scholarly journals A Network-Based Bioinformatics Approach to Identify Molecular Biomarkers for Type 2 Diabetes that Are Linked to the Progression of Neurological Diseases

2020 ◽  
Vol 17 (3) ◽  
pp. 1035 ◽  
Author(s):  
Md Habibur Rahman ◽  
Silong Peng ◽  
Xiyuan Hu ◽  
Chen Chen ◽  
Md Rezanur Rahman ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Interaction Analysis ◽  
Neurological Diseases ◽  
Molecular Pathways ◽  
Literature Searching ◽  
Multilayer Network ◽  
Protein Protein Interaction ◽  
Unaffected Control ◽  
Go Terms

Neurological diseases (NDs) are progressive disorders, the progression of which can be significantly affected by a range of common diseases that present as comorbidities. Clinical studies, including epidemiological and neuropathological analyses, indicate that patients with type 2 diabetes (T2D) have worse progression of NDs, suggesting pathogenic links between NDs and T2D. However, finding causal or predisposing factors that link T2D and NDs remains challenging. To address these problems, we developed a high-throughput network-based quantitative pipeline using agnostic approaches to identify genes expressed abnormally in both T2D and NDs, to identify some of the shared molecular pathways that may underpin T2D and ND interaction. We employed gene expression transcriptomic datasets from control and disease-affected individuals and identified differentially expressed genes (DEGs) in tissues of patients with T2D and ND when compared to unaffected control individuals. One hundred and ninety seven DEGs (99 up-regulated and 98 down-regulated in affected individuals) that were common to both the T2D and the ND datasets were identified. Functional annotation of these identified DEGs revealed the involvement of significant cell signaling associated molecular pathways. The overlapping DEGs (i.e., seen in both T2D and ND datasets) were then used to extract the most significant GO terms. We performed validation of these results with gold benchmark databases and literature searching, which identified which genes and pathways had been previously linked to NDs or T2D and which are novel. Hub proteins in the pathways were identified (including DNM2, DNM1, MYH14, PACSIN2, TFRC, PDE4D, ENTPD1, PLK4, CDC20B, and CDC14A) using protein-protein interaction analysis which have not previously been described as playing a role in these diseases. To reveal the transcriptional and post-transcriptional regulators of the DEGs we used transcription factor (TF) interactions analysis and DEG-microRNAs (miRNAs) interaction analysis, respectively. We thus identified the following TFs as important in driving expression of our T2D/ND common genes: FOXC1, GATA2, FOXL1, YY1, E2F1, NFIC, NFYA, USF2, HINFP, MEF2A, SRF, NFKB1, USF2, HINFP, MEF2A, SRF, NFKB1, PDE4D, CREB1, SP1, HOXA5, SREBF1, TFAP2A, STAT3, POU2F2, TP53, PPARG, and JUN. MicroRNAs that affect expression of these genes include mir-335-5p, mir-16-5p, mir-93-5p, mir-17-5p, mir-124-3p. Thus, our transcriptomic data analysis identifies novel potential links between NDs and T2D pathologies that may underlie comorbidity interactions, links that may include potential targets for therapeutic intervention. In sum, our neighborhood-based benchmarking and multilayer network topology methods identified novel putative biomarkers that indicate how type 2 diabetes (T2D) and these neurological diseases interact and pathways that, in the future, may be targeted for treatment.

scholarly journals Hypothetical molecular interconnection between type 2 diabetes and dyslexia

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tuğba Bülbül ◽  
Maryam Baharlooie ◽  
Zahra Safaeinejad ◽  
Ali Osmay Gure ◽  
Kamran Ghaedi
Keyword(s):  
Type 2 Diabetes ◽  
Learning Disabilities ◽  
Literature Review ◽  
Interaction Analysis ◽  
Large Population ◽  
Protein Protein Interaction ◽  
Gene Associations ◽  
Common Learning ◽  
Type Two Diabetes

