A Network of Conserved Damage Survival Pathways Revealed by a Genomic RNAi Screen

Damage initiates a pleiotropic cellular response aimed at cellular survival when appropriate. To identify genes required for damage survival, we used a cell-based RNAi screen against the Drosophila genome and the alkylating agent methyl methanesulphonate (MMS). Similar studies performed in other model organisms report that damage response may involve pleiotropic cellular processes other than the central DNA repair components, yet an intuitive systems level view of the cellular components required for damage survival, their interrelationship, and contextual importance has been lacking. Further, by comparing data from different model organisms, identification of conserved and presumably core survival components should be forthcoming. We identified 307 genes, representing 13 signaling, metabolic, or enzymatic pathways, affecting cellular survival of MMS–induced damage. As expected, the majority of these pathways are involved in DNA repair; however, several pathways with more diverse biological functions were also identified, including the TOR pathway, transcription, translation, proteasome, glutathione synthesis, ATP synthesis, and Notch signaling, and these were equally important in damage survival. Comparison with genomic screen data from Saccharomyces cerevisiae revealed no overlap enrichment of individual genes between the species, but a conservation of the pathways. To demonstrate the functional conservation of pathways, five were tested in Drosophila and mouse cells, with each pathway responding to alkylation damage in both species. Using the protein interactome, a significant level of connectivity was observed between Drosophila MMS survival proteins, suggesting a higher order relationship. This connectivity was dramatically improved by incorporating the components of the 13 identified pathways within the network. Grouping proteins into “pathway nodes” qualitatively improved the interactome organization, revealing a highly organized “MMS survival network.” We conclude that identification of pathways can facilitate comparative biology analysis when direct gene/orthologue comparisons fail. A biologically intuitive, highly interconnected MMS survival network was revealed after we incorporated pathway data in our interactome analysis

Identification of Novel Human Damage Response Proteins Targeted through Yeast Orthology

Studies in Saccharomyces cerevisiae show that many proteins influence cellular survival upon exposure to DNA damaging agents. We hypothesized that human orthologs of these S. cerevisiae proteins would also be required for cellular survival after treatment with DNA damaging agents. For this purpose, human homologs of S. cerevisiae proteins were identified and mapped onto the human protein-protein interaction network. The resulting human network was highly modular and a series of selection rules were implemented to identify 45 candidates for human toxicity-modulating proteins. The corresponding transcripts were targeted by RNA interference in human cells. The cell lines with depleted target expression were challenged with three DNA damaging agents: the alkylating agents MMS and 4-NQO, and the oxidizing agent t-BuOOH. A comparison of the survival revealed that the majority (74%) of proteins conferred either sensitivity or resistance. The identified human toxicity-modulating proteins represent a variety of biological functions: autophagy, chromatin modifications, RNA and protein metabolism, and telomere maintenance. Further studies revealed that MMS-induced autophagy increase the survival of cells treated with DNA damaging agents. In summary, we show that damage recovery proteins in humans can be identified through homology to S. cerevisiae and that many of the same pathways are represented among the toxicity modulators

Microanatomy of Adult Zebrafish Extraocular Muscles

Binocular vision requires intricate control of eye movement to align overlapping visual fields for fusion in the visual cortex, and each eye is controlled by 6 extraocular muscles (EOMs). Disorders of EOMs are an important cause of symptomatic vision loss. Importantly, EOMs represent specialized skeletal muscles with distinct gene expression profile and susceptibility to neuromuscular disorders. We aim to investigate and describe the anatomy of adult zebrafish extraocular muscles (EOMs) to enable comparison with human EOM anatomy and facilitate the use of zebrafish as a model for EOM research. Using differential interference contrast (DIC), epifluorescence microscopy, and precise sectioning techniques, we evaluate the anatomy of zebrafish EOM origin, muscle course, and insertion on the eye. Immunofluorescence is used to identify components of tendons, basement membrane and neuromuscular junctions (NMJs), and to analyze myofiber characteristics. We find that adult zebrafish EOM insertions on the globe parallel the organization of human EOMs, including the close proximity of specific EOM insertions to one another. However, analysis of EOM origins reveals important differences between human and zebrafish, such as the common rostral origin of both oblique muscles and the caudal origin of the lateral rectus muscles. Thrombospondin 4 marks the EOM tendons in regions that are highly innervated, and laminin marks the basement membrane, enabling evaluation of myofiber size and distribution. The NMJs appear to include both en plaque and en grappe synapses, while NMJ density is much higher in EOMs than in somatic muscles. In conclusion, zebrafish and human EOM anatomy are generally homologous, supporting the use of zebrafish for studying EOM biology. However, anatomic differences exist, revealing divergent evolutionary pressures

Sequestration of Highly Expressed mRNAs in Cytoplasmic Granules, P-Bodies, and Stress Granules Enhances Cell Viability

