University of Alabama at Birmingham Nathan Shock Center: comparative energetics of aging.

The UAB Nathan Shock Center focuses on comparative energetics and aging. Energetics, as defined for this purpose, encompasses the causes, mechanisms, and consequences of the acquisition, storage, and use of metabolizable energy. Comparative energetics is the study of metabolic processes at multiple scales and across multiple species as it relates to health and aging. The link between energetics and aging is increasingly understood in terms of dysregulated mitochondrial function, altered metabolic signaling, and aberrant nutrient responsiveness with increasing age. The center offers world-class expertise in comprehensive, integrated energetic assessment and analysis from the level of the organelle to the organism and across species from the size of worms to rats as well as state-of-the-art data analytics. The range of services offered by our three research cores, (1) The Organismal Energetics Core, (2) Mitometabolism Core, and (3) Data Analytics Core, is described herein.

A cell-nonautonomous mechanism of yeast chronological aging regulated by caloric restriction and one-carbon metabolism.

Caloric restriction (CR) improves health span and life span of organisms ranging from yeast to mammals. Understanding the mechanisms involved will uncover future interventions for aging-associated diseases. In budding yeast, Saccharomyces cerevisiae, CR is commonly defined by reduced glucose in the growth medium, which extends both replicative and chronological life span (CLS). We found that conditioned media collected from stationary-phase CR cultures extended CLS when supplemented into nonrestricted (NR) cultures, suggesting a potential cell-nonautonomous mechanism of CR-induced life span regulation. Chromatography and untargeted metabolomics of the conditioned media, as well as transcriptional responses associated with the longevity effect, pointed to specific amino acids enriched in the CR conditioned media (CRCM) as functional molecules, with L-serine being a particularly strong candidate. Indeed, supplementing L-serine into NR cultures extended CLS through a mechanism dependent on the one-carbon metabolism pathway, thus implicating this conserved and central metabolic hub in life span regulation.

High-resolution yeast quiescence profiling in human-like media reveals complex influences of auxotrophy and nutrient availability.

Yeast cells survive in stationary phase culture by entering quiescence, which is measured by colony-forming capacity upon nutrient re-exposure. Yeast chronological lifespan (CLS) studies, employing the comprehensive collection of gene knockout strains, have correlated weakly between independent laboratories, which is hypothesized to reflect differential interaction between the deleted genes, auxotrophy, media composition, and other assay conditions influencing quiescence. This hypothesis was investigated by high-throughput quiescence profiling of the parental prototrophic strain, from which the gene deletion strain libraries were constructed, and all possible auxotrophic allele combinations in that background. Defined media resembling human cell culture media promoted long-term quiescence and was used to assess effects of glucose, ammonium sulfate, auxotrophic nutrient availability, target of rapamycin signaling, and replication stress. Frequent, high-replicate measurements of colony-forming capacity from cultures aged past 60 days provided profiles of quiescence phenomena such as gasping and hormesis. Media acidification was assayed in parallel to assess correlation. Influences of leucine, methionine, glucose, and ammonium sulfate metabolism were clarified, and a role for lysine metabolism newly characterized, while histidine and uracil perturbations had less impact. Interactions occurred between glucose, ammonium sulfate, auxotrophy, auxotrophic nutrient limitation, aeration, TOR signaling, and/or replication stress. Weak correlation existed between media acidification and maintenance of quiescence. In summary, experimental factors, uncontrolled across previous genome-wide yeast CLS studies, influence quiescence and interact extensively, revealing quiescence as a complex metabolic and developmental process that should be studied in a prototrophic context, omitting ammonium sulfate from defined media, and employing highly replicable protocols.

Slowing ribosome velocity restores folding and function of mutant CFTR.

