Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes
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{"title"=>"Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes", "type"=>"journal", "authors"=>[{"first_name"=>"Priyanka", "last_name"=>"Jain", "scopus_author_id"=>"57199743216"}, {"first_name"=>"Saurabh", "last_name"=>"Vig", "scopus_author_id"=>"37666270500"}, {"first_name"=>"Malabika", "last_name"=>"Datta", "scopus_author_id"=>"7103022780"}, {"first_name"=>"Dinesh", "last_name"=>"Jindel", "scopus_author_id"=>"55551677900"}, {"first_name"=>"Ashok Kumar", "last_name"=>"Mathur", "scopus_author_id"=>"7201657339"}, {"first_name"=>"Sandeep Kumar", "last_name"=>"Mathur", "scopus_author_id"=>"17735484600"}, {"first_name"=>"Abhay", "last_name"=>"Sharma", "scopus_author_id"=>"55482786600"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84872133461", "pui"=>"368086134", "sgr"=>"84872133461", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "issn"=>"19326203", "pmid"=>"23308243", "doi"=>"10.1371/journal.pone.0053522"}, "id"=>"e5c8185f-f445-3d96-9040-0450089845c7", "abstract"=>"Genome-wide association studies (GWASs) have discovered association of several loci with Type 2 diabetes (T2D), a common complex disease characterized by impaired insulin secretion by pancreatic β cells and insulin signaling in target tissues. However, effect of genetic risk variants on continuous glycemic measures in nondiabetic subjects mainly elucidates perturbation of insulin secretion. Also, the disease associated genes do not clearly converge on functional categories consistent with the known aspects of T2D pathophysiology. We used a systems biology approach to unravel genome to phenome correlation in T2D. We first examined enrichment of pathways in genes identified in T2D GWASs at genome-wide or lower levels of significance. Genes at lower significance threshold showed enrichment of insulin secretion related pathway. Notably, physical and genetic interaction network of these genes showed robust enrichment of insulin signaling and other T2D pathophysiology related pathways including insulin secretion. The network also overrepresented genes reported to interact with insulin secretion and insulin action targeting antidiabetic drugs. The drug interacting genes themselves showed overrepresentation of insulin signaling and other T2D relevant pathways. Next, we generated genome-wide expression profiles of multiple insulin responsive tissues from nondiabetic and diabetic patients. Remarkably, the differentially expressed genes showed significant overlap with the network genes, with the intersection showing enrichment of insulin signaling and other pathways consistent with T2D pathophysiology. Literature search led our genomic, interactomic, transcriptomic and toxicogenomic evidence to converge on TGF-beta signaling, a pathway known to play a crucial role in pancreatic islets development and function, and insulin signaling. Cumulatively, we find that GWAS genes relate directly to insulin secretion and indirectly, through collaborating with other genes, to insulin resistance. This seems to support the epidemiological evidence that environmentally triggered insulin resistance interacts with genetically programmed β cell dysfunction to precipitate diabetes.", "link"=>"http://www.mendeley.com/research/systems-biology-approach-reveals-genome-phenome-correlation-type-2-diabetes", "reader_count"=>61, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>3, "Researcher"=>16, "Student > Ph. D. Student"=>16, "Student > Postgraduate"=>3, "Student > Master"=>6, "Other"=>4, "Student > Bachelor"=>9, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>3, "Researcher"=>16, "Student > Ph. D. Student"=>16, "Student > Postgraduate"=>3, "Student > Master"=>6, "Other"=>4, "Student > Bachelor"=>9, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Engineering"=>3, "Unspecified"=>3, "Biochemistry, Genetics and Molecular Biology"=>8, "Nursing and Health Professions"=>1, "Agricultural and Biological Sciences"=>28, "Medicine and Dentistry"=>13, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Computer Science"=>4}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>13}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>28}, "Computer Science"=>{"Computer Science"=>4}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>8}, "Unspecified"=>{"Unspecified"=>3}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}}, "reader_count_by_country"=>{"Sweden"=>1, "United States"=>2, "Japan"=>1, "Brazil"=>3, "Italy"=>1, "United Kingdom"=>1, "France"=>1, "Chile"=>1, "Switzerland"=>1}, "group_count"=>3}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/513301"], "description"=>"*<p>Bejamini-Hochberg correction.