Confirmation of region-specific patterns of gene expression in the human brain

Identification of a novel therapeutic candidate, NRK, in primary cancer-associated fibroblasts of lung adenocarcinoma microenvironment

PURPOSE

Lung adenocarcinoma (LUAD) accounts for approximately half of patients in lung cancer. Cancer-associated fibroblasts (CAFs) are the major component in the tumor microenvironment (TME). Targeting CAFs is a promising therapeutic strategy for cancer treatment. However, therapeutic targets of CAFs in LUAD remains largely unclear.

METHODS

Seven CAFs and nine normal fibroblasts (NFs) were isolated from tumor and paratumor tissues of LUAD patients undergoing surgery, respectively. RNA-seq and bioinformatics analysis were performed to identify the differentially expressed genes (DEGs) and their functions in CAFs compared with NFs. DEGs of ten overlaying were obtained from RNA-seq, our previously reported lncRNA microarray and public datasets (E-MTAB-6149, E-MTAB-6653) and validated by RT-qPCR. Nik-related kinase (NRK) was further validated by RT-qPCR, immunofluorescence (IF), Western Blot (WB) in vitro, and in Cancer Cell Line Encyclopedia (CCLE) database. Survival analysis was performed on Kaplan-Meier plotter.

RESULTS

A total of 1799 DEGs were identified, including 650 upregulated DEGs and 1149 downregulated DEGs. The upregulated and downregulated DEGs were mostly enriched in extracellular matrix (ECM) functions and in glycolysis/gluconeogenesis pathways. Interestingly, NRK was the most significantly upregulated overlaying DEGs which was rarely associated with CAFs before. NRK was predominantly expressed in CAFs, but weakly expressed in NFs, normal lung bronchial epithelial cell line BEAS-2B, LUAD cell lines A549 and H1299, as well as in the majority of 191 lung cancer cell lines including LUAD. Moreover, elevated NRK predicted poor survival in LUAD patients.

CONCLUSION

Here, we first report that NRK is significantly elevated in LUAD-associated CAFs and may function as a promising therapeutic target for cancer combination treatment. Besides, modulation of ECM and glycolysis/gluconeogenesis pathways may be an efficient approach to alter CAFs functionality in LUAD.

Expression of Nik-related kinase in smooth muscle cells attenuates vascular inflammation and intimal hyperplasia

Inflammation of the vascular microenvironment modulates distinct types of vascular cells, and plays important roles in promoting atherosclerosis, stenosis/restenosis, and vascular-related diseases. Nik-related kinase (Nrk), a member of the Ste20-type kinase family, has been reported to be selectively expressed in embryonic skeletal muscle. However, whether Nrk is expressed in adult vascular smooth muscle, and if it influences intimal hyperplasia is unclear. Here, we found that Nrk is abundantly expressed in cultured vascular smooth muscle cells (VSMC) and mouse arterial intima. Treatment of mouse VSMCs with lipopolysaccharide (LPS) or platelet-derived growth factor significantly reduced Nrk expression. In addition, expression of Nrk was significantly reduced in regions of neointimal formation caused by guide-wire carotid artery injuries in mice, as well as in human atherosclerotic tissues, when compared to normal vessels. We identified that expression of matrix metalloproteinases (MMP3, MMP8 and MMP12) and inflammatory cytokines/chemokines (CCL6, CCL8, CCL11, CXCL1, CXCL3, CXCL5 and CXCL9) are synergistically induced by Nrk siRNA in LPS-treated mouse VSMCs. Moreover, we found that resveratrol significantly impaired LPS- and Nrk siRNA-induced expression of MMP3, CCL8, CCL11, CXCL3 and CXCL5. These results suggested that Nrk may play important roles in regulating pathological progression of atherosclerosis or neointimal- hyperplasia-related vascular diseases.

miR-1202 is a primate-specific and brain-enriched microRNA involved in major depression and antidepressant treatment

Major depressive disorder (MDD) is a prevalent mood disorder that is associated with differential prefrontal brain expression patterns. Treatment of MDD includes a variety of biopsychosocial approaches. In medical practice, antidepressant drugs are the most common treatment for depressive episodes, and they are among the most prescribed medications in North America. Although antidepressants are clearly effective, particularly for moderate to severe depressive episodes, there is variability in how individuals respond to antidepressant treatment. Failure to respond has individual, economic and social consequences for patients and their families. Several lines of evidence demonstrate that genes are regulated through the activity of microRNAs (miRNAs), which act as fine-tuners and on-off switches of gene expression. Here we report on complementary studies using postmortem human brain samples, cellular assays and samples from clinical trials of patients with depression and show that miR-1202, a miRNA specific to primates and enriched in the human brain, is differentially expressed in individuals with depression. Additionally, miR-1202 regulates expression of the gene encoding metabotropic glutamate receptor-4 (GRM4) and predicts antidepressant response at baseline. These results suggest that miR-1202 is associated with the pathophysiology of depression and is a potential target for new antidepressant treatments.