Cystic fibrosis (CF) is caused by mutations in the CF transmembrane conductance regulator (CFTR), with approximately 90% of patients harboring at least one copy of the disease-associated variant F508del. We utilized a yeast phenomic system to identify genetic modifiers of F508del-CFTR biogenesis, from which ribosomal protein L12 (RPL12/uL11) emerged as a molecular target. In the present study, we investigated mechanism(s) by which suppression of RPL12 rescues F508del protein synthesis and activity. Using ribosome profiling, we found that rates of translation initiation and elongation were markedly slowed by RPL12 silencing. However, proteolytic stability and patch-clamp assays revealed RPL12 depletion significantly increased F508del-CFTR steady-state expression, interdomain assembly, and baseline open-channel probability. We next evaluated whether Rpl12-corrected F508del-CFTR could be further enhanced with concomitant pharmacologic repair (e.g., using clinically approved modulators lumacaftor and tezacaftor) and demonstrated additivity of these treatments. Rpl12 knockdown also partially restored maturation of specific CFTR variants in addition to F508del, and WT Cftr biogenesis was enhanced in the pancreas, colon, and ileum of Rpl12 haplosufficient mice. Modulation of ribosome velocity therefore represents a robust method for understanding both CF pathogenesis and therapeutic response.

A yeast phenomic model for the influence of Warburg metabolism on genetic buffering of doxorubicin.

BACKGROUND: The influence of the Warburg phenomenon on chemotherapy response is unknown. Saccharomyces cerevisiae mimics the Warburg effect, repressing respiration in the presence of adequate glucose. Yeast phenomic experiments were conducted to assess potential influences of Warburg metabolism on gene-drug interaction underlying the cellular response to doxorubicin. Homologous genes from yeast phenomic and cancer pharmacogenomics data were analyzed to infer evolutionary conservation of gene-drug interaction and predict therapeutic relevance. METHODS: Cell proliferation phenotypes (CPPs) of the yeast gene knockout/knockdown library were measured by quantitative high-throughput cell array phenotyping (Q-HTCP), treating with escalating doxorubicin concentrations under conditions of respiratory or glycolytic metabolism. Doxorubicin-gene interaction was quantified by departure of CPPs observed for the doxorubicin-treated mutant strain from that expected based on an interaction model. Recursive expectation-maximization clustering (REMc) and Gene Ontology (GO)-based analyses of interactions identified functional biological modules that differentially buffer or promote doxorubicin cytotoxicity with respect to Warburg metabolism. Yeast phenomic and cancer pharmacogenomics data were integrated to predict differential gene expression causally influencing doxorubicin anti-tumor efficacy. RESULTS: Yeast compromised for genes functioning in chromatin organization, and several other cellular processes are more resistant to doxorubicin under glycolytic conditions. Thus, the Warburg transition appears to alleviate requirements for cellular functions that buffer doxorubicin cytotoxicity in a respiratory context. We analyzed human homologs of yeast genes exhibiting gene-doxorubicin interaction in cancer pharmacogenomics data to predict causality for differential gene expression associated with doxorubicin cytotoxicity in cancer cells. This analysis suggested conserved cellular responses to doxorubicin due to influences of homologous recombination, sphingolipid homeostasis, telomere tethering at nuclear periphery, actin cortical patch localization, and other gene functions. CONCLUSIONS: Warburg status alters the genetic network required for yeast to buffer doxorubicin toxicity. Integration of yeast phenomic and cancer pharmacogenomics data suggests evolutionary conservation of gene-drug interaction networks and provides a new experimental approach to model their influence on chemotherapy response. Thus, yeast phenomic models could aid the development of precision oncology algorithms to predict efficacious cytotoxic drugs for cancer, based on genetic and metabolic profiles of individual tumors.

A Humanized Yeast Phenomic Model of Deoxycytidine Kinase to Predict Genetic Buffering of Nucleoside Analog Cytotoxicity.

