Experiences of racial discrimination and adverse gene expression among black individuals in a level 1 trauma center sample.

Up to 40 % of individuals who sustain traumatic injuries are at risk for posttraumatic stress disorder (PTSD) and the conditional risk for developing PTSD is even higher for Black individuals. Exposure to racial discrimination, including at both interpersonal and structural levels, helps explain this health inequity. Yet, the relationship between racial discrimination and biological processes in the context of traumatic injury has yet to be fully explored. The current study examined whether racial discrimination is associated with a cumulative measure of biological stress, the gene expression profile conserved transcriptional response to adversity (CTRA), in Black trauma survivors. Two-weeks (T1) and six-months (T2) post-injury, Black participants (N = 94) provided a blood specimen and completed assessments of lifetime racial discrimination and PTSD symptoms. Mixed effect linear models evaluated the relationship between change in CTRA gene expression and racial discrimination while adjusting for age, gender, body mass index (BMI), smoking history, heavy alcohol use history, and trauma-related variables (mechanism of injury, lifetime trauma). Results revealed that for individuals exposed to higher levels of lifetime racial discrimination, CTRA significantly increased between T1 and T2. Conversely, CTRA did not increase significantly over time in individuals exposed to lower levels of lifetime racial discrimination. Thus, racial discrimination appeared to lead to a more sensitized biological profile which was further amplified by the effects of a recent traumatic injury. These findings replicate and extend previous research elucidating the processes by which racial discrimination targets biological systems.

Sirt6 regulates lifespan in Drosophila melanogaster.

Sirt6 is a multifunctional enzyme that regulates diverse cellular processes such as metabolism, DNA repair, and aging. Overexpressing Sirt6 extends lifespan in mice, but the underlying cellular mechanisms are unclear. Drosophila melanogaster are an excellent model to study genetic regulation of lifespan; however, despite extensive study in mammals, very little is known about Sirt6 function in flies. Here, we characterized the Drosophila ortholog of Sirt6, dSirt6, and examined its role in regulating longevity; dSirt6 is a nuclear and chromatin-associated protein with NAD+-dependent histone deacetylase activity. dSirt6 overexpression (OE) in flies produces robust lifespan extension in both sexes, while reducing dSirt6 levels shortens lifespan. dSirt6 OE flies have normal food consumption and fertility but increased resistance to oxidative stress and reduced protein synthesis rates. Transcriptomic analyses reveal that dSirt6 OE reduces expression of genes involved in ribosome biogenesis, including many dMyc target genes. dSirt6 OE partially rescues many effects of dMyc OE, including increased nuclear size, up-regulation of ribosome biogenesis genes, and lifespan shortening. Last, dMyc haploinsufficiency does not convey additional lifespan extension to dSirt6 OE flies, suggesting dSirt6 OE is upstream of dMyc in regulating lifespan. Our results provide insight into the mechanisms by which Sirt6 OE leads to longer lifespan.

Differences in the Platelet mRNA Landscape Portend Racial Disparities in Platelet Function and Suggest Novel Therapeutic Targets.

The African American (AA) population displays a 1.6 to 3-fold higher incidence of thrombosis and stroke mortality compared with European Americans (EAs). Current antiplatelet therapies target the ADP-mediated signaling pathway, which displays significant pharmacogenetic variation for platelet reactivity. The focus of this study was to define underlying population differences in platelet function in an effort to identify novel molecular targets for future antiplatelet therapy. We performed deep coverage RNA-Seq to compare gene expression levels in platelets derived from a cohort of healthy volunteers defined by ancestry determination. We identified > 13,000 expressed platelet genes of which 480 were significantly differentially expressed genes (DEGs) between AAs and EAs. DEGs encoding proteins known or predicted to modulate platelet aggregation, morphology, or platelet count were upregulated in AA platelets. Numerous G-protein coupled receptors, ion channels, and pro-inflammatory cytokines not previously associated with platelet function were likewise differentially expressed. Many of the signaling proteins represent potential pharmacologic targets of intervention. Notably, we confirmed the differential expression of cytokines IL32 and PROK2 in an independent cohort by quantitative real-time polymerase chain reaction, and provide functional validation of the opposing actions of these two cytokines on collagen-induced AA platelet aggregation. Using Genotype-Tissue Expression whole blood data, we identified 516 expression quantitative trait locuses with Fst values > 0.25, suggesting that population-differentiated alleles may contribute to differences in gene expression. This study identifies gene expression differences at the population level that may affect platelet function and serve as potential biomarkers to identify cardiovascular disease risk. Additionally, our analysis uncovers candidate novel druggable targets for future antiplatelet therapies.