Abstract Background Dyslexia is one of the most common learning disabilities, especially among children. Type 2 diabetes is a metabolic disorder that affects a large population globally, with metabolic disorders. There have been several genes that are identified as causes of Dyslexia, and in recent studies, it has been found out that some of those genes are also involved in several metabolic pathways. For several years, it has been known that type 2 diabetes causes several neurodegenerative disorders, such as Alzheimer’s disease and Parkinson’s disease. Furthermore, in several studies, it was suggested that type 2 diabetes also has some associations with learning disabilities. This raises the question of whether “Is there a connection between type 2 diabetes and dyslexia?”. In this study, this question is elaborated by linking their developmental processes via bioinformatics analysis about these two diseases individually and collectively. Result The literature review for dyslexia and type two diabetes was completed. As the result of this literature review, the genes that are associated to type 2 diabetes and dyslexia were identified. The biological pathways of dyslexia, and dyslexia associated genes, type 2 diabetes, and type 2 diabetes associated genes were identified. The association of these genes, regarding to their association with pathways were analysed, and using STRING database the gene associations were analysed and identified. Conclusion The findings of this research included the interaction analysis via gene association, co-expression and protein–protein interaction. These findings clarified the interconnection between dyslexia and type 2 diabetes in molecular level and it will be the beginning of an answer regarding to the relationship between T2D and dyslexia. Finally, by improving the understanding this paper aims to open the way for the possible future approach to examine this hypothesis.

scholarly journals The development of multimorbidity during 16 years after diagnosis of type 2 diabetes

2018 ◽  
Vol 8 (1) ◽  
pp. 2235042X1880165 ◽  
Author(s):  
Sandra Pouplier ◽  
Maria Åhlander Olsen ◽  
Tora Grauers Willadsen ◽  
Håkon Sandholdt ◽  
Volkert Siersma ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Chronic Disease ◽  
Diabetes Care ◽  
Cox Regression ◽  
Neurological Diseases ◽  
Gastrointestinal Diseases ◽  
Diabetes Diagnosis ◽  
Prevalent Disease ◽  
Diabetes Patients

Objective: The aims of this study were to (1) quantify the development and composition of multimorbidity (MM) during 16 years following the diagnosis of type 2 diabetes and (2) evaluate whether the effectiveness of structured personal diabetes care differed between patients with and without MM. Research design and methods: One thousand three hundred eighty-one patients with newly diagnosed type 2 diabetes were randomized to receive either structured personal diabetes care or routine diabetes care. Patients were followed up for 19 years in Danish nationwide registries for the occurrence of outcomes. We analyzed the prevalence and degree of MM based on 10 well-defined disease groups. The effect of structured personal care in diabetes patients with and without MM was analyzed with Cox regression models. Results: The proportion of patients with MM increased from 31.6% at diabetes diagnosis to 80.4% after 16 years. The proportion of cardiovascular and gastrointestinal diseases in surviving patients decreased, while, for example, musculoskeletal, eye, and neurological diseases increased. The effect of the intervention was not different between type 2 diabetes patients with or without coexisting chronic disease. Conclusions: In general, the proportion of patients with MM increased after diabetes diagnosis, but the composition of chronic disease changed during the 16 years. We found cardiovascular and musculoskeletal disease to be the most prevalent disease groups during all 16 years of follow-up. The post hoc analysis of the intervention showed that its effectiveness was not different among patients who developed MM compared to those who continued to have diabetes alone.

scholarly journals Genetic effect of Type 2 diabetes to the progression of neurological diseases

2018 ◽  
Author(s):  
Md Habibur Rahman ◽  
Silong Peng ◽  
Chen Chen ◽  
Pietro Lio’ ◽  
Mohammad Ali Moni
Keyword(s):  
Gene Expression ◽  
Type 2 Diabetes ◽  
Drug Targets ◽  
Neurological Diseases ◽  
Genetic Effect ◽  
Neurological Dysfunction ◽  
Transcriptional Analysis ◽  
Increased Risk ◽  
Benchmark Datasets

