scholarly journals Diverse biological processes coordinate the transcriptional response to nutritional changes in a Drosophila melanogaster multiparent population

2019 ◽  
Author(s):  
E. Ng’oma ◽  
P.A. Williams-Simon ◽  
A. Rahman ◽  
E.G. King
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Sequence Data ◽  
Nutrient Sensing ◽  
Gene Set Enrichment Analysis ◽  
Tissue Type ◽  
Model Organisms ◽  
Laboratory Model ◽  
Fisher Exact Test ◽  
Tissue Samples

AbstractBackgroundEnvironmental variation in the amount of resources available to populations challenge individuals to optimize the allocation of those resources to key fitness functions. This coordination of resource allocation relative to resource availability is commonly attributed to key nutrient sensing gene pathways in laboratory model organisms, chiefly the insulin/TOR signaling pathway. However, the genetic basis of diet-induced variation in gene expression is less clear.ResultsTo describe the natural genetic variation underlying nutrient-dependent differences, we used an outbred panel derived from a multiparental population, the Drosophila Synthetic Population Resource. We analyzed RNA sequence data from multiple female tissue samples dissected from flies reared in three nutritional conditions: high sugar (HS), dietary restriction (DR), and control (C) diets. A large proportion of genes in the experiment (19.6% or 2,471 genes) were significantly differentially expressed for the effect of diet, 7.8% (978 genes) for the effect of the interaction between diet and tissue type (LRT, Padj. < 0.05). Interestingly, we observed similar patterns of gene expression relative to the C diet, in the DR and HS treated flies, a response likely reflecting diet component ratios. Hierarchical clustering identified 21 robust gene modules showing intra-modularly similar patterns of expression across diets, all of which were highly significant for diet or diet-tissue interaction effects (false discovery rate, FDR Padj. < 0.05). Gene set enrichment analysis for different diet-tissue combinations revealed a diverse set of pathways and gene ontology (GO) terms (two-sample t-test, FDR < 0.05). GO analysis on individual co-expressed modules likewise showed a large number of terms encompassing a large number of cellular and nuclear processes (Fisher exact test, Padj. < 0.01). Although a handful of genes in the IIS/TOR pathway including Ilp5, Rheb, and Sirt2 showed significant elevation in expression, known key genes such as InR, chico, insulin peptide genes, and the nutrient-sensing pathways were not observed.ConclusionsOur results suggest that a more diverse network of pathways and gene networks mediate the diet response in our population. These results have important implications for future studies focusing on diet responses in natural populations.

scholarly journals Diverse biological processes coordinate the transcriptional response to nutritional changes in a Drosophila melanogaster multiparent population

2019 ◽  
Author(s):  
Enoch Ng'oma ◽  
Patricka A. Williams-Simon ◽  
Aniqa Rahman ◽  
Elizabeth G. King
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Sequence Data ◽  
Nutrient Sensing ◽  
Gene Set Enrichment Analysis ◽  
Tissue Type ◽  
Model Organisms ◽  
Laboratory Model ◽  
Fisher Exact Test ◽  
Tissue Samples

Abstract Background: Environmental variation in the amount of resources available to populations challenge individuals to optimize the allocation of those resources to key fitness functions. This coordination of resource allocation relative to resource availability is commonly attributed to key nutrient sensing gene pathways in laboratory model organisms, chiefly the insulin/TOR signaling pathway. However, the genetic basis of diet-induced variation in gene expression is less clear. Results: To describe the natural genetic variation underlying nutrient-dependent differences, we used an outbred panel derived from a multiparental population, the Drosophila Synthetic Population Resource. We analyzed RNA sequence data from multiple female tissue samples dissected from flies reared in three nutritional conditions: high sugar (HS), dietary restriction (DR), and control (C) diets. A large proportion of genes in the experiment (19.6% or 2,471 genes) were significantly differentially expressed for the effect of diet, 7.8% (978 genes) for the effect of the interaction between diet and tissue type (LRT, P adj. < 0.05). Interestingly, we observed similar patterns of gene expression relative to the C diet, in the DR and HS treated flies, a response likely reflecting diet component ratios. Hierarchical clustering identified 21 robust gene modules showing intra-modularly similar patterns of expression across diets, all of which were highly significant for diet or diet-tissue interaction effects (false discovery rate, FDR P adj. < 0.05). Gene set enrichment analysis for different diet-tissue combinations revealed a diverse set of pathways and gene ontology (GO) terms (two-sample t-test, FDR < 0.05). GO analysis on individual co-expressed modules likewise showed a large number of terms encompassing a large number of cellular and nuclear processes (Fisher exact test, P adj. < 0.01). Although a handful of genes in the IIS/TOR pathway including Ilp5 , Rheb , and Sirt2 showed significant elevation in expression, known key genes such as InR , chico , insulin peptide genes, and the nutrient-sensing pathways were not observed. Conclusions: Our results suggest that a more diverse network of pathways and gene networks mediate the diet response in our population. These results have important implications for future studies focusing on diet responses in natural populations.

