Riluzole attenuates glutamatergic tone and cognitive decline in AβPP/PS1 mice.

We have previously demonstrated hippocampal hyperglutamatergic signaling occurs prior to plaque accumulation in AβPP/PS1 mice. Here, we evaluate 2-Amino-6-(trifluoromethoxy) benzothiazole (riluzole) as an early intervention strategy for Alzheimer\u27s disease (AD), aimed at restoring glutamate neurotransmission prior to substantial Beta amyloid (Aβ) plaque accumulation and cognitive decline. Male AβPP/PS1 mice, a model of progressive cerebral amyloidosis, were treated with riluzole from 2-6 months of age. Morris water maze, in vivo electrochemistry, and immunofluorescence were performed to assess cognition, glutamatergic neurotransmission, and pathology, respectively, at 12 months. Four months of prodromal riluzole treatment in AβPP/PS1 mice resulted in long-lasting procognitive effects and attenuated glutamatergic tone that was observed six months after discontinuing riluzole treatment. Riluzole-treated AβPP/PS1 mice had significant improvement in long-term memory compared to vehicle-treated AβPP/PS1 mice that was similar to normal aging C57BL/6J control mice. Furthermore, basal glutamate concentration and evoked-glutamate release levels, which were elevated in vehicle-treated AβPP/PS1 mice, were restored to levels observed in age-matched C57BL/6J mice in AβPP/PS1 mice receiving prodromal riluzole treatment. Aβ plaque accumulation was not altered with riluzole treatment. This study supports that interventions targeting the glutamatergic system during the early stages of AD progression have long-term effects on disease outcome, and importantly may prevent cognitive decline. Our observations provide preclinical support for targeting glutamate neurotransmission in patients at risk for developing AD

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

Identification and characterization of gene and microRNA networks associated with cancer survival and drug abuse

The study of the dysregulation of the transcriptome in diseases like cancer and drug abuse can offer insights into preventive and therapeutical remedies, as well as targets for future basic and applied research. The identification of reliable transcriptome biomarkers requires the simultaneous consideration of regulatory and target elements including microRNAs (miRNAs), transcription factors (TFs), and target genes. Previously, there has been limited validation of reported associations between these diseases and miRNAs, TFs, and target mRNA in independent studies. This may be due to several reasons. Few studies simultaneously analyze multiple miRNAs, TFs, and target mRNA. Also, most studies do not consider clinical or cohort-dependent factors when characterizing the associations between the transcriptome and disease. Lastly, most transcriptome studies tend to be small, and the individual analysis has limited statistical power to detect accurate and precise associations between transcripts and diseases. This thesis aims to address the previous limitations and identify replicable biomarkers of cancer and drug abuse. Functional and network analyses were performed to identify and study targets of microRNA biomarkers associated with glioblastoma multiforme survival within and across race, gender, recurrence, and therapy cohorts. A Cox survival model was applied to profiles from 253 individuals, 534 microRNAs, and the results were confirmed using cross-validation, discriminant analyses, and cross-study comparisons. All 45 microRNAs revealed were confirmed in independent cancer studies, and 25 of those were further confirmed in glioblastoma studies. Thirty-nine and six microRNAs were associated with one and multiple glioblastoma survival indicators, respectively. Nineteen and 26 microRNAs exhibited cohort-dependent and independent associations with glioblastoma, respectively. An approach integrating survival analysis, feature selection, and regulatory network visualization was used to identify reliable biomarkers of ovarian cancer survival and recurrence. Expression profiles of 799 miRNAs, 17,814 TFs and target genes and cohort clinical records on 272 patients diagnosed with ovarian cancer were simultaneously considered and results were validated on an independent group of 146 patients. This study confirmed 19 miRNAs previously associated with ovarian cancer and identified two miRNAs that have previously been associated with other cancer types. In total, the expression of 838 and 734 target genes and 12 and eight TFs were associated (FDR-adjusted P-value <0.05) with ovarian cancer survival and recurrence, respectively. The simultaneous analysis of co-expression profiles along with consideration of clinical characteristics of patients allowed reliable microRNA-transcription factor-target gene networks associated with ovarian cancer survival to be inferred. Illicit drug exposure brings about changes in the brain transcriptome that result in the dysregulation of pathways. To detect the progression of drug exposure pathways, meta-analysis of five individual microarray experiments measuring gene expression in the brain of mice under acute and chronic drug exposure was performed. Functional analysis and network visualization offered insights into the network changes across drug exposure levels. Meta-analyses uncovered 263 and 2,641 genes differentially expression (FDR-adjusted P-value <0.1) between control and acute and chronic exposure, respectively. Individual genes in these processes have been previously associated with drug exposure and reward-dependent behaviors. The MAPK signaling pathway and the molecular functions of protein dimerization and leucine zipper transcription factor were enriched in response to acute exposure. This study was able to detect the progression of drug exposure pathways using meta, functional, and network analyses

