Interactome-transcriptome analysis discovers signatures complementary to GWAS Loci of Type 2 Diabetes

Longitudinal association between DNA methylation and type 2 diabetes: findings from the KORA F4/FF4 study

BACKGROUND

Type 2 diabetes (T2D) has been linked to changes in DNA methylation levels, which can, in turn, alter transcriptional activity. However, most studies for epigenome-wide associations between T2D and DNA methylation comes from cross-sectional design. Few large-scale investigations have explored these associations longitudinally over multiple time-points.

METHODS

In this longitudinal study, we examined data from the Cooperative Health Research in the Region of Augsburg (KORA) F4 and FF4 studies, conducted approximately seven years apart. Leucocyte DNA methylation was assessed using the Illumina EPIC and 450K arrays. Linear mixed-effects models were employed to identify significant associations between methylation sites and diabetes status, as well as with fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), homoeostasis model assessment of beta cell function (HOMA-B), and homoeostasis model assessment of insulin resistance (HOMA-IR). Interaction effects between diabetes status and follow-up time were also examined. Additionally, we explored CpG sites associated with persistent prediabetes or T2D, as well as the progression from normal glucose tolerance (NGT) to prediabetes or T2D. Finally, we assessed the associations between the identified CpG sites and their corresponding gene expression levels.

RESULTS

A total of 3,501 observations from 2,556 participants, with methylation measured at least once across two visits, were included in the analyses. We identified 64 sites associated with T2D including 15 novel sites as well as known associations like those with the thioredoxin-interacting protein (TXNIP) and ATP-binding cassette sub-family G member 1 (ABCG1) genes. Of these, eight CpG sites exhibited different rates of annual methylation change between the NGT and T2D groups, and seven CpG sites were linked to the progression from NGT to prediabetes or T2D, including those annotated to mannosidase alpha class 2a member 2 (MAN2A2) and carnitine palmitoyl transferase 1 A (CPT1A). Longitudinal analysis revealed significant associations between methylation and FPG at 128 sites, HbA1c at 41 sites, and HOMA-IR at 57 sites. Additionally, we identified 104 CpG-transcript pairs in whole blood, comprising 40 unique CpG sites and 96 unique gene transcripts.

CONCLUSIONS

Our study identified novel differentially methylated loci linked to T2D as well as to changes in diabetes status through a longitudinal approach. We report CpG sites with different rates of annual methylation change and demonstrate that DNA methylation associated with T2D is linked to following transcriptional differences. These findings provide new insights into the molecular mechanisms of diabetes development.

Reduced methylation correlates with diabetic nephropathy risk in type 1 diabetes

Diabetic nephropathy (DN) is a polygenic disorder with few risk variants showing robust replication in large-scale genome-wide association studies. To understand the role of DNA methylation, it is important to have the prevailing genomic view to distinguish key sequence elements that influence gene expression. This is particularly challenging for DN because genome-wide methylation patterns are poorly defined. While methylation is known to alter gene expression, the importance of this causal relationship is obscured by array-based technologies since coverage outside promoter regions is low. To overcome these challenges, we performed methylation sequencing using leukocytes derived from participants of the Finnish Diabetic Nephropathy (FinnDiane) type 1 diabetes (T1D) study (n = 39) that was subsequently replicated in a larger validation cohort (n = 296). Gene body-related regions made up more than 60% of the methylation differences and emphasized the importance of methylation sequencing. We observed differentially methylated genes associated with DN in 3 independent T1D registries originating from Denmark (n = 445), Hong Kong (n = 107), and Thailand (n = 130). Reduced DNA methylation at CTCF and Pol2B sites was tightly connected with DN pathways that include insulin signaling, lipid metabolism, and fibrosis. To define the pathophysiological significance of these population findings, methylation indices were assessed in human renal cells such as podocytes and proximal convoluted tubule cells. The expression of core genes was associated with reduced methylation, elevated CTCF and Pol2B binding, and the activation of insulin-signaling phosphoproteins in hyperglycemic cells. These experimental observations also closely parallel methylation-mediated regulation in human macrophages and vascular endothelial cells.

