System network analysis of genomics and transcriptomics data identified type 1 diabetes-associated pathway and genes

Abstract Background Our study aimed to investigate signature RNAs and their potential roles in type 1 diabetes mellitus (T1DM) using a competing endogenous RNA regulatory network analysis. Methods Expression profiles of GSE55100, deposited from peripheral blood mononuclear cells of 12 T1DM patients and 10 normal controls, were downloaded from the Gene Expression Omnibus to uncover differentially expressed long non-coding RNAs (lncRNAs), mRNAs, and microRNAs (miRNAs). The ceRNA regulatory network was constructed, then functional and pathway enrichment analysis was conducted. AT1DM-related ceRNA regulatory network was established based on the Human microRNA Disease Database to carry out pathway enrichment analysis. Meanwhile, the T1DM-related pathways were retrieved from the Comparative Toxicogenomics Database (CTD). Results In total, 847 mRNAs, 41 lncRNAs, and 38 miRNAs were significantly differentially expressed. The ceRNA regulatory network consisted of 12 lncRNAs, 10 miRNAs, and 24 mRNAs. Two miRNAs (hsa-miR-181a and hsa-miR-1275) were screened as T1DM-related miRNAs to build the T1DM-related ceRNA regulatory network, in which genes were considerably enriched in seven pathways. Moreover, three overlapping pathways, including the phosphatidylinositol signaling system (involving PIP4K2A, INPP4A, PIP4K2C, and CALM1); dopaminergic synapse (involving CALM1 and PPP2R5C); and the insulin signaling pathway (involving CBLB and CALM1) were revealed by comparing with T1DM-related pathways in the CTD, which involved four lncRNAs (LINC01278, TRG-AS1, MIAT, and GAS5-AS1). Conclusion The identified signature RNAs may serve as important regulators in the pathogenesis of T1DM.

Download Full-text

Gene Co-Expression Network Analysis for Identifying Modules and Functionally Enriched Pathways in Type 1 Diabetes

PLoS ONE ◽

10.1371/journal.pone.0156006 ◽

2016 ◽

Vol 11 (6) ◽

pp. e0156006 ◽

Cited By ~ 23

Author(s):

Ignacio Riquelme Medina ◽

Zelmina Lubovac-Pilav

Keyword(s):

Type 1 Diabetes ◽

Network Analysis

Download Full-text

Mapping Individual Differences Online: A Case Study of the Type 1 Diabetes Community (Preprint)

10.2196/preprints.30756 ◽

2021 ◽

Author(s):

Cianna Bedford-Petersen ◽

Sara J Weston

Keyword(s):

Type 1 Diabetes ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Online Community ◽

Latent Dirichlet Allocation ◽

Emotional Health ◽

Low Cost ◽

Sources Of Information

BACKGROUND Social media platforms, such as Twitter, are increasingly popular among communities of people with chronic conditions, including people with Type 1 diabetes (T1D). OBJECTIVE The current study attempts to document the major themes of Twitter posts using a natural language processes method to identify topics of interest in the T1D online community. METHODS Through Twitter scraping, we gathered a dataset of 691,691 Tweets from 8,557 accounts which represent people with T1D, their caregivers, health practitioners, and advocates. Tweet content was analyzed for sentiment and topic using Latent Dirichlet allocation. We used social network analysis to examine the degree to which identified topics are siloed within specific groups or disseminated through the broader T1D online community. RESULTS Tweets were, on average, positive in sentiment. Through topic modeling, we identified six broad bandwidth topics, ranging from clinical to advocacy to daily management to emotional health, which can inform researchers and practitioners interested in the needs of people with T1D. Moreover, social network analysis suggests that users are likely to see a mix of these topics discussed by accounts they follow. CONCLUSIONS Twitter online communities are sources of information for people with T1D and members related to that community. Topics identified reveal key concerns of the T1D community and may be useful to practitioners and researchers alike. The methods used are efficient (low cost) while providing researchers with enormous amounts of data. We provide code to facilitate the use of these methods with other populations.