Long-lasting alterations to DNA methylation and ncRNAs could underlie the effects of fetal alcohol exposure in mice

Fetal alcohol spectrum disorders (FASDs) are characterized by life-long changes in gene expression, neurodevelopment and behavior. What mechanisms initiate and maintain these changes are not known, but current research suggests a role for alcohol-induced epigenetic changes. In this study we assessed alterations to adult mouse brain tissue by assaying DNA cytosine methylation and small noncoding RNA (ncRNA) expression, specifically the microRNA (miRNA) and small nucleolar RNA (snoRNA) subtypes. We found long-lasting alterations in DNA methylation as a result of fetal alcohol exposure, specifically in the imprinted regions of the genome harboring ncRNAs and sequences interacting with regulatory proteins. A large number of major nodes from the identified networks, such as Pten signaling, contained transcriptional repressor CTCF-binding sites in their promoters, illustrating the functional consequences of alcohol-induced changes to DNA methylation. Next, we assessed ncRNA expression using two independent array platforms and quantitative PCR. The results identified 34 genes that are targeted by the deregulated miRNAs. Of these, four (Pten, Nmnat1, Slitrk2 and Otx2) were viewed as being crucial in the context of FASDs given their roles in the brain. Furthermore, ∼20% of the altered ncRNAs mapped to three imprinted regions (Snrpn-Ube3a, Dlk1-Dio3 and Sfmbt2) that showed differential methylation and have been previously implicated in neurodevelopmental disorders. The findings of this study help to expand on the mechanisms behind the long-lasting changes in the brain transcriptome of FASD individuals. The observed changes could contribute to the initiation and maintenance of the long-lasting effect of alcohol.

How much of reinforcement learning is working memory, not reinforcement learning? A behavioral, computational, and neurogenetic analysis

Instrumental learning involves corticostriatal circuitry and the dopaminergic system. This system is typically modeled in the reinforcement learning (RL) framework by incrementally accumulating reward values of states and actions. However, human learning also implicates prefrontal cortical mechanisms involved in higher level cognitive functions. The interaction of these systems remains poorly understood, and models of human behavior often ignore working memory (WM) and therefore incorrectly assign behavioral variance to the RL system. Here we designed a task that highlights the profound entanglement of these two processes, even in simple learning problems. By systematically varying the size of the learning problem and delay between stimulus repetitions, we separately extracted WM-specific effects of load and delay on learning. We propose a new computational model that accounts for the dynamic integration of RL and WM processes observed in subjects' behavior. Incorporating capacity-limited WM into the model allowed us to capture behavioral variance that could not be captured in a pure RL framework even if we (implausibly) allowed separate RL systems for each set size. The WM component also allowed for a more reasonable estimation of a single RL process. Finally, we report effects of two genetic polymorphisms having relative specificity for prefrontal and basal ganglia functions. Whereas the COMT gene coding for catechol-O-methyl transferase selectively influenced model estimates of WM capacity, the GPR6 gene coding for G-protein-coupled receptor 6 influenced the RL learning rate. Thus, this study allowed us to specify distinct influences of the high-level and low-level cognitive functions on instrumental learning, beyond the possibilities offered by simple RL models.

Network methods for describing sample relationships in genomic datasets: application to Huntington's disease

BACKGROUND

Genomic datasets generated by new technologies are increasingly prevalent in disparate areas of biological research. While many studies have sought to characterize relationships among genomic features, commensurate efforts to characterize relationships among biological samples have been less common. Consequently, the full extent of sample variation in genomic studies is often under-appreciated, complicating downstream analytical tasks such as gene co-expression network analysis.

RESULTS

Here we demonstrate the use of network methods for characterizing sample relationships in microarray data generated from human brain tissue. We describe an approach for identifying outlying samples that does not depend on the choice or use of clustering algorithms. We introduce a battery of measures for quantifying the consistency and integrity of sample relationships, which can be compared across disparate studies, technology platforms, and biological systems. Among these measures, we provide evidence that the correlation between the connectivity and the clustering coefficient (two important network concepts) is a sensitive indicator of homogeneity among biological samples. We also show that this measure, which we refer to as cor(K,C), can distinguish biologically meaningful relationships among subgroups of samples. Specifically, we find that cor(K,C) reveals the profound effect of Huntington's disease on samples from the caudate nucleus relative to other brain regions. Furthermore, we find that this effect is concentrated in specific modules of genes that are naturally co-expressed in human caudate nucleus, highlighting a new strategy for exploring the effects of disease on sets of genes.

