Expression Profile of Genes Potentially Associated with Adequate Glycemic Control in Patients with Type 2 Diabetes Mellitus

Multi-omics Analysis of Umbilical Cord Hematopoietic Stem Cells from a Multi-ethnic Cohort of Hawaii Reveals the Transgenerational Effect of Maternal Pre-Pregnancy Obesity

Background

Maternal obesity is a health concern that may predispose newborns to a high risk of medical problems later in life. To understand the transgenerational effect of maternal obesity, we conducted a multi-omics study, using DNA methylation and gene expression in the CD34+/CD38-/Lin-umbilical cord blood hematopoietic stem cells (uHSCs) and metabolomics of the cord blood, all from a multi-ethnic cohort (n=72) from Kapiolani Medical Center for Women and Children in Honolulu, Hawaii (collected between 2016 and 2018).

Results

Differential methylation (DM) analysis unveiled a global hypermethylation pattern in the maternal pre-pregnancy obese group (BH adjusted p<0.05), after adjusting for major clinical confounders. Comprehensive functional analysis showed hypermethylation in promoters of genes involved in cell cycle, protein synthesis, immune signaling, and lipid metabolism. Utilizing Shannon entropy on uHSCs methylation, we discerned notably higher quiescence of uHSCs impacted by maternal obesity. Additionally, the integration of multi-omics data-including methylation, gene expression, and metabolomics-provided further evidence of dysfunctions in adipogenesis, erythropoietin production, cell differentiation, and DNA repair, aligning with the findings at the epigenetic level. Furthermore, the CpG sites associated with maternal obesity from these pathways also predicted highly accurately (average AUC = 0.8687) between cancer vs. normal tissues in 14 cancer types in The Cancer Genome Atlas (TCGA).

Conclusions

This study revealed the significant correlation between pre-pregnancy maternal obesity and multi-omics level molecular changes in the uHSCs of offspring, particularly in DNA methylation. Moreover, these maternal obesity epigenetic markers in uHSCs may predispose offspring to higher cancer risks.

PPIC-labeled CAFs: Key players in neoadjuvant chemotherapy resistance for gastric cancer

BACKGROUND

Gastric cancer (GC) is the fourth leading cause of cancer deaths, with advanced cases having a median survival of less than one year. Neoadjuvant chemotherapy (NCT) is vital but faces drug resistance issues, partly due to cancer-associated fibroblasts (CAFs). Yet, specific CAF subpopulations contributing to resistance are poorly understood.

METHODS

Differentially expressed genes (DEGs) between chemosensitive and resistant GC patients were identified using GEO2R. Single-cell sequencing (scRNA-seq) identified CAF-related genes. Immunohistochemistry verified key genes in NCT-treated GC samples, analyzing their correlation with tumor regression grade (TRG) and clinicopathological characteristics.

RESULTS

PPIC as a gene highly expressed in CAFs was closely associated with NCT resistance in gastric cancer. Immunohistochemistry results revealed positivity for the expression of cyclophilin C (CypC), encoded by PPIC, in the 5-fluorouracil and cisplatin NCT resistant and -sensitive groups of gastric cancer patients at rates of 69.7 % (76/109) and 43.6 % (24/55), respectively (p < 0.001). The high expression of CypC in CAFs was positively correlated to tumor size (p = 0.025), T stage (p = 0.004), TNM stage (p = 0.004), and vascular invasion (p = 0.027). In cancer cells the expression of CypC was associated with OS (p = 0.026). However, in CAFs, CypC expression was not related to OS (p = 0.671).

CONCLUSIONS

PPIC-labeled CAF subgroups are related to NCT resistance and poor prognosis in GC and they may cause drug resistance through signaling pathways such as glucose metabolism and extracellular matrix remodeling. However, the exact mechanism behind the involvement of PPIC-labeled CAF in drug resistance of GC requires further study.

Comprehensive analysis of epigenomics and transcriptome data to identify potential target genes associated with obesity

DNA methylation is closely related to the occurrence and development of many diseases, but its role in obesity is still unclear. This study aimed to find the potential differentially methylated genes associated with obesity occurrence and development. By combining methylation and transcriptome analysis, we identified the key genes in adipose tissue affecting the occurrence and development of obesity and revealed the possible molecular mechanisms involved in obesity pathogenesis. We first screened 14 methylation-related differential genes and verified their expression in adipose tissue by quantitative polymerase chain reaction (qPCR). Seven genes with the same expression pattern were identified as key genes, namely, CCRL2, GPT, LGALS12, PC, SLC27A2, SLC4A4, and TTC36. Then, the immune microenvironment of adipose tissue was quantified by CIBERSORT, and we found that the content of M0 macrophages and T follicular helper cells in adipose tissue was significantly increased and decreased, respectively, in the obese group. Furthermore, the relationship between key genes and the immune microenvironment was analyzed. Additionally, the metabolic pathway activity of each sample was calculated based on the ssGSEA algorithm, and the key gene–metabolic network was constructed. Moreover, we performed a CMAP analysis based on the differential genes in adipose tissue to screen out drugs potentially effective in obesity treatment. In conclusion, we identified seven methylation-related key genes closely related to obesity pathogenesis and explored the potential mechanism of their role in obesity. This study provided novel insights into the molecular mechanisms and management of obesity.