Knowledge about synthetic lethality can be applied to enhance the efficacy of anticancer therapies in individual patients harboring genetic alterations in their cancer that specifically render it vulnerable. We investigated the potential for high-resolution phenomic analysis in yeast to predict such genetic vulnerabilities by systematic, comprehensive, and quantitative assessment of drug-gene interaction for gemcitabine and cytarabine, substrates of deoxycytidine kinase that have similar molecular structures yet distinct antitumor efficacy. Human deoxycytidine kinase (dCK) was conditionally expressed in the Saccharomycescerevisiae genomic library of knockout and knockdown (YKO/KD) strains, to globally and quantitatively characterize differential drug-gene interaction for gemcitabine and cytarabine. Pathway enrichment analysis revealed that autophagy, histone modification, chromatin remodeling, and apoptosis-related processes influence gemcitabine specifically, while drug-gene interaction specific to cytarabine was less enriched in gene ontology. Processes having influence over both drugs were DNA repair and integrity checkpoints and vesicle transport and fusion. Non-gene ontology (GO)-enriched genes were also informative. Yeast phenomic and cancer cell line pharmacogenomics data were integrated to identify yeast-human homologs with correlated differential gene expression and drug efficacy, thus providing a unique resource to predict whether differential gene expression observed in cancer genetic profiles are causal in tumor-specific responses to cytotoxic agents.

Ribosomal Stalk Protein Silencing Partially Corrects the ΔF508-CFTR Functional Expression Defect.

The most common cystic fibrosis (CF) causing mutation, deletion of phenylalanine 508 (ΔF508 or Phe508del), results in functional expression defect of the CF transmembrane conductance regulator (CFTR) at the apical plasma membrane (PM) of secretory epithelia, which is attributed to the degradation of the misfolded channel at the endoplasmic reticulum (ER). Deletion of phenylalanine 670 (ΔF670) in the yeast oligomycin resistance 1 gene (YOR1, an ABC transporter) of Saccharomyces cerevisiae phenocopies the ΔF508-CFTR folding and trafficking defects. Genome-wide phenotypic (phenomic) analysis of the Yor1-ΔF670 biogenesis identified several modifier genes of mRNA processing and translation, which conferred oligomycin resistance to yeast. Silencing of orthologues of these candidate genes enhanced the ΔF508-CFTR functional expression at the apical PM in human CF bronchial epithelia. Although knockdown of RPL12, a component of the ribosomal stalk, attenuated the translational elongation rate, it increased the folding efficiency as well as the conformational stability of the ΔF508-CFTR, manifesting in 3-fold augmented PM density and function of the mutant. Combination of RPL12 knockdown with the corrector drug, VX-809 (lumacaftor) restored the mutant function to ~50% of the wild-type channel in primary CFTRΔF508/ΔF508 human bronchial epithelia. These results and the observation that silencing of other ribosomal stalk proteins partially rescue the loss-of-function phenotype of ΔF508-CFTR suggest that the ribosomal stalk modulates the folding efficiency of the mutant and is a potential therapeutic target for correction of the ΔF508-CFTR folding defect.

Gene-nutrient interaction markedly influences yeast chronological lifespan.

