Integrative Pathway-Based Approach for Genome-Wide Association Studies: Identification of New Pathways for Rheumatoid Arthritis and Type 1 Diabetes

Genome-wide association studies (GWAS) led to the identification of numerous novel loci for a number of complex diseases. Pathway-based approaches using genotypic data provide tangible leads which cannot be identified by single marker approaches as implemented in GWAS. The available pathway analysis approaches mainly differ in the employed databases and in the applied statistics for determining the significance of the associated disease markers.So far, pathway-based approaches using GWAS data failed to consider the overlapping of genes among different pathways or the influence of protein–interactions. We performed a multistage integrative pathway (MIP) analysis on three common diseases - Crohn''s disease (CD), rheumatoid arthritis (RA) and type 1 diabetes (T1D) - incorporating genotypic, pathway, protein- and domain-interaction data to identify novel associations between these diseases and pathways. Additionally, we assessed the sensitivity of our method by studying the influence of the most significant SNPs on the pathway analysis by removing those and comparing the corresponding pathway analysis results. Apart from confirming many previously published associations between pathways and RA, CD and T1D, our MIP approach was able to identify three new associations between disease phenotypes and pathways. This includes a relation between the influenza-A pathway and RA, as well as a relation between T1D and the phagosome and toxoplasmosis pathways. These results provide new leads to understand the molecular underpinnings of these diseases.The developed software herein used is available at http://www.cogsys.cs.uni-tuebingen.de/software/GWASPathwayIdentifier/index.htm.     

Genome Destabilizing Mutator Alleles Drive Specific Mutational Trajectories in Saccharomyces cerevisiae

In addition to environmental factors and intrinsic variations in base substitution rates, specific genome-destabilizing mutations can shape the mutational trajectory of genomes. How specific alleles influence the nature and position of accumulated mutations in a genomic context is largely unknown. Understanding the impact of genome-destabilizing alleles is particularly relevant to cancer genomes where biased mutational signatures are identifiable. We first created a more complete picture of cellular pathways that impact mutation rate using a primary screen to identify essential Saccharomyces cerevisiae gene mutations that cause mutator phenotypes. Drawing primarily on new alleles identified in this resource, we measure the impact of diverse mutator alleles on mutation patterns directly by whole-genome sequencing of 68 mutation-accumulation strains derived from wild-type and 11 parental mutator genotypes. The accumulated mutations differ across mutator strains, displaying base-substitution biases, allele-specific mutation hotspots, and break-associated mutation clustering. For example, in mutants of POLα and the Cdc13–Stn1–Ten1 complex, we find a distinct subtelomeric bias for mutations that we show is independent of the target sequence. Together our data suggest that specific genome-instability mutations are sufficient to drive discrete mutational signatures, some of which share properties with mutation patterns seen in tumors. Thus, in a population of cells, genome-instability mutations could influence clonal evolution by establishing discrete mutational trajectories for genomes.     

Too Much of a Good Thing: The Unique and Repeated Paths Toward Copper Adaptation

Copper is a micronutrient essential for growth due to its role as a cofactor in enzymes involved in respiration, defense against oxidative damage, and iron uptake. Yet too much of a good thing can be lethal, and yeast cells typically do not have tolerance to copper levels much beyond the concentration in their ancestral environment. Here, we report a short-term evolutionary study of Saccharomyces cerevisiae exposed to levels of copper sulfate that are inhibitory to the initial strain. We isolated and identified adaptive mutations soon after they arose, reducing the number of neutral mutations, to determine the first genetic steps that yeast take when adapting to copper. We analyzed 34 such strains through whole-genome sequencing and by assaying fitness within different environments; we also isolated a subset of mutations through tetrad analysis of four lines. We identified a multilayered evolutionary response. In total, 57 single base-pair mutations were identified across the 34 lines. In addition, gene amplification of the copper metallothionein protein, CUP1-1, was rampant, as was chromosomal aneuploidy. Four other genes received multiple, independent mutations in different lines (the vacuolar transporter genes VTC1 and VTC4; the plasma membrane H+-ATPase PMA1; and MAM3, a protein required for normal mitochondrial morphology). Analyses indicated that mutations in all four genes, as well as CUP1-1 copy number, contributed significantly to explaining variation in copper tolerance. Our study thus finds that evolution takes both common and less trodden pathways toward evolving tolerance to an essential, but highly toxic, micronutrient.     