Overview of Transcriptomic Research on Type 2 Diabetes: Challenges and Perspectives

Type 2 diabetes (T2D) is a common chronic disease whose etiology is known to have a strong genetic component. Standard genetic approaches, although allowing for the detection of a number of gene variants associated with the disease as well as differentially expressed genes, cannot fully explain the hereditary factor in T2D. The explosive growth in the genomic sequencing technologies over the last decades provided an exceptional impetus for transcriptomic studies and new approaches to gene expression measurement, such as RNA-sequencing (RNA-seq) and single-cell technologies. The transcriptomic analysis has the potential to find new biomarkers to identify risk groups for developing T2D and its microvascular and macrovascular complications, which will significantly affect the strategies for early diagnosis, treatment, and preventing the development of complications. In this article, we focused on transcriptomic studies conducted using expression arrays, RNA-seq, and single-cell sequencing to highlight recent findings related to T2D and challenges associated with transcriptome experiments.

Altered human alveolar bone gene expression in type 2 diabetes-A cross-sectional study

OBJECTIVE

The objective of this cross-sectional study is to investigate alveolar bone gene expression in health and diabetes through ribonucleic acid (RNA) sequencing and bioinformatics analysis.

BACKGROUND

It is relatively unknown how type 2 diabetes modulates gene expression in alveolar bone in humans. Clinical concern regarding increased implant failure rate in patients with diabetes has been discussed in the literature. Previous studies in animal models and humans have suggested an imbalance between the genes regulating bone formation with data suggesting bone resorption in diabetes. However, there is lack of data regarding a comprehensive gene expression from human alveolar bone in diabetes.

METHODS

Alveolar bone was collected from healthy and type 2 diabetic subjects undergoing periodontal and implant surgeries. The homogenized RNA sample was then extracted and analyzed for quantity and quality. RNA samples were further purified using ribosomal RNA depletion technique and processed for RNA sequencing and analysis. Expression levels for mRNAs were performed by calculating FPKM ([total_exon_fragments/mapped reads (millions) × exon length (kB)]), and differentially expressed mRNAs were selected with log₂ (fold change) >1 or log₂ (fold change) ≤1 and with a parametric F test comparing nested linear models.

RESULTS

Eighteen bone samples (10 healthy, 8 patients with diabetes) were analyzed for gene expression. The mean age and HbA1c% of healthy versus diabetic subjects were as follows: age (55.3 ± 17.5 vs 63.9 ± 8.7 years) and HbA1c% (5.6 ± 0.29 vs 7.3 ± 2.4), respectively. Sequencing analysis showed that expression of genes that regulate bone turnover like TGFB1, LTBP4, IGF1, BMP2, BMP4, BMP6, SMAD1, RUNX2, MCSF, and THRA was significantly downregulated in diabetes samples compared with healthy controls with overall reduced expression of genes in the bone regulation pathway in patients with diabetes. Bioinformatics analysis for the altered genes highlighted several pathways related to bone homeostasis and inflammation in diabetes. Periodontitis did not affect the gene expression pattern based on diabetes status.

CONCLUSIONS

Altered expression of genes due to downregulation of certain pathways that are involved in bone turnover and inflammation suggests that overall wound healing and bone homeostasis may be compromised in type 2 diabetes.

Rocha C, V. Maurer-Morelli C, Perez Orrico SR, Cirelli JA, Von Zuben FJ, Mantuaneli Scarel-Caminaga R. Using association rule mining to jointly detect clinical features and differentially expressed genes related to chronic inflammatory diseases

OBJECTIVE

It is increasingly common to find patients affected by a combination of type 2 diabetes mellitus (T2DM), dyslipidemia (DLP) and periodontitis (PD), which are chronic inflammatory diseases. More studies able to capture unknown relationships among these diseases will contribute to raise biological and clinical evidence. The aim of this study was to apply association rule mining (ARM) to discover whether there are consistent patterns of clinical features (CFs) and differentially expressed genes (DEGs) relevant to these diseases. We intend to reinforce the evidence of the T2DM-DLP-PD-interplay and demonstrate the ARM ability to provide new insights into multivariate pattern discovery.

METHODS

We utilized 29 clinical glycemic, lipid and periodontal parameters from 143 patients divided into five groups based upon diabetic, dyslipidemic and periodontal conditions (including a healthy-control group). At least 5 patients from each group were selected to assess the transcriptome by microarray. ARM was utilized to assess relevant association rules considering: (i) only CFs; and (ii) CFs+DEGs, such that the identified DEGs, specific to each group of patients, were submitted to gene expression validation by quantitative polymerase chain reaction (qPCR).

RESULTS

We obtained 78 CF-rules and 161 CF+DEG-rules. Based on their clinical significance, Periodontists and Geneticist experts selected 11 CF-rules, and 5 CF+DEG-rules. From the five DEGs prospected by the rules, four of them were validated by qPCR as significantly different from the control group; and two of them validated the previous microarray findings.

