reductions in its ecotone habitat over the coming century.
Electronic supplementary material
The online version of this article (doi:10.1186/s12862-014-0275-z) contains supplementary material, which is available to authorized users.
PMCID: PMC4314735  PMID: 25608567
3.  The population genetics of wild chimpanzees in Cameroon and Nigeria suggests a positive role for selection in the evolution of chimpanzee subspecies 
Chimpanzees (Pan troglodytes) can be divided into four subspecies. Substantial phylogenetic evidence suggests that these subspecies can be grouped into two distinct lineages: a western African group that includes P. t. verus and P. t. ellioti and a central/eastern African group that includes P. t. troglodytes and P. t. schweinfurthii. The geographic division of these two lineages occurs in Cameroon, where the rages of P. t. ellioti and P. t. troglodytes appear to converge at the Sanaga River. Remarkably, few population genetic studies have included wild chimpanzees from this region.
We analyzed microsatellite genotypes of 187 wild, unrelated chimpanzees, and mitochondrial control region sequencing data from 604 chimpanzees. We found that chimpanzees in Cameroon and eastern Nigeria comprise at least two, and likely three populations. Both the mtDNA and microsatellite data suggest that there is a primary separation of P. t. troglodytes in southern Cameroon from P. t. ellioti north and west of the Sanaga River. These two populations split ~200-250 thousand years ago (kya), but have exchanged one migrant per generation since separating. In addition, P. t. ellioti consists of two populations that split from one another ~4 kya. One population is located in the rainforests of western Cameroon and eastern Nigeria, whereas the second population appears to be confined to a savannah-woodland mosaic in central Cameroon.
Our findings suggest that there are as many as three genetically distinct populations of chimpanzees in Cameroon and eastern Nigeria. P. t. troglodytes in southern Cameroon comprises one population that is separated from two populations of P. t. ellioti in western and central Cameroon, respectively. P. t. ellioti and P. t. troglodytes appear to be characterized by a pattern of isolation-with-migration, and thus, we propose that neutral processes alone can not explain the differentiation of P. t. ellioti and P. t. troglodytes.
Electronic supplementary material
The online version of this article (doi:10.1186/s12862-014-0276-y) contains supplementary material, which is available to authorized users.
PMCID: PMC4314757  PMID: 25608610
4.  Environmental variation and rivers govern the structure of chimpanzee genetic diversity in a biodiversity hotspot 
BMC Evolutionary Biology  2015;15(1):1.
The mechanisms that underlie the diversification of tropical animals remain poorly understood, but new approaches that combine geo-spatial modeling with spatially explicit genetic data are providing fresh insights on this topic. Data about the diversification of tropical mammals remain particularly sparse, and vanishingly few opportunities exist to study endangered large mammals that increasingly exist only in isolated pockets. The chimpanzees of Cameroon represent a unique opportunity to examine the mechanisms that promote genetic differentiation in tropical mammals because the region is home to two chimpanzee subspecies: Pan troglodytes ellioti and P. t. trogolodytes. Their ranges converge in central Cameroon, which is a geographically, climatically and environmentally complex region that presents an unparalleled opportunity to examine the roles of rivers and/or environmental variation in influencing the evolution of chimpanzee populations.
We analyzed microsatellite genotypes and mtDNA HVRI sequencing data from wild chimpanzees sampled at a fine geographic scale across Cameroon and eastern Nigeria using a spatially explicit approach based upon Generalized Dissimilarity Modeling. Both the Sanaga River and environmental variation were found to contribute to driving separation of the subspecies. The importance of environmental variation differed among subspecies. Gene-environment associations were weak in P. t. troglodytes, whereas environmental variation was found to play a much larger role in shaping patterns of genetic differentiation in P. t. ellioti.
We found that both the Sanaga River and environmental variation likely play a role in shaping patterns of chimpanzee genetic diversity. Future studies using single nucleotide polymorphism (SNP) data are necessary to further understand how rivers and environmental variation contribute to shaping patterns of genetic variation in chimpanzees.
Electronic supplementary material
The online version of this article (doi:10.1186/s12862-014-0274-0) contains supplementary material, which is available to authorized users.
PMCID: PMC4314796  PMID: 25608511
5.  Bladder Cancer Biomarker Discovery Using Global Metabolomic Profiling of Urine 
PLoS ONE  2014;9(12):e115870.
