GeneFishing to reconstruct context specific portraits of biological processes.

Rapid advances in genomic technologies have led to a wealth of diverse data, from which novel discoveries can be gleaned through the application of robust statistical and computational methods. Here, we describe GeneFishing, a semisupervised computational approach to reconstruct context-specific portraits of biological processes by leveraging gene-gene coexpression information. GeneFishing incorporates multiple high-dimensional statistical ideas, including dimensionality reduction, clustering, subsampling, and results aggregation, to produce robust results. To illustrate the power of our method, we applied it using 21 genes involved in cholesterol metabolism as "bait" to "fish out" (or identify) genes not previously identified as being connected to cholesterol metabolism. Using simulation and real datasets, we found that the results obtained through GeneFishing were more interesting for our study than those provided by related gene prioritization methods. In particular, application of GeneFishing to the GTEx liver RNA sequencing (RNAseq) data not only reidentified many known cholesterol-related genes, but also pointed to glyoxalase I (GLO1) as a gene implicated in cholesterol metabolism. In a follow-up experiment, we found that GLO1 knockdown in human hepatoma cell lines increased levels of cellular cholesterol ester, validating a role for GLO1 in cholesterol metabolism. In addition, we performed pantissue analysis by applying GeneFishing on various tissues and identified many potential tissue-specific cholesterol metabolism-related genes. GeneFishing appears to be a powerful tool for identifying related components of complex biological systems and may be used across a wide range of applications.

Transcriptomics and Functional Analysis of Copper Stress Response in the Sulfate-Reducing Bacterium Desulfovibrio alaskensis G20.

Copper (Cu) is an essential micronutrient required as a co-factor in the catalytic center of many enzymes. However, excess Cu can generate pleiotropic effects in the microbial cell. In addition, leaching of Cu from pipelines results in elevated Cu concentration in the environment, which is of public health concern. Sulfate-reducing bacteria (SRB) have been demonstrated to grow in toxic levels of Cu. However, reports on Cu toxicity towards SRB have primarily focused on the degree of toxicity and subsequent elimination. Here, Cu(II) stress-related effects on a model SRB, Desulfovibrio alaskensis G20, is reported. Cu(II) stress effects were assessed as alterations in the transcriptome through RNA-Seq at varying Cu(II) concentrations (5 µM and 15 µM). In the pairwise comparison of control vs. 5 µM Cu(II), 61.43% of genes were downregulated, and 38.57% were upregulated. In control vs. 15 µM Cu(II), 49.51% of genes were downregulated, and 50.5% were upregulated. The results indicated that the expression of inorganic ion transporters and translation machinery was massively modulated. Moreover, changes in the expression of critical biological processes such as DNA transcription and signal transduction were observed at high Cu(II) concentrations. These results will help us better understand the Cu(II) stress-response mechanism and provide avenues for future research.

Dynamic motif occupancy (DynaMO) analysis identifies transcription factors and their binding sites driving dynamic biological processes.

Biological processes are usually associated with genome-wide remodeling of transcription driven by transcription factors (TFs). Identifying key TFs and their spatiotemporal binding patterns are indispensable to understanding how dynamic processes are programmed. However, most methods are designed to predict TF binding sites only. We present a computational method, dynamic motif occupancy analysis (DynaMO), to infer important TFs and their spatiotemporal binding activities in dynamic biological processes using chromatin profiling data from multiple biological conditions such as time-course histone modification ChIP-seq data. In the first step, DynaMO predicts TF binding sites with a random forests approach. Next and uniquely, DynaMO infers dynamic TF binding activities at predicted binding sites using their local chromatin profiles from multiple biological conditions. Another landmark of DynaMO is to identify key TFs in a dynamic process using a clustering and enrichment analysis of dynamic TF binding patterns. Application of DynaMO to the yeast ultradian cycle, mouse circadian clock and human neural differentiation exhibits its accuracy and versatility. We anticipate DynaMO will be generally useful for elucidating transcriptional programs in dynamic processes.

