Diane M. Rassi

Identifying common and specific microRNAs expressed in peripheral blood mononuclear cell of type 1, type 2, and gestational diabetes mellitus patients

BackgroundRegardless the regulatory function of microRNAs (miRNA), their differential expression pattern has been used to define miRNA signatures and to disclose disease biomarkers. To address the question of whether patients presenting the different types of diabetes mellitus could be distinguished on the basis of their miRNA and mRNA expression profiling, we obtained peripheral blood mononuclear cell (PBMC) RNAs from 7 type 1 (T1D), 7 type 2 (T2D), and 6 gestational diabetes (GDM) patients, which were hybridized to Agilent miRNA and mRNA microarrays. Data quantification and quality control were obtained using the Feature Extraction software, and data distribution was normalized using quantile function implemented in the Aroma light package. Differentially expressed miRNAs/mRNAs were identified using Rank products, comparing T1DxGDM, T2DxGDM and T1DxT2D. Hierarchical clustering was performed using the average linkage criterion with Pearson uncentered distance as metrics.ResultsThe use of the same microarrays platform permitted the identification of sets of shared or specific miRNAs/mRNA interaction for each type of diabetes. Nine miRNAs (hsa-miR-126, hsa-miR-1307, hsa-miR-142-3p, hsa-miR-142-5p, hsa-miR-144, hsa-miR-199a-5p, hsa-miR-27a, hsa-miR-29b, and hsa-miR-342-3p) were shared among T1D, T2D and GDM, and additional specific miRNAs were identified for T1D (20 miRNAs), T2D (14) and GDM (19) patients. ROC curves allowed the identification of specific and relevant (greater AUC values) miRNAs for each type of diabetes, including: i) hsa-miR-1274a, hsa-miR-1274b and hsa-let-7f for T1D; ii) hsa-miR-222, hsa-miR-30e and hsa-miR-140-3p for T2D, and iii) hsa-miR-181a and hsa-miR-1268 for GDM. Many of these miRNAs targeted mRNAs associated with diabetes pathogenesis.ConclusionsThese results indicate that PBMC can be used as reporter cells to characterize the miRNA expression profiling disclosed by the different diabetes mellitus manifestations. Shared miRNAs may characterize diabetes as a metabolic and inflammatory disorder, whereas specific miRNAs may represent biological markers for each type of diabetes, deserving further attention.

MicroRNA expression profiling and functional annotation analysis of their targets in patients with type 1 diabetes mellitus.

Type 1 diabetes mellitus (T1DM) results from an autoimmune attack against the insulin-producing pancreatic β-cells, leading to elimination of insulin production. The exact cause of this disorder is still unclear. Although the differential expression of microRNAs (miRNAs), small non-coding RNAs that control gene expression in a post-transcriptional manner, has been identified in many diseases, including T1DM, only scarce information exists concerning miRNA expression profile in T1DM. Thus, we employed the microarray technology to examine the miRNA expression profiles displayed by peripheral blood mononuclear cells (PBMCs) from T1DM patients compared with healthy subjects. Total RNA extracted from PBMCs from 11 T1DM patients and nine healthy subjects was hybridized onto Agilent human miRNA microarray slides (V3), 8x15K, and expression data were analyzed on R statistical environment. After applying the rank products statistical test, the receiver-operating characteristic (ROC) curves were generated and the areas under the ROC curves (AUC) were calculated. To examine the functions of the differentially expressed (p-value<0.01, percentage of false-positives <0.05) miRNAs that passed the AUC cutoff value ≥ 0.90, the database miRWalk was used to predict their potential targets, which were afterwards submitted to the functional annotation tool provided by the Database for Annotation, Visualization, and Integrated Discovery (DAVID), version 6.7, using annotations from the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. We found 57 probes, corresponding to 44 different miRNAs (35 up-regulated and 9 down-regulated), that were differentially expressed in T1DM and passed the AUC threshold of 0.90. The hierarchical clustering analysis indicated the discriminatory power of those miRNAs, since they were able to clearly distinguish T1DM patients from healthy individuals. Target prediction indicated that 47 candidate genes for T1DM are potentially regulated by the differentially expressed miRNAs. After performing functional annotation analysis of the predicted targets, we observed 22 and 12 annotated KEGG pathways for the induced and repressed miRNAs, respectively. Interestingly, many pathways were enriched for the targets of both up- and down-regulated miRNAs and the majority of those pathways have been previously associated with T1DM, including many cancer-related pathways. In conclusion, our study indicated miRNAs that may be potential biomarkers of T1DM as well as provided new insights into the molecular mechanisms involved in this disorder.