scholarly journals Diverse biological processes coordinate the transcriptional response to nutritional changes in a Drosophila melanogaster multiparent population

2019 ◽  
Author(s):  
Enoch Ng'oma ◽  
Patricka A. Williams-Simon ◽  
Aniqa Rahman ◽  
Elizabeth G. King
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Sequence Data ◽  
Nutrient Sensing ◽  
Gene Set Enrichment Analysis ◽  
Tissue Type ◽  
Model Organisms ◽  
Laboratory Model ◽  
Fisher Exact Test ◽  
Tissue Samples

Abstract Background Environmental variation in the amount of resources available to populations challenge individuals to optimize the allocation of those resources to key fitness functions. This coordination of resource allocation relative to resource availability is commonly attributed to key nutrient sensing gene pathways in laboratory model organisms, chiefly the insulin/TOR signaling pathway. However, the genetic basis of diet-induced variation in gene expression is less clear. Results To describe the natural genetic variation underlying nutrient-dependent differences, we used an outbred panel derived from a multiparental population, the Drosophila Synthetic Population Resource. We analyzed RNA sequence data from multiple female tissue samples dissected from flies reared in three nutritional conditions: high sugar (HS), dietary restriction (DR), and control (C) diets. A large proportion of genes in the experiment (19.6% or 2,471 genes) were significantly differentially expressed for the effect of diet, 7.8% (978 genes) for the effect of the interaction between diet and tissue type (LRT, Padj. < 0.05). Interestingly, we observed similar patterns of gene expression relative to the C diet, in the DR and HS treated flies, a response likely reflecting diet component ratios. Hierarchical clustering identified 21 robust gene modules showing intra-modularly similar patterns of expression across diets, all of which were highly significant for diet or diet-tissue interaction effects (false discovery rate, FDR Padj. < 0.05). Gene set enrichment analysis for different diet-tissue combinations revealed a diverse set of pathways and gene ontology (GO) terms (two-sample t-test, FDR < 0.05). GO analysis on individual co-expressed modules likewise showed a large number of terms encompassing a large number of cellular and nuclear processes (Fisher exact test, Padj. < 0.01). Although a handful of genes in the IIS/TOR pathway including Ilp5, Rheb, and Sirt2 showed significant elevation in expression, known key genes such as InR, chico, insulin peptide genes, and the nutrient-sensing pathways were not observed. Conclusions Our results suggest that a more diverse network of pathways and gene networks mediate the diet response in our population. These results have important implications for future studies focusing on diet responses in natural populations.

scholarly journals Diverse biological processes coordinate the transcriptional response to nutritional changes in a Drosophila melanogaster multiparent population

2019 ◽  
Author(s):  
Enoch Ng'oma ◽  
Patricka A. Williams-Simon ◽  
Aniqa Rahman ◽  
Elizabeth G. King
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Sequence Data ◽  
Nutrient Sensing ◽  
Gene Set Enrichment Analysis ◽  
Tissue Type ◽  
Model Organisms ◽  
Laboratory Model ◽  
Fisher Exact Test ◽  
Tissue Samples