Comprehensive Interactome Mapping of Nuclear Receptors Using Proximity Biotinylation

Nuclear receptors, including hormone receptors, perform their cellular activities by modulating their protein-protein interactions. They engage with specific ligands and translocate to the nucleus, where they bind the DNA and activate extensive transcriptional programs. Therefore, gaining a comprehensive overview of the protein-protein interactions they establish requires methods that function effectively throughout the cell with fast dynamics and high reproducibility. Focusing on estrogen receptor alpha (ESR1), the founding member of the nuclear receptor family, this chapter describes a new lentiviral system that allows the expression of TurboID-hemagglutinin (HA)-2 × Strep tagged proteins in mammalian cells to perform fast proximity biotinylation assays. Key validation steps for these reagents and their use in interactome mapping experiments in two distinct breast cancer cell lines are described. Our protocol enabled the quantification of ESR1 interactome generated by cellular contexts that were hormone-sensitive or not.

JAZF1 heterozygous knockout mice show altered adipose development and metabolism

BACKGROUND

Juxtaposed with another zinc finger protein 1 (JAZF1) is associated with metabolic disorders, including type 2 diabetes mellitus (T2DM). Several studies showed that JAZF1 and body fat mass are closely related. We attempted to elucidate the JAZF1 functions on adipose development and related metabolism using in vitro and in vivo models.

RESULTS

The JAZF1 expression was precisely regulated during adipocyte differentiation of 3T3-L1 preadipocyte and mouse embryonic fibroblasts (MEFs). Homozygous JAZF1 deletion (JAZF1-KO) resulted in impaired adipocyte differentiation in MEF. The JAZF1 role in adipocyte differentiation was demonstrated by the regulation of PPARγ-a key regulator of adipocyte differentiation. Heterozygous JAZF1 deletion (JAZF1-Het) mice fed a normal diet (ND) or a high-fat diet (HFD) had less adipose tissue mass and impaired glucose homeostasis than the control (JAZF1-Cont) mice. However, other metabolic organs, such as brown adipose tissue and liver, were negligible effect on JAZF1 deficiency.

CONCLUSION

Our findings emphasized the JAZF1 role in adipocyte differentiation and related metabolism through the heterozygous knockout mice. This study provides new insights into the JAZF1 function in adipose development and metabolism, informing strategies for treating obesity and related metabolic disorders.

Multi-omics profiling: the way towards precision medicine in metabolic diseases

Metabolic diseases including type 2 diabetes mellitus (T2DM), non-alcoholic fatty liver disease (NAFLD), and metabolic syndrome (MetS) are alarming health burdens around the world, while therapies for these diseases are far from satisfying as their etiologies are not completely clear yet. T2DM, NAFLD, and MetS are all complex and multifactorial metabolic disorders based on the interactions between genetics and environment. Omics studies such as genetics, transcriptomics, epigenetics, proteomics, and metabolomics are all promising approaches in accurately characterizing these diseases. And the most effective treatments for individuals can be achieved via omics pathways, which is the theme of precision medicine. In this review, we summarized the multi-omics studies of T2DM, NAFLD, and MetS in recent years, provided a theoretical basis for their pathogenesis and the effective prevention and treatment, and highlighted the biomarkers and future strategies for precision medicine.

Jazf1 acts as a regulator of insulin-producing β-cell differentiation in induced pluripotent stem cells and glucose homeostasis in mice

Genetic susceptibility of type 2 diabetes and Juxtaposed with another zinc finger protein 1 (Jazf1) has been reported; however, the precise role of Jazf1 in metabolic processes remains elusive. In this study, using Jazf1-knockout (KO)-induced pluripotent stem cells (iPSC), pancreatic beta cell line MIN6 cells, and Jazf-1 heterozygous KO (Jazf1^+/- ) mice, the effect of Jazf1 on gradual differentiation was investigated. We checked the alterations of the genes related with β-cell specification, maturation, and insulin release against glucose treatment by the gain and loss of the Jazf1 gene in the MIN6 cells. Because undifferentiated Jazf1-KO iPSC were not significantly different from wild-type (WT) iPSC, the size and endoderm marker expression after embryoid body (EB) and teratoma formation were investigated. Compared to EB and teratomas formed with WT iPSC, the EB and teratomas from with Jazf1-KO iPSC were smaller, and in teratomas, the expression of proliferation markers was reduced. Moreover, the expression of the gene sets for β-cell differentiation and the levels of insulin and C-peptide secreted by insulin precursor cells were notably reduced in β-cells differentiated from Jazf1-KO iPSC compared with those differentiated from WT iPSC. A comparison of Jazf1^+/- and WT mice showed that Jazf1^+/- mice had lower levels of serum insulin, pancreatic insulin expression, and decreased pancreatic β-cell size, which resulted in defects in the glucose homeostasis. These findings suggest that Jazf1 plays a pivotal role in the differentiation of β-cells and glucose homeostasis.