Transcriptome analyses indicate that a core 10%–15% of the yeast genome is modulated by a variety of different stresses. However, not all the induced genes undergo translation, and null mutants of many induced genes do not show elevated sensitivity to the particular stress. Elucidation of the RNA lifecycle reveals accumulation of non-translating mRNAs in cytoplasmic granules, P-bodies, and stress granules for future regulation. P-bodies contain enzymes for mRNA degradation; under stress conditions mRNAs may be transferred to stress granules for storage and return to translation. Protein degradation by the ubiquitin-proteasome system is elevated by stress; and here we analyzed the steady state levels, decay, and subcellular localization of the mRNA of the gene encoding the F-box protein, UFO1, that is induced by stress. Using the MS2L mRNA reporter system UFO1 mRNA was observed in granules that colocalized with P-bodies and stress granules. These P-bodies stored diverse mRNAs. Granules of two mRNAs transported prior to translation, ASH1-MS2L and OXA1-MS2L, docked with P-bodies. HSP12 mRNA that gave rise to highly elevated protein levels was not observed in granules under these stress conditions. ecd3, pat1 double mutants that are defective in P-body formation were sensitive to mRNAs expressed ectopically from strong promoters. These highly expressed mRNAs showed elevated translation compared with wild-type cells, and the viability of the mutants was strongly reduced. ecd3, pat1 mutants also exhibited increased sensitivity to different stresses. Our interpretation is that sequestration of highly expressed mRNAs in P-bodies is essential for viability. Storage of mRNAs for future regulation may contribute to the discrepancy between the steady state levels of many stress-induced mRNAs and their proteins. Sorting of mRNAs for future translation or decay by individual cells could generate potentially different phenotypes in a genetically identical population and enhance its ability to withstand stress

Identification and Analysis of Co-Occurrence Networks with NetCutter

BACKGROUND: Co-occurrence analysis is a technique often applied in text mining, comparative genomics, and promoter analysis. The methodologies and statistical models used to evaluate the significance of association between co-occurring entities are quite diverse, however. METHODOLOGY/PRINCIPAL FINDINGS: We present a general framework for co-occurrence analysis based on a bipartite graph representation of the data, a novel co-occurrence statistic, and software performing co-occurrence analysis as well as generation and analysis of co-occurrence networks. We show that the overall stringency of co-occurrence analysis depends critically on the choice of the null-model used to evaluate the significance of co-occurrence and find that random sampling from a complete permutation set of the bipartite graph permits co-occurrence analysis with optimal stringency. We show that the Poisson-binomial distribution is the most natural co-occurrence probability distribution when vertex degrees of the bipartite graph are variable, which is usually the case. Calculation of Poisson-binomial P-values is difficult, however. Therefore, we propose a fast bi-binomial approximation for calculation of P-values and show that this statistic is superior to other measures of association such as the Jaccard coefficient and the uncertainty coefficient. Furthermore, co-occurrence analysis of more than two entities can be performed using the same statistical model, which leads to increased signal-to-noise ratios, robustness towards noise, and the identification of implicit relationships between co-occurring entities. Using NetCutter, we identify a novel protein biosynthesis related set of genes that are frequently coordinately deregulated in human cancer related gene expression studies. NetCutter is available at http://bio.ifom-ieo-campus.it/NetCutter/). CONCLUSION: Our approach can be applied to any set of categorical data where co-occurrence analysis might reveal functional relationships such as clinical parameters associated with cancer subtypes or SNPs associated with disease phenotypes. The stringency of our approach is expected to offer an advantage in a variety of applications

Molecular analysis of the vaginal response to estrogens in the ovariectomized rat and postmenopausal woman

<p>Abstract</p> <p>Background</p> <p>Vaginal atrophy (VA) is the thinning of the vaginal epithelial lining, typically the result of lowered estrogen levels during menopause. Some of the consequences of VA include increased susceptibility to bacterial infection, pain during sexual intercourse, and vaginal burning or itching. Although estrogen treatment is highly effective, alternative therapies are also desired for women who are not candidates for post-menopausal hormone therapy (HT). The ovariectomized (OVX) rat is widely accepted as an appropriate animal model for many estrogen-dependent responses in humans; however, since reproductive biology can vary significantly between mammalian systems, this study examined how well the OVX rat recapitulates human biology.</p> <p>Methods</p> <p>We analyzed 19 vaginal biopsies from human subjects pre and post 3-month 17β-estradiol treated by expression profiling. Data were compared to transcriptional profiling generated from vaginal samples obtained from ovariectomized rats treated with 17β-estradiol for 6 hrs, 3 days or 5 days. The level of differential expression between pre- vs. post- estrogen treatment was calculated for each of the human and OVX rat datasets. Probe sets corresponding to orthologous rat and human genes were mapped to each other using NCBI Homologene.</p> <p>Results</p> <p>A positive correlation was observed between the rat and human responses to estrogen. Genes belonging to several biological pathways and GO categories were similarly differentially expressed in rat and human. A large number of the coordinately regulated biological processes are already known to be involved in human VA, such as inflammation, epithelial development, and EGF pathway activation.</p> <p>Conclusion</p> <p>At the transcriptional level, there is evidence of significant overlap of the effects of estrogen treatment between the OVX rat and human VA samples.</p