Construction of Decision Trees Based on Gene Expression Omnibus Data to Classify Bladder Cancer and Its Subtypes.

BACKGROUND Bladder cancer is a malignant tumor of the genitourinary system. Different subtypes of bladder cancer have different treatment methods and prognoses. Therefore, identifying hub genes affecting other genes is of great significance for the treatment of bladder cancer. MATERIAL AND METHODS We obtained expression profiles from the GSE13507 and GSE77952 datasets from the Gene Expression Omnibus database. First, principal component analysis was used to identify the difference in gene expression in different types of tissues. Differential expression analysis was used to find the differentially expressed genes between normal and tumor tissues, and between tumors with and without muscle infiltration. Further, based on differentially expressed genes, we constructed 2 decision trees for differentiating between tumor and normal tissues, and between muscle-infiltrating and non-muscle-infiltrating tumor tissues. A receiver operating characteristic curve was used to evaluate the prediction effect of the decision trees. RESULTS FAM107A and C8orf4 showed significantly lower expression in bladder cancer tissues than in normal tissues. Regarding muscle infiltration, CTHRC1 showed lower expression and HMGCS2 showed higher expression in non-muscle-infiltrating samples than in those with muscle infiltration. We constructed 2 decision trees for differentiating between tumor and normal tissue, and between tissues with and without muscle infiltration. Both decision trees showed good prediction results. CONCLUSIONS These newly discovered hub genes will be helpful in understanding the occurrence and development of different subtypes of bladder cancer, and will provide new therapeutic targets and biomarkers for bladder cancer.

A Targeted Bioinformatics Assessment of Adrenocortical Carcinoma Reveals Prognostic Implications of GABA System Gene Expression.

Adrenocortical carcinoma (ACC) is a rare but deadly cancer for which few treatments exist. Here, we have undertaken a targeted bioinformatics study of The Cancer Genome Atlas (TCGA) ACC dataset focusing on the 30 genes encoding the γ-aminobutyric acid (GABA) system-an under-studied, evolutionarily-conserved system that is an emerging potential player in cancer progression. Our analysis identified a subset of ACC patients whose tumors expressed a distinct GABA system transcriptome. Transcript levels of ABAT (encoding a key GABA shunt enzyme), were upregulated in over 40% of tumors, and this correlated with several favorable clinical outcomes including patient survival; while enrichment and ontology analysis implicated two cancer-related biological pathways involved in metastasis and immune response. The phenotype associated with ABAT upregulation revealed a potential metabolic heterogeneity among ACC tumors associated with enhanced mitochondrial metabolism. Furthermore, many GABAA receptor subunit-encoding transcripts were expressed, including two (GABRB2 and GABRD) prognostic for patient survival. Transcripts encoding GABAB receptor subunits and GABA transporters were also ubiquitously expressed. The GABA system transcriptome of ACC tumors is largely mirrored in the ACC NCI-H295R cell line, suggesting that this cell line may be appropriate for future functional studies investigating the role of the GABA system in ACC cell growth phenotypes and metabolism.

Assessment of prognostic implication of a panel of oncogenes in bladder cancer and identification of a 3-gene signature associated with recurrence and progression risk in non-muscle-invasive bladder cancer.

This study evaluated the prognostic value of a panel of 29 oncogenes derived from the analysis of The Cancer Genome Atlas (TCGA data) or from the recent literature on bladder tumors on a well-characterized series of muscle-invasive bladder cancer (MIBC) and non-MIBC (NMIBC) samples and tried to identify molecular prognostic markers. Mutations of HRAS, FGFR3, PIK3CA and TERT were found in 2.9%, 27.2%, 14.9% and 76.7% of tumor samples, respectively. Concerning NMIBC, on multivariate analysis, RXRA and FGFR3 levels were associated with recurrence-free survival (RFS) (p = 0.0022 and p = 0.0069) and RXRA level was associated with progression to muscle-invasive disease (p = 0.0068). We identified a 3-gene molecular signature associated with NMIBC prognosis. FGFR3 overexpression was associated with reduced response to Bacillus Calmette-Guerin treatment (p = 0.037). As regards MIBC, on multivariate analysis, ERCC2 overexpression was associated with RFS (p = 0.0011) and E2F3 and EGFR overexpression were associated with overall survival (p = 0.014 and p = 0.035). RT-PCR findings were confirmed by IHC for FGFR3. Genomic alterations in MIBC revealed in TCGA data also concern NMIBC and seem to be associated with prognosis in terms of recurrence and progression. Correcting these alterations by targeted therapies seems a promising pharmacological approach.

