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Hypoglycemia has been recognized as a risk factor for diabetic vascular complications and cognitive decline. In this work, gene networks of hypoglycemia and cardiovascular disease, diabetic retinopathy, diabetic nephropathy, diabetic neuropathy, cognitive decline, and Alzheimer’s disease were reconstructed. The gene network of hypoglycemia included 141 genes and 2467 interactions. Hypoglycemia-related genes were overrepresented in the reconstructed gene networks of diabetic complications and comorbidity. Some GO biological processes, including glucose homeostasis, nitric oxide biosynthesis, smooth muscle cell proliferation, ERK1 and ERK2 cascade, were overrepresented in all reconstructed networks.
- hypoglycemia
- diabetes
- cardiovascular disease
- diabetic retinopathy
- diabetic nephropathy
- cognitive dysfunction
- Alzheimer’s disease
- diabetic neuropathy
- gene networks
- ANDsystem
1. Introduction
Hypoglycemia is a life-threatening complication and a barrier to achieving good glycemic control in patients with diabetes [1]. The long-term consequences of hypoglycemia include cardiovascular events, as well as cognitive and psychological problems [2]. In both type 1 and type 2 diabetes, self-reported episodes of severe hypoglycemia are related to increased risk of death [3]. Large prospective clinical studies have documented the association between severe diabetes-related hypoglycemia and major adverse cardiovascular events: cardiovascular and all-cause mortality [4,5,6]. In diabetes, the link between severe hypoglycemia and cardiovascular events is time-dependent and bidirectional: this means increased cardiovascular risk after severe hypoglycemia, as well as greater risk of severe hypoglycemia after a cardiovascular event [7]. Recent studies indicate a promoting role of hypoglycemia in the progression of microvascular diabetic complications, including retinopathy [8,9] and chronic kidney disease [10]. Decreased kidney function, in turn, increases the risk of hypoglycemia [11].
In individuals with diabetes, severe hypoglycemia is an established risk factor for cognitive decline and dementia [12,13,14]. Recurrent symptomatic or asymptomatic hypoglycemia has been suggested to induce sub-clinical brain damage and permanent cognitive dysfunction [15]. Recent clinical observations suggest that hypoglycemia is a risk factor for both vascular dementia and dementia due to Alzheimer’s disease (AD) in elderly patients with type 2 diabetes [16]. These data are consistent with the results of experimental studies indicating that glucose deprivation triggers tau pathology and synaptic dysfunction in the brain, the hallmarks of AD [17,18]. It should be noted that type 2 diabetes can also increase the risk of AD [14].
It is well known that a glucose-deprived condition triggers a cascade of adaptive and pathophysiological events in the cardiovascular and nervous systems. Cardiovascular effects of hypoglycemia include increase in cardiac work load and potential attenuation of myocardial perfusion, potentially arrhythmogenic electrophysiological changes, induction of a prothrombotic state, and release of inflammatory mediators [19]. An episode of hypoglycemia induces an adaptive counter-regulatory response, which involves enhanced glucagon, epinephrine, cortisol and growth hormone secretion; the suppression of insulin release; and the modulation of the autonomic nervous system. Recurrent or chronic hypoglycemia induces multiple shifts in the brain’s metabolism, including glycogen mobilization; the utilization of alternate sources of energy, such as lactate and ketones; changes in glucose uptake; and changes in cellular respiration [20]. However, the molecular mechanisms of the effects of hypoglycemia in the target organs are not fully understood.
Artificial intelligence and bioinformatics open up new possibilities for systems analysis of molecular events in human diseases. Text-mining is a field in artificial intelligence that aims to extract information from collections of text documents based on machine learning and natural language processing techniques. Text-mining is considered a useful tool for integrative biomedical research involving genes, proteins and phenotypes [21].
The main goal of the ANDSystem is to allow the generation of new hypotheses related to understanding of the molecular mechanisms of complex biological processes by reconstruction and analysis of associative molecular (gene) networks where biological objects are presented as nodes, and interactions between them are presented as edges. For that purpose, a high-throughput technology of automatic knowledge extraction from texts of scientific publications is utilized. For the first step, the text-mining approach performs automated recognition of the names of biological entities in texts. For the second step, it reveals interactions between biological objects using more than 3000 specific semantic templates. The information extracted by the text-mining is stored in the huge ANDCell knowledge base which is updated annually. Information from the ANDCell knowledge base could be queried by users through the ANDVisio client module. The ANDVisio supports network visualization and analysis. For example, ANDVisio functions allow to calculate the connectivity and the centrality coefficients of nodes [22,23,24]. Previously, the ANDSystem was applied successfully to analyze the molecular basis of a number of human diseases and comorbidity [25,26,27,28].
