Global gene expression in neuroendocrine tumors from patients with the MEN1 syndrome

BACKGROUND: Multiple Endocrine Neoplasia type 1 (MEN1, OMIM 131100) is an autosomal dominant disorder characterized by endocrine tumors of the parathyroids, pancreatic islets and pituitary. The disease is caused by the functional loss of the tumor suppressor protein menin, coded by the MEN1 gene. The protein sequence has no significant homology to known consensus motifs. In vitro studies have shown menin binding to JunD, Pem, Smad3, NF-kappaB, nm23H1, and RPA2 proteins. However, none of these binding studies have led to a convincing theory of how loss-of-menin leads to neoplasia. RESULTS: Global gene expression studies on eight neuroendocrine tumors from MEN1 patients and 4 normal islet controls was performed utilizing Affymetrix U95Av2 chips. Overall hierarchical clustering placed all tumors in one group separate from the group of normal islets. Within the group of tumors, those of the same type were mostly clustered together. The clustering analysis also revealed 19 apoptosis-related genes that were under-expressed in the group of tumors. There were 193 genes that were increased/decreased by at least 2-fold in the tumors relative to the normal islets and that had a t-test significance value of p < = 0.005. Forty-five of these genes were increased and 148 were decreased in the tumors relative to the controls. One hundred and four of the genes could be classified as being involved in cell growth, cell death, or signal transduction. The results from 11 genes were selected for validation by quantitative RT-PCR. The average correlation coefficient was 0.655 (range 0.235–0.964). CONCLUSION: This is the first analysis of global gene expression in MEN1-associated neuroendocrine tumors. Many genes were identified which were differentially expressed in neuroendocrine tumors arising in patients with the MEN1 syndrome, as compared with normal human islet cells. The expression of a group of apoptosis-related genes was significantly suppressed, suggesting that these genes may play crucial roles in tumorigenesis in this syndrome. We identified a number of genes which are attractive candidates for further investigation into the mechanisms by which menin loss causes tumors in pancreatic islets. Of particular interest are: FGF9 which may stimulate the growth of prostate cancer, brain cancer and endometrium; and IER3 (IEX-1), PHLDA2 (TSS3), IAPP (amylin), and SST, all of which may play roles in apoptosis

Immunological and hematological outcomes following protracted low dose/low dose rate ionizing radiation and simulated microgravity

Using a ground-based model to simulate spaceflight [21-days of single-housed, hindlimb unloading (HLU) combined with continuous low-dose gamma irradiation (LDR, total dose of 0.04 Gy)], an in-depth survey of the immune and hematological systems of mice at 7-days post-exposure was performed. Collected blood was profiled with a hematology analyzer and spleens were analyzed by whole transcriptome shotgun sequencing (RNA-sequencing). The results revealed negligible differences in immune differentials. However, hematological system analyses of whole blood indicated large disparities in red blood cell differentials and morphology, suggestive of anemia. Murine Reactome networks indicated majority of spleen cells displayed differentially expressed genes (DEG) involved in signal transduction, metabolism, cell cycle, chromatin organization, and DNA repair. Although immune differentials were not changed, DEG analysis of the spleen revealed expression profiles associated with inflammation and dysregulated immune function persist to 1-week post-simulated spaceflight. Additionally, specific regulation pathways associated with human blood disease gene orthologs, such as blood pressure regulation, transforming growth factor-β receptor signaling, and B cell differentiation were noted. Collectively, this study revealed differential immune and hematological outcomes 1-week post-simulated spaceflight conditions, suggesting recovery from spaceflight is an unremitting process