Biological Processes Discovered by High-Throughput Sequencing.

Advances in DNA and RNA sequencing technologies have completely transformed the field of genomics. High-throughput sequencing (HTS) is now a widely used and accessible technology that allows scientists to sequence an entire transcriptome or genome in a timely and cost-effective manner. Application of HTS techniques has led to many key discoveries, including the identification of long noncoding RNAs, microDNAs, a family of small extrachromosomal circular DNA species, and tRNA-derived fragments, which are a group of small non-miRNAs that are derived from tRNAs. Furthermore, public sequencing repositories provide unique opportunities for laboratories to parse large sequencing databases to identify proteins and noncoding RNAs at a scale that was not possible a decade ago. Herein, we review how HTS has led to the discovery of novel nucleic acid species and uncovered new biological processes during the course.

eQTL Regulating Transcript Levels Associated with Diverse Biological Processes in Tomato.

Variation in gene expression, in addition to sequence polymorphisms, is known to influence developmental, physiological, and metabolic traits in plants. Genetic mapping populations have facilitated identification of expression quantitative trait loci (eQTL), the genetic determinants of variation in gene expression patterns. We used an introgression population developed from the wild desert-adapted Solanum pennellii and domesticated tomato (Solanum lycopersicum) to identify the genetic basis of transcript level variation. We established the effect of each introgression on the transcriptome and identified approximately 7,200 eQTL regulating the steady-state transcript levels of 5,300 genes. Barnes-Hut t-distributed stochastic neighbor embedding clustering identified 42 modules revealing novel associations between transcript level patterns and biological processes. The results showed a complex genetic architecture of global transcript abundance pattern in tomato. Several genetic hot spots regulating a large number of transcript level patterns relating to diverse biological processes such as plant defense and photosynthesis were identified. Important eQTL regulating transcript level patterns were related to leaf number and complexity as well as hypocotyl length. Genes associated with leaf development showed an inverse correlation with photosynthetic gene expression, but eQTL regulating genes associated with leaf development and photosynthesis were dispersed across the genome. This comprehensive eQTL analysis details the influence of these loci on plant phenotypes and will be a valuable community resource for investigations on the genetic effects of eQTL on phenotypic traits in tomato.

Comprehensive profiling of biological processes reveals two major prognostic subtypes in breast cancer.

Heterogeneity is the major obstacle to breast cancer target therapy. Classification of breast cancer with significant biological process may reduce the influence of heterogeneity of intrinsic tumor. We used survival analysis to filter 95 gene sets and classify 638 breast cancer samples into two subtypes based on those gene sets associated with prognosis. Clinical outcome of two subtypes were evaluated with disease-free survival, distant metastasis-free survival, and overall survival levels in three databases and ER+, PR+ HER2+, and TNBC groups. We established a novel classification with 95 prognostic gene sets. In the training and validation cohorts, the subtype 1 was characterized by significant gene sets associated with regulation of metabolic process and enzyme activity and predicted obviously improved clinical outcome than subtype 2, which was enriched by tumor cell division, mitosis, and cell cycle-related gene sets (P < 0.05). When evaluated prognostic impact of subtypes in ER+, PR+ HER2+, and TNBC groups, we found that patients in subtype 1 showed better prognosis in ER+ and PR+ groups (P < 0.05) but had no difference from prognosis of subtype 2 in HER2+ and TNBC groups. These findings may have implications in understanding of breast cancer and filtering effective therapeutic strategies for targeted therapy.

Network-based prediction for sources of transcriptional dysregulation using latent pathway identification analysis.