Transcription Factor-MicroRNA-Target Gene Networks Associated with Ovarian Cancer Survival and Recurrence

<div><p>The identification of reliable transcriptome biomarkers requires the simultaneous consideration of regulatory and target elements including microRNAs (miRNAs), transcription factors (TFs), and target genes. A novel approach that integrates multivariate survival analysis, feature selection, and regulatory network visualization was used to identify reliable biomarkers of ovarian cancer survival and recurrence. Expression profiles of 799 miRNAs, 17,814 TFs and target genes and cohort clinical records on 272 patients diagnosed with ovarian cancer were simultaneously considered and results were validated on an independent group of 146 patients. Three miRNAs (hsa-miR-16, hsa-miR-22*, and ebv-miR-BHRF1-2*) were associated with both ovarian cancer survival and recurrence and 27 miRNAs were associated with either one hazard. Two miRNAs (hsa-miR-521 and hsa-miR-497) were cohort-dependent, while 28 were cohort-independent. This study confirmed 19 miRNAs previously associated with ovarian cancer and identified two miRNAs that have previously been associated with other cancer types. In total, the expression of 838 and 734 target genes and 12 and eight TFs were associated (FDR-adjusted P-value <0.05) with ovarian cancer survival and recurrence, respectively. Functional analysis highlighted the association between cellular and nucleotide metabolic processes and ovarian cancer. The more direct connections and higher centrality of the miRNAs, TFs and target genes in the survival network studied suggest that network-based approaches to prognosticate or predict ovarian cancer survival may be more effective than those for ovarian cancer recurrence. This study demonstrated the feasibility to infer reliable miRNA-TF-target gene networks associated with survival and recurrence of ovarian cancer based on the simultaneous analysis of co-expression profiles and consideration of the clinical characteristics of the patients.</p> </div

Transcription factors associated with ovarian cancer recurrence.

1<p>C.I.: Confidence Interval;</p>O<p>Associated with Ovarian Cancer;</p>Z<p>Associated with other cancer type.</p

Network of microRNA, transcription factors, and target genes associated with ovarian cancer recurrence.

<p>(Node Shape: microRNA = diamond, target gene = circle, transcription factor = square; Node Color: Red indicates increased hazard with high expression, Green indicates decreased hazard with high expression; Node Size: larger indicates a more extreme association (P-value <0.006), smaller indicates a less extreme association.).</p

Probability of ovarian cancer non-recurrence for patients receiving the treatment chemotherapy only, chemotherapy along with another treatment, or some other treatment or combination of treatments except chemotherapy that have high or low levels of hsa-miR-497.

<p>Probability of ovarian cancer non-recurrence for patients receiving the treatment chemotherapy only, chemotherapy along with another treatment, or some other treatment or combination of treatments except chemotherapy that have high or low levels of hsa-miR-497.</p

Transcription factors associated with ovarian cancer survival.

1<p>C.I.: Confidence Interval;</p>2<p>NA: No information found;</p>O<p>Associated with Ovarian Cancer;</p>Z<p>Associated with other cancer type.</p

Differentially enriched Gene Ontology biological processes among all target genes segmented by low and high hazard of ovarian cancer death or recurrence identified by set enrichment analyses.

1<p>− hazard genes: number of genes that have a negative association between the hazard of ovarian cancer death (higher survival) or recurrence and expression.</p>2<p>+ hazard genes: number of genes that have a positive association between the hazard of ovarian cancer death (lower survival) or recurrence and expression.</p>3<p>Log_e(Odds Ratio): values >1 indicate that the category was more enriched among the genes that have a negative association with hazard than among the genes that have a positive association with hazard; values <1 indicate that the category was more enriched among the genes that have a positive association with hazard than among the genes that have a negative association with hazard; Extreme values indicate higher difference in the enrichment percentages between the negative and positive association groups. Values close to zero indicate similar enrichment percentages between positive and negative association groups.</p>4<p>FDR-adjusted P-value: False discovery rate adjusted P-value of the log odds ratio test. Enrichment at FDR-adjusted Pvalue <0.05) and ≥75 genes in the category.</p

Probability of ovarian cancer survival for patients that have lower grade (I and II) tumors (black lines) or higher (Rest) grade tumors (gray lines) and high (dashed lines) or low (solid line) levels of hsa-miR-521.

<p>Probability of ovarian cancer survival for patients that have lower grade (I and II) tumors (black lines) or higher (Rest) grade tumors (gray lines) and high (dashed lines) or low (solid line) levels of hsa-miR-521.</p