Assessment of IGF-1 expression in the peripheral blood of women with recurrent breast cancer.

Breast cancer is the most common malignancy affecting women worldwide. The insulin-like growth factor 1 (IGF-1) gene encodes a protein responsible for a wide variety of physiological processes, including differentiation and cell proliferation. Despite several studies on tumor tissues, no study has evaluated IGF-1 expression in the peripheral blood of women with recurrent breast cancer.In this cross-sectional study, IGF-1 expression in the peripheral blood of 146 women with breast cancer treated approximately 5 years ago was quantified by quantitative reverse transcription polymerase chain. The women were divided into 2 groups: non-recurrence (n = 85) and recurrence (n = 61). Statistical analysis of the data was performed using ANOVA, Mann-Whitney, and Chi-squared tests (P < .05).The results showed no significant difference in IGF-1 expression between the non-recurrence and recurrence groups (P = .988). In the subgroups of patients with lymph node involvement, no statistically significant difference was observed in IGF-1 expression between women with recurrence and those non-recurrence (P = .113). In patients without lymph node metastases, IGF-1 messenger ribonucleic acid (mRNA) expression levels were significantly higher in the non-recurrence group than in the recurrence group (P = .019). Furthermore, using the median IGF-1 mRNA expression as the cutoff point, it was obtained a statistically significant difference in tumor histological grade among women with recurrent breast cancer (P = .042).These data showed significantly higher IGF-1 expression in women without lymph node metastases in the non-recurrence group compared with the recurrence group. In addition, a significant difference was observed in median IGF-1 mRNA expression in relation to tumor histological grade in women with recurrent breast cancer.

Assessment of renal fibrosis in a rat model of unilateral ureteral obstruction with diffusion kurtosis imaging: Comparison with α-SMA expression and 18F-FDG PET.

PURPOSE: To investigate the utility of diffusion kurtosis imaging (DKI) MRI for evaluation of renal fibrosis in rats with unilateral ureteral obstruction (UUO). METHODS: Twenty-five rats had UUO, and ten rats were subjected to sham operation as control. DKI was performed on a 3.0 T MRI scanner on days 1, 3, 5, and 7 after ligation. All rats then underwent 18F-FDG dynamic PET to evaluate unilateral renal function, followed by histological analysis to examine α-smooth muscle actin (α-SMA) expression. DKI metrics were assessed among the time points and between two sides, and compared with maximum standardized uptake value (SUVmax), serum levels of creatinine and urea, and fibrosis marker α-SMA. RESULTS: Mean kurtosis (MK) on day 7, axial kurtosis (Ka) on days 3 and 7, mean diffusivity (MD) on days 1, 3, 5, and 7, and fractional anisotropy (FA) on days 3, 5, and 7 of cortex and medulla between the UUO and contralateral sides were significantly different (all p < 0.05). Over the course of UUO progression, there were significant changes in Ka, MD and FA of medulla (all p < 0.05). FA of medulla was positively correlated with SUVmax (r = 0.641, p < 0.001), and MD of cortex was negatively correlated with urea (r = -0.534, p = 0.001). MD of cortex was negatively correlated with α-SMA on UUO sides (r = -0.710, p < 0.001). CONCLUSIONS: DKI shows the potential for noninvasive assessment of renal fibrosis and unilateral renal function induced by UUO.

A feature selection strategy for gene expression time series experiments with hidden Markov models.