An epidemic of poliomyelitis in southern Kerala

An epidemic of poliomyelitis was recognized in May 1987 when there was a sharp increase in the number of children with acute paralytic poliomyelitis admitted to the SAT Hospital in Trivandrum in Kerala State. From May through September, 392 cases were admitted; the total admitted cases in 1987 were 458 in contrast to 119 in 1986: Evidence for type 1 poliovirus infection was found in 33 (85%) of the 39 children in whom virological investigations were done during the epidemic. In addition, evidence for poliovirus type 3 infection was found in four children. Data on the immunization status was available on 231 affected children in the epidemic; 175 (76%) had not received oral polio vaccine (OPV); 55 (24%) had received one or two doses and only one child had received three doses. Thus, lack of immunization was a major risk factor for disease. The estimated vaccine coverage with three doses of OPV in Kerala, based on the quantity of vaccine distributed during the years 1985, 1986 and 1987 were 94%, 100% and 91%, respectively. This outbreak occurred in spite of high vaccine coverage, and it illustrates the need for even higher coverage rates; the usefulness of hospitals as sentinel surveillance centres; the need for decentralized vaccine coverage data in order to prevent build-up of unimmunized susceptible children in any region; and the urgent need of a mechanism to respond to an epidemic quickly, with immunization, in order to curtail it

Potential of Ayurgenomics Approach in Complex Trait Research: Leads from a Pilot Study on Rheumatoid Arthritis

<div><h3>Background</h3><p>Inconsistent results across association studies including Genome-wide association, have posed a major challenge in complex disease genetics. Of the several factors which contribute to this, phenotypic heterogeneity is a serious limitation encountered in modern medicine. On the other hand, Ayurveda, a holistic Indian traditional system of medicine, enables subgrouping of individuals into three major categories namely <em>Vata</em>, <em>Pitta</em> and <em>Kapha,</em> based on their physical and mental constitution, referred to as <em>Prakriti</em>. We hypothesised that conditioning association studies on prior risk, predictable in Ayurveda, will uncover much more variance and potentially open up more predictive health.</p> <h3>Objectives and Methods</h3><p>Identification of genetic susceptibility markers by combining the <em>prakriti</em> based subgrouping of individuals with genetic analysis tools was attempted in a Rheumatoid arthritis (RA) cohort. Association of 21 markers from commonly implicated inflammatory and oxidative stress pathways was tested using a case-control approach in a total cohort comprising 325 cases and 356 controls and in the three subgroups separately. We also tested few postulates of Ayurveda on the disease characteristics in different <em>prakriti</em> groups using clinico-genetic data.</p> <h3>Results</h3><p>Inflammatory genes like <em>IL1β</em> (C-C-C haplotype, p = 0.0005, OR = 3.09) and CD40 (rs4810485 allelic, p = 0.04, OR = 2.27) seem to be the determinants in <em>Vata</em> subgroup whereas oxidative stress pathway genes are observed in <em>Pitta (SOD3</em> rs699473<b>,</b> p = 0.004, OR = 1.83; rs2536512 p<b> = </b>0.005; OR = 1.88 and <em>PON1</em> rs662, p = 0.04, OR = 1.53) and <em>Kapha</em> (<em>SOD3</em> rs2536512, genotypic, p = 0.02, OR = 2.39) subgroups. Fixed effect analysis of the associated markers from <em>CD40, SOD3</em> and <em>TNFα</em> with genotype X <em>prakriti</em> interaction terms suggests heterogeneity of effects within the subgroups. Further, disease characteristics such as severity was most pronounced in <em>Vata</em> group.</p> <h3>Conclusions</h3><p>This exploratory study suggests discrete causal pathways for RA etiology in <em>prakriti</em> based subgroups, thereby, validating concepts of <em>prakriti</em> and personalized medicine in Ayurveda. Ayurgenomics approach holds promise for biomarker discovery in complex diseases.</p> </div

<i>Prakriti</i>-wise distribution and demography of the study samples.

<p><i>Prakriti</i>-wise distribution and demography of the study samples.</p

Regression analysis of covariates showing significance (p<0.2) in different <i>prakriti</i> subgroups.

<p>Significant associations are indicated in bold.</p