Genetic Networks Inducing Invasive Growth in Saccharomyces cerevisiae Identified Through Systematic Genome-Wide Overexpression

The budding yeast Saccharomyces cerevisiae can respond to nutritional and environmental stress by implementing a morphogenetic program wherein cells elongate and interconnect, forming pseudohyphal filaments. This growth transition has been studied extensively as a model signaling system with similarity to processes of hyphal development that are linked with virulence in related fungal pathogens. Classic studies have identified core pseudohyphal growth signaling modules in yeast; however, the scope of regulatory networks that control yeast filamentation is broad and incompletely defined. Here, we address the genetic basis of yeast pseudohyphal growth by implementing a systematic analysis of 4909 genes for overexpression phenotypes in a filamentous strain of S. cerevisiae. Our results identify 551 genes conferring exaggerated invasive growth upon overexpression under normal vegetative growth conditions. This cohort includes 79 genes lacking previous phenotypic characterization. Pathway enrichment analysis of the gene set identifies networks mediating mitogen-activated protein kinase (MAPK) signaling and cell cycle progression. In particular, overexpression screening suggests that nuclear export of the osmoresponsive MAPK Hog1p may enhance pseudohyphal growth. The function of nuclear Hog1p is unclear from previous studies, but our analysis using a nuclear-depleted form of Hog1p is consistent with a role for nuclear Hog1p in repressing pseudohyphal growth. Through epistasis and deletion studies, we also identified genetic relationships with the G2 cyclin Clb2p and phenotypes in filamentation induced by S-phase arrest. In sum, this work presents a unique and informative resource toward understanding the breadth of genes and pathways that collectively constitute the molecular basis of filamentation.     

Identifying Relationships among Genomic Disease Regions: Predicting Genes at Pathogenic SNP Associations and Rare Deletions

Translating a set of disease regions into insight about pathogenic mechanisms requires not only the ability to identify the key disease genes within them, but also the biological relationships among those key genes. Here we describe a statistical method, Gene Relationships Among Implicated Loci (GRAIL), that takes a list of disease regions and automatically assesses the degree of relatedness of implicated genes using 250,000 PubMed abstracts. We first evaluated GRAIL by assessing its ability to identify subsets of highly related genes in common pathways from validated lipid and height SNP associations from recent genome-wide studies. We then tested GRAIL, by assessing its ability to separate true disease regions from many false positive disease regions in two separate practical applications in human genetics. First, we took 74 nominally associated Crohn''s disease SNPs and applied GRAIL to identify a subset of 13 SNPs with highly related genes. Of these, ten convincingly validated in follow-up genotyping; genotyping results for the remaining three were inconclusive. Next, we applied GRAIL to 165 rare deletion events seen in schizophrenia cases (less than one-third of which are contributing to disease risk). We demonstrate that GRAIL is able to identify a subset of 16 deletions containing highly related genes; many of these genes are expressed in the central nervous system and play a role in neuronal synapses. GRAIL offers a statistically robust approach to identifying functionally related genes from across multiple disease regions—that likely represent key disease pathways. An online version of this method is available for public use (http://www.broad.mit.edu/mpg/grail/).     

