Protein Complexes are Central in the Yeast Genetic Landscape

If perturbing two genes together has a stronger or weaker effect than expected, they are said to genetically interact. Genetic interactions are important because they help map gene function, and functionally related genes have similar genetic interaction patterns. Mapping quantitative (positive and negative) genetic interactions on a global scale has recently become possible. This data clearly shows groups of genes connected by predominantly positive or negative interactions, termed monochromatic groups. These groups often correspond to functional modules, like biological processes or complexes, or connections between modules. However it is not yet known how these patterns globally relate to known functional modules. Here we systematically study the monochromatic nature of known biological processes using the largest quantitative genetic interaction data set available, which includes fitness measurements for ∼5.4 million gene pairs in the yeast Saccharomyces cerevisiae. We find that only 10% of biological processes, as defined by Gene Ontology annotations, and less than 1% of inter-process connections are monochromatic. Further, we show that protein complexes are responsible for a surprisingly large fraction of these patterns. This suggests that complexes play a central role in shaping the monochromatic landscape of biological processes. Altogether this work shows that both positive and negative monochromatic patterns are found in known biological processes and in their connections and that protein complexes play an important role in these patterns. The monochromatic processes, complexes and connections we find chart a hierarchical and modular map of sensitive and redundant biological systems in the yeast cell that will be useful for gene function prediction and comparison across phenotypes and organisms. Furthermore the analysis methods we develop are applicable to other species for which genetic interactions will progressively become more available

Genetic analyses reveal cryptic introgression in secretive marsh bird populations

Hybridization is common in bird populations but can be challenging for management, especially if one of the two parent species is of greater conservation concern than the other. King rails (Rallus elegans) and clapper rails (R. crepitans) are two marsh bird species with similar morphologies, behaviors, and overlapping distributions. The two species are found along a salinity gradient with the king rail in freshwater marshes and the clapper in estuarine marshes. However, this separation is not absolute; they are occasionally sympatric, and there are reports of interbreeding. In Virginia, USA, both king and clapper rails are identified by the state as Species of Greater Conservation Need, although clappers are thought to be more abundant and king rails have a higher priority ranking. We used a mitochondrial DNA marker and 13 diagnostic nuclear single nucleotide polymorphisms (SNPs) to identify species, classify the degree of introgression, and explore the evolutionary history of introgression in two putative clapper rail focal populations along a salinity gradient in coastal Virginia. Genetic analyses revealed cryptic introgression with site‐specific rates of admixture. We identified a pattern of introgression where clapper rail alleles predominate in brackish marshes. These results suggest clapper rails may be displacing king rails in Virginia coastal waterways, most likely as a result of ecological selection. As introgression can result in various outcomes from outbreeding depression to local adaptation, continued monitoring of these populations would allow further exploration of hybrid fitness and inform conservation management

Bringing order to protein disorder through comparative genomics and genetic interactions

Abstract Background Intrinsically disordered regions are widespread, especially in proteomes of higher eukaryotes. Recently, protein disorder has been associated with a wide variety of cellular processes and has been implicated in several human diseases. Despite its apparent functional importance, the sheer range of different roles played by protein disorder often makes its exact contribution difficult to interpret. Results We attempt to better understand the different roles of disorder using a novel analysis that leverages both comparative genomics and genetic interactions. Strikingly, we find that disorder can be partitioned into three biologically distinct phenomena: regions where disorder is conserved but with quickly evolving amino acid sequences (flexible disorder); regions of conserved disorder with also highly conserved amino acid sequences (constrained disorder); and, lastly, non-conserved disorder. Flexible disorder bears many of the characteristics commonly attributed to disorder and is associated with signaling pathways and multi-functionality. Conversely, constrained disorder has markedly different functional attributes and is involved in RNA binding and protein chaperones. Finally, non-conserved disorder lacks clear functional hallmarks based on our analysis. Conclusions Our new perspective on protein disorder clarifies a variety of previous results by putting them into a systematic framework. Moreover, the clear and distinct functional association of flexible and constrained disorder will allow for new approaches and more specific algorithms for disorder detection in a functional context. Finally, in flexible disordered regions, we demonstrate clear evolutionary selection of protein disorder with little selection on primary structure, which has important implications for sequence-based studies of protein structure and evolution

Androgen Receptor Inhibition Suppresses Anti-Tumor Neutrophil Response Against Bone Metastatic Prostate Cancer via Regulation of TβRI Expression