Sustained Stimulation of β2AR Inhibits Insulin Signaling in H9C2 Cardiomyoblast Cells Through the PKA-Dependent Signaling Pathway

INTRODUCTION

This study aimed to investigate the role of β2 adrenergic receptor (β2AR) in insulin signaling transduction in H9C2 cardiomyoblast cells to understand the formation of the β2AR-insulin receptor (IR) protein complex and its role in insulin-induced Glut4 expression.

METHODS

H9C2 cells were treated with various protein inhibitors (CGP, β1AR inhibitor CGP20712; ICI, β2AR inhibitor ICI 118,551; PKI, PKA inhibitor myristoylated PKI; PD 0325901, MEK inhibitor; SP600125, JNK inhibitor) with or without insulin or isoproterenol (ISO) before RNA-sequencing (RNA-Seq) and quantitative-PCR (Q-PCR). Yeast two-hybrid, co-immunoprecipitation and His-tag pull-down assay were carried out to investigate the formation of the β2AR-IR protein complex. The intracellular concentrations of cAMP in H9C2 cells were tested by high performance liquid chromatography (HPLC) and the phosphorylation of JNK was tested by Western blot.

RESULTS

Gene Ontology (GO) analysis revealed that the most significantly enriched processes in the domain of molecular function (MF) were catalytic activity and binding, whereas in the domain of biological processes (BP) were metabolic process and cellular process. Furthermore, the enriched processes in the domain of cellular components (CC) were cell and cell parts. The Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis showed that the most significant pathways that have been altered included the PI3K-Akt and MAPK signaling pathways. Q-PCR, which was performed to verify the gene expression levels exhibited consistent results. In evaluating the signaling pathways, the sustained stimulation of β2AR by ISO inhibited insulin signalling, and the effect was primarily through the cAMP-PKA-JNK pathway and MEK/JNK signaling pathway. Yeast two-hybrid, co-immunoprecipitation and His-tag pull-down assay revealed that β2AR, IR, insulin receptor substrate 1 (IRS1), Grb2-associated binding protein 1 (GAB1) and Grb2 existed in the same protein complex.

CONCLUSION

The sustained stimulation of β2AR might inhibit insulin signaling transduction through the cAMP-PKA-JNK and MEK/JNK pathways in H9C2 cells.

Mitochondria under the spotlight: On the implications of mitochondrial dysfunction and its connectivity to neuropsychiatric disorders

Neuropsychiatric disorders (NPDs) such as bipolar disorder (BD), schizophrenia (SZ) and mood disorder (MD) are hard to manage due to overlapping symptoms and lack of biomarkers. Risk alleles of BD/SZ/MD are emerging, with evidence suggesting mitochondrial (mt) dysfunction as a critical factor for disease onset and progression. Mood stabilizing treatments for these disorders are scarce, revealing the need for biomarker discovery and artificial intelligence approaches to design synthetically accessible novel therapeutics. Here, we show mt involvement in NPDs by associating 245 mt proteins to BD/SZ/MD, with 7 common players in these disease categories. Analysis of over 650 publications suggests that 245 NPD-linked mt proteins are associated with 800 other mt proteins, with mt impairment likely to rewire these interactions. High dosage of mood stabilizers is known to alleviate manic episodes, but which compounds target mt pathways is another gap in the field that we address through mood stabilizer-gene interaction analysis of 37 prescriptions and over-the-counter psychotropic treatments, which we have refined to 15 mood-stabilizing agents. We show 26 of the 245 NPD-linked mt proteins are uniquely or commonly targeted by one or more of these mood stabilizers. Further, induced pluripotent stem cell-derived patient neurons and three-dimensional human brain organoids as reliable BD/SZ/MD models are outlined, along with multiomics methods and machine learning-based decision making tools for biomarker discovery, which remains a bottleneck for precision psychiatry medicine.