</p>", "links"=>[], "tags"=>["pathways", "t2d", "inteactome-t2d", "transcriptome", "intersection", "tgf-beta", "signaling"], "article_id"=>183789, "categories"=>["Chemistry", "Genetics"], "users"=>["Priyanka Jain", "Saurabh Vig", "Malabika Datta", "Dinesh Jindel", "Ashok Kumar Mathur", "Sandeep Kumar Mathur", "Abhay Sharma"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0053522.t006", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Enriched_pathways_in_T2D_inteactome_T2D_transcriptome_intersection_without_TGF_beta_signaling_genes_/183789", "title"=>"Enriched pathways in T2D inteactome-T2D transcriptome intersection without TGF-beta signaling genes.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-01-07 01:03:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/513380"], "description"=>"<p>Pathways enriched in all four conditions are shown in italics.</p>*<p>Bejamini-Hochberg correction.</p>", "links"=>[], "tags"=>["pathways", "t2d", "interactome-t2d", "transcriptome", "intersection"], "article_id"=>183867, "categories"=>["Chemistry", "Genetics"], "users"=>["Priyanka Jain", "Saurabh Vig", "Malabika Datta", "Dinesh Jindel", "Ashok Kumar Mathur", "Sandeep Kumar Mathur", "Abhay Sharma"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0053522.t004", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Enriched_pathways_in_T2D_interactome_T2D_transcriptome_intersection_genes_/183867", "title"=>"Enriched pathways in T2D interactome-T2D transcriptome intersection genes.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-01-07 01:04:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/512938"], "description"=>"<p>Mean±S.E.M of fold change in gene expression in T2D patients, as compared to controls, is shown for (A) female visceral adipose, (B) male visceral adipose, (C) male skeletal muscle, and (D) female subcutaneous adipose. Fold change was calculated for two to six technical replicates, each representing three biological replicates. The source of RNA used in qRT-PCR analysis was same as in microarray profiling. A total of 47 genes were used for validation. The rationale behind the subsets of genes selected was two fold. One, the genes should represent, wherever applicable, one or more enriched pathways (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0053522#pone-0053522-t003\" target=\"_blank\"><b>Table 3</b></a>) in a given condition. Second, the genes should maximally represent those which are differentially expressed at adjusted <i>p</i> value cutoff (<b>Dataset S4</b>) in more than one condition, so that validation of microarrays can be examined more widely. The selected genes, besides others, covered all the pathways that were enriched in the above microarray gene lists. Notably, up- or down- regulation observed in qRT-PCR, shown in red and green, respectively, was consistent with microarrays, for all comparisons.</p>", "links"=>[], "tags"=>["microarrays"], "article_id"=>183433, "categories"=>["Chemistry", "Genetics"], "users"=>["Priyanka Jain", "Saurabh Vig", "Malabika Datta", "Dinesh Jindel", "Ashok Kumar Mathur", "Sandeep Kumar Mathur", "Abhay Sharma"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0053522.g006", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Validation_of_microarrays_using_qRT_PCR_/183433", "title"=>"Validation of microarrays using qRT-PCR.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-01-07 00:57:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/512588"], "description"=>"<p>Compared to total interactome of 14,306 genes, the T2D interactome of 561 genes represent significantly greater number of antidiabetic drug interacting genes. A statistically significant overrepresentation was observed for all the drugs except pioglitazone in hypergeometric test with Bonferroni adjustment of <i>p</i> values for multiple hypotheses testing. Overrepresentation with a borderline significance was nonetheless observed even for pioglitazone. The adjusted enrichment <i>p</i> values are indicated.</p>", "links"=>[], "tags"=>["antidiabetic", "interacting", "genes", "t2d"], "article_id"=>183080, "categories"=>["Chemistry", "Genetics"], "users"=>["Priyanka Jain", "Saurabh Vig", "Malabika Datta", "Dinesh Jindel", "Ashok Kumar Mathur", "Sandeep Kumar Mathur", "Abhay Sharma"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0053522.g003", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overlap_between_antidiabetic_drug_interacting_genes_and_T2D_interactome_/183080", "title"=>"Overlap between antidiabetic drug interacting genes and T2D interactome.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-01-07 00:51:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/513179"], "description"=>"<p>T2D genome, T2D interactome, T2D transcriptome and antidiabetic drug interacting genes are mapped on to KEGG pathway for TGF-beta signaling. Red: genome, interactome and transcriptome; brown: interactome, transcriptome and antidiabetic drug interacting genes; green: interactome and transcriptome; yellow: interactome; grey: antidiabetic drug interacting genes.