No-match ORESTES explored as tumor markers

Sequencing technologies and new bioinformatics tools have led to the complete sequencing of various genomes. However, information regarding the human transcriptome and its annotation is yet to be completed. The Human Cancer Genome Project, using ORESTES (open reading frame EST sequences) methodology, contributed to this objective by generating data from about 1.2 million expressed sequence tags. Approximately 30% of these sequences did not align to ESTs in the public databases and were considered no-match ORESTES. On the basis that a set of these ESTs could represent new transcripts, we constructed a cDNA microarray. This platform was used to hybridize against 12 different normal or tumor tissues. We identified 3421 transcribed regions not associated with annotated transcripts, representing 83.3% of the platform. The total number of differentially expressed sequences was 1007. Also, 28% of analyzed sequences could represent noncoding RNAs. Our data reinforces the knowledge of the human genome being pervasively transcribed, and point out molecular marker candidates for different cancers. To reinforce our data, we confirmed, by real-time PCR, the differential expression of three out of eight potentially tumor markers in prostate tissues. Lists of 1007 differentially expressed sequences, and the 291 potentially noncoding tumor markers were provided

No-match ORESTES explored as tumor markers

Sequencing technologies and new bioinformatics tools have led to the complete sequencing of various genomes. However, information regarding the human transcriptome and its annotation is yet to be completed. The Human Cancer Genome Project, using ORESTES (open reading frame EST sequences) methodology, contributed to this objective by generating data from about 1.2 million expressed sequence tags. Approximately 30% of these sequences did not align to ESTs in the public databases and were considered no-match ORESTES. On the basis that a set of these ESTs could represent new transcripts, we constructed a cDNA microarray. This platform was used to hybridize against 12 different normal or tumor tissues. We identified 3421 transcribed regions not associated with annotated transcripts, representing 83.3% of the platform. The total number of differentially expressed sequences was 1007. Also, 28% of analyzed sequences could represent noncoding RNAs. Our data reinforces the knowledge of the human genome being pervasively transcribed, and point out molecular marker candidates for different cancers. To reinforce our data, we confirmed, by real-time PCR, the differential expression of three out of eight potentially tumor markers in prostate tissues. Lists of 1007 differentially expressed sequences, and the 291 potentially noncoding tumor markers were provided

Gestational age acceleration is associated with epigenetic biomarkers of prenatal physiologic stress exposure

BACKGROUND: Physiological maternal stress response, such as imbalance in the glucocorticoid pathway and immune system seems to be mediated by DNA methylation (DNAm) and might translate intrauterine stress exposures into phenotypic changes in a sex-specific manner. DNAm in specific sites can also predict newborn gestational age and gestational age acceleration (GAA). GAA occurs when the predicted biological age is higher than the chronological age. In adults, poor health outcomes related to this deviance are well documented and raise questions for the interpretation and prediction in early stages of life. Boys seem to be more vulnerable to intrauterine stress exposure than girls; however, the mechanisms of adaptive sex-specific responses are still unclear. We hypothesize that intrauterine stress exposure is associated with GAA and could be different in boys and girls if inflammatory or glucocorticoid pathways exposure is considered. RESULTS: Using the Western Region Birth Cohort (ROC-Sao Paulo, Brazil) (n = 83), we calculated DNAm age and GAA from cord blood samples. Two epigenetic risk scores were calculated as an indirect proxy for low-grade inflammation (i-ePGS) and for glucocorticoid exposure (GES). Multivariate linear regression models were applied to investigate associations of GAA with prenatal exposures. The i-ePGS and GES were included in different models with the same co-variates considering sex interactions. The first multivariate model investigating inflammatory exposure (adj. R(2) = 0.31, p = < 0.001) showed that GAA was positively associated with i-ePGS (CI, 0.26-113.87, p = 0.049) and negative pregnancy-related feelings (CI, 0.04-0.48 p = 0.019). No sex interaction was observed. The second model investigating glucocorticoid exposure (adj. R(2) = 0.32, p = < 0.001) showed that the higher was the GAA was associated with a lower the lower was the GES in girls (CI, 0.04-2.55, p = 0.044). In both models, maternal self-reported mental disorder was negatively associated with GAA. CONCLUSION: Prenatal epigenetic score of exposure to low-grade inflammatory was a predictor of GAA for both sexes. Glucocorticoid epigenetic score seems to be more important to GAA in girls. This study supports the evidence of sex-specificity in stress response, suggesting the glucocorticoid as a possible pathway adopted by girls to accelerate the maturation in an adverse condition