Download Full-text

Identification of dysregulated pathways through SLC30A8 protein interaction in type 1 diabetes mellitus

AIMS Molecular Science ◽

10.3934/molsci.2021023 ◽

2021 ◽

Vol 8 (4) ◽

pp. 301-310

Author(s):

Afreen Bhatty ◽

◽

Zile Rubab ◽

Hafiz Syed Mohammad Osama Jafri ◽

Sheh Zano

Keyword(s):

Diabetes Mellitus ◽

Type 1 Diabetes ◽

Network Analysis ◽

Type 1 Diabetes Mellitus ◽

Protein Interaction ◽

Interaction Network ◽

Signalling Pathway ◽

Functional Enrichment ◽

Protein Protein Interaction

<abstract><sec> <title>Objective</title> The aim of the current study was to explore the gene enrichment and dysregulated pathways on the basis of interaction network analysis of <italic>SLC30A8</italic> in type 1 diabetes mellitus (T1DM). <italic>SLC30A8</italic> polymorphism could be characterized as a beneficial tool to identify the interacting gene in developing T1DM. </sec><sec> <title>Materials and methods</title> <italic>SLC30A8</italic> interacting protein interaction network was obtained by String Interaction network Version 11.0. Ten proteins were identified interacting with <italic>SLC30A8</italic> and were analysed by protein-protein interaction and enrichment network analysis along with Functional Enrichment analysis tool (FunRich 3.1.3) to map the gene data sets. In entire analysis, FunRich database was used as background against all annotated gene/protein list. Protein-protein interaction (PPI) and enrichment network analysis of the selected protein: <italic>SLC30A8</italic> gene along with gene mapping and pathway enrichment were performed using FunRich 3.1.3 and String Interaction network Version 11.0. </sec><sec> <title>Results</title> Biological pathway grouping displayed enriched proteins in TRAIL signalling pathway (<italic>p</italic> < 0.001). <italic>PTPRN, GAD2</italic> and <italic>TCF7L2</italic> were enriched in TRAIL Signalling pathway when <italic>INS</italic> was made focused gene and directly interacting with <italic>SLC30A8</italic>. </sec><sec> <title>Conclusions</title> TRAIL signalling pathways were enriched in T1DM. Therefore, <italic>SLC30A8</italic> along with <italic>PTPRN, GAD2</italic> and <italic>TCF7L2</italic> involved in TRAIL pathway must be further explored to understand their in vivo role in T1DM. </sec></abstract>

Download Full-text

Type 1 Diabetes Mellitus Virtual Patient Network as a Peer Support Community: Protocol for Social Network Analysis and Content Analysis

JMIR Research Protocols ◽

10.2196/18714 ◽

2020 ◽

Vol 9 (8) ◽

pp. e18714

Author(s):

Nancy Wu ◽

Anne-Sophie Brazeau ◽

Meranda Nakhla ◽

Deborah Chan ◽

Deborah Da Costa ◽

...

Keyword(s):