Circulating lymphocytes and monocytes transcriptomic analysis of patients with type 2 diabetes mellitus, dyslipidemia and periodontitis

Type 2 diabetes mellitus (T2DM), dyslipidemia and periodontitis are frequently associated pathologies; however, there are no studies showing the peripheral blood transcript profile of these combined diseases. Here we identified the differentially expressed genes (DEGs) of circulating lymphocytes and monocytes to reveal potential biomarkers that may be used as molecular targets for future diagnosis of each combination of these pathologies (compared to healthy patients) and give insights into the underlying molecular mechanisms of these diseases. Study participants (n = 150) were divided into groups: (H) systemically and periodontal healthy (control group); (P) with periodontitis, but systemically healthy; (DL-P) with dyslipidemia and periodontitis; (T2DMwell-DL-P) well-controlled type 2 diabetes mellitus with dyslipidemia and periodontitis; and (T2DMpoorly-DL-P) poorly-controlled type 2 diabetes mellitus with dyslipidemia and periodontitis. We preprocessed the microarray data using the Robust Multichip Average (RMA) strategy, followed by the RankProd method to identify candidates for DEGs. Furthermore, we performed functional enrichment analysis using Ingenuity Pathway Analysis and Gene Set Enrichment Analysis. DEGs were submitted to pairwise comparisons, and selected DEGs were validated by quantitative polymerase chain reaction. Validated DEGs verified from T2DMpoorly-DL-P versus H were: TGFB1I1, VNN1, HLADRB4 and CXCL8; T2DMwell-DL-P versus H: FN1, BPTF and PDE3B; DL-P versus H: DAB2, CD47 and HLADRB4; P versus H: IGHDL-P, ITGB2 and HLADRB4. In conclusion, we identified that circulating lymphocytes and monocytes of individuals simultaneously affected by T2DM, dyslipidemia and periodontitis, showed an altered molecular profile mainly associated to inflammatory response, immune cell trafficking, and infectious disease pathways. Altogether, these results shed light on novel potential targets for future diagnosis, monitoring or development of targeted therapies for patients sharing these conditions.

Revealing the Interactions Between Diabetes, Diabetes-Related Diseases, and Cancers Based on the Network Connectivity of Their Related Genes

Diabetes-related diseases (DRDs), especially cancers pose a big threat to public health. Although people have explored pathological pathways of a few common DRDs, there is a lack of systematic studies on important biological processes (BPs) connecting diabetes and its related diseases/cancers. We have proposed and compared 10 protein-protein interaction (PPI)-based computational methods to study the connections between diabetes and 254 diseases, among which a method called DIconnectivity_eDMN performs the best in the sense that it infers a disease rank (according to its relation with diabetes) most consistent with that by literature mining. DIconnectivity_eDMN takes diabetes-related genes, other disease-related genes, a PPI network, and genes in BPs as input. It first maps genes in a BP into the PPI network to construct a BP-related subnetwork, which is expanded (in the whole PPI network) by a random walk with restart (RWR) process to generate a so-called expanded modularized network (eMN). Since the numbers of known disease genes are not high, an RWR process is also performed to generate an expanded disease-related gene list. For each eMN and disease, the expanded diabetes-related genes and disease-related genes are mapped onto the eMN. The association between diabetes and the disease is measured by the reachability of their genes on all eMNs, in which the reachability is estimated by a method similar to the Kolmogorov-Smirnov (KS) test. DIconnectivity_eDMN achieves an area under receiver operating characteristic curve (AUC) of 0.71 for predicting both Type 1 DRDs and Type 2 DRDs. In addition, DIconnectivity_eDMN reveals important BPs connecting diabetes and DRDs. For example, "respiratory system development" and "regulation of mRNA metabolic process" are critical in associating Type 1 diabetes (T1D) and many Type 1 DRDs. It is also found that the average proportion of diabetes-related genes interacting with DRDs is higher than that of non-DRDs.

JAZF1, a relevant metabolic regulator in type 2 diabetes

Excessive adiposity and metabolic inflammation are the key risk factors of type 2 diabetes mellitus (T2DM). Juxtaposed with another zinc finger gene 1 (JAZF1) has been identified as a novel transcriptional cofactor, with function of regulating glucose and lipid homeostasis and inflammation. JAZF1 is involved in metabolic process of T2DM via interaction with several nuclear receptors and protein kinases. Additionally, increasing evidence from genome-wide association studies (GWAS) has shown that JAZF1 polymorphisms are closely associated with T2DM. In this review, we have updated the latest research advances on JAZF1 and discussed its regulatory network in T2DM. The association between JAZF1 polymorphisms and T2DM is discussed as well. The information provided is of importance for guiding future studies as well as for the design of JAZF1-based T2DM therapy.