Bone metastatic disease of prostate cancer (PCa) is incurable and progression in bone is largely dictated by tumor-stromal interactions in the bone microenvironment. We showed previously that bone neutrophils initially inhibit bone metastatic PCa growth yet metastatic PCa becomes resistant to neutrophil response. Further, neutrophils isolated from tumor-bone lost their ability to suppress tumor growth through unknown mechanisms. With this study, our goal was to define the impact of metastatic PCa on neutrophil function throughout tumor progression and to determine the potential of neutrophils as predictive biomarkers of metastatic disease. Using patient peripheral blood polymorphonuclear neutrophils (PMNs), we identified that PCa progression dictates PMN cell surface markers and gene expression, but not cytotoxicity against PCa. Importantly, we also identified a novel phenomenon in which second generation androgen deprivation therapy (ADT) suppresses PMN cytotoxicity via increased transforming growth factor beta receptor I (TβRI). High dose testosterone and genetic or pharmacologic TβRI inhibition rescued androgen receptor-mediated neutrophil suppression and restored neutrophil anti-tumor immune response. These studies highlight the ability to leverage standard-care ADT to generate neutrophil anti-tumor responses against bone metastatic PCa

A Systems Biology Approach Reveals the Endocrine Disrupting Potential of Aflatoxin B1

Background Aflatoxin B1 (AFB1) a mycotoxin produced by Aspergillus flavus and A. parasiticus is a potent carcinogen and causative agent of hepatocellular carcinoma (HCC). It is a food contaminant which presents a major risk to human health. AFB1 contamination poses a significant economic burden, as 25% of the world's food crops need to be destroyed annually. The mechanism of action (MOA) of aflatoxins remains to be fully elucidated. Recent findings suggest that AFB1 mediated endocrine disruption may occur in the population of regions with high contamination, even without evidence of direct dietary intake. Objective An integrative systems biology approach was undertaken to decipher the estrogenic component of the mechanism of action (MOA) of AFB1. Methods Molecular Docking and Molecular dynamics simulations were performed to examine the binding affinity of AFB1 and its metabolite aflatoxin Q1 (AFQ1) with the Estrogen Receptors (ERs). Differential gene expression (DGE), gene ontology (GO) and pathway analyses were carried out on hepatic transcriptomic data generated from in vivo AFB1 exposures. In parallel exposures to the synthetic estrogen ethinylestradiol (EE2) were examined for overlapping effects. Finally, protein–protein interaction (PPI) network analysis assessed the involvement of estrogen responsive targets (ERTs) associated with aflatoxin exposure. Results The free energies of binding affinity and estimated equilibrium dissociation constants (KD) demonstrated that AFB1 and AFQ1 can interact with the ERα and ERβ. DGE and GO analyses highlighted overlap in the responses between AFB1 and EE2 treatments with the activation of key processes involved in estrogenic signaling. PPI network analyses after AFBI exposure revealed a dynamic response to AFB1 treatments with the solid involvement of ERTs in regulatory networks. Conclusions This study revealed molecular interactions between aflatoxins (AFB1, AFQ1) and ERs in addition to overlap in differentially expressed genes and biological processes following AFB1 and EE2 exposures. The estrogenic components at the core of the PPI networks suggest that ER-mediated signaling pathways are a major component in the MOA of aflatoxins

Extensive Association of Functionally and Cytotopically Related mRNAs with Puf Family RNA-Binding Proteins in Yeast

Genes encoding RNA-binding proteins are diverse and abundant in eukaryotic genomes. Although some have been shown to have roles in post-transcriptional regulation of the expression of specific genes, few of these proteins have been studied systematically. We have used an affinity tag to isolate each of the five members of the Puf family of RNA-binding proteins in Saccharomyces cerevisiae and DNA microarrays to comprehensively identify the associated mRNAs. Distinct groups of 40–220 different mRNAs with striking common themes in the functions and subcellular localization of the proteins they encode are associated with each of the five Puf proteins: Puf3p binds nearly exclusively to cytoplasmic mRNAs that encode mitochondrial proteins; Puf1p and Puf2p interact preferentially with mRNAs encoding membrane-associated proteins; Puf4p preferentially binds mRNAs encoding nucleolar ribosomal RNA-processing factors; and Puf5p is associated with mRNAs encoding chromatin modifiers and components of the spindle pole body. We identified distinct sequence motifs in the 3′-untranslated regions of the mRNAs bound by Puf3p, Puf4p, and Puf5p. Three-hybrid assays confirmed the role of these motifs in specific RNA–protein interactions in vivo. The results suggest that combinatorial tagging of transcripts by specific RNA-binding proteins may be a general mechanism for coordinated control of the localization, translation, and decay of mRNAs and thus an integral part of the global gene expression program

