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1.  Aging and energetics’ ‘Top 40’ future research opportunities 2010-2013 
F1000Research  2014;3:219.
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.
PMCID: PMC4197746  PMID: 25324965
2.  The SWI/SNF Chromatin Remodeling Complex Influences Transcription by RNA Polymerase I in Saccharomyces cerevisiae 
PLoS ONE  2013;8(2):e56793.
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.
PMCID: PMC3577654  PMID: 23437238
3.  A yeast phenomic model for the gene interaction network modulating CFTR-ΔF508 protein biogenesis 
Genome Medicine  2012;4(12):103.
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-ΔF508) accounts for most of the disease. In cell models, CFTR-ΔF508 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-ΔF508. 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-ΔF would manifest phenotypically.
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-ΔF508-equivalent residue of a yeast homolog is mutated (Yor1-ΔF670), and where the genome is scanned quantitatively for interaction. We first confirmed that Yor1-ΔF undergoes protein misfolding and has reduced half-life, analogous to CFTR-ΔF. Gene interaction was then assessed quantitatively by growth curves for approximately 5,000 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.
From a comparative genomic perspective, yeast gene interactions influencing Yor1-ΔF biogenesis were representative of human homologs previously found to modulate processing of CFTR-ΔF in mammalian cells. Additional evolutionarily conserved pathways were implicated by the study, and a ΔF-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-ΔF508. 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.
We propose phenomic analysis of Yor1-ΔF as a model for investigating gene interaction networks that can modulate cystic fibrosis disease severity. Although the clinical relevance of the Yor1-ΔF gene interaction network for cystic fibrosis remains to be defined, the model appears to be informative with respect to human cell models of CFTR-ΔF. 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.
PMCID: PMC3906889  PMID: 23270647
Gene interaction; Genetic buffering; Genotype-phenotype complexity; Phenomics; Quantitative high throughput cell array phenotyping (Q-HTCP); Cystic fibrosis transmembrane conductance regulator (CFTR); ER membrane complex (EMC); ATP binding cassette (ABC) transporter; Membrane protein biogenesis; Yeast model of human disease; Comparative functional genomics
4.  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.
PMCID: PMC3102257  PMID: 21281130
5.  Accurate, precise modeling of cell proliferation kinetics from time-lapse imaging and automated image analysis of agar yeast culture arrays 
Genome-wide mutant strain collections have increased demand for high throughput cellular phenotyping (HTCP). For example, investigators use HTCP to investigate interactions between gene deletion mutations and additional chemical or genetic perturbations by assessing differences in cell proliferation among the collection of 5000 S. cerevisiae gene deletion strains. Such studies have thus far been predominantly qualitative, using agar cell arrays to subjectively score growth differences. Quantitative systems level analysis of gene interactions would be enabled by more precise HTCP methods, such as kinetic analysis of cell proliferation in liquid culture by optical density. However, requirements for processing liquid cultures make them relatively cumbersome and low throughput compared to agar. To improve HTCP performance and advance capabilities for quantifying interactions, YeastXtract software was developed for automated analysis of cell array images.
YeastXtract software was developed for kinetic growth curve analysis of spotted agar cultures. The accuracy and precision for image analysis of agar culture arrays was comparable to OD measurements of liquid cultures. Using YeastXtract, image intensity vs. biomass of spot cultures was linearly correlated over two orders of magnitude. Thus cell proliferation could be measured over about seven generations, including four to five generations of relatively constant exponential phase growth. Spot area normalization reduced the variation in measurements of total growth efficiency. A growth model, based on the logistic function, increased precision and accuracy of maximum specific rate measurements, compared to empirical methods. The logistic function model was also more robust against data sparseness, meaning that less data was required to obtain accurate, precise, quantitative growth phenotypes.
Microbial cultures spotted onto agar media are widely used for genotype-phenotype analysis, however quantitative HTCP methods capable of measuring kinetic growth rates have not been available previously. YeastXtract provides objective, automated, quantitative, image analysis of agar cell culture arrays. Fitting the resulting data to a logistic equation-based growth model yields robust, accurate growth rate information. These methods allow the incorporation of imaging and automated image analysis of cell arrays, grown on solid agar media, into HTCP-driven experimental approaches, such as global, quantitative analysis of gene interaction networks.
PMCID: PMC1847469  PMID: 17408510
6.  Systematic quantification of gene interactions by phenotypic array analysis 
Genome Biology  2004;5(7):R49.
A phenotypic array method, developed for quantifying cell growth, was applied to the haploid and homozygous diploid yeast deletion strain sets. A growth index was developed to screen for non-additive interacting effects between gene deletion and induced perturbations.
A phenotypic array method, developed for quantifying cell growth, was applied to the haploid and homozygous diploid yeast deletion strain sets. A growth index was developed to screen for non-additive interacting effects between gene deletion and induced perturbations. From a genome screen for hydroxyurea (HU) chemical-genetic interactions, 298 haploid deletion strains were selected for further analysis. The strength of interactions was quantified using a wide range of HU concentrations affecting reference strain growth. The selectivity of interaction was determined by comparison with drugs targeting other cellular processes. Bio-modules were defined as gene clusters with shared strength and selectivity of interaction profiles. The functions and connectivity of modules involved in processes such as DNA repair, protein secretion and metabolic control were inferred from their respective gene composition. The work provides an example of, and a general experimental framework for, quantitative analysis of gene interaction networks that buffer cell growth.
PMCID: PMC463315  PMID: 15239834

Results 1-6 (6)