13C Metabolic Flux Analysis Identifies an Unusual Route for Pyruvate Dissimilation in Mycobacteria which Requires Isocitrate Lyase and Carbon Dioxide Fixation

Mycobacterium tuberculosis requires the enzyme isocitrate lyase (ICL) for growth and virulence in vivo. The demonstration that M. tuberculosis also requires ICL for survival during nutrient starvation and has a role during steady state growth in a glycerol limited chemostat indicates a function for this enzyme which extends beyond fat metabolism. As isocitrate lyase is a potential drug target elucidating the role of this enzyme is of importance; however, the role of isocitrate lyase has never been investigated at the level of in vivo fluxes. Here we show that deletion of one of the two icl genes impairs the replication of Mycobacterium bovis BCG at slow growth rate in a carbon limited chemostat. In order to further understand the role of isocitrate lyase in the central metabolism of mycobacteria the effect of growth rate on the in vivo fluxes was studied for the first time using ¹³C-metabolic flux analysis (MFA). Tracer experiments were performed with steady state chemostat cultures of BCG or M. tuberculosis supplied with ¹³C labeled glycerol or sodium bicarbonate. Through measurements of the ¹³C isotopomer labeling patterns in protein-derived amino acids and enzymatic activity assays we have identified the activity of a novel pathway for pyruvate dissimilation. We named this the GAS pathway because it utilizes the Glyoxylate shunt and Anapleurotic reactions for oxidation of pyruvate, and Succinyl CoA synthetase for the generation of succinyl CoA combined with a very low flux through the succinate – oxaloacetate segment of the tricarboxylic acid cycle. We confirm that M. tuberculosis can fix carbon from CO₂ into biomass. As the human host is abundant in CO₂ this finding requires further investigation in vivo as CO₂ fixation may provide a point of vulnerability that could be targeted with novel drugs. This study also provides a platform for further studies into the metabolism of M. tuberculosis using ¹³C-MFA.     

A Cytogenetic Abnormality and Rare Coding Variants Identify ABCA13 as a Candidate Gene in Schizophrenia, Bipolar Disorder, and Depression

Schizophrenia and bipolar disorder are leading causes of morbidity across all populations, with heritability estimates of ∼80% indicating a substantial genetic component. Population genetics and genome-wide association studies suggest an overlap of genetic risk factors between these illnesses but it is unclear how this genetic component is divided between common gene polymorphisms, rare genomic copy number variants, and rare gene sequence mutations. We report evidence that the lipid transporter gene ABCA13 is a susceptibility factor for both schizophrenia and bipolar disorder. After the initial discovery of its disruption by a chromosome abnormality in a person with schizophrenia, we resequenced ABCA13 exons in 100 cases with schizophrenia and 100 controls. Multiple rare coding variants were identified including one nonsense and nine missense mutations and compound heterozygosity/homozygosity in six cases. Variants were genotyped in additional schizophrenia, bipolar, depression (n > 1600), and control (n > 950) cohorts and the frequency of all rare variants combined was greater than controls in schizophrenia (OR = 1.93, p = 0.0057) and bipolar disorder (OR = 2.71, p = 0.00007). The population attributable risk of these mutations was 2.2% for schizophrenia and 4.0% for bipolar disorder. In a study of 21 families of mutation carriers, we genotyped affected and unaffected relatives and found significant linkage (LOD = 4.3) of rare variants with a phenotype including schizophrenia, bipolar disorder, and major depression. These data identify a candidate gene, highlight the genetic overlap between schizophrenia, bipolar disorder, and depression, and suggest that rare coding variants may contribute significantly to risk of these disorders.     

Spatial heterogeneity increases the importance of species richness for an ecosystem process

The role of biodiversity in mediating ecosystem processes has been the subject of focused theoretical and empirical attention since the mid-1990s. Theory predicts that the balance between species richness and identity effects will critically depend on the degree of environmental heterogeneity, which dictates the extent to which differences between species in patterns of resource use can be expressed. We conducted a mesocosm experiment to explicitly test this hypothesis. We manipulated the richness and identity of intertidal molluscan grazers, as well as the spatial heterogeneity of the substrate upon which they grazed. The magnitude of algal consumption was used as our focal ecosystem process. The grazer treatments consisted of three monocultures and a single polyculture including all three species; heterogeneity was represented as the proportion of topographically complex and flat substrate. Species identity had strong effects on homogeneous substrates, with the identity of the best-performing species dependent on the substrate. On the heterogeneous substrate, suitable conditions for all three species were represented, allowing the expression of spatial complementarity of resource use and the enhancement of total algal consumption. Our findings provide the first explicit experimental evidence that spatial heterogeneity of the physical environment can play a key role in mediating effects of species diversity.     

PFP: Automated prediction of gene ontology functional annotations with confidence scores using protein sequence data

Protein function prediction is a central problem in bioinformatics, increasing in importance recently due to the rapid accumulation of biological data awaiting interpretation. Sequence data represents the bulk of this new stock and is the obvious target for consideration as input, as newly sequenced organisms often lack any other type of biological characterization. We have previously introduced PFP (Protein Function Prediction) as our sequence-based predictor of Gene Ontology (GO) functional terms. PFP interprets the results of a PSI-BLAST search by extracting and scoring individual functional attributes, searching a wide range of E-value sequence matches, and utilizing conventional data mining techniques to fill in missing information. We have shown it to be effective in predicting both specific and low-resolution functional attributes when sufficient data is unavailable. Here we describe (1) significant improvements to the PFP infrastructure, including the addition of prediction significance and confidence scores, (2) a thorough benchmark of performance and comparisons to other related prediction methods, and (3) applications of PFP predictions to genome-scale data. We applied PFP predictions to uncharacterized protein sequences from 15 organisms. Among these sequences, 60-90% could be annotated with a GO molecular function term at high confidence (>or=80%). We also applied our predictions to the protein-protein interaction network of the Malaria plasmodium (Plasmodium falciparum). High confidence GO biological process predictions (>or=90%) from PFP increased the number of fully enriched interactions in this dataset from 23% of interactions to 94%. Our benchmark comparison shows significant performance improvement of PFP relative to GOtcha, InterProScan, and PSI-BLAST predictions. This is consistent with the performance of PFP as the overall best predictor in both the AFP-SIG '05 and CASP7 function (FN) assessments. PFP is available as a web service at http://dragon.bio.purdue.edu/pfp/.     

Quantification of hydroxycinnamic acids and lignin in perennial forage and energy grasses by Fourier-transform infrared spectroscopy and partial least squares regression

Levels of lignin and hydroxycinnamic acid wall components in three genera of forage grasses (Lolium,Festuca and Dactylis) have been accurately predicted by Fourier-transform infrared spectroscopy using partial least squares models correlated to analytical measurements. Different models were derived that predicted the concentrations of acid detergent lignin, total hydroxycinnamic acids, total ferulate monomers plus dimers, p-coumarate and ferulate dimers in independent spectral test data from methanol extracted samples of perennial forage grass with accuracies of 92.8%, 86.5%, 86.1%, 59.7% and 84.7% respectively, and analysis of model projection scores showed that the models relied generally on spectral features that are known absorptions of these compounds. Acid detergent lignin was predicted in samples of two species of energy grass, (Phalaris arundinacea and Pancium virgatum) with an accuracy of 84.5%.