One of the well-established ways to find relations between gene sets obtained in the research and the studied conditions (biological processes, diseases, phenotypes, etc.) is the gene set enrichment analysis. As a result of applying this method, it is possible to identify sets of genes for which the frequency of occurrence in the analyzed set, associated with the target condition, is significantly different from the background frequency (for example, the frequency in the entire genome). Such sets of genes are called overrepresented (if the frequency is higher than the background) or underrepresented (if the frequency is below the background). The hypergeometric distribution is commonly used as a statistical model to assess the significance of enrichment. The examples of web tools that perform the gene set enrichment analysis are DAVID [29] and TopAnat function of Bgee [30]. DAVID is aimed to perform comprehensive functional annotation for revealing the biological meaning of a large list of tested genes. It is in particular able to identify enriched Gene Ontology terms [29]. Bgee is a database containing information on gene expression patterns in different tissues and cells. Its TopAnat function allows to find enrichment of anatomical terms related to genes by expression patterns [30].
2. Gene Network of Hypoglycemia
The gene network related to hypoglycemia is shonw in Figure 1.
[1]Figure 1. Molecular network of hypoglycemia visualized with the ANDSystem.
The network of hypoglycemia consisted of molecules with different structures and functions. It included insulin and other hormones, cytokines and growth factors, enzymes, transporters, transcription factors, neuropeptides, structural and binding proteins, and microRNAs (Table 1).
Table 1. Molecules of the hypoglycemia network.
| Group of Molecules | Genes |
|---|---|
| Hormones | ADIPOQ, AVP, EPO, GCG, GH1, GIP, IAPP, INS, LEP, PRL, PTH, REN, SCT, and SST |
| Cytokines and growth factors | ANGPTL4, CCL2, CSF3, EDN1, FGF2, FGF21, IGF1, IGF2, IL1B, IL6, TNF, and VEGFA |
| Receptors | ADRB2, ADRB3, AGTR2, CD36, CD40, EGFR, GCGR, GHRHR, GLP1R, GPR142, IGF1R, IGF2R, INSR, LEPR, MC2R, NOTCH1, NR3C1, SORCS1, and SSTR2 |
| Enzymes | ACE, AKT1, AKT2, CHAT, CPT1A, CYP2C9, CYP3A4, DNMT1, DPP4, EIF2AK3, FBP1, G6PC1, GCK, GPD1, GPD2, GPX3, GSR, GYS2, H6PD, HK1, MAP4K2, MBOAT4, METAP2, NAMPT, NOS1, PARK7, PDK4, PGM1, SERPINA1, SIAH2, SIRT1, SIRT6, SOD2, TGM1, TIGAR, UQCRC2, VHL, and WWOX |
| Transporters | ABCC8, AQP4, AQP7, CACNA1C, KCNJ11, KCNH2, MPC2, RAMP1, SLC5A1, SLC5A2, SLC5A4, SLC16A1, SLC2A1, SLC2A2, SLC22A1, SLC30A8, SLC25A20, SLC30A10, and UCP2 |
| Transcription factors | ARNTL, DDIT3, FOS, FOXO1, HNF1A, HNF4A, NFE2L2, NR3C1, PPARA, PROP1, SOX17, and TCF7L2 |
| Neuropeptides | ADCYAP1, CHGA, GRP, HCRT, NPY, PPY, and UCN |
| Structural proteins | CLDN5 and MAP2 |
| Other proteins | ALB, CDKN1A, CISD1, CRP, IGFBP1, IGFBP2, IGFBP3, IGFBP6, NRP1, PRNP, PSMB9, PSMG1, SELE, SELP, SERPINE1, TRAF6, and VAMP8 |
| MicroRNAs | MIR155 and MIR410 |
Expectedly, genes encoding hormones that regulate glucose metabolism including insulin (INS), glucagon (GCG), glucagon-like peptide 1 (GCG), glucose-dependent insulinotropic polypeptide (GIP), islet amyloid polypeptide (IAPP), growth hormone (GH1), and some hormonal receptors (INSR, GLP1R, ADRB2, ADRB3) turned out to be the central hubs of this network. Among identified hormones, insulin plays a key role as an inducer of hypoglycemia, glucagon and growth hormone are involved in the response to hypoglycemia, and other hormones act as modulators of insulin secretion or sensitivity. Alternatively, hypoglycemia itself may affect the secretion of a number of these regulators [31,32]. Some of the identified transcription factors (HNF1A, HNF4A, and TCF7L2) are essential for glucose homeostasis. A group of neuropeptides included modulators of the neuroendocrine system, such as adenylate cyclase-activating polypeptide 1(ADCYAP1), neuromedin C (GRP), and chromogranin A (CHGA), and some regulators of appetite and food intake, namely, hypocretin neuropeptide precursor (HCRT), neuropeptide Y (NPY), pancreatic polypeptide Y (PPY), and urocortin (UCN) participated in the network.
Two microRNAs genes (MIR155 and MIR410) identified in the networks were both involved in glucose metabolism. Specifically, in mice, global overexpression of miR155 resulted in hypoglycemia, improved glucose tolerance and enhanced insulin sensitivity of peripheral tissues [33]. MiR-410 enhanced glucose-stimulated insulin secretion in vitro [34]. It is also involved in the brain response to oxygen-glucose deprivation [35].