Understanding the systemic biological pathways and the key cellular mechanisms that dictate disease states, drug response, and altered cellular function poses a significant challenge. Although high-throughput measurement techniques, such as transcriptional profiling, give some insight into the altered state of a cell, they fall far short of providing by themselves a complete picture. Some improvement can be made by using enrichment-based methods to, for example, organize biological data of this sort into collections of dysregulated pathways. However, such methods arguably are still limited to primarily a transcriptional view of the cell. Augmenting these methods still further with networks and additional -omics data has been found to yield pathways that play more fundamental roles. We propose a previously undescribed method for identification of such pathways that takes a more direct approach to the problem than any published to date. Our method, called latent pathway identification analysis (LPIA), looks for statistically significant evidence of dysregulation in a network of pathways constructed in a manner that implicitly links pathways through their common function in the cell. We describe the LPIA methodology and illustrate its effectiveness through analysis of data on (i) metastatic cancer progression, (ii) drug treatment in human lung carcinoma cells, and (iii) diagnosis of type 2 diabetes. With these analyses, we show that LPIA can successfully identify pathways whose perturbations have latent influences on the transcriptionally altered genes.

Quantitative protein and mRNA profiling shows selective post-transcriptional control of protein expression by vasopressin in kidney cells.

Previous studies in yeast have supported the view that post-transcriptional regulation of protein abundances may be more important than previously believed. Here we ask the question: "In a physiological regulatory process (the response of mammalian kidney cells to the hormone vasopressin), what fraction of the expressed proteome undergoes a change in abundance and what fraction of the regulated proteins have corresponding changes in mRNA levels?" In humans and other mammals, vasopressin fulfills a vital homeostatic role (viz. regulation of renal water excretion) by regulating the water channel aquaporin-2 in collecting duct cells. To address the question posed, we utilized large-scale quantitative protein mass spectrometry (LC-MS/MS) employing stable isotopic labeling in cultured mpkCCD cells ('SILAC') coupled with transcriptomic profiling using oligonucleotide expression arrays (Affymetrix). Preliminary studies analyzing two nominally identical control samples by SILAC LC-MS/MS yielded a relative S.D. of 13% (for ratios), establishing the precision of the SILAC approach in our hands. We quantified nearly 3000 proteins with nontargeted SILAC LC-MS/MS, comparing vasopressin- versus vehicle-treated samples. Of these proteins 786 of them were quantified in each of 3 experiments, allowing statistical analysis and 188 of these showed significant vasopressin-induced changes in abundance, including aquaporin-2 (20-fold increase). Among the proteins with statistically significant abundance changes, a large fraction (at least one-third) was found to lack changes in the corresponding mRNA species (despite sufficient statistical power), indicating that post-transcriptional regulation of protein abundance plays an important role in the vasopressin response. Bioinformatic analysis of the regulated proteins (versus all transcripts) shows enrichment of glutathione S-transferase isoforms as well as proteins involved in organization of the actin cytoskeleton. The latter suggests that long-term regulatory processes may contribute to actomyosin-dependent trafficking of the water channel aquaporin-2. The results provide impetus for increased focus on translational regulation and regulation of protein degradation in physiological control in mammalian epithelial cells.

How selective sweeps in domestic animals provide new insight into biological mechanisms.

Genetic studies of domestic animals are of general interest because there is more phenotypic diversity to explore in these species than in any experimental organism. Some mutations with favourable phenotypic effects have been highly enriched and gone through selective sweeps during the process of domestication and selective breeding. Three such selective sweeps are described in this review. All three mutations are intronic and constitute cis-acting regulatory mutations. Two of the mutations constitute structural changes (one duplication and one copy number expansion). These examples illustrate a general trend that noncoding mutations and structural changes have both contributed significantly to the evolution of phenotypic diversity in domestic animals. How the molecular characterization of trait loci in domestic animals can provide new basic knowledge of relevance for human medicine is discussed.

Global landscape of a co-expressed gene network in barley and its application to gene discovery in Triticeae crops.