PURPOSE: Research into the genetic mechanisms of aging has expanded rapidly over the past two decades. This has in part been the result of the use of model organisms (particularly yeast, worms and flies) and high-throughput technologies, combined with a growing interest in aging research. Despite this progress, widespread consensus regarding the pathways that are fundamental to the modulation of cellular aging and lifespan for all organisms has been limited due to discrepancies between different studies. We have compared results from published genome-wide, chronological lifespan (CLS) screens of individual gene deletion strains in Saccharomyces cerevisiae in order to identify gene deletion strains with consistent influences on longevity as possible indicators of fundamental aging processes from this single-celled, eukaryotic model organism. METHODS: Three previous reports have described genetic modifiers of chronological aging in the budding yeast (S. cerevisiae) using the yeast gene deletion strain collection. We performed a comparison among the data sets using correlation and decile distribution analysis to describe concordance between screens and identify strains that consistently increased or decreased CLS. We used gene enrichment analysis in an effort to understand the biology underlying genes identified in multiple studies. We attempted to replicate the different experimental conditions employed by the screens to identify potential sources of variability in CLS worth further investigating. RESULTS: Among 3209 strains present in all three screens, nine deletions strains were in common in the longest-lived decile (2.80%) and thirteen were in common in the shortest-lived decile (4.05%) of all three screens. Similarly, pairwise overlap between screens was low. When the same comparison was extended to three deciles to include more mutants studied in common between the three screens, enrichment of cellular processes based on gene ontology analysis in the long-lived strains remained very limited. To test the hypothesis that different parental strain auxotrophic requirements or media formulations employed by the respective genome-wide screens might contribute to the lack of concordance, different CLS assay conditions were assessed in combination with strains having different ploidy and auxotrophic requirements (all relevant to differences in the way the three genome-wide CLS screens were performed). This limited but systematic analysis of CLS with respect to auxotrophy, ploidy, and media revealed several instances of gene-nutrient interaction. CONCLUSIONS: There is surprisingly little overlap between the results of three independently performed genome-wide screens of CLS in S. cerevisiae. However, differences in strain genetic background (ploidy and specific auxotrophic requirements) were present, as well as different media and experimental conditions (e.g., aeration and pooled vs. individual culturing), which, along with stochastic effects such as genetic drift or selection of secondary mutations that suppress the loss of function from gene deletion, could in theory account for some of the lack of consensus between results. Considering the lack of overlap in CLS phenotypes among the set of genes reported by all three screens, and the results of a CLS experiment that systematically tested (incorporating extensive controls) for interactions between variables existing between the screens, we propose that discrepancies can be reconciled through deeper understanding of the influence of cell intrinsic factors such as auxotrophic requirements ploidy status, extrinsic factors such as media composition and aeration, as well as interactions that may occur between them, for example as a result of different pooling vs. individually aging cultures. Such factors may have a more significant impact on CLS outcomes than previously realized. Future studies that systematically account for these contextual factors, and can thus clarify the interactions between genetic and nutrient factors that alter CLS phenotypes, should aid more complete understanding of the underlying biology so that genetic principles of CLS in yeast can be extrapolated to differential cellular aging observed in animal models.

Long-range coupling between the extracellular gates and the intracellular ATP binding domains of multidrug resistance protein pumps and cystic fibrosis transmembrane conductance regulator channels.

The ABCC transporter subfamily includes pumps, the long and short multidrug resistance proteins (MRPs), and an ATP-gated anion channel, the cystic fibrosis transmembrane conductance regulator (CFTR). We show that despite their thermodynamic differences, these ABCC transporter subtypes use broadly similar mechanisms to couple their extracellular gates to the ATP occupancies of their cytosolic nucleotide binding domains. A conserved extracellular phenylalanine at this gate was a prime location for producing gain of function (GOF) mutants of a long MRP in yeast (Ycf1p cadmium transporter), a short yeast MRP (Yor1p oligomycin exporter), and human CFTR channels. Extracellular gate mutations rescued ATP binding mutants of the yeast MRPs and CFTR by increasing ATP sensitivity. Control ATPase-defective MRP mutants could not be rescued by this mechanism. A CFTR double mutant with an extracellular gate mutation plus a cytosolic GOF mutation was highly active (single-channel open probability >0.3) in the absence of ATP and protein kinase A, each normally required for CFTR activity. We conclude that all 3 ABCC transporter subtypes use similar mechanisms to couple their extracellular gates to ATP occupancy, and highly active CFTR channels that bypass defects in ATP binding or phosphorylation can be produced.

Yeast Phenomics: An Experimental Approach for Modeling Gene Interaction Networks that Buffer Disease.

The genome project increased appreciation of genetic complexity underlying disease phenotypes: many genes contribute each phenotype and each gene contributes multiple phenotypes. The aspiration of predicting common disease in individuals has evolved from seeking primary loci to marginal risk assignments based on many genes. Genetic interaction, defined as contributions to a phenotype that are dependent upon particular digenic allele combinations, could improve prediction of phenotype from complex genotype, but it is difficult to study in human populations. High throughput, systematic analysis of S. cerevisiae gene knockouts or knockdowns in the context of disease-relevant phenotypic perturbations provides a tractable experimental approach to derive gene interaction networks, in order to deduce by cross-species gene homology how phenotype is buffered against disease-risk genotypes. Yeast gene interaction network analysis to date has revealed biology more complex than previously imagined. This has motivated the development of more powerful yeast cell array phenotyping methods to globally model the role of gene interaction networks in modulating phenotypes (which we call yeast phenomic analysis). The article illustrates yeast phenomic technology, which is applied here to quantify gene X media interaction at higher resolution and supports use of a human-like media for future applications of yeast phenomics for modeling human disease.