Studies conducted in time series could be far more informative than those that only capture a specific moment in time. However, when it comes to transcriptomic data, time points are sparse creating the need for a constant search for methods capable of extracting information out of experiments of this kind. We propose a feature selection algorithm embedded in a hidden Markov model applied to gene expression time course data on either single or even multiple biological conditions. For the latter, in a simple case-control study features or genes are selected under the assumption of no change over time for the control samples, while the case group must have at least one change. The proposed model reduces the feature space according to a two-state hidden Markov model. The two states define change/no-change in gene expression. Features are ranked in consonance with three scores: number of changes across time, magnitude of such changes and quality of replicates as a measure of how much they deviate from the mean. An important highlight is that this strategy overcomes the few samples limitation, common in transcriptome experiments through a process of data transformation and rearrangement. To prove this method, our strategy was applied to three publicly available data sets. Results show that feature domain is reduced by up to 90% leaving only few but relevant features yet with findings consistent to those previously reported. Moreover, our strategy proved to be robust, stable and working on studies where sample size is an issue otherwise. Hence, even with two biological replicates and/or three time points our method proves to work well.

Detection of dynamic protein complexes through Markov Clustering based on Elephant Herd Optimization Approach.

The accessibility of a huge amount of protein-protein interaction (PPI) data has allowed to do research on biological networks that reveal the structure of a protein complex, pathways and its cellular organization. A key demand in computational biology is to recognize the modular structure of such biological networks. The detection of protein complexes from the PPI network, is one of the most challenging and significant problems in the post-genomic era. In Bioinformatics, the frequently employed approach for clustering the networks is Markov Clustering (MCL). Many of the researches for protein complex detection were done on the static PPI network, which suffers from a few drawbacks. To resolve this problem, this paper proposes an approach to detect the dynamic protein complexes through Markov Clustering based on Elephant Herd Optimization Approach (DMCL-EHO). Initially, the proposed method divides the PPI network into a set of dynamic subnetworks under various time points by combining the gene expression data and secondly, it employs the clustering analysis on every subnetwork using the MCL along with Elephant Herd Optimization approach. The experimental analysis was employed on different PPI network datasets and the proposed method surpasses various existing approaches in terms of accuracy measures. This paper identifies the common protein complexes that are expressively enriched in gold-standard datasets and also the pathway annotations of the detected protein complexes using the KEGG database.

Experienced discrimination and racial differences in leukocyte gene expression.

Racial disparities in health outcomes between African Americans and European Americans have been well-documented, but not fully understood. Chronic inflammation contributes to several of the diseases showing racial disparities (e.g., Human Immunodeficiency Virus [HIV]), and racial differences in stress exposure (e.g., experiences of racial discrimination) that stimulate pro-inflammatory processes that may contribute to differential health outcomes. We performed a cross-sectional bioinformatic analyses relating perceived discrimination (as measured by the Perceived Ethnic Discrimination Questionnaire [PED-Q]) to the activity of pro-inflammatory, neuroendocrine, and antiviral transcription control pathways relevant to the conserved transcriptional response to adversity (CTRA) in peripheral blood leukocytes. Subjects were 71 individuals (37 HIV-seropositive (HIV+); 34 HIV-seronegative (HIV-)) (mean age = 53 years, range 27-63), who self-identified either as African American/Black (n = 48) or European American/White (n = 23). This provided the opportunity to examine the independent effects of race and HIV, as well as the modifying role of perceived discrimination on pathways involved in CTRA. Exploratory analysis examined the interactive effects of HIV and race on pathways involved in CTRA. Relative to European Americans, African Americans showed increased activity of two key pro-inflammatory transcription control pathways (NF- кB and AP-1) and two stress-responsive signaling pathways (CREB and glucocorticoid receptor); these effects did not differ significantly as a function of HIV infection (HIV x Race interaction, all p > .10). Results suggested that differences in experiences of racial discrimination could potentially account for more than 50% of the total race-related difference in pro-inflammatory transcription factor activity. In sum, differential exposure to racial discrimination may contribute to racial disparities in health outcomes in part by activating threat-related molecular programs that stimulate inflammation and contribute to increased risk of chronic illnesses.

Classical and Bayesian random-effects meta-analysis models with sample quality weights in gene expression studies.