Genetic Analysis of Resistance and Sensitivity to 2-Deoxyglucose in Saccharomyces cerevisiae

Aerobic glycolysis is a metabolic pathway utilized by human cancer cells and also by yeast cells when they ferment glucose to ethanol. Both cancer cells and yeast cells are inhibited by the presence of low concentrations of 2-deoxyglucose (2DG). Genetic screens in yeast used resistance to 2-deoxyglucose to identify a small set of genes that function in regulating glucose metabolism. A recent high throughput screen for 2-deoxyglucose resistance identified a much larger set of seemingly unrelated genes. Here, we demonstrate that these newly identified genes do not in fact confer significant resistance to 2-deoxyglucose. Further, we show that the relative toxicity of 2-deoxyglucose is carbon source dependent, as is the resistance conferred by gene deletions. Snf1 kinase, the AMP-activated protein kinase of yeast, is required for 2-deoxyglucose resistance in cells growing on glucose. Mutations in the SNF1 gene that reduce kinase activity render cells hypersensitive to 2-deoxyglucose, while an activating mutation in SNF1 confers 2-deoxyglucose resistance. Snf1 kinase activated by 2-deoxyglucose does not phosphorylate the Mig1 protein, a known Snf1 substrate during glucose limitation. Thus, different stimuli elicit distinct responses from the Snf1 kinase.     

Integrating Constitutive Gene Expression and Chemoactivity: Mining the NCI60 Anticancer Screen

Studies into the genetic origins of tumor cell chemoactivity pose significant challenges to bioinformatic mining efforts. Connections between measures of gene expression and chemoactivity have the potential to identify clinical biomarkers of compound response, cellular pathways important to efficacy and potential toxicities; all vital to anticancer drug development. An investigation has been conducted that jointly explores tumor-cell constitutive NCI60 gene expression profiles and small-molecule NCI60 growth inhibition chemoactivity profiles, viewed from novel applications of self-organizing maps (SOMs) and pathway-centric analyses of gene expressions, to identify subsets of over- and under-expressed pathway genes that discriminate chemo-sensitive and chemo-insensitive tumor cell types. Linear Discriminant Analysis (LDA) is used to quantify the accuracy of discriminating genes to predict tumor cell chemoactivity. LDA results find 15% higher prediction accuracies, using ∼30% fewer genes, for pathway-derived discriminating genes when compared to genes derived using conventional gene expression-chemoactivity correlations. The proposed pathway-centric data mining procedure was used to derive discriminating genes for ten well-known compounds. Discriminating genes were further evaluated using gene set enrichment analysis (GSEA) to reveal a cellular genetic landscape, comprised of small numbers of key over and under expressed on- and off-target pathway genes, as important for a compound’s tumor cell chemoactivity. Literature-based validations are provided as support for chemo-important pathways derived from this procedure. Qualitatively similar results are found when using gene expression measurements derived from different microarray platforms. The data used in this analysis is available at http://pubchem.ncbi.nlm.nih.gov/and http://www.ncbi.nlm.nih.gov/projects/geo ({"type":"entrez-geo","attrs":{"text":"GPL96","term_id":"96"}}GPL96, {"type":"entrez-geo","attrs":{"text":"GSE32474","term_id":"32474"}}GSE32474).     

A Genome-Wide RNAi Screen for Enhancers of par Mutants Reveals New Contributors to Early Embryonic Polarity in Caenorhabditis elegans

The par genes of Caenorhabditis elegans are essential for establishment and maintenance of early embryo polarity and their homologs in other organisms are crucial polarity regulators in diverse cell types. Forward genetic screens and simple RNAi depletion screens have identified additional conserved regulators of polarity in C. elegans; genes with redundant functions, however, will be missed by these approaches. To identify such genes, we have performed a genome-wide RNAi screen for enhancers of lethality in conditional par-1 and par-4 mutants. We have identified 18 genes for which depletion is synthetically lethal with par-1 or par-4, or both, but produces little embryo lethality in wild type. Fifteen of the 18 genes identified in our screen are not previously known to function in C. elegans embryo polarity and 11 of them also increase lethality in a par-2 mutant. Among the strongest synthetic lethal genes, polarity defects are more apparent in par-2 early embryos than in par-1 or par-4, except for strd-1(RNAi), which enhances early polarity phenotypes in all three mutants. One strong enhancer of par-1 and par-2 lethality, F25B5.2, corresponds to nop-1, a regulator of actomyosin contractility for which the molecular identity was previously unknown. Other putative polarity enhancers identified in our screen encode cytoskeletal and membrane proteins, kinases, chaperones, and sumoylation and deubiquitylation proteins. Further studies of these genes should give mechanistic insight into pathways regulating establishment and maintenance of cell polarity.     