Diabetes Mellitus ◽

Social Support ◽

Type 1 Diabetes ◽

Content Analysis ◽

Social Network ◽

Network Analysis ◽

Type 1 Diabetes Mellitus ◽

Virtual Patient ◽

Interaction Data

Background Type 1 Diabetes Mellitus Virtual Patient Network (T1DM-VPN) is a private Facebook group for youths with type 1 diabetes mellitus (T1DM) in Canada intended to facilitate peer-to-peer support. It was built on the finding that stigma is prevalent among youth with T1DM and impedes self-management. Objective We aim to determine if T1DM-VPN provides support as intended and to ascertain what type of members provide support. Specifically, we will (1) identify text consistent with any one of 5 social support categories, (2) describe the network by visualizing its structure and reporting basic engagement statistics, and (3) determine whether being a designated peer leader is related to a member’s centrality (ie, importance in the network) and how frequently they offer social support. Methods We will manually extract interaction data from the Facebook group (posts, comments, likes/reactions, seen) generated from June 21, 2017 (addition of first member), to March 1, 2020. Two researchers will independently code posts and comments according to an existing framework of 5 social support categories—informational, emotional, esteem, network, and tangible—with an additional framework for nonsocial support categories. We will calculate how frequently each code is used. We will also report basic engagement statistics (eg, number of posts made per person-month) and generate a visualization of the network. We will identify stable time intervals in the history of T1DM-VPN by modeling monthly membership growth as a Poisson process. Within each interval, each member’s centrality will be calculated and standardized to that of the most central member. We will use a centrality formula that considers both breadth and depth of connections (centrality = 0.8 × total No. of connections + 0.2 × total No. of interactions). Finally, we will construct multivariate linear regression models to assess whether peer leader status predicts member centrality and the frequency of offering social support. Other variables considered for inclusion in the models are gender and age at diagnosis. Results T1DM-VPN was launched in June 2017. As of March 1, 2020, it has 196 patient-members. This research protocol received ethics approval from the McGill University Health Centre Research Ethics Board on May 20, 2020. Baseline information about each group member was collected upon addition into the group, and collection of interaction data is ongoing as of May 2020. Conclusions This content analysis and social network analysis study of a virtual patient network applies epidemiological methods to account for dynamic growth and activity. The results will allow for an understanding of the topics of importance to youth with T1DM and how a virtual patient network evolves over time. This work is intended to serve as a foundation for future action to help youth improve their experience of living with diabetes. International Registered Report Identifier (IRRID) PRR1-10.2196/18714

Download Full-text

Type 1 Diabetes: Urinary Proteomics and Protein Network Analysis Support Perturbation of Lysosomal Function

Theranostics ◽

10.7150/thno.19679 ◽

2017 ◽

Vol 7 (10) ◽

pp. 2704-2717 ◽

Cited By ~ 11

Author(s):

Harinder Singh ◽

Yanbao Yu ◽

Moo-Jin Suh ◽

Manolito G Torralba ◽

Robert D. Stenzel ◽

...

Keyword(s):

Type 1 Diabetes ◽

Network Analysis ◽

Protein Network ◽

Lysosomal Function ◽

Urinary Proteomics

Download Full-text

Type 1 Diabetes Mellitus Virtual Patient Network as a Peer Support Community: Protocol for Social Network Analysis and Content Analysis (Preprint)

10.2196/preprints.18714 ◽

2020 ◽

Author(s):

Nancy Wu ◽

Anne-Sophie Brazeau ◽

Meranda Nakhla ◽

Deborah Chan ◽

Deborah Da Costa ◽

...

Keyword(s):