An Integrative Multi-Network and Multi-Classifier Approach to Predict Genetic Interactions

Genetic interactions occur when a combination of mutations results in a surprising phenotype. These interactions capture functional redundancy, and thus are important for predicting function, dissecting protein complexes into functional pathways, and exploring the mechanistic underpinnings of common human diseases. Synthetic sickness and lethality are the most studied types of genetic interactions in yeast. However, even in yeast, only a small proportion of gene pairs have been tested for genetic interactions due to the large number of possible combinations of gene pairs. To expand the set of known synthetic lethal (SL) interactions, we have devised an integrative, multi-network approach for predicting these interactions that significantly improves upon the existing approaches. First, we defined a large number of features for characterizing the relationships between pairs of genes from various data sources. In particular, these features are independent of the known SL interactions, in contrast to some previous approaches. Using these features, we developed a non-parametric multi-classifier system for predicting SL interactions that enabled the simultaneous use of multiple classification procedures. Several comprehensive experiments demonstrated that the SL-independent features in conjunction with the advanced classification scheme led to an improved performance when compared to the current state of the art method. Using this approach, we derived the first yeast transcription factor genetic interaction network, part of which was well supported by literature. We also used this approach to predict SL interactions between all non-essential gene pairs in yeast (http://sage.fhcrc.org/downloads/downloads/predicted_yeast_genetic_interactions.zip). This integrative approach is expected to be more effective and robust in uncovering new genetic interactions from the tens of millions of unknown gene pairs in yeast and from the hundreds of millions of gene pairs in higher organisms like mouse and human, in which very few genetic interactions have been identified to date

Mitochondrial-Nuclear DNA Interactions Contribute to the Regulation of Nuclear Transcript Levels as Part of the Inter-Organelle Communication System

Nuclear and mitochondrial organelles must maintain a communication system. Loci on the mitochondrial genome were recently reported to interact with nuclear loci. To determine whether this is part of a DNA based communication system we used genome conformation capture to map the global network of DNA-DNA interactions between the mitochondrial and nuclear genomes (Mito-nDNA) in Saccharomyces cerevisiae cells grown under three different metabolic conditions. The interactions that form between mitochondrial and nuclear loci are dependent on the metabolic state of the yeast. Moreover, the frequency of specific mitochondrial - nuclear interactions (i.e. COX1-MSY1 and Q0182-RSM7) showed significant reductions in the absence of mitochondrial encoded reverse transcriptase machinery. Furthermore, these reductions correlated with increases in the transcript levels of the nuclear loci (MSY1 and RSM7). We propose that these interactions represent an inter-organelle DNA mediated communication system and that reverse transcription of mitochondrial RNA plays a role in this process

clusterMaker: a multi-algorithm clustering plugin for Cytoscape

<p>Abstract</p> <p>Background</p> <p>In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns within them. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. Here we present <it>clusterMaker</it>, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. <it>clusterMaker </it>is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view), k-means, k-medoid, SCPS, AutoSOME, and native (Java) MCL.</p> <p>Results</p> <p>Results are presented in the form of three scenarios of use: analysis of protein expression data using a recently published mouse interactome and a mouse microarray data set of nearly one hundred diverse cell/tissue types; the identification of protein complexes in the yeast <it>Saccharomyces cerevisiae</it>; and the cluster analysis of the vicinal oxygen chelate (VOC) enzyme superfamily. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. Cytoscape session files for all three scenarios are provided in the Additional Files section.</p> <p>Conclusions</p> <p>The Cytoscape plugin <it>clusterMaker </it>provides a number of clustering algorithms and visualizations that can be used independently or in combination for analysis and visualization of biological data sets, and for confirming or generating hypotheses about biological function. Several of these visualizations and algorithms are only available to Cytoscape users through the <it>clusterMaker </it>plugin. <it>clusterMaker </it>is available via the Cytoscape plugin manager.</p