Aging and energetics' 'Top 40' future research opportunities 2010-2013.

BACKGROUND: As part of a coordinated effort to expand our research activity at the interface of Aging and Energetics a team of investigators at The University of Alabama at Birmingham systematically assayed and catalogued the top research priorities identified in leading publications in that domain, believing the result would be useful to the scientific community at large. OBJECTIVE: To identify research priorities and opportunities in the domain of aging and energetics as advocated in the 40 most cited papers related to aging and energetics in the last 4 years. DESIGN: The investigators conducted a search for papers on aging and energetics in Scopus, ranked the resulting papers by number of times they were cited, and selected the ten most-cited papers in each of the four years that include 2010 to 2013, inclusive. RESULTS:   Ten research categories were identified from the 40 papers.  These included: (1) Calorie restriction (CR) longevity response, (2) role of mTOR (mechanistic target of Rapamycin) and related factors in lifespan extension, (3) nutrient effects beyond energy (especially resveratrol, omega-3 fatty acids, and selected amino acids), 4) autophagy and increased longevity and health, (5) aging-associated predictors of chronic disease, (6) use and effects of mesenchymal stem cells (MSCs), (7) telomeres relative to aging and energetics, (8) accretion and effects of body fat, (9) the aging heart,  and (10) mitochondria, reactive oxygen species, and cellular energetics. CONCLUSION: The field is rich with exciting opportunities to build upon our existing knowledge about the relations among aspects of aging and aspects of energetics and to better understand the mechanisms which connect them.

Phenomic assessment of genetic buffering by kinetic analysis of cell arrays.

Quantitative high-throughput cell array phenotyping (Q-HTCP) is applied to the genomic collection of yeast gene deletion mutants for systematic, comprehensive assessment of the contribution of genes and gene combinations to any phenotype of interest (phenomic analysis). Interacting gene networks influence every phenotype. Genetic buffering refers to how gene interaction networks stabilize or destabilize a phenotype. Like genomics, phenomics varies in its resolution with there being a trade-off allocating a greater number of measurements per sample to enhance quantification of the phenotype vs. increasing the number of different samples by obtaining fewer measurements per sample. The Q-HTCP protocol we describe assesses 50,000-70,000 cultures per experiment by obtaining kinetic growth curves from time series imaging of agar cell arrays. This approach was developed for the yeast gene deletion strains, but it could be applied as well to other microbial mutant arrays grown on solid agar media. The methods we describe are for creation and maintenance of frozen stocks, liquid source array preparation, agar destination plate printing, image scanning, image analysis, curve fitting, and evaluation of gene interaction.

Conserved allosteric hot spots in the transmembrane domains of cystic fibrosis transmembrane conductance regulator (CFTR) channels and multidrug resistance protein (MRP) pumps.

ATP-binding cassette (ABC) transporters are an ancient family of transmembrane proteins that utilize ATPase activity to move substrates across cell membranes. The ABCC subfamily of the ABC transporters includes active drug exporters (the multidrug resistance proteins (MRPs)) and a unique ATP-gated ion channel (cystic fibrosis transmembrane conductance regulator (CFTR)). The CFTR channel shares gating principles with conventional ligand-gated ion channels, but the allosteric network that couples ATP binding at its nucleotide binding domains (NBDs) with conformational changes in its transmembrane helices (TMs) is poorly defined. It is also unclear whether the mechanisms that govern CFTR gating are conserved with the thermodynamically distinct MRPs. Here we report a new class of gain of function (GOF) mutation of a conserved proline at the base of the pore-lining TM6. Multiple substitutions of this proline promoted ATP-free CFTR activity and activation by the weak agonist, 5'-adenylyl-ß,γ-imidodiphosphate (AMP-PNP). TM6 proline mutations exhibited additive GOF effects when combined with a previously reported GOF mutation located in an outer collar of TMs that surrounds the pore-lining TMs. Each TM substitution allosterically rescued the ATP sensitivity of CFTR gating when introduced into an NBD mutant with defective ATP binding. Both classes of GOF mutations also rescued defective drug export by a yeast MRP (Yor1p) with ATP binding defects in its NBDs. We conclude that the conserved TM6 proline helps set the energy barrier to both CFTR channel opening and MRP-mediated drug efflux and that CFTR channels and MRP pumps utilize similar allosteric mechanisms for coupling conformational changes in their translocation pathways to ATP binding at their NBDs.