DAG Expression: High-Throughput Gene Expression Analysis of Real-Time PCR Data Using Standard Curves for Relative Quantification

Background

Real-time quantitative PCR (qPCR) is still the gold-standard technique for gene-expression quantification. Recent technological advances of this method allow for the high-throughput gene-expression analysis, without the limitations of sample space and reagent used. However, non-commercial and user-friendly software for the management and analysis of these data is not available.

Results

The recently developed commercial microarrays allow for the drawing of standard curves of multiple assays using the same n-fold diluted samples. Data Analysis Gene (DAG) Expression software has been developed to perform high-throughput gene-expression data analysis using standard curves for relative quantification and one or multiple reference genes for sample normalization. We discuss the application of DAG Expression in the analysis of data from an experiment performed with Fluidigm technology, in which 48 genes and 115 samples were measured. Furthermore, the quality of our analysis was tested and compared with other available methods.

Conclusions

DAG Expression is a freely available software that permits the automated analysis and visualization of high-throughput qPCR. A detailed manual and a demo-experiment are provided within the DAG Expression software at http://www.dagexpression.com/dage.zip.     

Carbohydrate Metabolism Differences between Subgroup A1 and B2 Strains of Bacillus anthracis as Assessed by Comparative Genomics and Functional Genetics

In France, Bacillus anthracis subgroup B2 strains do not metabolize starch or glycogen but can use gluconate, whereas subgroup A1 strains show the inverse pattern. Functional genetic analysis revealed that mutations in the amyS and gntK genes encoding an alpha-amylase and a gluconate kinase, respectively, were responsible for these phenotypes.Bacillus anthracis, the etiological agent of anthrax, is a gram-positive, aerobic soil bacterium. Multilocus variable-number tandem repeat analysis of a collection of French isolates shows that the main groups of B. anthracis groups A (subgroup A1) and B (subgroup B2) described worldwide are represented (1, 2). Subgroup B2 isolates are the most common isolates in France and are found particularly in southern mountain regions, but they are extremely rare elsewhere in the world. Biochemical characterization of French isolates indicates that subgroup A1 and B2 strains have different carbohydrate utilization patterns (P. Vaissaire, A. Fouet, K. L. Smith, C. Keys, C. Le Doujet, P. Sylvestre, M. Levy, P. Keim, and M. Mock, presented at the 5th International Conference on Anthrax and 3rd International Workshop on the Molecular Biology of Bacillus cereus, B. anthracis and B. thuringiensis, 30 March to 3 April 2003, Nice, France). French subgroup A1 strains metabolize starch and glycogen but not gluconate, and the inverse is true for subgroup B2 strains. The genomes of several B. anthracis strains are available on the NCBI website (http://www.ncbi.nlm.nih.gov/), and two of these strains, Ames and CNEVA, are representative of groups A and B, respectively. We compared the genomic sequences of Ames and CNEVA to identify mutations that may affect metabolic activities involved in the phenotypic differences.The Kegg pathway database (http://www.genome.jp/kegg/pathway.html) was used to select enzyme activities involved in the metabolic pathways for starch, glycogen, and gluconate. BLAST analysis of the corresponding open reading frame in the Ames (subgroup A3) and CNEVA (subgroup B2) genomes was then used to identify the selected genes that were interrupted or mutated. The functions and localizations of these open reading frames were then investigated with the Pfam (http://pfam.sanger.ac.uk/), CDD (http://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml), SMART (http://smart.embl-heidelberg.de/), SignalP (http://www.cbs.dtu.dk/services/SignalP/), and TMHMM (http://www.cbs.dtu.dk/services/TMHMM-2.0/) search programs. A single-base deletion in the amyS gene (BA3551) encoding an alpha-amylase linked to starch and glycogen metabolism was found in the CNEVA genome. The wild-type AmyS protein contains 513 amino acids, and its predicted molecular mass is 58.4 kDa. In subgroup B2, there is a frameshift due to deletion of an adenosine in the 7th position of the nucleotide sequence that leads to a premature stop codon in the 13th position. In the Ames genome, a single-base substitution was found in the gntK gene (BA0162) encoding a gluconate kinase linked to gluconate metabolism. The predicted wild-type GntK protein contains 511 amino acids, and its predicted molecular mass is 56.7 kDa. The mutation identified is a cytosine-to-adenosine substitution at position 530 of the nucleotide sequence that leads to a premature stop codon at amino acid position 176. We confirmed the presence of these two mutations in the other B. anthracis subgroup genomes accessible in the NCBI unfinished microbial genome database and sequenced 12 isolates with various genotypes belonging to subgroups A1 and B2 (6 isolates in each subgroup) originating from outbreaks that occurred in different regions of France over the last 15 years. These analyses revealed that the deletion in amyS is restricted to strains belonging to group B subgroups, whereas the substitution in gntK is restricted to strains belonging to group A subgroups. The mutations identified in amyS and gntK both result in premature stop codons that lead to a loss of the enzymatic activities and may thus account for the observed phenotypic differences between subgroup A1 and B2 strains. We therefore focused on these two genes and used French strains 9602R and RA3R belonging to subgroups A1 and B2, respectively, for further analysis.     