Accumulated transcriptome data can be used to investigate regulatory networks of genes involved in various biological systems. Co-expression analysis data sets generated from comprehensively collected transcriptome data sets now represent efficient resources that are capable of facilitating the discovery of genes with closely correlated expression patterns. In order to construct a co-expression network for barley, we analyzed 45 publicly available experimental series, which are composed of 1,347 sets of GeneChip data for barley. On the basis of a gene-to-gene weighted correlation coefficient, we constructed a global barley co-expression network and classified it into clusters of subnetwork modules. The resulting clusters are candidates for functional regulatory modules in the barley transcriptome. To annotate each of the modules, we performed comparative annotation using genes in Arabidopsis and Brachypodium distachyon. On the basis of a comparative analysis between barley and two model species, we investigated functional properties from the representative distributions of the gene ontology (GO) terms. Modules putatively involved in drought stress response and cellulose biogenesis have been identified. These modules are discussed to demonstrate the effectiveness of the co-expression analysis. Furthermore, we applied the data set of co-expressed genes coupled with comparative analysis in attempts to discover potentially Triticeae-specific network modules. These results demonstrate that analysis of the co-expression network of the barley transcriptome together with comparative analysis should promote the process of gene discovery in barley. Furthermore, the insights obtained should be transferable to investigations of Triticeae plants. The associated data set generated in this analysis is publicly accessible at http://coexpression.psc.riken.jp/barley/.

Role of the autonomic nervous system in rat liver regeneration.

To study the regulatory role of autonomic nervous system in rat regenerating liver, surgical operations of rat partial hepatectomy (PH) and its operation control (OC), sympathectomy combining partial hepatectomy (SPH), vagotomy combining partial hepatectomy (VPH), and total liver denervation combining partial hepatectomy (TDPH) were performed, then expression profiles of regenerating livers at 2 h after operation were detected using Rat Genome 230 2.0 array. It was shown that the expressions of 97 genes in OC, 230 genes in PH, 253 genes in SPH, 187 genes in VPH, and 177 genes in TDPH were significantly changed in biology. The relevance analysis showed that in SPH, genes involved in stimulus response, immunity response, amino acids and K(+) transport, amino acid catabolism, cell adhesion, cell proliferation mediated by JAK-STAT, Ca(+), and platelet-derived growth factor receptor, cell growth and differentiation through JAK-STAT were up-regulated, while the genes involved in chromatin assembly and disassembly, and cell apoptosis mediated by MAPK were down-regulated. In VPH, the genes associated with chromosome modification-related transcription factor, oxygen transport, and cell apoptosis mediated by MAPK pathway were up-regulated, but the genes associated with amino acid catabolism, histone acetylation-related transcription factor, and cell differentiation mediated by Wnt pathway were down-regulated. In TDPH, the genes related to immunity response, growth and development of regenerating liver, cell growth by MAPK pathway were up-regulated. Our data suggested that splanchnic and vagal nerves could regulate the expressions of liver regeneration-related genes.

Correlation analysis between genome-wide expression profiles and cytoarchitectural abnormalities in the prefrontal cortex of psychiatric disorders.

Cytoarchitectural abnormalities have been described in the prefrontal cortex of subjects with schizophrenia, bipolar disorder and depression. However, little is known about the gene expression profiles associated with these abnormalities. Genome-wide expression profiling technology provides an unbiased approach to identifying candidate genes and biological processes that may be associated with complex biological traits such as cytoarchitecture. In this study, we explored expression profiles associated with the abnormalities by using publicly available microarray metadata and cytoarchitectural data from post-mortem samples of the frontal cortex from 54 subjects (schizophrenia, n=14; bipolar disorder, n=13; depression, n=12 and controls n=15). Correlation analysis between genome-wide expression levels and cytoarchitectural traits revealed that 818 genes were significantly correlated with a decrease in the number of perineuronal oligodendrocytes across all subjects. A total of 600 genes were significantly correlated with a decrease in density of calbindin-positive interneurons across all subjects. Multiple biological processes including cellular metabolism, central nervous system development, cell motility and programmed cell death were significantly overrepresented in both correlated gene lists. These findings may provide novel insights into the molecular mechanisms that underlie the cytoarchitectural abnormalities of perineuronal oligodendrocytes and calbindin-containing GABAergic interneurons in the prefrontal cortex of the major psychiatric disorders.