The SWI/SNF chromatin remodeling complex influences transcription by RNA polymerase I in Saccharomyces cerevisiae.

SWI/SNF is a chromatin remodeling complex that affects transcription initiation and elongation by RNA polymerase II. Here we report that SWI/SNF also plays a role in transcription by RNA polymerase I (Pol I) in Saccharomyces cerevisiae. Deletion of the genes encoding the Snf6p or Snf5p subunits of SWI/SNF was lethal in combination with mutations that impair Pol I transcription initiation and elongation. SWI/SNF physically associated with ribosomal DNA (rDNA) within the coding region, with an apparent peak near the 5' end of the gene. In snf6Δ cells there was a ∼2.5-fold reduction in rRNA synthesis rate compared to WT, but there was no change in average polymerase occupancy per gene, the number of rDNA gene repeats, or the percentage of transcriptionally active rDNA genes. However, both ChIP and EM analyses showed a small but reproducible increase in Pol I density in a region near the 5' end of the gene. Based on these data, we conclude that SWI/SNF plays a positive role in Pol I transcription, potentially by modifying chromatin structure in the rDNA repeats. Our findings demonstrate that SWI/SNF influences the most robust transcription machinery in proliferating cells.

A yeast phenomic model for the gene interaction network modulating CFTR-ΔF508 protein biogenesis.

BACKGROUND: The overall influence of gene interaction in human disease is unknown. In cystic fibrosis (CF) a single allele of the cystic fibrosis transmembrane conductance regulator (CFTR-[increment]F508) accounts for most of the disease. In cell models, CFTR-[increment]F508 exhibits defective protein biogenesis and degradation rather than proper trafficking to the plasma membrane where CFTR normally functions. Numerous genes function in the biogenesis of CFTR and influence the fate of CFTR-[increment]F508. However it is not known whether genetic variation in such genes contributes to disease severity in patients. Nor is there an easy way to study how numerous gene interactions involving CFTR-[increment]F would manifest phenotypically. METHODS: To gain insight into the function and evolutionary conservation of a gene interaction network that regulates biogenesis of a misfolded ABC-transporter, we employed yeast genetics to develop a "phenomic" model, in which the CFTR-[increment]F508-equivalent residue of a yeast homolog is mutated (Yor1-[increment]F670), and where the genome is scanned quantitatively for interaction. We first confirmed that Yor1-[increment]F undergoes protein misfolding and has reduced half-life, analogous to CFTR-[increment]F. Gene interaction was then assessed quantitatively by growth curves for all ~5000 double mutants, based on alteration in the dose response to growth inhibition by oligomycin, a toxin extruded from the cell at the plasma membrane by Yor1. RESULTS: From a comparative genomic perspective, yeast gene interaction influencing Yor1-[increment]F biogenesis was representative of human homologs previously found to modulate processing of CFTR-[increment]F in mammalian cells. Additional evolutionarily conserved pathways were implicated by the study, and a [increment]F-specific pro-biogenesis function of the recently discovered ER Membrane Complex (EMC) was evident from the yeast screen. This novel function was validated biochemically by siRNA of an EMC ortholog in a human cell line expressing CFTR-[increment]F508. The precision and accuracy of quantitative high throughput cell array phenotyping (Q-HTCP), which captures tens of thousands of growth curves simultaneously, provided powerful resolution to measure gene interaction on a phenomic scale, based on discrete cell proliferation parameters. CONCLUSION: We propose phenomic analysis of Yor1-[increment]F as a model for investigating gene interaction networks that can modulate cystic fibrosis disease severity. Although the clinical relevance of the Yor1-[increment]F gene interaction network for cystic fibrosis remains to be defined, the model appears to be informative with respect to human cell models of CFTR-[increment]F. Moreover, the general strategy of yeast phenomics can be employed in a systematic manner to model gene interaction for other diseases relating to pathologies that result from protein misfolding or potentially any disease involving evolutionarily conserved genetic pathways.