CONCLUSIONS

ARM was a powerful data analysis technique to identify multivariate patterns involving clinical and molecular profiles of patients affected by specific pathological panels. ARM proved to be an effective mining approach to analyze gene expression with the advantage of including patient's CFs. A combination of CFs and DEGs might be employed in modeling the patient's chance to develop complex diseases, such as those studied here.

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.

MicroRNA-184 alleviates insulin resistance in cardiac myocytes and high fat diet-induced cardiac dysfunction in mice through the LPP3/DAG pathway

AIM

Cardiovascular complication is a major cause of mortality and morbidity in patients with diabetes. Insulin sensitivity loss is a major contributor to the pathogenesis of cardiovascular diseases in diabetes. Based on our previous research, diacylglycerol (DAG) levels play an important role in high saturated fatty acid-induced insulin resistance. Phosphatidic acid phosphatase (LPP3), a key enzyme for synthesizing DAG, is indispensable for normal cardiac functions and vascular health. However, adipose knockdown of LPP3 increases insulin sensitivity, suggesting that LPP3 regulation may be complicated in hearts. The aim of this study was to investigate LPP3 roles in diabetic cardiac insulin sensitivity and to identify potential upstream targets implicated in diabetic cardiomyopathy.

METHODS AND RESULTS

Mice were fed a high fat diet (HF) or a low fat diet (control) for up to 24 weeks. After 24 weeks, we found that high fat diet-induced cardiac dysfunction is linked to elevated LPP3 compared to the control group (P < 0.05). In addition, knockdown of LPP3 rescued the glucose uptake that was impaired by palmitate treatment alone in cardiomyoblasts (P < 0.05). Furthermore, we identified miR-184 as an upstream regulator targeting LPP3 and further confirmed the link between DAG and insulin sensitivity. MiR-184 mimic transfection rescued the glucose uptake and glucose consumption that had been impaired by palmitate treatment alone (P < 0.05).

CONCLUSION

In hearts of high fat diet-fed mice, increased LPP3 contributes to insulin resistance via increased DAG levels. A small non-coding RNA, miR-184, at least partially regulates this signal pathway to alleviate insulin resistance.

Short- and long-term impact of hyperoxia on the blood and retinal cells' transcriptome in a mouse model of oxygen-induced retinopathy

BACKGROUND

We aimed to identify global blood and retinal gene expression patterns in murine oxygen-induced retinopathy (OIR), a common model of retinopathy of prematurity, which may allow better understanding of the pathogenesis of this severe ocular prematurity complication and identification of potential blood biomarkers.

METHODS

A total of 120 C57BL/6J mice were randomly divided into an OIR group, in which 7-day-old pups were maintained in 75% oxygen for 5 days, or a control group. RNA was extracted from the whole-blood mononuclear cells and retinal cells on days 12, 17, and 28. Gene expression in the RNA samples was evaluated with mouse gene expression microarrays.

RESULTS

There were 38, 1370 and 111 genes, the expression of which differed between the OIR and control retinas on days 12, 17, and 28, respectively. Gene expression in the blood mononuclear cells was significantly altered only on day 17. Deptor and Nol4 genes showed reduced expression both in the blood and retinal cells on day 17.

CONCLUSION

There are sustained marked changes in the global pattern of gene expression in the OIR mice retinas. An altered expression of Deptor and Nol4 genes in the blood mononuclear cells requires further investigation as they may indicate retinal neovascularization.

Integrated analysis of hepatic mRNA and miRNA profiles identified molecular networks and potential biomarkers of NAFLD

To enhance our understanding of molecular mechanisms and mine novel biomarkers of non-alcoholic fatty liver disease (NAFLD), RNA sequencing was performed to gain hepatic expression profiles of mRNAs and miRNAs in NAFLD and normal rats. Using DESeq with thresholds of a two-fold change and a false discovery rate (FDR) less than 0.05, 336 mRNAs and 21 miRNAs were identified as differentially expressed. Among those, 17 miRNAs (e.g., miR-144-3p, miR-99a-3p, miR-200b-3p, miR-200b-5p, miR-200c-3p, etc.) might serve as novel biomarkers of NAFLD. MiRNA target genes (13565) were predicted by the miRWalk database. Using DAVID 6.8, the intersection (195 genes) of differentially expressed mRNAs and miRNA-predicted target genes were enriched in 47 gene ontology (GO) terms and 28 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Using Cytoscape, pathway interaction and protein-protein interaction (PPI) networks were constructed, and hub genes (e.g., Abcg8, Cyp1a1, Cyp51, Hmgcr, etc.) associated with NAFLD were obtained. Moreover, 673 miRNA-mRNA negative regulatory pairs were obtained, and networks were constructed. Finally, several representative miRNAs and mRNAs were validated by real-time qPCR. In conclusion, potential molecular mechanisms of NAFLD could be inferred from integrated analysis of mRNA and miRNA profiles, which may indicate novel biomarkers of NAFLD.