Abstract Background: Environmental variation in the amount of resources available to populations challenge individuals to optimize the allocation of those resources to key fitness functions. This coordination of resource allocation relative to resource availability is commonly attributed to key nutrient sensing gene pathways in laboratory model organisms, chiefly the insulin/TOR signaling pathway. However, the genetic basis of diet-induced variation in gene expression is less clear. Results: To describe the natural genetic variation underlying nutrient-dependent differences, we used an outbred panel derived from a multiparental population, the Drosophila Synthetic Population Resource. We analyzed RNA sequence data from multiple female tissue samples dissected from flies reared in three nutritional conditions: high sugar (HS), dietary restriction (DR), and control (C) diets. A large proportion of genes in the experiment (19.6% or 2,471 genes) were significantly differentially expressed for the effect of diet, 7.8% (978 genes) for the effect of the interaction between diet and tissue type (LRT, Padj. < 0.05). Interestingly, we observed similar patterns of gene expression relative to the C diet, in the DR and HS treated flies, a response likely reflecting diet component ratios. Hierarchical clustering identified 21 robust gene modules showing intra-modularly similar patterns of expression across diets, all of which were highly significant for diet or diet-tissue interaction effects (false discovery rate, FDR Padj. < 0.05). Gene set enrichment analysis for different diet-tissue combinations revealed a diverse set of pathways and gene ontology (GO) terms (two-sample t-test, FDR < 0.05). GO analysis on individual co-expressed modules likewise showed a large number of terms encompassing a large number of cellular and nuclear processes (Fisher exact test, Padj. < 0.01). Although a handful of genes in the IIS/TOR pathway including Ilp5, Rheb, and Sirt2 showed significant elevation in expression, known key genes such as InR, chico, insulin peptide genes, and the nutrient-sensing pathways were not observed. Conclusions: Our results suggest that a more diverse network of pathways and gene networks mediate the diet response in our population. These results have important implications for future studies focusing on diet responses in natural populations.

scholarly journals Diverse biological processes coordinate the transcriptional response to nutritional changes in a Drosophila melanogaster multiparent population

2020 ◽  
Author(s):  
Enoch Ng'oma ◽  
Patricka A. Williams-Simon ◽  
Aniqa Rahman ◽  
Elizabeth G. King
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Sequence Data ◽  
Nutrient Sensing ◽  
Gene Set Enrichment Analysis ◽  
Tissue Type ◽  
Model Organisms ◽  
Laboratory Model ◽  
Fisher Exact Test ◽  
Tissue Samples

Abstract Background: Environmental variation in the amount of resources available to populations challenge individuals to optimize the allocation of those resources to key fitness functions. This coordination of resource allocation relative to resource availability is commonly attributed to key nutrient sensing gene pathways in laboratory model organisms, chiefly the insulin/TOR signaling pathway. However, the genetic basis of diet-induced variation in gene expression is less clear. Results: To describe the natural genetic variation underlying nutrient-dependent differences, we used an outbred panel derived from a multiparental population, the Drosophila Synthetic Population Resource. We analyzed RNA sequence data from multiple female tissue samples dissected from flies reared in three nutritional conditions: high sugar (HS), dietary restriction (DR), and control (C) diets. A large proportion of genes in the experiment (19.6% or 2,471 genes) were significantly differentially expressed for the effect of diet, and 7.8% (978 genes) for the effect of the interaction between diet and tissue type (LRT, P adj. < 0.05). Interestingly, we observed similar patterns of gene expression relative to the C diet, in the DR and HS treated flies, a response likely reflecting diet component ratios. Hierarchical clustering identified 21 robust gene modules showing intra-modularly similar patterns of expression across diets, all of which were highly significant for diet or diet-tissue interaction effects (FDR P adj. < 0.05). Gene set enrichment analysis for different diet-tissue combinations revealed a diverse set of pathways and gene ontology (GO) terms (two-sample t-test, FDR < 0.05). GO analysis on individual co-expressed modules likewise showed a large number of terms encompassing many cellular and nuclear processes (Fisher exact test, P adj. < 0.01). Although a handful of genes in the IIS/TOR pathway including Ilp5 , Rheb , and Sirt2 showed significant elevation in expression, many key genes such as InR , chico , most insulin peptide genes, and the nutrient-sensing pathways were not observed. Conclusions: Our results suggest that a more diverse network of pathways and gene networks mediate the diet response in our population. These results have important implications for future studies focusing on diet responses in natural populations.

scholarly journals Cross-species alcohol dependence-associated gene networks: Co-analysis of mouse brain gene expression and human genome-wide association data