A screen to identify small molecule inhibitors of protein-protein interactions in mycobacteria.

Despite extensive efforts in tuberculosis (TB) drug research, very few novel inhibitors have been discovered. This issue emphasizes the need for innovative methods to discover new anti-TB drugs. In this study, we established a new high-throughput screen (HTS) platform technology that differs from traditional TB drug screens because it utilizes Mycobacterial-Protein Fragment Complementation (M-PFC) to identify small molecule inhibitors of protein-protein interactions in mycobacteria. Several examples of protein-protein interactions were tested with M-PFC to highlight the diversity of selectable drug targets that could be used for screening. These included interactions of essential regulators (IdeR dimerization), enzymatic complexes (LeuCD), secretory antigens (Cfp10-Esat6), and signaling pathways (DevR dimerization). The feasibility of M-PFC in a HTS platform setting was tested by performing a proof-of-concept quantitative HTS of 3,600 small molecule compounds on DevR-DevR interaction, which was chosen because of its strong implications in Mycobacterium tuberculosis persistence and the need for effective drugs against latent TB. The calculated Z'-factor was consistently ≥0.8, indicating a robust and reproducible assay. Completion of the proof-of-concept screen allowed for the identification of advantages and disadvantages in the current assay design, where improvements made will further pioneer M-PFC-based applications in a large-scale HTS format.

Global analysis of phase locking in gene expression during cell cycle: the potential in network modeling.

BACKGROUND: In nonlinear dynamic systems, synchrony through oscillation and frequency modulation is a general control strategy to coordinate multiple modules in response to external signals. Conversely, the synchrony information can be utilized to infer interaction. Increasing evidence suggests that frequency modulation is also common in transcription regulation. RESULTS: In this study, we investigate the potential of phase locking analysis, a technique to study the synchrony patterns, in the transcription network modeling of time course gene expression data. Using the yeast cell cycle data, we show that significant phase locking exists between transcription factors and their targets, between gene pairs with prior evidence of physical or genetic interactions, and among cell cycle genes. When compared with simple correlation we found that the phase locking metric can identify gene pairs that interact with each other more efficiently. In addition, it can automatically address issues of arbitrary time lags or different dynamic time scales in different genes, without the need for alignment. Interestingly, many of the phase locked gene pairs exhibit higher order than 1:1 locking, and significant phase lags with respect to each other. Based on these findings we propose a new phase locking metric for network reconstruction using time course gene expression data. We show that it is efficient at identifying network modules of focused biological themes that are important to cell cycle regulation. CONCLUSIONS: Our result demonstrates the potential of phase locking analysis in transcription network modeling. It also suggests the importance of understanding the dynamics underlying the gene expression patterns.

Recursive expectation-maximization clustering: a method for identifying buffering mechanisms composed of phenomic modules.