Bladder cancer (BCa) is a common malignancy worldwide and has a high probability of recurrence after initial diagnosis and treatment. As a result, recurrent surveillance, primarily involving repeated cystoscopies, is a critical component of post diagnosis patient management. Since cystoscopy is invasive, expensive and a possible deterrent to patient compliance with regular follow-up screening, new non-invasive technologies to aid in the detection of recurrent and/or primary bladder cancer are strongly needed. In this study, mass spectrometry based metabolomics was employed to identify biochemical signatures in human urine that differentiate bladder cancer from non-cancer controls. Over 1000 distinct compounds were measured including 587 named compounds of known chemical identity. Initial biomarker identification was conducted using a 332 subject sample set of retrospective urine samples (cohort 1), which included 66 BCa positive samples. A set of 25 candidate biomarkers was selected based on statistical significance, fold difference and metabolic pathway coverage. The 25 candidate biomarkers were tested against an independent urine sample set (cohort 2) using random forest analysis, with palmitoyl sphingomyelin, lactate, adenosine and succinate providing the strongest predictive power for differentiating cohort 2 cancer from non-cancer urines. Cohort 2 metabolite profiling revealed additional metabolites, including arachidonate, that were higher in cohort 2 cancer vs. non-cancer controls, but were below quantitation limits in the cohort 1 profiling. Metabolites related to lipid metabolism may be especially interesting biomarkers. The results suggest that urine metabolites may provide a much needed non-invasive adjunct diagnostic to cystoscopy for detection of bladder cancer and recurrent disease management.
PMCID: PMC4277370  PMID: 25541698
7.  Assessment of Genetically Modified Soybean in Relation to Natural Variation in the Soybean Seed Metabolome 
Scientific Reports  2013;3:3082.
Genetically modified (GM) crops currently constitute a significant and growing part of agriculture. An important aspect of GM crop adoption is to demonstrate safety and equivalence with respect to conventional crops. Untargeted metabolomics has the ability to profile diverse classes of metabolites and thus could be an adjunct for GM crop substantial equivalence assessment. To account for environmental effects and introgression of GM traits into diverse genetic backgrounds, we propose that the assessment for GM crop metabolic composition should be understood within the context of the natural variation for the crop. Using a non-targeted metabolomics platform, we profiled 169 metabolites and established their dynamic ranges from the seeds of 49 conventional soybean lines representing the current commercial genetic diversity. We further demonstrated that the metabolome of a GM line had no significant deviation from natural variation within the soybean metabolome, with the exception of changes in the targeted engineered pathway.
PMCID: PMC3812653  PMID: 24170158
8.  Mining the Unknown: A Systems Approach to Metabolite Identification Combining Genetic and Metabolic Information 
PLoS Genetics  2012;8(10):e1003005.
Recent genome-wide association studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. The identification of these “unknown metabolites” is still a demanding and intricate task, limiting their usability as functional markers of metabolic processes. As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the analysis. Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. We apply our method to original data of 517 metabolic traits, of which 225 are unknowns, and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. We report previously undescribed genotype–metabotype associations for six distinct gene loci (SLC22A2, COMT, CYP3A5, CYP2C18, GBA3, UGT3A1) and one locus not related to any known gene (rs12413935). Overlaying the inferred genetic associations, metabolic networks, and knowledge-based pathway information, we derive testable hypotheses on the biochemical identities of 106 unknown metabolites. As a proof of principle, we experimentally confirm nine concrete predictions. We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification, hypertension, and insulin resistance. Our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms.
Author Summary
Genome-wide association studies on metabolomics data have demonstrated that genetic variation in metabolic enzymes and transporters leads to concentration changes in the respective metabolite levels. The conventional goal of these studies is the detection of novel interactions between the genome and the metabolic system, providing valuable insights for both basic research as well as clinical applications. In this study, we borrow the metabolomics GWAS concept for a novel, entirely different purpose. Metabolite measurements frequently produce signals where a certain substance can be reliably detected in the sample, but it has not yet been elucidated which specific metabolite this signal actually represents. The concept is comparable to a fingerprint: each one is uniquely identifiable, but as long as it is not registered in a database one cannot tell to whom this fingerprint belongs. Obviously, this issue tremendously reduces the usability of a metabolomics analyses. The genetic associations of such an “unknown,” however, give us concrete evidence of the metabolic pathway this substance is most probably involved in. Moreover, we complement the approach with a specific measure of correlation between metabolites, providing further evidence of the metabolic processes of the unknown. For a number of cases, this even allows for a concrete identity prediction, which we then experimentally validate in the lab.