A pilot study of a Community Health Agent-led type 2 diabetes self-management program using Motivational Interviewing-based approaches in a public primary care center in São Paulo, Brazil

Abstract Background Rates of noncommunicable diseases (NCDs) such as type 2 diabetes are escalating in low and middle-income countries such as Brazil. Scalable primary care-based interventions are needed to improve self-management and clinical outcomes of adults with diabetes. This pilot study examines the feasibility, acceptability, and outcomes of training community health agents (CHAs) in Motivational Interviewing (MI)-based counseling for patients with poorly controlled diabetes in a primary care center in São Paulo, Brazil. Methods Nineteen salaried CHAs participated in 32 h of training in MI and behavioral action planning. With support from booster training sessions, they used these skills in their regular monthly home visits over a 6 month period with 57 diabetes patients with baseline HbA1cs > 7.0%. The primary outcome was patients’ reports of the quality of diabetes care as measured by the Portuguese version of the Patient Assessment of Chronic Illness Care (PACIC) scale. Secondary outcomes included changes in patients’ reported diabetes self-management behaviors and in A1c, blood pressure, cholesterol and triglycerides. We also examined CHAs’ fidelity to and experiences with the intervention. Results Patients reported improvements over the 6 month period in quality of diabetes care received (PACIC score improved 33 (+/−19) to 68 (+/−21) (p < .001)). They reported increases in physical activity (p = .001), consumption of fruits and vegetables (p < .001) and medication adherence (p = .002), but no decreases in consumption of high-fat foods (p = .402) or sweets (p = .436). Participants had mean 6-month A1c levels 0.34% points lower than at baseline (p = .08) and improved mean LDL (−16.1 mg/dL, p = .005) and triglyceride levels (−38.725 mg/dL, p = .002). Of the 16 CHAs observed in fidelity assessments, 13 were categorized as medium- or high-performing on MI skills, while 3 were low-performing. CHAs expressed enthusiasm about learning new skills, and many described a shift from advice-giving to encouraging patients to define their own goals. Conclusion In resource-scarce settings, it is essential to fully utilize existing primary care resources to stem the epidemic of diabetes and other NCDs. Our pilot results support the potential of training CHAs to incorporate effective diabetes self-management support into their routine patient encounters. Trial registration NCT02994095 12/14/2016 Registered retrospectively.http://deepblue.lib.umich.edu/bitstream/2027.42/135718/1/12913_2016_Article_1968.pd

Simcluster: clustering enumeration gene expression data on the simplex space

Transcript enumeration methods such as SAGE, MPSS, and sequencing-by-synthesis EST &#x22;digital northern&#x22;, are important high-throughput techniques for digital gene expression measurement. As other counting or voting processes, these measurements constitute compositional data exhibiting properties particular to the simplex space where the summation of the components is constrained. These properties are not present on regular Euclidean spaces, on which hybridization-based microarray data is often modeled. Therefore, pattern recognition methods commonly used for microarray data analysis may be non-informative for the data generated by transcript enumeration techniques since they ignore certain fundamental properties of this space.&#xd;&#xa;&#xd;&#xa;Here we present a software tool, Simcluster, designed to perform clustering analysis for data on the simplex space. We present Simcluster as a stand-alone command-line C package and as a user-friendly on-line tool. Both versions are available at: http://xerad.systemsbiology.net/simcluster.&#xd;&#xa;&#xd;&#xa;Simcluster is designed in accordance with a well-established mathematical framework for compositional data analysis, which provides principled procedures for dealing with the simplex space, and is thus applicable in a number of contexts, including enumeration-based gene expression data