Diabetes Mellitus ◽

Social Support ◽

Type 1 Diabetes ◽

Content Analysis ◽

Social Network ◽

Network Analysis ◽

Type 1 Diabetes Mellitus ◽

Virtual Patient ◽

Interaction Data

BACKGROUND Type 1 Diabetes Mellitus Virtual Patient Network (T1DM-VPN) is a private Facebook group for youths with type 1 diabetes mellitus (T1DM) in Canada intended to facilitate peer-to-peer support. It was built on the finding that stigma is prevalent among youth with T1DM and impedes self-management. OBJECTIVE We aim to determine if T1DM-VPN provides support as intended and to ascertain what type of members provide support. Specifically, we will (1) identify text consistent with any one of 5 social support categories, (2) describe the network by visualizing its structure and reporting basic engagement statistics, and (3) determine whether being a designated peer leader is related to a member’s centrality (ie, importance in the network) and how frequently they offer social support. METHODS We will manually extract interaction data from the Facebook group (posts, comments, likes/reactions, seen) generated from June 21, 2017 (addition of first member), to March 1, 2020. Two researchers will independently code posts and comments according to an existing framework of 5 social support categories—informational, emotional, esteem, network, and tangible—with an additional framework for nonsocial support categories. We will calculate how frequently each code is used. We will also report basic engagement statistics (eg, number of posts made per person-month) and generate a visualization of the network. We will identify stable time intervals in the history of T1DM-VPN by modeling monthly membership growth as a Poisson process. Within each interval, each member’s centrality will be calculated and standardized to that of the most central member. We will use a centrality formula that considers both breadth and depth of connections (centrality = 0.8 × total No. of connections + 0.2 × total No. of interactions). Finally, we will construct multivariate linear regression models to assess whether peer leader status predicts member centrality and the frequency of offering social support. Other variables considered for inclusion in the models are gender and age at diagnosis. RESULTS T1DM-VPN was launched in June 2017. As of March 1, 2020, it has 196 patient-members. This research protocol received ethics approval from the McGill University Health Centre Research Ethics Board on May 20, 2020. Baseline information about each group member was collected upon addition into the group, and collection of interaction data is ongoing as of May 2020. CONCLUSIONS This content analysis and social network analysis study of a virtual patient network applies epidemiological methods to account for dynamic growth and activity. The results will allow for an understanding of the topics of importance to youth with T1DM and how a virtual patient network evolves over time. This work is intended to serve as a foundation for future action to help youth improve their experience of living with diabetes. INTERNATIONAL REGISTERED REPORT PRR1-10.2196/18714

Download Full-text

Competing endogenous RNA network analysis explores the key lncRNAs, miRNAs, and mRNAs in type 1 diabetes

BMC Medical Genomics ◽

10.1186/s12920-021-00877-3 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Chang Li ◽

Bo Wei ◽

Jianyu Zhao

Keyword(s):

Gene Expression ◽

Type 1 Diabetes ◽

Network Analysis ◽

Real Time ◽

Real Time Pcr ◽

Interaction Network ◽

Enrichment Analysis ◽

Rapid Onset ◽

Gene Set Enrichment Analysis

Abstract Background Type 1 diabetes (T1D, named insulin-dependent diabetes) has a relatively rapid onset and significantly decreases life expectancy. This study is conducted to reveal the long non-coding RNA (lncRNA)-microRNA (miRNA)-mRNA regulatory axises implicated in T1D. Methods The gene expression profile under GSE55100 (GPL570 and GPL8786 datasets; including 12 T1D samples and 10 normal samples for each dataset) was extracted from Gene Expression Omnibus database. Using limma package, the differentially expressed mRNAs (DE-mRNAs), miRNAs (DE-miRNAs), and lncRNAs (DE-lncRNAs) between T1D and normal samples were analyzed. For the DE-mRNAs, the functional terms were enriched by DAVID tool, and the significant pathways were enriched using gene set enrichment analysis. The interactions among DE-lncRNAs, DE-miRNAs and DE-mRNAs were predicted using mirwalk and starbase. The lncRNA-miRNA-mRNA interaction network analysis was visualized by Cytoscape. The key genes in the interaction network were verified by quantitatively real-time PCR. Results In comparison to normal samples, 236 DE-mRNAs, 184 DE-lncRNAs, and 45 DE-miRNAs in T1D samples were identified. For the 236 DE-mRNAs, 16 Gene Ontology (GO)_biological process (BP) terms, four GO_cellular component (CC) terms, and 57 significant pathways were enriched. A network involving 36 DE-mRNAs, 8 DE- lncRNAs, and 15 DE-miRNAs was built, such as TRG-AS1—miR-23b/miR-423—PPM1L and GAS5—miR-320a/miR-23b/miR-423—SERPINA1 regulatory axises. Quantitatively real-time PCR successfully validated the expression levels of TRG-AS1- miR-23b -PPM1L and GAS5-miR-320a- SERPINA1. Conclusion TRG-AS1—miR-23b—PPM1L and GAS5—miR-320a—SERPINA1 regulatory axises might impact the pathogenesis of T1D.

Download Full-text