PMCID: PMC3475673  PMID: 23093944
9.  Metabolomic differences in heart failure patients with and without major depression 
Metabolomics is an emerging technology that allows researchers to characterize hundreds of small molecules that comprise the metabolome. We sought to determine metabolic differences in depressed and non-depressed subjects. The sample consisted of a depressed group of patients with heart failure enrolled in an NIMH-supported clinical trial of sertraline versus placebo in depressed heart failure patients, and a non-depressed comparator group of heart failure patients. Plasma was obtained from blood samples provided by participants at baseline, and samples were profiled on GC-MS and LC-MS metabolomics platforms for biochemical content. A number of biochemicals were significantly different between groups, with depressed subjects showing higher concentrations of several amino acids and dicarboxylic fatty acids. These results are consistent with prior findings where changes in neurotransmitter systems and fatty acid metabolism were shown to associate with the depressed state. It is unclear what role heart failure may have played in these differing concentrations.
PMCID: PMC3279728  PMID: 20101071
10.  Metabolomic Profiling Reveals Biochemical Pathways and Biomarkers Associated with Pathogenesis in Cystic Fibrosis Cells* 
The Journal of Biological Chemistry  2010;285(40):30516-30522.
Cystic fibrosis (CF) is a life-shortening disease caused by a mutation in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. To gain an understanding of the epithelial dysfunction associated with CF mutations and discover biomarkers for therapeutics development, untargeted metabolomic analysis was performed on primary human airway epithelial cell cultures from three separate cohorts of CF patients and non-CF subjects. Statistical analysis revealed a set of reproducible and significant metabolic differences between the CF and non-CF cells. Aside from changes that were consistent with known CF effects, such as diminished cellular regulation against oxidative stress and osmotic stress, new observations on the cellular metabolism in the disease were generated. In the CF cells, the levels of various purine nucleotides, which may function to regulate cellular responses via purinergic signaling, were significantly decreased. Furthermore, CF cells exhibited reduced glucose metabolism in glycolysis, pentose phosphate pathway, and sorbitol pathway, which may further exacerbate oxidative stress and limit the epithelial cell response to environmental pressure. Taken together, these findings reveal novel metabolic abnormalities associated with the CF pathological process and identify a panel of potential biomarkers for therapeutic development using this model system.
PMCID: PMC2945545  PMID: 20675369
Cystic Fibrosis; Energy Metabolism; Metabolomics; Oxidative Stress; Purine
11.  α-Hydroxybutyrate Is an Early Biomarker of Insulin Resistance and Glucose Intolerance in a Nondiabetic Population 
PLoS ONE  2010;5(5):e10883.
Insulin resistance is a risk factor for type 2 diabetes and cardiovascular disease progression. Current diagnostic tests, such as glycemic indicators, have limitations in the early detection of insulin resistant individuals. We searched for novel biomarkers identifying these at-risk subjects.
Using mass spectrometry, non-targeted biochemical profiling was conducted in a cohort of 399 nondiabetic subjects representing a broad spectrum of insulin sensitivity and glucose tolerance (based on the hyperinsulinemic euglycemic clamp and oral glucose tolerance testing, respectively).
Random forest statistical analysis selected α-hydroxybutyrate (α–HB) as the top-ranked biochemical for separating insulin resistant (lower third of the clamp-derived MFFM = 33 [12] µmol·min−1·kgFFM−1, median [interquartile range], n = 140) from insulin sensitive subjects (MFFM = 66 [23] µmol·min−1·kgFFM−1) with a 76% accuracy. By targeted isotope dilution assay, plasma α–HB concentrations were reciprocally related to MFFM; and by partition analysis, an α–HB value of 5 µg/ml was found to best separate insulin resistant from insulin sensitive subjects. α–HB also separated subjects with normal glucose tolerance from those with impaired fasting glycemia or impaired glucose tolerance independently of, and in an additive fashion to, insulin resistance. These associations were also independent of sex, age and BMI. Other metabolites from this global analysis that significantly correlated to insulin sensitivity included certain organic acid, amino acid, lysophospholipid, acylcarnitine and fatty acid species. Several metabolites are intermediates related to α-HB metabolism and biosynthesis.
α–hydroxybutyrate is an early marker for both insulin resistance and impaired glucose regulation. The underlying biochemical mechanisms may involve increased lipid oxidation and oxidative stress.
PMCID: PMC2878333  PMID: 20526369

Results 1-11 (11)