Identification of indels in next-generation sequencing data

Background

The discovery and mapping of genomic variants is an essential step in most analysis done using sequencing reads. There are a number of mature software packages and associated pipelines that can identify single nucleotide polymorphisms (SNPs) with a high degree of concordance. However, the same cannot be said for tools that are used to identify the other types of variants. Indels represent the second most frequent class of variants in the human genome, after single nucleotide polymorphisms. The reliable detection of indels is still a challenging problem, especially for variants that are longer than a few bases.

Results

We have developed a set of algorithms and heuristics collectively called indelMINER to identify indels from whole genome resequencing datasets using paired-end reads. indelMINER uses a split-read approach to identify the precise breakpoints for indels of size less than a user specified threshold, and supplements that with a paired-end approach to identify larger variants that are frequently missed with the split-read approach. We use simulated and real datasets to show that an implementation of the algorithm performs favorably when compared to several existing tools.

Conclusions

indelMINER can be used effectively to identify indels in whole-genome resequencing projects. The output is provided in the VCF format along with additional information about the variant, including information about its presence or absence in another sample. The source code and documentation for indelMINER can be freely downloaded from www.bx.psu.edu/miller_lab/indelMINER.tar.gz.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0483-6) contains supplementary material, which is available to authorized users.     

A network map of apelin-mediated signaling

The apelin receptor (APLNR) is a class A (rhodopsin-like) G-protein coupled receptor with a wide distribution throughout the human body. Activation of the apelin/APLNR system regulates AMPK/PI3K/AKT/mTOR and RAF/ERK1/2 mediated signaling pathways. APLNR activation orchestrates several downstream signaling cascades, which play diverse roles in physiological effects, including effects upon vasoconstriction, heart muscle contractility, energy metabolism regulation, and fluid homeostasis angiogenesis. We consolidated a network map of the APLNR signaling map owing to its biomedical importance. The curation of literature data pertaining to the APLNR system was performed manually by the NetPath criteria. The described apelin receptor signaling map comprises 35 activation/inhibition events, 38 catalysis events, 4 molecular associations, 62 gene regulation events, 113 protein expression types, and 4 protein translocation events. The APLNR signaling pathway map data is made freely accessible through the WikiPathways Database (https://www.wikipathways.org/index.php/Pathway:WP5067).Supplementary InformationThe online version contains supplementary material available at 10.1007/s12079-021-00614-6.