Particle Swarm Optimization with Reinforcement Learning for the Prediction of CpG Islands in the Human Genome

BACKGROUND: Regions with abundant GC nucleotides, a high CpG number, and a length greater than 200 bp in a genome are often referred to as CpG islands. These islands are usually located in the 5' end of genes. Recently, several algorithms for the prediction of CpG islands have been proposed. METHODOLOGY/PRINCIPAL FINDINGS: We propose here a new method called CPSORL to predict CpG islands, which consists of a complement particle swarm optimization algorithm combined with reinforcement learning to predict CpG islands more reliably. Several CpG island prediction tools equipped with the sliding window technique have been developed previously. However, the quality of the results seems to rely too much on the choices that are made for the window sizes, and thus these methods leave room for improvement. CONCLUSIONS/SIGNIFICANCE: Experimental results indicate that CPSORL provides results of a higher sensitivity and a higher correlation coefficient in all selected experimental contigs than the other methods it was compared to (CpGIS, CpGcluster, CpGProd and CpGPlot). A higher number of CpG islands were identified in chromosomes 21 and 22 of the human genome than with the other methods from the literature. CPSORL also achieved the highest coverage rate (3.4%). CPSORL is an application for identifying promoter and TSS regions associated with CpG islands in entire human genomic. When compared to CpGcluster, the islands predicted by CPSORL covered a larger region in the TSS (12.2%) and promoter (26.1%) region. If Alu sequences are considered, the islands predicted by CPSORL (Alu) covered a larger TSS (40.5%) and promoter (67.8%) region than CpGIS. Furthermore, CPSORL was used to verify that the average methylation density was 5.33% for CpG islands in the entire human genome

Feature Selection via Chaotic Antlion Optimization

Selecting a subset of relevant properties from a large set of features that describe a dataset is a challenging machine learning task. In biology, for instance, the advances in the available technologies enable the generation of a very large number of biomarkers that describe the data. Choosing the more informative markers along with performing a high-accuracy classification over the data can be a daunting task, particularly if the data are high dimensional. An often adopted approach is to formulate the feature selection problem as a biobjective optimization problem, with the aim of maximizing the performance of the data analysis model (the quality of the data training fitting) while minimizing the number of features used.This work was partially supported by the IPROCOM Marie Curie initial training network, funded through the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013/ under REA grants agreement No. 316555, and by the Romanian National Authority for Scientific Research, CNDIUEFISCDI, project number PN-II-PT-PCCA-2011-3.2- 0917. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

A prospective descriptive study of cryptococcal meningitis in HIV uninfected patients in Vietnam - high prevalence of Cryptococcus neoformans var grubii in the absence of underlying disease

<p>Abstract</p> <p>Background</p> <p>Most cases of cryptococcal meningitis occur in patients with HIV infection: the course and outcome of disease in the apparently immunocompetent is much more poorly understood. We describe a cohort of HIV uninfected Vietnamese patients with cryptococcal meningitis in whom underlying disease is uncommon, and relate presenting features of patients and the characteristics of the infecting species to outcome.</p> <p>Methods</p> <p>A prospective descriptive study of HIV negative patients with cryptococcal meningitis based at the Hospital for Tropical Diseases, Ho Chi Minh City. All patients had comprehensive clinical assessment at baseline, were cared for by a dedicated study team, and were followed up for 2 years. Clinical presentation was compared by infecting isolate and outcome.</p> <p>Results</p> <p>57 patients were studied. <it>Cryptococcus neoformans var grubii </it>molecular type VN1 caused 70% of infections; <it>C. gattii </it>accounted for the rest. Most patients did not have underlying disease (81%), and the rate of underlying disease did not differ by infecting species. 11 patients died while in-patients (19.3%). Independent predictors of death were age ≥ 60 years and a history of convulsions (odds ratios and 95% confidence intervals 8.7 (1 - 76), and 16.1 (1.6 - 161) respectively). Residual visual impairment was common, affecting 25 of 46 survivors (54.3%). Infecting species did not influence clinical phenotype or outcome. The minimum inhibitory concentrations of flucytosine and amphotericin B were significantly higher for <it>C. neoformans var grubii </it>compared with <it>C. gattii </it>(p < 0.001 and p = 0.01 respectively).</p> <p>Conclusion</p> <p>In HIV uninfected individuals in Vietnam, cryptococcal meningitis occurs predominantly in people with no clear predisposing factor and is most commonly due to <it>C. neoformans var grubii</it>. The rates of mortality and visual loss are high and independent of infecting species. There are detectable differences in susceptibility to commonly used antifungal drugs between species, but the clinical significance of this is not clear.</p