Neurological diseases (NDs) are progressive disorder often advances with age and comorbidities of Type 2 diabetes (T2D). Epidemiological, clinical and neuropathological evidence advocate that patients with T2D are at an increased risk of getting NDs. However, it is very little known how T2D affects the risk and severity of NDs. To tackle these problems, we employed a transcriptional analysis of affected tissues using agnostic approaches to identify overlapping cellular functions. In this study, we examined gene expression microarray human datasets along with control and disease-affected individuals. Differentially expressed genes (DEG) were identified for both T2D and NDs that includes Alzheimer Disease (AD), Parkinson Disease (PD), Amyotrophic Lateral Sclerosis (ALS), Epilepsy Disease (ED), Huntington Disease (HD), Cerebral Palsy (CP) and Multiple Sclerosis Disease (MSD). We have developed genetic association and diseasome network of T2D and NDs based on the neighborhood-based benchmarking and multilayer network topology approaches. Overlapping DEG sets go through protein-protein interaction and gene enrichment using pathway analysis and gene ontology methods, identifying numerous candidate common genes and pathways. Gene expression analysis platforms have been extensively used to investigate altered pathways and to identify potential biomarkers and drug targets. Finally, we validated our identified biomarkers using the gold benchmark datasets which identified corresponding relations of T2D and NDs. Therapeutic targets aimed at attenuating identified altered pathway could ameliorate neurological dysfunction in a T2D patient.

scholarly journals Functional Interactomes of Genes Showing Association with Type-2 Diabetes and Its Intermediate Phenotypic Traits Point towards Adipo-Centric Mechanisms in Its Pathophysiology

Biomolecules ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 601
Author(s):  
Aditya Saxena ◽  
Nitin Wahi ◽  
Anshul Kumar ◽  
Sandeep Kumar Mathur
Keyword(s):  
Type 2 Diabetes ◽  
Adipose Tissue ◽  
Network Analysis ◽  
Potential Role ◽  
Phenotypic Traits ◽  
Protein Protein Interaction ◽  
Cellular Processes ◽  
Antidiabetic Treatment

The pathogenic mechanisms causing type 2 diabetes (T2D) are still poorly understood; a greater awareness of its causation can lead to the development of newer and better antidiabetic drugs. In this study, we used a network-based approach to assess the cellular processes associated with protein–protein interaction subnetworks of glycemic traits—HOMA-β and HOMA-IR. Their subnetworks were further analyzed in terms of their overlap with the differentially expressed genes (DEGs) in pancreatic, muscle, and adipose tissue in diabetics. We found several DEGs in these tissues showing an overlap with the HOMA-β subnetwork, suggesting a role of these tissues in β-cell failure. Many genes in the HOMA-IR subnetwork too showed an overlap with the HOMA-β subnetwork. For understanding the functional theme of these subnetworks, a pathway-to-pathway complementary network analysis was done, which identified various adipose biology-related pathways, containing genes involved in both insulin secretion and action. In conclusion, network analysis of genes showing an association between T2D and its intermediate phenotypic traits suggests their potential role in beta cell failure. These genes enriched the adipo-centric pathways and were expressed in both pancreatic and adipose tissue and, therefore, might be one of the potential targets for future antidiabetic treatment.

scholarly journals Reconstruction of Protein-Protein Interaction Network of Insulin Signaling inHomo Sapiens

2010 ◽  
Vol 2010 ◽  
pp. 1-7 ◽  
Author(s):  
Saliha Durmuş Tekir ◽  
Pelin Ümit ◽  
Aysun Eren Toku ◽  
Kutlu Ö. Ülgen
Keyword(s):  
Type 2 Diabetes ◽  
Insulin Signaling ◽  
Homo Sapiens ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Signaling Mechanisms ◽  
Reconstructed Network ◽  
Protein Protein Interaction Network