2018 ◽  
Author(s):  
Kristin M. Mignogna ◽  
Silviu A. Bacanu ◽  
Brien P. Riley ◽  
Aaron R. Wolen ◽  
Michael F. Miles
Keyword(s):  
Gene Expression ◽  
Alcohol Dependence ◽  
Mouse Brain ◽  
Gene Networks ◽  
Association Studies ◽  
Complex Trait ◽  
Genome Wide Association ◽  
Model Organisms ◽  
Genome Wide Association Studies ◽  
Genome Wide

AbstractGenome-wide association studies on alcohol dependence, by themselves, have yet to account for the estimated heritability of the disorder and provide incomplete mechanistic understanding of this complex trait. Integrating brain ethanol-responsive gene expression networks from model organisms with human genetic data on alcohol dependence could aid in identifying dependence-associated genes and functional networks in which they are involved. This study used a modification of the Edge-Weighted Dense Module Searching for genome-wide association studies (EW-dmGWAS) approach to co-analyze whole-genome gene expression data from ethanol-exposed mouse brain tissue, human protein-protein interaction databases and alcohol dependence-related genome-wide association studies. Results revealed novel ethanol-regulated and alcohol dependence-associated gene networks in prefrontal cortex, nucleus accumbens, and ventral tegmental area. Three of these networks were overrepresented with genome-wide association signals from an independent dataset. These networks were significantly overrepresented for gene ontology categories involving several mechanisms, including actin filament-based activity, transcript regulation, Wnt and Syndecan-mediated signaling, and ubiquitination. Together, these studies provide novel insight for brain mechanisms contributing to alcohol dependence.

An Enriched Multigen Low-Density Array To Predict Outcome in Advanced Hodgkin Lymphoma Paraffin-Embedded Samples.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 383-383
Author(s):  
Beatriz Sanchez-Espiridion ◽  
Abel Sanchez-Aguilera ◽  
Carlos Montalban ◽  
Monica Garcia-Cosio ◽  
Carmen Bellas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hodgkin Lymphoma ◽  
Cell Lines ◽  
Mapk Pathway ◽  
Unfavorable Outcome ◽  
Gene Set Enrichment Analysis ◽  
Specific Gene ◽  
Low Density ◽  
Tissue Samples ◽  
Increased Risk

Abstract Despite the major advances in the treatment of classical Hodgkin Lymphoma (cHL) patients, around 30% to 40% of cases in advanced stages may relapse or die as result of the disease, and current markers to predict prognosis are rather unreliable. The identification of molecular events and biological processes associated with treatment failure are essential to develop new predictive tools. We used gene expression data from 29 samples of advanced cHL patients and HL-derived cell lines in order to identify transcriptional patterns from both tumoral cells and cell microenvironment. Student t-test was used to detect genes differentially overexpressed in cell lines and in tumor samples, thus creating two databases that report for genes expressed by the tumor HRS cells and genes expressed by the microenvironment. Using Gene Set Enrichment analysis (GSEA) we identified specific gene sets enriched in both databases in patients with favorable and unfavorable outcome, respectively. To validate these pathways we designed a novel Taqman low-density array (LDA) to examine the expression of the most relevant genes in 60 formalin-fixed, paraffin embedded (FFPE) tissue samples, and correlated the results with treatment outcome. Functional pathways related to unfavorable outcome significantly enriched in the HRS cells included the regulation of the G2/M checkpoint of the cell cycle, S phase and G1/S transition, chaperons, histone modification and other signaling pathways with an important representation of the MAPK pathway. On the other hand, genes reporting for specific T-cell populations (T-cytotoxic and T-regulatory cells) and macrophage activation were found to be overexpressed in the microenvironment. The final model presents a balanced representation of these genes, including also genes encoding factors implicated in drug resistance (RRM2, TYMS and TOP2A). RNA extracted from FFPE sections yielded analyzable data for 80% of samples. LDA analysis of the genes included in the model confirmed the feasibility of this approach, and the capacity for identifying cases with increased risk of failure.LDA provides an effective technique for analyzing gene expression in FFPE tissues, and it can be used for clinical prediction in diagnostic samples, using a selection of genes identified after GSEA analysis of the initial molecular signatures. This novel Taqman LDA will be used to develop a new molecular predictor of the outcome of patients with advanced cHL.