BACKGROUND: Random-effects (RE) models are commonly applied to account for heterogeneity in effect sizes in gene expression meta-analysis. The degree of heterogeneity may differ due to inconsistencies in sample quality. High heterogeneity can arise in meta-analyses containing poor quality samples. We applied sample-quality weights to adjust the study heterogeneity in the DerSimonian and Laird (DSL) and two-step DSL (DSLR2) RE models and the Bayesian random-effects (BRE) models with unweighted and weighted data, Gibbs and Metropolis-Hasting (MH) sampling algorithms, weighted common effect, and weighted between-study variance. We evaluated the performance of the models through simulations and illustrated application of the methods using Alzheimer's gene expression datasets. RESULTS: Sample quality adjusting within study variance (wP6) models provided an appropriate reduction of differentially expressed (DE) genes compared to other weighted functions in classical RE models. The BRE model with a uniform(0,1) prior was appropriate for detecting DE genes as compared to the models with other prior distributions. The precision of DE gene detection in the heterogeneous data was increased with the DSLR2wP6 weighted model compared to the DSLwP6 weighted model. Among the BRE weighted models, the wP6weighted- and unweighted-data models and both Gibbs- and MH-based models performed similarly. The wP6 weighted common-effect model performed similarly to the unweighted model in the homogeneous data, but performed worse in the heterogeneous data. The wP6weighted data were appropriate for detecting DE genes with high precision, while the wP6weighted between-study variance models were appropriate for detecting DE genes with high overall accuracy. Without the weight, when the number of genes in microarray increased, the DSLR2 performed stably, while the overall accuracy of the BRE model was reduced. When applying the weighted models in the Alzheimer's gene expression data, the number of DE genes decreased in all metadata sets with the DSLR2wP6weighted and the wP6weighted between study variance models. Four hundred and forty-six DE genes identified by the wP6weighted between study variance model could be potentially down-regulated genes that may contribute to good classification of Alzheimer's samples. CONCLUSIONS: The application of sample quality weights can increase precision and accuracy of the classical RE and BRE models; however, the performance of the models varied depending on data features, levels of sample quality, and adjustment of parameter estimates.

Subjective social status and inflammatory gene expression.

OBJECTIVE: There exists a well-established link between low perceived social status and poorer health outcomes. However, the molecular mechanisms associated with this link remain unclear. This study begins to fill this gap by investigating the effects of low perceived subjective social status on health-related gene expression. METHOD: Participants were 47 healthy heterosexual women (mean age 20.5 years) from a large American university. Participants gave 10 mL of peripheral blood and completed questionnaires assessing subjective social status (SSS), perceived childhood socioeconomic status (SES), health, and relevant demographics. Putatively associated genes were subject to TELiS promoter-based bioinformatic analysis to assess activity of proinflammatory, anti-inflammatory, and antiviral transcription factors. RESULTS: In analyses controlling for perceived childhood socioeconomic status (SES) and other covariates, 84 transcripts showed >1.5-fold difference in average expression across the range of SSS. TELiS bioinformatics analyses implicated the proinflammatory transcription factors, NF-κB and AP-1, in driving expression of genes that were up-regulated in low-SSS individuals. Results also indicated increased activity of CREB family transcription factors but no differential activity of the anti-inflammatory glucocorticoid receptor of interferon response factors. Transcript origin analysis implicated monocytes and dendritic cells as cellular mediators. CONCLUSION: In this first study examining the molecular correlates of SSS, experiences of low social status are associated with transcriptional effects similar to those previously observed for objective adversity conditions such as low SES, social isolation, and chronic stress. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Elevated pro-inflammatory gene expression in the third trimester of pregnancy in mothers who experienced stressful life events.

BACKGROUND: Stress exposure is associated with risk for adverse pregnancy outcomes, potentially in part through dysregulated immune and inflammatory activity. Evidence suggests that stress during pregnancy is associated with inflammation during pregnancy, consistent with risk for preterm birth. However, research has not tested whether complementary changes are reflected in immune cell gene expression, or upstream regulation of inflammation. The purpose of this study was to test associations between preconception and prenatal stress exposure and third trimester immune cell gene expression, focusing specifically on sets of genes previously linked to stress in non-pregnant samples: Pro-inflammatory genes, and antiviral and antibody genes. METHODS: A sample of 116 low-income, diverse women was recruited from 5 U.S. sites by the Community Child and Health Network at the birth of a child. This study is of the subgroup of women who became pregnant again over the two-year follow-up period, and provided information on stressful life events that occurred both preconception and during the third trimester of the subsequent pregnancy. Dried blood spots (DBS) were collected in the third trimester of pregnancy, and used for gene expression analysis. RESULTS: Women with more prenatal stressful life events had higher expression of pro-inflammatory genes when compared to those with fewer life events, and the effect was driven by increased activation of pro-inflammatory transcription factors, NF-κB and AP-1. Preconception stressful life event exposure was not associated with gene expression profiles. When entered into models simultaneously, only prenatal stressful life events were associated with up-regulation of pro-inflammatory genes. No differences between high or low stress groups emerged for antiviral or antibody genes. CONCLUSIONS: Prenatal stress exposure was associated with up-regulated pro-inflammatory gene expression during pregnancy, and increased activity of NF-κB and AP-1. In contrast, stress occurring preconception was not associated with gene expression. These results are consistent with the hypothesis that stress-induced activation of pro-inflammatory transcriptional pathways in pregnancy, but not earlier, may increase risk for inflammation-driven adverse pregnancy outcomes.