Diabetes is one of the most prevalent diseases in the world. Type 1 diabetes is characterized by the failure of synthesizing and secreting of insulin because of destroyed pancreaticβ-cells. Type 2 diabetes, on the other hand, is described by the decreased synthesis and secretion of insulin because of the defect in pancreaticβ-cells as well as by the failure of responding to insulin because of malfunctioning of insulin signaling. In order to understand the signaling mechanisms of responding to insulin, it is necessary to identify all components in the insulin signaling network. Here, an interaction network consisting of proteins that have statistically high probability of being biologically related to insulin signaling inHomo sapienswas reconstructed by integrating Gene Ontology (GO) annotations and interactome data. Furthermore, within this reconstructed network, interacting proteins which mediate the signal from insulin hormone to glucose transportation were identified using linear paths. The identification of key components functioning in insulin action on glucose metabolism is crucial for the efforts of preventing and treating type 2 diabetes mellitus.

A Pathway and Network Oriented Approach to Enlighten Molecular Mechanisms of Type 2 Diabetes Using Multiple Association Studies

2020 ◽  
Author(s):  
Burcu Bakir-Gungor ◽  
Miray Unlu Yazici ◽  
Gokhan Goy ◽  
Mustafa Temiz
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Protein Interaction ◽  
Molecular Mechanisms ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Oriented Approach

AbstractDiabetes Mellitus (DM) is a group of metabolic disorder that is characterized by pancreatic dysfunction in insulin producing beta cells, glucagon secreting alpha cells, and insulin resistance or insulin in-functionality related hyperglycemia. Type 2 Diabetes Mellitus (T2D), which constitutes 90% of the diabetes cases, is a complex multifactorial disease. In the last decade, genome-wide association studies (GWASs) for type 2 diabetes (T2D) successfully pinpointed the genetic variants (typically single nucleotide polymorphisms, SNPs) that associate with disease risk. However, traditional GWASs focus on the ‘the tip of the iceberg’ SNPs, and the SNPs with mild effects are discarded. In order to diminish the burden of multiple testing in GWAS, researchers attempted to evaluate the collective effects of interesting variants. In this regard, pathway-based analyses of GWAS became popular to discover novel multi-genic functional associations. Still, to reveal the unaccounted 85 to 90% of T2D variation, which lies hidden in GWAS datasets, new post-GWAS strategies need to be developed. In this respect, here we reanalyze three meta-analysis data of GWAS in T2D, using the methodology that we have developed to identify disease-associated pathways by combining nominally significant evidence of genetic association with the known biochemical pathways, protein-protein interaction (PPI) networks, and the functional information of selected SNPs. In this research effort, to enlighten the molecular mechanisms underlying T2D development and progress, we integrated different in-silico approaches that proceed in top-down manner and bottom-up manner, and hence presented a comprehensive analysis at protein subnetwork, pathway, and pathway subnetwork levels. Our network and pathway-oriented approach is based on both the significance level of an affected pathway and its topological relationship with its neighbor pathways. Using the mutual information based on the shared genes, the identified protein subnetworks and the affected pathways of each dataset were compared. While, most of the identified pathways recapitulate the pathophysiology of T2D, our results show that incorporating SNP functional properties, protein-protein interaction networks into GWAS can dissect leading molecular pathways, which cannot be picked up using traditional analyses. We hope to bridge the knowledge gap from sequence to consequence.

scholarly journals Development of GMDR-GPU for Gene-Gene Interaction Analysis and Its Application to WTCCC GWAS Data for Type 2 Diabetes

PLoS ONE ◽  
2013 ◽  
Vol 8 (4) ◽  
pp. e61943 ◽  
Author(s):  
Zhixiang Zhu ◽  
Xiaoran Tong ◽  
Zhihong Zhu ◽  
Meimei Liang ◽  
Wenyan Cui ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Interaction Analysis ◽  
Gene Interaction ◽  
Gwas Data

scholarly journals Genetic effect of Type 2 diabetes to the progression of neurological diseases