Association between RUNX3 promoter methylation and gastric cancer: a meta-analysis

<p>Abstract</p> <p>Background</p> <p>Runt-related transcription factor 3 (RUNX3) is a member of the runt-domain family of transcription factors and has been reported to be a candidate tumor suppressor in gastric cancer. However, the association between RUNX3 promoter methylation and gastric cancer remains unclear.</p> <p>Methods</p> <p>We systematically reviewed studies of RUNX3 promoter methylation and gastric cancer published in English or Chinese from January 2000 to January 2011, and quantified the association between RUNX3 promoter methylation and gastric cancer using meta-analysis methods.</p> <p>Results</p> <p>A total of 1740 samples in 974 participants from seventeen studies were included in the meta-analysis. A significant association was observed between RUNX3 promoter methylation and gastric cancer, with an aggregated odds ratio (OR) of 5.63 (95%CI 3.15, 10.07). There was obvious heterogeneity among studies. Subgroup analyses (including by tissue origin, country and age), meta-regression were performed to determine the source of the heterogeneity. Meta-regression showed that the trend in ORs was inversely correlated with age. No publication bias was detected. The ORs for RUNX3 methylation in well-differentiated <it>vs </it>undifferentiated gastric cancers, and in intestinal-type <it>vs </it>diffuse-type carcinomas were 0.59 (95%CI: 0.30, 1.16) and 2.62 (95%CI: 1.33, 5.14), respectively. There were no significant differences in RUNX3 methylation in cancer tissues in relation to age, gender, TNM stage, invasion of tumors into blood vessel or lymphatic ducts, or tumor stage.</p> <p>Conclusions</p> <p>This meta-analysis identified a strong association between methylation of the RUNX3 promoter and gastric cancer, confirming the role of RUNX3 as a tumor suppressor gene.</p

Cell cycle and aging, morphogenesis, and response to stimuli genes are individualized biomarkers of glioblastoma progression and survival

<p>Abstract</p> <p>Background</p> <p>Glioblastoma is a complex multifactorial disorder that has swift and devastating consequences. Few genes have been consistently identified as prognostic biomarkers of glioblastoma survival. The goal of this study was to identify general and clinical-dependent biomarker genes and biological processes of three complementary events: lifetime, overall and progression-free glioblastoma survival.</p> <p>Methods</p> <p>A novel analytical strategy was developed to identify general associations between the biomarkers and glioblastoma, and associations that depend on cohort groups, such as race, gender, and therapy. Gene network inference, cross-validation and functional analyses further supported the identified biomarkers.</p> <p>Results</p> <p>A total of 61, 47 and 60 gene expression profiles were significantly associated with lifetime, overall, and progression-free survival, respectively. The vast majority of these genes have been previously reported to be associated with glioblastoma (35, 24, and 35 genes, respectively) or with other cancers (10, 19, and 15 genes, respectively) and the rest (16, 4, and 10 genes, respectively) are novel associations. <it>Pik3r1</it>, <it>E2f3, Akr1c3</it>, <it>Csf1</it>, <it>Jag2</it>, <it>Plcg1</it>, <it>Rpl37a</it>, <it>Sod2</it>, <it>Topors</it>, <it>Hras</it>, <it>Mdm2, Camk2g</it>, <it>Fstl1</it>, <it>Il13ra1</it>, <it>Mtap </it>and <it>Tp53 </it>were associated with multiple survival events.</p> <p>Most genes (from 90 to 96%) were associated with survival in a general or cohort-independent manner and thus the same trend is observed across all clinical levels studied. The most extreme associations between profiles and survival were observed for <it>Syne1</it>, <it>Pdcd4</it>, <it>Ighg1</it>, <it>Tgfa</it>, <it>Pla2g7</it>, and <it>Paics</it>. Several genes were found to have a cohort-dependent association with survival and these associations are the basis for individualized prognostic and gene-based therapies. <it>C2</it>, <it>Egfr</it>, <it>Prkcb</it>, <it>Igf2bp3</it>, and <it>Gdf10 </it>had gender-dependent associations; <it>Sox10</it>, <it>Rps20</it>, <it>Rab31</it>, and <it>Vav3 </it>had race-dependent associations; <it>Chi3l1</it>, <it>Prkcb</it>, <it>Polr2d</it>, and <it>Apool </it>had therapy-dependent associations. Biological processes associated glioblastoma survival included morphogenesis, cell cycle, aging, response to stimuli, and programmed cell death.</p> <p>Conclusions</p> <p>Known biomarkers of glioblastoma survival were confirmed, and new general and clinical-dependent gene profiles were uncovered. The comparison of biomarkers across glioblastoma phases and functional analyses offered insights into the role of genes. These findings support the development of more accurate and personalized prognostic tools and gene-based therapies that improve the survival and quality of life of individuals afflicted by glioblastoma multiforme.</p