Glioblastoma gene expression subtypes and correlation with clinical, molecular and immunohistochemical characteristics in a homogenously treated cohort: GLIOCAT project.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 2029-2029 ◽  
Author(s):  
Estela Pineda ◽  
Anna Esteve-Codina ◽  
Maria Martinez-Garcia ◽  
Francesc Alameda ◽  
Cristina Carrato ◽  
...  
Keyword(s):  
Gene Expression ◽  
Web Application ◽  
Nearest Neighbor ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
The Other ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Tissue Samples

2029 Background: Glioblastoma (GBM) gene expression subtypes have been described in last years, data in homogeneously treated patients is lacking. Methods: Clinical, molecular and immunohistochemistry (IHC) analysis from patients with newly diagnosed GBM homogeneously treated with standard radiochemotherapy were studied. Samples were classified based on the expression profiles into three different subtypes (classical, mesenchymal, proneural) using Support Vector Machine (SVM), the K-nearest neighbor (K-NN) and the single sample Gene Set Enrichment Analysis (ssGSEA) classification algorithms provided by GlioVis web application. Results: GLIOCAT Project recruited 432 patients from 6 catalan institutions, all of whom received standard first-line treatment (2004 -2015). Best paraffin tissue samples were selected for RNAseq and reliable data were obtained from 124. 82 cases (66%) were classified into the same subtype by all three classification algorithms. SVM and ssGEA algorithms obtain more similar results (87%). No differences in clinical variables were found between the 3 GBM subtypes. Proneural subtype was enriched with IDH1 mutated and G-CIMP positive tumors. Mesenchymal subtype (SVM) was enriched in unmethylated MGMT tumors (p = 0.008), and classical (SVM) in methylated MGMT tumors (p = 0.008). Long survivors ( > 30 months) were rarely classified as mesenchymal (0-7.5%) and were more frequently classified as Proneural (23.1-26.). Clinical (age, resection, KPS) and molecular ( IDH1, MGMT) known prognostic factors were confirmed in this serie. Overall, no differences in prognosis were observed between 3 subtypes, but a trend to worse survival in mesenchymal was observed in K-NN (9.6 vs 15 ). Mesenchymal subtype presented less expression of Olig2 (p < 0.001) and SOX2 (p = 0.003) by IHC, but more YLK-40 expression (p = 0.023, SVM). On the other hand, classical subtype expressed more Nestin (p = 0.004) compared to the other subtypes (K-NN). Conclusions: In our study we have not found correlation between glioblastoma expression subtype and outcome. This large serie provides reproducible data regarding clinical-molecular-immunohistochemistry features of glioblastoma genetic subtypes.

scholarly journals Sigmoid colon mucosal gene expression supports alterations of neuronal signaling in irritable bowel syndrome with constipation

2018 ◽  
Vol 315 (1) ◽  
pp. G140-G157 ◽  
Author(s):  
Elizabeth J. Videlock ◽  
Swapna Mahurkar-Joshi ◽  
Jill M. Hoffman ◽  
Dimitrios Iliopoulos ◽  
Charalabos Pothoulakis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Irritable Bowel Syndrome ◽  
Sigmoid Colon ◽  
Microarray Analysis ◽  
Gene Networks ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
Healthy Controls ◽  
Neuronal Signaling ◽  
Irritable Bowel

Peripheral factors likely play a role in at least a subset of irritable bowel syndrome (IBS) patients. Few studies have investigated mucosal gene expression using an unbiased approach. Here, we performed mucosal gene profiling in a sex-balanced sample to identify relevant signaling pathways and gene networks and compare with publicly available profiling data from additional cohorts. Twenty Rome III+ IBS patients [10 IBS with constipation (IBS-C), 10 IBS with diarrhea (IBS-D), 5 men/women each), and 10 age-/sex-matched healthy controls (HCs)] underwent sigmoidoscopy with biopsy for gene microarray analysis, including differential expression, weighted gene coexpression network analysis (WGCNA), gene set enrichment analysis, and comparison with publicly available data. Expression levels of 67 genes were validated in an expanded cohort, including the above samples and 18 additional participants (6 each of IBS-C, IBS-D, HCs) using NanoString nCounter technology. There were 1,270 differentially expressed genes (FDR < 0.05) in IBS-C vs. HCs but none in IBS or IBS-D vs. HCs. WGNCA analysis identified activation of the cAMP/protein kinase A signaling pathway. Nine of 67 genes were validated by the NanoString nCounter technology (FDR < 0.05) in the expanded sample. Comparison with publicly available microarray data from the Mayo Clinic and University of Nottingham supports the reproducibility of 17 genes from the microarray analysis and three of nine genes validated by nCounter in IBS-C vs. HCs. This study supports the involvement of peripheral mechanisms in IBS-C, particularly pathways mediating neuronal signaling. NEW & NOTEWORTHY Peripheral factors play a role in the pathophysiology of irritable bowel syndrome (IBS), which, to date, has been mostly evident in IBS with diarrhea. Here, we show that sigmoid colon mucosal gene expression profiles differentiate IBS with constipation from healthy controls. These profiling data and analysis of additional cohorts also support the concept that peripheral neuronal pathways contribute to IBS pathophysiology.