2018 ◽  
Author(s):  
Md Habibur Rahman ◽  
Silong Peng ◽  
Chen Chen ◽  
Pietro Lio’ ◽  
Mohammad Ali Moni
Keyword(s):  
Gene Expression ◽  
Type 2 Diabetes ◽  
Drug Targets ◽  
Neurological Diseases ◽  
Genetic Effect ◽  
Neurological Dysfunction ◽  
Transcriptional Analysis ◽  
Increased Risk ◽  
Benchmark Datasets

Neurological diseases (NDs) are progressive disorder often advances with age and comorbidities of Type 2 diabetes (T2D). Epidemiological, clinical and neuropathological evidence advocate that patients with T2D are at an increased risk of getting NDs. However, it is very little known how T2D affects the risk and severity of NDs. To tackle these problems, we employed a transcriptional analysis of affected tissues using agnostic approaches to identify overlapping cellular functions. In this study, we examined gene expression microarray human datasets along with control and disease-affected individuals. Differentially expressed genes (DEG) were identified for both T2D and NDs that includes Alzheimer Disease (AD), Parkinson Disease (PD), Amyotrophic Lateral Sclerosis (ALS), Epilepsy Disease (ED), Huntington Disease (HD), Cerebral Palsy (CP) and Multiple Sclerosis Disease (MSD). We have developed genetic association and diseasome network of T2D and NDs based on the neighborhood-based benchmarking and multilayer network topology approaches. Overlapping DEG sets go through protein-protein interaction and gene enrichment using pathway analysis and gene ontology methods, identifying numerous candidate common genes and pathways. Gene expression analysis platforms have been extensively used to investigate altered pathways and to identify potential biomarkers and drug targets. Finally, we validated our identified biomarkers using the gold benchmark datasets which identified corresponding relations of T2D and NDs. Therapeutic targets aimed at attenuating identified altered pathway could ameliorate neurological dysfunction in a T2D patient.

Evaluation of the Associations of GC and CYP2R1 Genes and Gene-Obesity Interactions with Type 2 Diabetes Risk in a Chinese Rural Population

2020 ◽  
Vol 76 (3) ◽  
pp. 175-182
Author(s):  
Yanting Zhao ◽  
Gaoshuai Wang ◽  
Yuqian Li ◽  
Xiaotian Liu ◽  
Li Liu ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Interaction Analysis ◽  
Allele Carrier ◽  
Gene Variants ◽  
Nucleotide Polymorphisms ◽  
Additive Interaction ◽  
Asian Populations ◽  
Increased Risk ◽  
Gc Gene

Introduction: Group-specific component (GC) and cytochrome P450 family 2 subfamily R member 1 (CYP2R1) gene polymorphisms and obesity have been associated with an increased risk for development of type 2 diabetes mellitus (T2DM) in Asian populations. Objective: This study assessed the associations of interactions between GC gene variants and CYP2R1 gene variants and between genes and obesity with T2DM risk. Methods: A study that included 2,271 subjects was performed. Eight single nucleotide polymorphisms in the GC and CYP2R1 genes were genotyped. Interaction analysis was performed using rs7041 in the GC gene and rs1993116 in the CYP2R1 gene. The effects of multiplicative and additive gene-gene and gene-environment interactions on T2DM risk were assessed. Results: The T2DM risk was significantly associated with being overweight/obese, abdominal obesity, rs7041, and rs1993116. A significant additive interaction between rs1993116 and rs7041 was associated with T2DM. In addition, there was a significant multiplicative interaction between rs7041 and body mass index (BMI) associated with elevated blood glucose levels, and at a higher BMI (>28.47), the G allele carrier showed a stronger effect than the TT genotype. Conclusions: The interactions between GC rs7041-CYP2R1 rs1993116 and GC rs7041-BMI may explain the mechanisms by which these factors increase the risk of T2DM development.

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