Transcriptomic heterogeneity in multifocal prostate cancer.

BACKGROUND: Commercial gene expression assays are guiding clinical decision making in patients with prostate cancer, particularly when considering active surveillance. Given heterogeneity and multifocality of primary prostate cancer, such assays should ideally be robust to the coexistence of unsampled higher grade disease elsewhere in the prostate in order to have clinical utility. Herein, we comprehensively evaluated transcriptomic profiles of primary multifocal prostate cancer to assess robustness to clinically relevant multifocality. METHODS: We designed a comprehensive, multiplexed targeted RNA-sequencing assay capable of assessing multiple transcriptional classes and deriving commercially available prognostic signatures, including the Myriad Prolaris Cell Cycle Progression score, the Oncotype DX Genomic Prostate Score, and the GenomeDX Decipher Genomic Classifier. We applied this assay to a retrospective, multi-institutional cohort of 156 prostate cancer samples. Derived commercial biomarker scores for 120 informative primary prostate cancer samples from 44 cases were determined and compared. RESULTS: Derived expression scores were positively correlated with tumor grade (rS = 0.53-0.73; all P < 0.001), both within the same case and across the entire cohort. In cases of extreme grade-discordant multifocality (co-occurrence of grade group 1 [GG1] and ≥GG4 foci], gene expression scores were significantly lower in low- (GG1) versus high-grade (≥GG4) foci (all P < 0.001). No significant differences in expression scores, however, were observed between GG1 foci from prostates with and without coexisting higher grade cancer (all P > 0.05). CONCLUSIONS: Multifocal, low-grade and high-grade prostate cancer foci exhibit distinct prognostic expression signatures. These findings demonstrate that prognostic RNA expression assays performed on low-grade prostate cancer biopsy tissue may not provide meaningful information on the presence of coexisting unsampled aggressive disease. FUNDING: Prostate Cancer Foundation, National Institutes of Health (U01 CA214170, R01 CA183857, University of Michigan Prostate Specialized Program of Research Excellence [S.P.O.R.E.] P50 CA186786-05, Weill Cornell Medicine S.P.O.R.E. P50 CA211024-01A1), Men of Michigan Prostate Cancer Research Fund, University of Michigan Comprehensive Cancer Center core grant (2-P30-CA-046592-24), A. Alfred Taubman Biomedical Research Institute, and Department of Defense.

Incorporation of unique molecular identifiers in TruSeq adapters improves the accuracy of quantitative sequencing.

Quantitative analysis of next-generation sequencing (NGS) data requires discriminating duplicate reads generated by PCR from identical molecules that are of unique origin. Typically, PCR duplicates are identified as sequence reads that align to the same genomic coordinates using reference-based alignment. However, identical molecules can be independently generated during library preparation. Misidentification of these molecules as PCR duplicates can introduce unforeseen biases during analyses. Here, we developed a cost-effective sequencing adapter design by modifying Illumina TruSeq adapters to incorporate a unique molecular identifier (UMI) while maintaining the capacity to undertake multiplexed, single-index sequencing. Incorporation of UMIs into TruSeq adapters (TrUMIseq adapters) enables identification of bona fide PCR duplicates as identically mapped reads with identical UMIs. Using TrUMIseq adapters, we show that accurate removal of PCR duplicates results in improved accuracy of both allele frequency (AF) estimation in heterogeneous populations using DNA sequencing and gene expression quantification using RNA-Seq.

Co-expression of lipase isozymes for enhanced expression in Pichia pastoris.