</p>", "links"=>[], "tags"=>["phenome", "pathway", "tgf-beta"], "article_id"=>183669, "categories"=>["Chemistry", "Genetics"], "users"=>["Priyanka Jain", "Saurabh Vig", "Malabika Datta", "Dinesh Jindel", "Ashok Kumar Mathur", "Sandeep Kumar Mathur", "Abhay Sharma"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0053522.g008", "stats"=>{"downloads"=>1, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Genome_to_phenome_pathway_of_TGF_beta_signaling_/183669", "title"=>"Genome to phenome pathway of TGF-beta signaling.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-01-07 01:01:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/513416"], "description"=>"*<p>Bejamini-Hochberg correction.</p>", "links"=>[], "tags"=>["pathways", "differentially", "genes"], "article_id"=>183910, "categories"=>["Chemistry", "Genetics"], "users"=>["Priyanka Jain", "Saurabh Vig", "Malabika Datta", "Dinesh Jindel", "Ashok Kumar Mathur", "Sandeep Kumar Mathur", "Abhay Sharma"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0053522.t003", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Enriched_pathways_in_differentially_expressed_genes_between_people_with_and_without_T2D_/183910", "title"=>"Enriched pathways in differentially expressed genes between people with and without T2D.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-01-07 01:05:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/512802"], "description"=>"<p>Expression profiles of skeletal muscle, visceral adipose and subcutaneous adipose from male and/or female subjects were generated using Illumina HumanHT-12 v3 Expression BeadChip arrays that contain more than 25,000 annotated genes. IIlumina custom error model was used to identify up- and down- regulated genes in T2D as compared to controls, with or without Benjamini and Hochberg correction for multiple hypotheses testing. The differentially expressed genes were identified at ±13 Diff score threshold of Illumina custom algorithm, corresponding to a <i>p</i> value of 0.05.</p>", "links"=>[], "tags"=>["up-", "down-", "regulated", "genes", "insulin", "responsive", "tissues", "t2d"], "article_id"=>183284, "categories"=>["Chemistry", "Genetics"], "users"=>["Priyanka Jain", "Saurabh Vig", "Malabika Datta", "Dinesh Jindel", "Ashok Kumar Mathur", "Sandeep Kumar Mathur", "Abhay Sharma"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0053522.g005", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Numbers_of_up_and_down_regulated_genes_in_multiple_insulin_responsive_tissues_in_T2D_patients_/183284", "title"=>"Numbers of up- and down- regulated genes in multiple insulin responsive tissues in T2D patients.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-01-07 00:54:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/512654"], "description"=>"<p>Correlations between all the eight groups of samples analyzed in microarrays are plotted as a dendrogram. As expected, muscle and adipose form separate clusters. Also, in adipose cluster, subgroups of adipose type and gender are observed. Globally normalized data was used for constructing the dendrogram. Con, control subjects; T2D, diabetic patients; SA, subcutaneous adipose; VA, visceral adipose; SM, skeletal muscle.</p>", "links"=>[], "tags"=>["samples"], "article_id"=>183150, "categories"=>["Chemistry", "Genetics"], "users"=>["Priyanka Jain", "Saurabh Vig", "Malabika Datta", "Dinesh Jindel", "Ashok Kumar Mathur", "Sandeep Kumar Mathur", "Abhay Sharma"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0053522.g004", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Dendrogram_of_samples_based_on_gene_expression_profiling_/183150", "title"=>"Dendrogram of samples based on gene expression profiling.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-01-07 00:52:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/513480"], "description"=>"*<p>Bejamini-Hochberg correction.</p>", "links"=>[], "tags"=>["pathways", "t2d"], "article_id"=>183974, "categories"=>["Chemistry", "Genetics"], "users"=>["Priyanka Jain", "Saurabh Vig", "Malabika Datta", "Dinesh Jindel", "Ashok Kumar Mathur", "Sandeep Kumar Mathur", "Abhay Sharma"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0053522.t001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Enriched_pathways_in_T2D_interactome_/183974", "title"=>"Enriched pathways in T2D interactome.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-01-07 01:06:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/512443"], "description"=>"<p>Genes at different <i>p</i> value cutoffs were examined for pathway enrichment. The pathway along with corresponding genes and enrichment <i>p</i> values are indicated. Note highly significant enrichment of Maturity onset diabetes of the young in genes reported at 10<sup>−5 </sup><i>p</i> value threshold, dubbed “T2D genome” henceforth.