Simultaneous Clustering of Multiple Gene Expression and Physical Interaction Datasets

Many genome-wide datasets are routinely generated to study different aspects of biological systems, but integrating them to obtain a coherent view of the underlying biology remains a challenge. We propose simultaneous clustering of multiple networks as a framework to integrate large-scale datasets on the interactions among and activities of cellular components. Specifically, we develop an algorithm JointCluster that finds sets of genes that cluster well in multiple networks of interest, such as coexpression networks summarizing correlations among the expression profiles of genes and physical networks describing protein-protein and protein-DNA interactions among genes or gene-products. Our algorithm provides an efficient solution to a well-defined problem of jointly clustering networks, using techniques that permit certain theoretical guarantees on the quality of the detected clustering relative to the optimal clustering. These guarantees coupled with an effective scaling heuristic and the flexibility to handle multiple heterogeneous networks make our method JointCluster an advance over earlier approaches. Simulation results showed JointCluster to be more robust than alternate methods in recovering clusters implanted in networks with high false positive rates. In systematic evaluation of JointCluster and some earlier approaches for combined analysis of the yeast physical network and two gene expression datasets under glucose and ethanol growth conditions, JointCluster discovers clusters that are more consistently enriched for various reference classes capturing different aspects of yeast biology or yield better coverage of the analysed genes. These robust clusters, which are supported across multiple genomic datasets and diverse reference classes, agree with known biology of yeast under these growth conditions, elucidate the genetic control of coordinated transcription, and enable functional predictions for a number of uncharacterized genes

Fasting Induces the Expression of PGC-1α and ERR Isoforms in the Outer Stripe of the Outer Medulla (OSOM) of the Mouse Kidney

Peroxisome proliferator-activated receptor-γ co-activator-1α (PGC-1α) is a member of the transcriptional coactivator family that plays a central role in the regulation of cellular energy metabolism under various physiological stimuli. During fasting, PGC-1α is induced in the liver and together with estrogen-related receptor a and γ (ERRα and ERRγ, orphan nuclear receptors with no known endogenous ligand, regulate sets of genes that participate in the energy balance program. We found that PGC-1α, ERRα and ERRγ was highly expressed in human kidney HK2 cells and that PGC-1α induced dynamic protein interactions on the ERRα chromatin. However, the effect of fasting on the expression of endogenous PGC-1α, ERRα and ERRγ in the kidney is not known.In this study, we demonstrated by qPCR that the expression of PGC-1α, ERRα and ERRγ was increased in the mouse kidney after fasting. By using immunohistochemistry (IHC), we showed these three proteins are co-localized in the outer stripe of the outer medulla (OSOM) of the mouse kidney. We were able to collect this region from the kidney using the Laser Capture Microdissection (LCM) technique. The qPCR data showed significant increase of PGC-1α, ERRα and ERRγ mRNA in the LCM samples after fasting for 24 hours. Furthermore, the known ERRα target genes, mitochondrial oxidative phosphorylation gene COX8H and the tricarboxylic acid (TCA) cycle gene IDH3A also showed an increase. Taken together, our data suggest that fasting activates the energy balance program in the OSOM of the kidney

Elevated Proteasome Capacity Extends Replicative Lifespan in Saccharomyces cerevisiae

Aging is characterized by the accumulation of damaged cellular macromolecules caused by declining repair and elimination pathways. An integral component employed by cells to counter toxic protein aggregates is the conserved ubiquitin/proteasome system (UPS). Previous studies have described an age-dependent decline of proteasomal function and increased longevity correlates with sustained proteasome capacity in centenarians and in naked mole rats, a long-lived rodent. Proof for a direct impact of enhanced proteasome function on longevity, however, is still lacking. To determine the importance of proteasome function in yeast aging, we established a method to modulate UPS capacity by manipulating levels of the UPS–related transcription factor Rpn4. While cells lacking RPN4 exhibit a decreased non-adaptable proteasome pool, loss of UBR2, an ubiquitin ligase that regulates Rpn4 turnover, results in elevated Rpn4 levels, which upregulates UPS components. Increased UPS capacity significantly enhances replicative lifespan (RLS) and resistance to proteotoxic stress, while reduced UPS capacity has opposing consequences. Despite tight transcriptional co-regulation of the UPS and oxidative detoxification systems, the impact of proteasome capacity on lifespan is independent of the latter, since elimination of Yap1, a key regulator of the oxidative stress response, does not affect lifespan extension of cells with higher proteasome capacity. Moreover, since elevated proteasome capacity results in improved clearance of toxic huntingtin fragments in a yeast model for neurodegenerative diseases, we speculate that the observed lifespan extension originates from prolonged elimination of damaged proteins in old mother cells. Epistasis analyses indicate that proteasome-mediated modulation of lifespan is at least partially distinct from dietary restriction, Tor1, and Sir2. These findings demonstrate that UPS capacity determines yeast RLS by a mechanism that is distinct from known longevity pathways and raise the possibility that interventions to promote enhanced proteasome function will have beneficial effects on longevity and age-related disease in humans