HLA-G Expression in the Skin of Patients with Systemic Sclerosis

Objective. To determine HLA-G expression in skin biopsies from patients with systemic sclerosis (SSc), and its association with epidemiological, clinical, and laboratory variables and survival. Methods. Paraffin-embedded skin biopsies obtained from 21 SSc patients (14 limited SSc, 7 diffuse SSc) and from 28 healthy controls were studied. HLA-G expression was evaluated by immunohistochemistry. Results. HLA-G molecules were detected in 57% of skin biopsies from patients with SSc (9 from limited SSc, 3 from diffuse SSc), whereas no control sample expressed HLA-G (p = 0.000004). In patients, HLA-G molecules were consistently observed within epidermal and some dermal cells. HLA-G expression was associated with a lower frequency of vascular cutaneous ulcers (p = 0.0004), telangiectasias (p = 0.008), and inflammatory polyarthralgia (p = 0.02). After a 15-year followup, SSc patients who exhibited HLA-G survived longer than patients who did not. Conclusion. HLA-G is expressed in skin biopsies from patients with SSc, and this is associated with a better disease prognosis. This suggests a modulatory role of HLA-G in SSc, as observed in other skin disorders.

Integrative analysis of the transcriptome profiles observed in type 1, type 2 and gestational diabetes mellitus reveals the role of inflammation

BackgroundType 1 diabetes (T1D) is an autoimmune disease, while type 2 (T2D) and gestational diabetes (GDM) are considered metabolic disturbances. In a previous study evaluating the transcript profiling of peripheral mononuclear blood cells obtained from T1D, T2D and GDM patients we showed that the gene profile of T1D patients was closer to GDM than to T2D. To understand the influence of demographical, clinical, laboratory, pathogenetic and treatment features on the diabetes transcript profiling, we performed an analysis integrating these features with the gene expression profiles of the annotated genes included in databases containing information regarding GWAS and immune cell expression signatures.MethodsSamples from 56 (19 T1D, 20 T2D, and 17 GDM) patients were hybridized to whole genome one-color Agilent 4x44k microarrays. Non-informative genes were filtered by partitioning, and differentially expressed genes were obtained by rank product analysis. Functional analyses were carried out using the DAVID database, and module maps were constructed using the Genomica tool.ResultsThe functional analyses were able to discriminate between T1D and GDM patients based on genes involved in inflammation. Module maps of differentially expressed genes revealed that modulated genes: i) exhibited transcription profiles typical of macrophage and dendritic cells; ii) had been previously associated with diabetic complications by association and by meta-analysis studies, and iii) were influenced by disease duration, obesity, number of gestations, glucose serum levels and the use of medications, such as metformin.ConclusionThis is the first module map study to show the influence of epidemiological, clinical, laboratory, immunopathogenic and treatment features on the transcription profiles of T1D, T2D and GDM patients.

HLA polymorphisms as incidence factor in the progression to end-stage renal disease in Brazilian patients awaiting kidney transplant.

Chronic renal failure (CRF) leads in the majority of instances to end-stage renal disease (ESRD) requiring renal replacement therapy. Age, gender, genetics, race, hypertension, and smoking among others are factors associated with ESRD. Our interest was to evaluate the possible associations of class I and II HLA antigens with ESRD renal disease independent of other factors, among patients with CRF, having various diagnoses in the Brazilian population of the São Paulo state. So 21 HLA-A, 31 HLA-B, and 13 HLA-DR were detected in 105 patients who were compared with 160 healthy controls of both sexes who were not related to the patients evaluated until 2005. We calculated allelic frequencies, haplotypes frequencies, etiological fractions (EF), preventive fractions, and relative risks (RR). We compared demographic data of patients and controls. The antigens positively associated with ESRD were: HLA-A78 (RR = 30.31 and EF = 0.96) and HLA-DR11 (RR = 18.87 and EF = 0.65). The antigens HLAB14 (RR = 29.90 and EF = 0.75) was present at a significantly lower frequency among patients compared with controls. In contrast, no haplotype frequency showed statically significant associations. Further molecular studies may clarify types and subtypes of alleles involved with ESRD progression.