</p>", "links"=>[], "tags"=>["pathways", "genes", "reported", "t2d", "gwass"], "article_id"=>182928, "categories"=>["Chemistry", "Genetics"], "users"=>["Priyanka Jain", "Saurabh Vig", "Malabika Datta", "Dinesh Jindel", "Ashok Kumar Mathur", "Sandeep Kumar Mathur", "Abhay Sharma"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0053522.g002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Enrichment_of_pathways_in_genes_reported_in_T2D_GWASs_at_various_association_p_value_thresholds_/182928", "title"=>"Enrichment of pathways in genes reported in T2D GWASs at various association <i>p</i> value thresholds.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-01-07 00:48:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/278683", "https://ndownloader.figshare.com/files/278696", "https://ndownloader.figshare.com/files/278717", "https://ndownloader.figshare.com/files/278731", "https://ndownloader.figshare.com/files/278765"], "description"=>"<div><p>Genome-wide association studies (GWASs) have discovered association of several loci with Type 2 diabetes (T2D), a common complex disease characterized by impaired insulin secretion by pancreatic β cells and insulin signaling in target tissues. However, effect of genetic risk variants on continuous glycemic measures in nondiabetic subjects mainly elucidates perturbation of insulin secretion. Also, the disease associated genes do not clearly converge on functional categories consistent with the known aspects of T2D pathophysiology. We used a systems biology approach to unravel genome to phenome correlation in T2D. We first examined enrichment of pathways in genes identified in T2D GWASs at genome-wide or lower levels of significance. Genes at lower significance threshold showed enrichment of insulin secretion related pathway. Notably, physical and genetic interaction network of these genes showed robust enrichment of insulin signaling and other T2D pathophysiology related pathways including insulin secretion. The network also overrepresented genes reported to interact with insulin secretion and insulin action targeting antidiabetic drugs. The drug interacting genes themselves showed overrepresentation of insulin signaling and other T2D relevant pathways. Next, we generated genome-wide expression profiles of multiple insulin responsive tissues from nondiabetic and diabetic patients. Remarkably, the differentially expressed genes showed significant overlap with the network genes, with the intersection showing enrichment of insulin signaling and other pathways consistent with T2D pathophysiology. Literature search led our genomic, interactomic, transcriptomic and toxicogenomic evidence to converge on TGF-beta signaling, a pathway known to play a crucial role in pancreatic islets development and function, and insulin signaling. Cumulatively, we find that GWAS genes relate directly to insulin secretion and indirectly, through collaborating with other genes, to insulin resistance. This seems to support the epidemiological evidence that environmentally triggered insulin resistance interacts with genetically programmed β cell dysfunction to precipitate diabetes.</p> </div>", "links"=>[], "tags"=>["systems", "reveals", "genome", "phenome", "diabetes"], "article_id"=>114958, "categories"=>["Chemistry", "Genetics"], "users"=>["Priyanka Jain", "Saurabh Vig", "Malabika Datta", "Dinesh Jindel", "Ashok Kumar Mathur", "Sandeep Kumar Mathur", "Abhay Sharma"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0053522.s001", "https://dx.doi.org/10.1371/journal.pone.0053522.s002", "https://dx.doi.org/10.1371/journal.pone.0053522.s003", "https://dx.doi.org/10.1371/journal.pone.0053522.s004", "https://dx.doi.org/10.1371/journal.pone.0053522.s005"], "stats"=>{"downloads"=>24, "page_views"=>33, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Systems_Biology_Approach_Reveals_Genome_to_Phenome_Correlation_in_Type_2_Diabetes__/114958", "title"=>"Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-01-07 01:22:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/513342"], "description"=>"<p>TGF-beta signaling genes in T2D interactome-T2D transcriptome intersection.</p>", "links"=>[], "tags"=>["signaling", "genes", "t2d", "interactome-t2d", "transcriptome"], "article_id"=>183833, "categories"=>["Chemistry", "Genetics"], "users"=>["Priyanka Jain", "Saurabh Vig", "Malabika Datta", "Dinesh Jindel", "Ashok Kumar Mathur", "Sandeep Kumar Mathur", "Abhay Sharma"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0053522.t005", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_TGF_beta_signaling_genes_in_T2D_interactome_T2D_transcriptome_intersection_/183833", "title"=>"TGF-beta signaling genes in T2D interactome-T2D transcriptome intersection.