Overview of genomics and post-genomics research on type 2 diabetes mellitus: Future perspectives and a framework for further studies

In this review, we briefly outlined salient features of pathophysiology and results of the genetic association studies hitherto conducted on type 2 diabetes. Primarily focusing on the current status of genomic research, we briefly discussed the limited progress made during the post-genomic era and tried to identify the limitations of the post-genomic research strategies. We suggested reanalysis of the existing genomic data through advanced statistical and computational methods and recommended integrated genomics-metabolomics approaches for future studies to facilitate understanding of the gene-environment interactions in the manifestation of the disease. We also propose a framework for research that may be apt for determining the effects of urbanization and changing lifestyles in the manifestation of complex genetic disorders like type 2 diabetes in the Indian populations and offset the confounding effects of both genetic and environmental factors in the natural way.

A network biology approach to unraveling inherited axonopathies

Inherited axonopathies represent a spectrum of disorders unified by the common pathological mechanism of length-dependent axonal degeneration. Progressive axonal degeneration can lead to both Charcot-Marie-Tooth type 2 (CMT2) and Hereditary Spastic Paraplegia (HSP) depending on the affected neurons: peripheral motor and sensory nerves or central nervous system axons of the corticospinal tract and dorsal columns, respectively. Inherited axonopathies display an extreme degree of genetic heterogeneity of Mendelian high-penetrance genes. High locus heterogeneity is potentially advantageous to deciphering disease etiology by providing avenues to explore biological pathways in an unbiased fashion. Here, we investigate ‘gene modules’ in inherited axonopathies through a network-based analysis of the Human Integrated Protein-Protein Interaction rEference (HIPPIE) database. We demonstrate that CMT2 and HSP disease proteins are significantly more connected than randomly expected. We define these connected disease proteins as ‘proto-modules’ and show the topological relationship of these proto-modules by evaluating their overlap through a shortest-path based measurement. In particular, we observe that the CMT2 and HSP proto-modules significantly overlapped, demonstrating a shared genetic etiology. Comparison of both modules with other diseases revealed an overlapping relationship between HSP and hereditary ataxia and between CMT2 + HSP and hereditary ataxia. We then use the DIseAse Module Detection (DIAMOnD) algorithm to expand the proto-modules into comprehensive disease modules. Analysis of disease modules thus obtained reveals an enrichment of ribosomal proteins and pathways likely central to inherited axonopathy pathogenesis, including protein processing in the endoplasmic reticulum, spliceosome, and mRNA processing. Furthermore, we determine pathways specific to each axonopathy by analyzing the difference of the axonopathy modules. CMT2-specific pathways include glycolysis and gluconeogenesis-related processes, while HSP-specific pathways include processes involved in viral infection response. Unbiased characterization of inherited axonopathy disease modules will provide novel candidate disease genes, improve interpretation of candidate genes identified through patient data, and guide therapy development.

An Artificial Neural Network Integrated Pipeline for Biomarker Discovery Using Alzheimer's Disease as a Case Study

The field of machine learning has allowed researchers to generate and analyse vast amounts of data using a wide variety of methodologies. Artificial Neural Networks (ANN) are some of the most commonly used statistical models and have been successful in biomarker discovery studies in multiple disease types. This review seeks to explore and evaluate an integrated ANN pipeline for biomarker discovery and validation in Alzheimer's disease, the most common form of dementia worldwide with no proven cause and no available cure. The proposed pipeline consists of analysing public data with a categorical and continuous stepwise algorithm and further examination through network inference to predict gene interactions. This methodology can reliably generate novel markers and further examine known ones and can be used to guide future research in Alzheimer's disease.

Using genetics to inform new therapeutics for diabetes

The genetic architecture of diabetes has been extensively studied. Numerous genetic markers for diabetes have been reported. However, the translation of such knowledge into clinical interventions has been inadequate. Areas covered: We performed a literature search on various frontiers in diabetes treatment that could be improved using genetic information: (1) understanding the mechanisms of existing antidiabetic drugs, (2) repurposing existing drugs for the treatment of diabetes, (3) complementing clinical trial findings; (4) finding novel treatment approaches; (5) better estimation of the efficacy of metabolic surgery. Expert commentary: The translation of genetic information to clinical intervention requires further study, including the development of an appropriate genetic risk score algorithm for type 2 diabetes. Genomic studies provide empirical explanations for clinical trial findings. Moreover, the mechanisms of antidiabetic drugs should be thoroughly investigated to enable clinical trials and pharmacogenomics studies of these drugs. As metabolic surgery becomes more prevalent for the treatment of diabetes, genetic approaches may improve patient prioritization.