Shared and unique gene expression in systemic lupus erythematosus depending on disease activity.

Patients presenting with active systemic lupus erythematosus (SLE) manifestations may exhibit distinct pathogenetic features in relation to inactive SLE. Also, cDNA microarrays may potentially discriminate the gene expression profile of a disease or disease variant. Therefore, we evaluated the expression profile of 4500 genes in peripheral blood lymphocytes (PBL) of SLE patients. We studied 11 patients with SLE (seven with active SLE and four with inactive SLE) and eight healthy controls. Total RNA was isolated from PBL, reverse transcribed into cDNA, and postlabeled with Cy3 fluorochrome. These probes were then hybridized to a glass slide cDNA microarray containing 4500 human IMAGE cDNA target sequences. An equimolar amount of total RNA from human cell lines served as reference. The microarray images were quantified, normalized, and analyzed using the R environment (ANOVA, significant analysis of microarrays, and cluster‐tree view algorithms). Disease activity was assessed by the SLE disease activity index. Compared to the healthy controls, 104 genes in active SLE patients (80 repressed and 24 induced) and 52 genes in nonactive SLE patients (31 induced and 21 repressed) were differentially expressed. The modulation of 12 genes, either induced or repressed, was found in both disease variants; however, each disease variant had differential expression of different genes. Taken together, these results indicate that the two lupus variants studied have common and unique differentially expressed genes. Although the biological significance of the differentially expressed genes discussed above has not been completely understood, they may serve as a platform to further explore the molecular basis of immune deregulation in SLE.

Metabolism genes are among the differentially expressed ones observed in lymphomononuclear cells of recently diagnosed type 1 diabetes mellitus patients.

Abstract:  The large‐scale differential gene expression in lymphomononuclear cells of six patients with recently diagnosed type ), and six normal individuals matched to patients for sex and age were studied. Glass slides containing 4608 cDNAs from the IMAGE library were spotted using robotic technology. Statistical analysis was carried out by the SAM program, and gene function assessed by the FATIGO program. Thirty differentially expressed genes (21 induced and 9 repressed) were disclosed when DM‐1 patients were compared with controls. Although presenting with distinct biological function, most of the induced or repressed genes were related with protein, phosphate, DNA, RNA, carboxylic acid, and fatty acid metabolism. Although some of these genes have been previously associated with the pathogenesis of T1DM, many other genes were identified for further studies.

Is HLA Class II Profile Relevant for the Study of Large-Scale Differentially Expressed Genes in Type 1 Diabetes Mellitus Patients?

Abstract:  We have previously identified 30 differentially expressed genes when comparing recently diagnosed type 1 diabetes mellitus (DM‐1) patients and controls paired for sex, age, and ethnic background. In this article we performed the hierarchical clustering of these genes taking into account the human‐leukocyte‐antigen (HLA)‐DRB1/DQB1 profile. The dendrogram obtained using the Cluster program grouped patients and controls into three clusters, one including individuals with no susceptibility alleles, another including individuals with at least three susceptibility alleles, and a third intermingling susceptibility/protective alleles. In addition to other variables, the results of the present article suggest that the major histocompatibility complex (MHC) class II profile may be of relevance for the study of a large‐scale differentially expressed genes.

TNFa-e Microsatellite‚ HLA-DRB1 and-DQB1 Alleles and Haplotypes in Brazilian Patients Presenting Recently Diagnosed Type 1 Diabetes Mellitus

Abstract:  TNF microsatellite and HLA class II polymorphisms were studied in 28 recently diagnosed Brazilian patients presenting type 1 diabetes mellitus (T1DM) and in 120 healthy controls. TNFa‐e and HLA‐DRB1/DQB1 alleles were identified using sets of sequence‐specific primers. Compared to controls, the DRB1*03 and DQB1*02 allele groups, TNFa1 allele, and the TNFa4‐b5‐c1‐d4‐e3 and TNFa10‐b5‐c1‐d4‐e3 haplotypes were overrepresented in patients. TNF microsatellite together with HLA polymorphisms is associated with type 1 diabetes in Brazilian patients, corroborating the participation of the MHC genes in disease susceptibility.