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-01-07 01:03:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/513261"], "description"=>"<p>TGF-beta signaling genes in T2D genome, interactome and transcriptome, and antidiabetic drug toxicogenome.</p>", "links"=>[], "tags"=>["signaling", "genes", "t2d", "interactome", "antidiabetic"], "article_id"=>183754, "categories"=>["Chemistry", "Genetics"], "users"=>["Priyanka Jain", "Saurabh Vig", "Malabika Datta", "Dinesh Jindel", "Ashok Kumar Mathur", "Sandeep Kumar Mathur", "Abhay Sharma"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0053522.t007", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_TGF_beta_signaling_genes_in_T2D_genome_interactome_and_transcriptome_and_antidiabetic_drug_toxicogenome_/183754", "title"=>"TGF-beta signaling genes in T2D genome, interactome and transcriptome, and antidiabetic drug toxicogenome.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-01-07 01:02:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/513449"], "description"=>"*<p>Bejamini-Hochberg correction.</p>", "links"=>[], "tags"=>["pathways", "antidiabetic", "interacting"], "article_id"=>183941, "categories"=>["Chemistry", "Genetics"], "users"=>["Priyanka Jain", "Saurabh Vig", "Malabika Datta", "Dinesh Jindel", "Ashok Kumar Mathur", "Sandeep Kumar Mathur", "Abhay Sharma"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0053522.t002", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Enriched_pathways_in_antidiabetic_drug_interacting_genes_/183941", "title"=>"Enriched pathways in antidiabetic drug interacting genes.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-01-07 01:05:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/513036"], "description"=>"<p>Compared to total interactome of 14,306 genes, the T2D interactome of 561 genes represent significantly greater number of genes differentially expressed at unadjusted <i>p</i> value threshold in microarray profiles, dubbed “T2D transcriptome” from now on. Bonferroni adjusted hypergeometric distribution <i>p</i> values for the overlaps are indicated.</p>", "links"=>[], "tags"=>["t2d", "interactome"], "article_id"=>183526, "categories"=>["Chemistry", "Genetics"], "users"=>["Priyanka Jain", "Saurabh Vig", "Malabika Datta", "Dinesh Jindel", "Ashok Kumar Mathur", "Sandeep Kumar Mathur", "Abhay Sharma"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0053522.g007", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overlap_between_T2D_interactome_and_T2D_transcriptome_/183526", "title"=>"Overlap between T2D interactome and T2D transcriptome.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-01-07 00:58:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/512321"], "description"=>"<p>T2D GWAS genes do not directly relate (indicated by ‘X’ on the left side) to pathways associated with disease pathophysiology. Conspicuously, effect of identified risk variants on continuous glycemic measures in nondiabetic subjects chiefly explains only perturbation of insulin secretion, not insulin resistance. Further, the genes found as associated with the disease do not clearly relate to processes and pathways consistent with the known aspects of T2D pathophysiology. The main aim of the present study was to ask the question (indicated by ‘?’ on the right side) if GWAS data when considered in conjunction with interactome, toxicogenome and disease transcriptome data reveal genome to phenome correlation in T2D. Data available in public domain for GWAS, interactome and toxicogenome was used in the analysis. For disease transcriptome, new experimental data was generated. We specifically examined if interaction network of genes reported in T2D GWAS, genes showing altered expression after treatment with various antidiabetic drugs, and genes that are differentially expressed in insulin responsive tissues in male and female T2D patients do converge on insulin secretion, insulin resistance and other T2D associated pathophysiological pathways.</p>", "links"=>[], "tags"=>["genetics and genomics", "diabetes and endocrinology"], "article_id"=>182809, "categories"=>["Chemistry", "Genetics"], "users"=>["Priyanka Jain", "Saurabh Vig", "Malabika Datta", "Dinesh Jindel", "Ashok Kumar Mathur", "Sandeep Kumar Mathur", "Abhay Sharma"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0053522.g001", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_representation_of_the_workflow_/182809", "title"=>"Schematic representation of the workflow.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-01-07 00:46:49"}

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Relative Metric

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