Large scale physiological readjustment during growth enables rapid, comprehensive and inexpensive systems analysis

Abstract Background Rapidly characterizing the operational interrelationships among all genes in a given organism is a critical bottleneck to significantly advancing our understanding of thousands of newly sequenced microbial and eukaryotic species. While evolving technologies for global profiling of transcripts, proteins, and metabolites are making it possible to comprehensively survey cellular physiology in newly sequenced organisms, these experimental techniques have not kept pace with sequencing efforts. Compounding these technological challenges is the fact that individual experiments typically only stimulate relatively small-scale cellular responses, thus requiring numerous expensive experiments to survey the operational relationships among nearly all genetic elements. Therefore, a relatively quick and inexpensive strategy for observing changes in large fractions of the genetic elements is highly desirable. Results We have discovered in the model organism Halobacterium salinarum NRC-1 that batch culturing in complex medium stimulates meaningful changes in the expression of approximately two thirds of all genes. While the majority of these changes occur during transition from rapid exponential growth to the stationary phase, several transient physiological states were detected beyond what has been previously observed. In sum, integrated analysis of transcript and metabolite changes has helped uncover growth phase-associated physiologies, operational interrelationships among two thirds of all genes, specialized functions for gene family members, waves of transcription factor activities, and growth phase associated cell morphology control. Conclusions Simple laboratory culturing in complex medium can be enormously informative regarding the activities of and interrelationships among a large fraction of all genes in an organism. This also yields important baseline physiological context for designing specific perturbation experiments at different phases of growth. The integration of such growth and perturbation studies with measurements of associated environmental factor changes is a practical and economical route for the elucidation of comprehensive systems-level models of biological systems

Meta-analysis of global metabolomic data identifies metabolites associated with life-span extension

The manipulation of distinct signaling pathways and transcription factors has been shown to influence life span in a cell-non-autonomous manner in multicellular model organisms such as Caenorhabditis elegans. These data suggest that coordination of whole-organism aging involves endocrine signaling, however, the molecular identities of such signals have not yet been determined and their potential relevance in humans is unknown. Here we describe a novel metabolomic approach to identify molecules directly associated with extended life span in C. elegans that represent candidate compounds for age-related endocrine signals. To identify metabolic perturbations directly linked to longevity, we developed metabolomic software for meta-analysis that enabled intelligent comparisons of multiple different mutants. Simple pairwise comparisons of long-lived glp-1, daf-2, and isp-1 mutants to their respective controls resulted in more than 11,000 dysregulated metabolite features of statistical significance. By using meta-analysis, we were able to reduce this number to six compounds most likely to be associated with life-span extension. Mass spectrometry-based imaging studies suggested that these metabolites might be localized to C. elegans muscle. We extended the metabolomic analysis to humans by comparing quadricep muscle tissue from young and old individuals and found that two of the same compounds associated with longevity in worms were also altered in human muscle with age. These findings provide candidate compounds that may serve as age-related endocrine signals and implicate muscle as a potential tissue regulating their levels in humans

A Computational Framework for Proteome-Wide Pursuit and Prediction of Metalloproteins Using ICP-MS and MS/MS Data

BACKGROUND: Metal-containing proteins comprise a diverse and sizable category within the proteomes of organisms, ranging from proteins that use metals to catalyze reactions to proteins in which metals play key structural roles. Unfortunately, reliably predicting that a protein will contain a specific metal from its amino acid sequence is not currently possible. We recently developed a generally-applicable experimental technique for finding metalloproteins on a genome-wide scale. Applying this metal-directed protein purification approach (ICP-MS and MS/MS based) to the prototypical microbe Pyrococcus furiosus conclusively demonstrated the extent and diversity of the uncharacterized portion of microbial metalloproteomes since a majority of the observed metal peaks could not be assigned to known or predicted metalloproteins. However, even using this technique, it is not technically feasible to purify to homogeneity all metalloproteins in an organism. In order to address these limitations and complement the metal-directed protein purification, we developed a computational infrastructure and statistical methodology to aid in the pursuit and identification of novel metalloproteins. RESULTS: We demonstrate that our methodology enables predictions of metal-protein interactions using an experimental data set derived from a chromatography fractionation experiment in which 870 proteins and 10 metals were measured over 2,589 fractions. For each of the 10 metals, cobalt, iron, manganese, molybdenum, nickel, lead, tungsten, uranium, vanadium, and zinc, clusters of proteins frequently occurring in metal peaks (of a specific metal) within the fractionation space were defined. This resulted in predictions that there are from 5 undiscovered vanadium- to 13 undiscovered cobalt-containing proteins in Pyrococcus furiosus. Molybdenum and nickel were chosen for additional assessment producing lists of genes predicted to encode metalloproteins or metalloprotein subunits, 22 for nickel including seven from known nickel-proteins, and 20 for molybdenum including two from known molybdo-proteins. The uncharacterized proteins are prime candidates for metal-based purification or recombinant approaches to validate these predictions. CONCLUSIONS: We conclude that the largely uncharacterized extent of native metalloproteomes can be revealed through analysis of the co-occurrence of metals and proteins across a fractionation space. This can significantly impact our understanding of metallobiochemistry, disease mechanisms, and metal toxicity, with implications for bioremediation, medicine and other fields