Gene set-based analysis of polymorphisms: finding pathways or biological processes associated to traits in genome-wide association studies.

Genome-wide association studies have become a popular strategy to find associations of genes to traits of interest. Despite the high-resolution available today to carry out genotyping studies, the success of its application in real studies has been limited by the testing strategy used. As an alternative to brute force solutions involving the use of very large cohorts, we propose the use of the Gene Set Analysis (GSA), a different analysis strategy based on testing the association of modules of functionally related genes. We show here how the Gene Set-based Analysis of Polymorphisms (GeSBAP), which is a simple implementation of the GSA strategy for the analysis of genome-wide association studies, provides a significant increase in the power testing for this type of studies. GeSBAP is freely available at http://bioinfo.cipf.es/gesbap/.

Systematic alteration of in vitro metabolic environments reveals empirical growth relationships in cancer cell phenotypes.

Cancer cells, like microbes, live in complex metabolic environments. Recent evidence suggests that microbial behavior across metabolic environments is well described by simple empirical growth relationships, or growth laws. Do such empirical growth relationships also exist in cancer cells? To test this question, we develop a high-throughput approach to extract quantitative measurements of cancer cell behaviors in systematically altered metabolic environments. Using this approach, we examine relationships between growth and three frequently studied cancer phenotypes: drug-treatment survival, cell migration, and lactate overflow. Drug-treatment survival follows simple linear growth relationships, which differ quantitatively between chemotherapeutics and EGFR inhibition. Cell migration follows a weak grow-and-go growth relationship, with substantial deviation in some environments. Finally, lactate overflow is mostly decoupled from growth rate and is instead determined by the cells' ability to maintain high sugar uptake rates. Altogether, this work provides a quantitative approach for formulating empirical growth laws of cancer.

Identification and function prediction of novel microRNAs in adenosine monophosphate activated protein kinase-activated Sertoli cells of immature boar.

This study was carried out with the objective to identify function prediction of novel microRNAs (miRNAs) in immature boar Sertoli cells (SCs) treated with 5-aminoimidazole-4-carboxamide-1-ß-D-ribofuranoside (AICAR), which is an agonist of adenosine monophosphate-activated protein kinase (AMPK) for regulating cellular energy homeostasis. Two small RNA libraries (control and AICAR treatment) prepared from immature boar SCs were constructed and sequenced by the Illumina small RNA deep sequencing. We identified 77 novel miRNAs and predicted 177 potential target genes for 26 differential novel miRNAs (four miRNAs up-regulation and 22 miRNAs down-regulation) in AICAR-treated SCs. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway suggested that target genes of differential novel miRNAs were implicated in many biological processes and metabolic pathways. Our findings provided useful information for the functional regulation of novel miRNAs and target mRNAs on AMPK-activated immature boar SCs.

Identification of a master transcription factor and a regulatory mechanism for desiccation tolerance in the anhydrobiotic cell line Pv11.

Water is essential for living organisms. Terrestrial organisms are incessantly exposed to the stress of losing water, desiccation stress. Avoiding the mortality caused by desiccation stress, many organisms acquired molecular mechanisms to tolerate desiccation. Larvae of the African midge, Polypedilum vanderplanki, and its embryonic cell line Pv11 tolerate desiccation stress by entering an ametabolic state, anhydrobiosis, and return to active life after rehydration. The genes related to desiccation tolerance have been comprehensively analyzed, but transcriptional regulatory mechanisms to induce these genes after desiccation or rehydration remain unclear. Here, we comprehensively analyzed the gene regulatory network in Pv11 cells and compared it with that of Drosophila melanogaster, a desiccation sensitive species. We demonstrated that nuclear transcription factor Y subunit gamma-like, which is important for drought stress tolerance in plants, and its transcriptional regulation of downstream positive feedback loops have a pivotal role in regulating various anhydrobiosis-related genes. This study provides an initial insight into the systemic mechanism of desiccation tolerance.