3. Comparative Analysis of the Gene Networks of Hypoglycemia and Diabetic Vascular Disease
Figure 3 represents a Venn diagram showing the intersection of the gene lists of the networks of hypoglycemia, cardiovascular disease, diabetic retinopathy, diabetic nephropathy, and diabetic neuropathy. It turned out that 14 genes were mutual for all analyzed gene networks. The products of these genes encode insulin (INS) and insulin receptor (INSR), endothelin-1 (EDN1), erythropoietin (EPO), adiponectin (ADIPOQ), interleukin-1β (IL1B), interleukin-6 (IL6), tumor necrosis factor α (TNF), glucagon-like peptide-1 receptor (GLP1R), insulin-like growth factor-1 (IGF1), vascular endothelial growth factor A (VEGFA), C-reactive protein (CRP), nuclear factor, erythroid 2 like 2 (NFE2L2) and neuropeptide Y (NPY). A wide range of biological activities of these molecules is consistent with the concept of their key role in the pathophysiology of diabetic vascular complications [44,45,46,47,48,49,50,51,52,53,54,55,56].
Figure 3. Venn diagram of intersection of gene sets associated simultaneously with hypoglycemia and cardiovascular disease, diabetic neuropathy, diabetic retinopathy, diabetic nephropathy, and Alzheimer’s disease.
In addition, the genes of neuropilin 1 (NRP1), adenylate cyclase-activating polypeptide 1 (ADCYAP1) and fibroblast growth factor 2 (FGF2) were mutual for the networks of hypoglycemia and all microvascular complications. Neuropilin-1 is a membrane-bound receptor for vascular endothelial growth factor and semaphorin family members, it is important for angiogenesis, axon guidance, cell survival, migration, and invasion. The role of neuropilin-1 in diabetic complications is discussed [57,58]. Adenylate cyclase-activating polypeptide 1, the product of ADCYAP1 gene, is involved in neuroendocrine stress response; in pancreatic islets, it may produce a glucose-sensitive effect and decrease insulin levels required to control hyperglycemia [59]. Fibroblast growth factor 2, being involved in cell growth, angiogenesis, atherogenesis, wound healing and other processes, is implicated in the development of diabetic nephropathy [60], diabetic retinopathy [61], diabetic neuropathy [62], and coronary artery disease [63].
4. Conclusions
Hypoglycemia is a trigger for a number of complications and comorbidities in diabetes, including cardiovascular events, microvascular diabetic complications, cognitive dysfunction, and AD.
There were 141 genes/proteins in the hypoglycemia-associated network. Among them, INS, IL6, LEP, TNF, IL1B, EGFR, and FOS were the principal central hubs, meanwhile, GPR142, MBOAT4, SLC5A4, IGFBP6, PPY, G6PC1, SLC2A2, GYS2, GCGR and AQP7 were the most specific, according to the CTS criterion. The enrichment analysis of GO biological processes showed that regulation of insulin secretion, glucose homeostasis, apoptosis, nitric oxide biosynthesis and cell signaling are significantly enriched for hypoglycemia. The anatomical structures that are overrepresented among those associated with hypoglycemia genes are the central nervous system, muscles, aorta, connective tissue, and others.
A step-by-step comparison of the hypoglycemic gene network with that for cardiovascular diseases, diabetic retinopathy, diabetic nephropathy, diabetic neuropathy, cognitive decline and AD showed that hypoglycemia-related genes are overrepresented for all hypoglycemia-triggered conditions according to the hypergeometric distribution. It was suggested that 14 genes (ADIPOQ, CRP, EDN1, EPO, GLP1R, IGF1, IL1B, IL6, INS, INSR, NFE2L2, NPY, TNF, and VEGFA) can significantly contribute to the development of hypoglycemia comorbidities. It turned out that genes associated with hypoglycemia, macro- and microvascular diabetes complications and Alzheimer’s disease are involved in nitric oxide biosynthesis, glucose homeostasis, ERK1 and ERK2 cascade, smooth muscle cell proliferation, and some others. In AD, hypoglycemia also regulates the neuron death process.
The obtained results expand the understanding of the molecular mechanisms of the deteriorating effect of hypoglycemia on the targeted organs in diabetes. Influencing the expression of many genes and intensity of physiological processes, hypoglycemia can play an important role in the promotion of diabetes-associated vascular disease and cognitive dysfunction.
This entry is adapted from the peer-reviewed paper 10.3390/ijms222212419
References
- Olga V. Saik; Vadim V. Klimontov; Hypoglycemia, Vascular Disease and Cognitive Dysfunction in Diabetes: Insights from Text Mining-Based Reconstruction and Bioinformatics Analysis of the Gene Networks. International Journal of Molecular Sciences 2021, 22, 12419, 10.3390/ijms222212419.
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