To enhance the overall expression level of lipase isozymes which catalyse the same reaction in Pichia pastoris through co-expression of isozymes from different sources; several types of co-expression ways were constructed to determine the co-expression efficiencies of lipase isozymes in P. pastoris. The results showed that the Kex2-mediated co-expression of lipase isozymes could express Rhizomucor miehei lipase (RML) and Thermomyces lanuginosus lipase (TLL) simultaneously, and GS-RMk-kTL displayed an average lipase activity of 306·91 U ml-1 , higher than GS-RML and GS-kTL (2·89 and 300·59 U ml-1 ) expressed independently in P. pastoris, and the sum of both (303·48 U ml-1 ), implying the potential of isozyme co-expression mediated by Kex2 in increasing the overall recombinant expression, but the low recombinant expression of RML in P. pastoris weakened the overall increasing effect on lipase expression in the isozyme co-expression strains. In addition, the fusion isozymes were successfully expressed, but with low lipase activities. Furthermore, 2A peptide could successfully mediate the co-expression and secretion of lipase isozymes, but it seriously affected the expression of TLL downstream of 2A peptide. SIGNIFICANCE AND IMPACT OF THE STUDY: The low production level is one of the limitation factors for decreasing the prices of enzymes and expanding their application in industry as the biocatalysts. This research focuses on developing lipase isozyme co-expression strategies in Pichia pastoris to enhance the expression level of overall lipase isozymes which catalyse the same reaction. The Kex2-mediated co-expression strategy of lipase isozymes could potentially enhance the overall isozyme expression, and isozyme co-expression might provide a new direction for improving the recombinant isozyme expression, and decreasing the production and application prices of these mixed enzymes as biocatalysts.

Blood assessment of the expression levels of matrix metalloproteinase 9 (MMP9) and its natural inhibitor, TIMP1 genes in Iranian schizophrenic patients.

Schizophrenia (SCZ) is the most severe chronic mental disorder characterized by abnormal social behavior and disrupted emotions and thought. Like other complex neuropsychological disease, SCZ is caused by a combination of genetic and environmental factors but with a high concordance rate. So far, different genetic factors are revealed to be associated with increased risk of developing SCZ. One of the best ways to investigate the genetic basis of the complex disease is to discover the genetic underlying mechanisms of the defective clinical aspects of the patients. In this regard, genes involved in the developmental mechanisms of the brain such as long-term potentiation (LTP) process that is the basis of synaptic plasticity, memory and learning are considered as strong candidates for SCZ. The aim of the present study was to evaluate the expression levels of two genes that are involved in the LTP regulation in the developing and adult brain, Matrix metallopeptidase9 (MMP9) and TIMP metallopeptidase inhibitor 1 (TIMP1) genes in a blood assessment of schizophrenic patients in comparison to healthy controls by means of quantitative real time PCR. The results of the study showed a significant difference in MMP9/TIPM1 ratio between SCZ patients and healthy controls (P = 0.01). However, no significant difference was detected in the expression level of individual MMP9 and TIMP1 genes in SCZ patients versus healthy controls either in total numbers of subject or in sex based subgroups. Considering the relatively small sample size of the current study, there is a need to replicate this study with further investigations about the mechanism of association of these genes and their functions in the pathogenesis of the SCZ.

Genome-Wide Profiling of RNA from Dried Blood Spots: Convergence with Bioinformatic Results Derived from Whole Venous Blood and Peripheral Blood Mononuclear Cells.

Genome-wide transcriptional profiling has emerged as a powerful tool for analyzing biological mechanisms underlying social gradients in health, but utilization in population-based studies has been hampered by logistical constraints and costs associated with venipuncture blood sampling. Dried blood spots (DBS) provide a minimally invasive, low-cost alternative to venipuncture, and in this article we evaluate how closely the substantive results from DBS transcriptional profiling correspond to those derived from parallel analyses of gold-standard venous blood samples (PAXgene whole blood and peripheral blood mononuclear cells [PBMC]). Analyses focused on differences in gene expression between African-Americans and Caucasians in a community sample of 82 healthy adults (age 18-70 years; mean 35). Across 19,679 named gene transcripts, DBS-derived values correlated r = .85 with both PAXgene and PBMC values. Results from bioinformatics analyses of gene expression derived from DBS samples were concordant with PAXgene and PBMC samples in identifying increased Type I interferon signaling and up-regulated activity of monocytes and natural killer (NK) cells in African-Americans compared to Caucasian participants. These findings demonstrate the feasibility of DBS in field-based studies of gene expression and encourage future studies of human transcriptome dynamics in larger, more representative samples than are possible with clinic- or lab-based research designs.