Two paralogous EcfG σ factors hierarchically orchestrate the activation of the General Stress Response in Sphingopyxis granuli TFA.

Under ever-changing environmental conditions, the General Stress Response (GSR) represents a lifesaver for bacteria in order to withstand hostile situations. In α-proteobacteria, the EcfG-type extracytoplasmic function (ECF) σ factors are the key activators of this response at the transcriptional level. In this work, we address the hierarchical function of the ECF σ factor paralogs EcfG1 and EcfG2 in triggering the GSR in Sphingopyxis granuli TFA and describe the role of EcfG2 as global switch of this response. In addition, we define a GSR regulon for TFA and use in vitro transcription analysis to study the relative contribution of each EcfG paralog to the expression of selected genes. We show that the features of each promoter ultimately dictate this contribution, though EcfG2 always produced more transcripts than EcfG1 regardless of the promoter. These first steps in the characterisation of the GSR in TFA suggest a tight regulation to orchestrate an adequate protective response in order to survive in conditions otherwise lethal.

Global variation of soil microbial carbon-use efficiency in relation to growth temperature and substrate supply.

Soil microbial carbon-use efficiency (CUE), which is defined as the ratio of growth over C uptake, is commonly assumed as a constant or estimated by a temperature-dependent function in current microbial-explicit soil carbon (C) models. The temperature-dependent function (i.e., CUE = CUE0 + m × (T - 20)) simulates the dynamic CUE based on the specific CUE at a given reference temperature (i.e., CUE0) and a temperature response coefficient (i.e., m). Here, based on 780 observations from 98 sites, we showed a divergent spatial distribution of the soil microbial CUE (0.5 ± 0.25; mean ± SD) at the global scale. Then, the key parameters CUE0 and m in the above equation were estimated as 0.475 and -0.016, respectively, based on the observations with the Markov chain Monte Carlo technique. We also found a strong dependence of microbial CUE on the type of C substrate. The multiple regression analysis showed that glucose influences the variation of measured CUE associated with the environmental factors. Overall, this study confirms the global divergence of soil microbial CUE and calls for the incorporation of C substrate beside temperature in estimating the microbial CUE in different biomes.

Symptom Science: Advocating for Inclusion of Functional Genetic Polymorphisms.

Incorporating biologically based data into symptom science research can contribute substantially to understanding commonly experienced symptoms across chronic conditions. The purpose of this literature review was to identify functional polymorphisms associated with common symptoms (i.e., pain, sleep disturbance, fatigue, affective and cognitive symptoms) with the goal of identifying a parsimonious list of functional genetic polymorphisms with evidence to advocate for their inclusion in symptom science research. PubMed was searched to identify genes and functional polymorphisms associated with symptoms across chronic conditions, revealing eight functional genetic polymorphisms in seven different genes that showed evidence of association with at least three or more symptoms and/or symptom clusters: BDNF rs6265, COMT rs4680, FKBP5 rs3800373, IL-6 rs1800795, NFKB2 rs1056890, SLC6A4 5-HTTLPR+rs25531, and TNFA rs1799964 and rs1800629. Of these genes, three represent protein biomarkers previously identified as common data elements for symptom science research (BDNF, IL-6, and TNFA), and the polymorphisms in these genes identified through the search are known to impact secretion or level of transcription of these protein biomarkers. Inclusion of genotype data for polymorphisms offers great potential to further advance scientific knowledge of the biological basis of individual symptoms and symptom clusters across studies. Additionally, these polymorphisms have the potential to be used as targets to optimize precision health through the identification of individuals at risk for poor symptom experiences as well as the development of symptom management interventions.