CONCLUSIONS

These results underscore the importance of systematically exploring sample relationships in large genomic datasets before seeking to analyze genomic feature activity. We introduce a standardized platform for this purpose using freely available R software that has been designed to enable iterative and interactive exploration of sample networks.

Pharmacogenomics of suicidal events

Pharmacogenomic studies of antidepressant treatment-emergent suicidal events in depressed patients report associations with polymorphisms in genes involved in transcription (CREB1), neuroprotection (BDNF and NTRK2), glutamatergic and noradrenergic neurotransmission (GRIA3, GRIK2 and ADRA2A), the stress and inflammatory responses (FKBP5 and IL28RA), and the synthesis of glycoproteins (PAPLN). Nearly all of the reported events in these studies were modest one-time increases in suicidal ideation. In 3231 unique subjects across six studies, 424 (13.1%) patients showed increases in suicidal ideation, eight (0.25%) attempted suicide and four (0.12%) completed suicide. Systems related to most of these genes have also been implicated in studies of suicidal behavior irrespective of treatment. Future pharmacogenomic studies should target events that are clinically significant, related clinical phenotypes of response and medication side effects, and biological pathways that are involved in these outcomes in order to improve treatment approaches.

Transcriptional profiling of fibroblasts from patients with mutations in MCT8 and comparative analysis with the human brain transcriptome

Thyroid hormone (TH) is crucial for normal brain development. TH transporters control TH homeostasis in brain as evidenced by the complex endocrine and neurological phenotype of patients with mutations in monocarboxylate transporter 8 (MCT8). We investigated the mechanisms of disease by analyzing gene expression profiles in fibroblasts from patients with MCT8 mutations. Studying MCT8 and its transcriptional context in different comprehensive spatial and temporal human brain transcriptome data sets revealed distinct region-specific MCT8 expression. Furthermore, MCT8 demonstrated a clear age-dependent decrease, suggesting its importance in early brain development. Performing comparative transcriptome analysis, we linked the genes differentially expressed (DE) in patient fibroblasts to the human brain transcriptome. DE genes in patient fibroblasts were strongly over-represented among genes highly correlated with MCT8 expression in brain. Furthermore, using the same approach we identified which genes in the classical TH signaling pathway are affected in patients. Finally, we provide evidence that the TRα2 receptor variant is closely connected to MCT8. The present study provides a molecular basis for understanding which pathways are likely affected in the brains of patients with mutations in MCT8. Our data regarding a functional relationship between MCT8 and TRα2 suggest an unanticipated role for TRα2 in the (patho)physiology of TH signaling in the brain. This study demonstrates how genome-wide expression data from patient-derived non-neuronal tissue related to the human brain transcriptome may be successfully employed to improve our understanding of neurological disease.

A cross-laboratory comparison of expression profiling data from normal human postmortem brain

Expression profiling of post-mortem human brain tissue has been widely used to study molecular changes associated with neuropsychiatric diseases as well as normal processes such as aging. Changes in expression associated with factors such as age, gender or postmortem interval are often more pronounced than changes associated with disease. Therefore in addition to being of interest in their own right, careful consideration of these effects are important in the interpretation of disease studies. We performed a large meta-analysis of genome-wide expression studies of normal human cortex to more fully catalogue the effects of age, gender, postmortem interval and brain pH, yielding a "meta-signature" of gene expression changes for each factor. We validated our results by showing a significant overlap with independent gene lists extracted from the literature. Importantly, meta-analysis identifies genes which are not significant in any individual study. Finally, we show that many schizophrenia candidate genes appear in the meta-signatures, reinforcing the idea that studies must be carefully controlled for interactions between these factors and disease. In addition to the inherent value of the meta-signatures, our results provide critical information for future studies of disease effects in the human brain.

The Allen Brain Atlas: 5 years and beyond

The Allen Brain Atlas, a Web-based, genome-wide atlas of gene expression in the adult mouse brain, was an experiment on a massive scale. The development of the atlas faced a combination of great technical challenges and a non-traditional open research model, and it encountered many hurdles on the path to completion and community adoption. Having overcome these challenges, it is now a fundamental tool for neuroscientists worldwide and has set the stage for the creation of other similar open resources. Nevertheless, there are many untapped opportunities for exploration.

Application of microarray outlier detection methodology to psychiatric research

BACKGROUND

Most microarray data processing methods negate extreme expression values or alter them so that they do not lie outside the mean level of variation of the system. While microarrays generate a substantial amount of false positive and spurious results, some of the extreme expression values may be valid and could represent true biological findings.

METHODS

We propose a simple method to screen brain microarray data to detect individual differences across a psychiatric sample set. We demonstrate in two different samples how this method can be applied.

RESULTS

This method targets high-throughput technology to psychiatric research on a subject-specific basis.

CONCLUSION

Assessing microarray data for both mean group effects and individual effects can lead to more robust findings in psychiatric genetics.