Prediction and Testing of Biological Networks Underlying Intestinal Cancer

Colorectal cancer progresses through an accumulation of somatic mutations, some of which reside in so-called “driver” genes that provide a growth advantage to the tumor. To identify points of intersection between driver gene pathways, we implemented a network analysis framework using protein interactions to predict likely connections – both precedented and novel – between key driver genes in cancer. We applied the framework to find significant connections between two genes, Apc and Cdkn1a (p21), known to be synergistic in tumorigenesis in mouse models. We then assessed the functional coherence of the resulting Apc-Cdkn1a network by engineering in vivo single node perturbations of the network: mouse models mutated individually at Apc (Apc1638N+/−) or Cdkn1a (Cdkn1a−/−), followed by measurements of protein and gene expression changes in intestinal epithelial tissue. We hypothesized that if the predicted network is biologically coherent (functional), then the predicted nodes should associate more specifically with dysregulated genes and proteins than stochastically selected genes and proteins. The predicted Apc-Cdkn1a network was significantly perturbed at the mRNA-level by both single gene knockouts, and the predictions were also strongly supported based on physical proximity and mRNA coexpression of proteomic targets. These results support the functional coherence of the proposed Apc-Cdkn1a network and also demonstrate how network-based predictions can be statistically tested using high-throughput biological data

Dramatic Co-Activation of WWOX/WOX1 with CREB and NF-κB in Delayed Loss of Small Dorsal Root Ganglion Neurons upon Sciatic Nerve Transection in Rats

BACKGROUND:Tumor suppressor WOX1 (also named WWOX or FOR) is known to participate in neuronal apoptosis in vivo. Here, we investigated the functional role of WOX1 and transcription factors in the delayed loss of axotomized neurons in dorsal root ganglia (DRG) in rats. METHODOLOGY/PRINCIPAL FINDINGS:Sciatic nerve transection in rats rapidly induced JNK1 activation and upregulation of mRNA and protein expression of WOX1 in the injured DRG neurons in 30 min. Accumulation of p-WOX1, p-JNK1, p-CREB, p-c-Jun, NF-kappaB and ATF3 in the nuclei of injured neurons took place within hours or the first week of injury. At the second month, dramatic nuclear accumulation of WOX1 with CREB (>65% neurons) and NF-kappaB (40-65%) occurred essentially in small DRG neurons, followed by apoptosis at later months. WOX1 physically interacted with CREB most strongly in the nuclei as determined by FRET analysis. Immunoelectron microscopy revealed the complex formation of p-WOX1 with p-CREB and p-c-Jun in vivo. WOX1 blocked the prosurvival CREB-, CRE-, and AP-1-mediated promoter activation in vitro. In contrast, WOX1 enhanced promoter activation governed by c-Jun, Elk-1 and NF-kappaB. WOX1 directly activated NF-kappaB-regulated promoter via its WW domains. Smad4 and p53 were not involved in the delayed loss of small DRG neurons. CONCLUSIONS/SIGNIFICANCE:Rapid activation of JNK1 and WOX1 during the acute phase of injury is critical in determining neuronal survival or death, as both proteins functionally antagonize. In the chronic phase, concurrent activation of WOX1, CREB, and NF-kappaB occurs in small neurons just prior to apoptosis. Likely in vivo interactions are: 1) WOX1 inhibits the neuroprotective CREB, which leads to eventual neuronal death, and 2) WOX1 enhances NF-kappaB promoter activation (which turns to be proapoptotic). Evidently, WOX1 is the potential target for drug intervention in mitigating symptoms associated with neuronal injury