Interactions between genetic and/or environmental factors are ubiquitous, affecting the phenotypes of organisms in complex ways. Knowledge about such interactions is becoming rate-limiting for our understanding of human disease and other biological phenomena. Phenomics refers to the integrative analysis of how all genes contribute to phenotype variation, entailing genome and organism level information. A systems biology view of gene interactions is critical for phenomics. Unfortunately the problem is intractable in humans; however, it can be addressed in simpler genetic model systems. Our research group has focused on the concept of genetic buffering of phenotypic variation, in studies employing the single-cell eukaryotic organism, S. cerevisiae. We have developed a methodology, quantitative high throughput cellular phenotyping (Q-HTCP), for high-resolution measurements of gene-gene and gene-environment interactions on a genome-wide scale. Q-HTCP is being applied to the complete set of S. cerevisiae gene deletion strains, a unique resource for systematically mapping gene interactions. Genetic buffering is the idea that comprehensive and quantitative knowledge about how genes interact with respect to phenotypes will lead to an appreciation of how genes and pathways are functionally connected at a systems level to maintain homeostasis. However, extracting biologically useful information from Q-HTCP data is challenging, due to the multidimensional and nonlinear nature of gene interactions, together with a relative lack of prior biological information. Here we describe a new approach for mining quantitative genetic interaction data called recursive expectation-maximization clustering (REMc). We developed REMc to help discover phenomic modules, defined as sets of genes with similar patterns of interaction across a series of genetic or environmental perturbations. Such modules are reflective of buffering mechanisms, i.e., genes that play a related role in the maintenance of physiological homeostasis. To develop the method, 297 gene deletion strains were selected based on gene-drug interactions with hydroxyurea, an inhibitor of ribonucleotide reductase enzyme activity, which is critical for DNA synthesis. To partition the gene functions, these 297 deletion strains were challenged with growth inhibitory drugs known to target different genes and cellular pathways. Q-HTCP-derived growth curves were used to quantify all gene interactions, and the data were used to test the performance of REMc. Fundamental advantages of REMc include objective assessment of total number of clusters and assignment to each cluster a log-likelihood value, which can be considered an indicator of statistical quality of clusters. To assess the biological quality of clusters, we developed a method called gene ontology information divergence z-score (GOid_z). GOid_z summarizes total enrichment of GO attributes within individual clusters. Using these and other criteria, we compared the performance of REMc to hierarchical and K-means clustering. The main conclusion is that REMc provides distinct efficiencies for mining Q-HTCP data. It facilitates identification of phenomic modules, which contribute to buffering mechanisms that underlie cellular homeostasis and the regulation of phenotypic expression.

Genomewide analysis reveals novel pathways affecting endoplasmic reticulum homeostasis, protein modification and quality control.

To gain new mechanistic insight into ER homeostasis and the biogenesis of secretory proteins, we screened a genomewide collection of yeast mutants for defective intracellular retention of the ER chaperone, Kar2p. We identified 87 Kar2p-secreting strains, including a number of known components in secretory protein modification and sorting. Further characterization of the 73 nonessential Kar2p retention mutants revealed roles for a number of novel gene products in protein glycosylation, GPI-anchor attachment, ER quality control, and retrieval of escaped ER residents. A subset of these mutants, required for ER retrieval, included the GET complex and two novel proteins that likely function similarly in membrane insertion of tail-anchored proteins. Finally, the variant histone, Htz1p, and its acetylation state seem to play an important role in maintaining ER retrieval pathways, suggesting a surprising link between chromatin remodeling and ER homeostasis.

Stringent mating-type-regulated auxotrophy increases the accuracy of systematic genetic interaction screens with Saccharomyces cerevisiae mutant arrays.

A genomic collection of haploid Saccharomyces cerevisiae deletion strains provides a unique resource for systematic analysis of gene interactions. Double-mutant haploid strains can be constructed by the synthetic genetic array (SGA) method, wherein a query mutation is introduced by mating to mutant arrays, selection of diploid double mutants, induction of meiosis, and selection of recombinant haploid double-mutant progeny. The mechanism of haploid selection is mating-type-regulated auxotrophy (MRA), by which prototrophy is restricted to a particular haploid genotype generated only as a result of meiosis. MRA escape leads to false-negative genetic interaction results because postmeiotic haploids that are supposed to be under negative selection instead proliferate and mate, forming diploids that are heterozygous at interacting loci, masking phenotypes that would be observed in a pure haploid double-mutant culture. This work identified factors that reduce MRA escape, including insertion of terminator and repressor sequences upstream of the MRA cassette, deletion of silent mating-type loci, and utilization of alpha-type instead of a-type MRA. Modifications engineered to reduce haploid MRA escape reduced false negative results in SGA-type analysis, resulting in >95% sensitivity for detecting gene-gene interactions.