scholarly journals The evolution of ovary-specific gene expression in Hawaiian Drosophilidae

2021 ◽  
Author(s):  
Samuel H Church ◽  
Catriona Munro ◽  
Casey Dunn ◽  
Cassandra G. Extavour
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Model Organisms ◽  
Laboratory Model ◽  
Developmental Genetics ◽  
Specific Gene ◽  
Ancestral State ◽  
Phylogenetic Distance ◽  
Evolutionary Changes

As detailed data on gene expression become accessible from more species, we have an opportunity to test the extent to which our understanding of developmental genetics from model organisms helps predict expression patterns across species. Central to this is the question: how much variation in gene expression do we expect to observe between species? Here we provide an answer by comparing RNAseq data between twelve species of Hawaiian Drosophilidae flies, focusing on gene expression differences between the ovary and other tissues. We show that there exists a cohort of ovary-specific genes that is stable across species, and that largely corresponds to described expression patterns from laboratory model Drosophila species. However, our results also show that, as phylogenetic distance increases, variation between species overwhelms variation between tissues. Using ancestral state reconstruction of expression, we describe the distribution of evolutionary changes in tissue-biased expression profiles, and use this to identify gains and losses of ovarian expression across these twelve species. We then use this distribution to calculate the correlation in expression evolution between genes, and demonstrate that genes with known interactions in D. melanogaster are significantly more correlated in their evolution than genes with no or unknown interactions. Finally, we use this correlation matrix to infer new networks of genes that have similar evolutionary trajectories, and we provide these as a dataset of novel testable hypotheses about genetic roles and interactions.

scholarly journals Efficient Proximal Gradient Algorithm for Inference of Differential Gene Networks

2018 ◽  
Author(s):  
Chen Wang ◽  
Feng Gao ◽  
Georgios B. Giannakis ◽  
Gennaro D’Urso ◽  
Xiaodong Cai
Keyword(s):  
Gene Expression ◽  
Computer Simulations ◽  
Gene Expression Data ◽  
Gene Networks ◽  
Gene Set Enrichment Analysis ◽  
Gradient Algorithm ◽  
Superior Performance ◽  
Expression Data ◽  
Gene Set ◽  
Proximal Gradient Algorithm

AbstractBackgroundGene networks in living cells can change depending on various conditions such as caused by different environments, tissue types, disease states, and development stages. Identifying the differential changes in gene networks is very important to understand molecular basis of various biological process. While existing algorithms can be used to infer two gene networks separately from gene expression data under two different conditions, and then to identify network changes, such an approach does not exploit the data jointly, and it is thus suboptimal. A desirable approach would be clearly to infer two gene networks jointly, which can yield improved estimates of network changes.ResultsIn this paper, we developed a proximal gradient algorithm for differential network (ProGAdNet) inference, that jointly infers two gene networks under different conditions and then identifies changes in the network structure. Computer simulations demonstrated that our ProGAdNet outperformed existing algorithms in terms of inference accuracy, and was much faster than a similar approach for joint inference of gene networks. Gene expression data of breast tumors and normal tissues in the TCGA database were analyzed with our ProGAdNet, and revealed that 268 genes were involved in the changed network edges. Gene set enrichment analysis of this set of 268 genes identified a number of gene sets related to breast cancer or other types of cancer, which corroborated the gene set identified by ProGAdNet was very informative about the cancer disease status. A software package implementing the ProGAdNet and computer simulations is available upon request.ConclusionWith its superior performance over existing algorithms, ProGAdNet provides a valuable tool for finding changes in gene networks, which may aid the discovery of gene-gene interactions changed under different conditions.

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