Metabolomics identifies shared lipid pathways in independent amyotrophic lateral sclerosis cohorts.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease lacking effective treatments. This is due, in part, to a complex and incompletely understood pathophysiology. To shed light, we conducted untargeted metabolomics on plasma from two independent cross-sectional ALS cohorts versus control participants to identify recurrent dysregulated metabolic pathways. Untargeted metabolomics was performed on plasma from two ALS cohorts (cohort 1, n = 125; cohort 2, n = 225) and healthy controls (cohort 1, n = 71; cohort 2, n = 104). Individual differential metabolites in ALS cases versus controls were assessed by Wilcoxon, adjusted logistic regression and partial least squares-discriminant analysis, while group lasso explored sub-pathway level differences. Adjustment parameters included age, sex and body mass index. Metabolomics pathway enrichment analysis was performed on metabolites selected using the above methods. Additionally, we conducted a sex sensitivity analysis due to sex imbalance in the cohort 2 control arm. Finally, a data-driven approach, differential network enrichment analysis (DNEA), was performed on a combined dataset to further identify important ALS metabolic pathways. Cohort 2 ALS participants were slightly older than the controls (64.0 versus 62.0 years, P = 0.009). Cohort 2 controls were over-represented in females (68%, P < 0.001). The most concordant cohort 1 and 2 pathways centred heavily on lipid sub-pathways, including complex and signalling lipid species and metabolic intermediates. There were differences in sub-pathways that were enriched in ALS females versus males, including in lipid sub-pathways. Finally, DNEA of the merged metabolite dataset of both ALS and control cohorts identified nine significant subnetworks; three centred on lipids and two encompassed a range of sub-pathways. In our analysis, we saw consistent and important shared metabolic sub-pathways in both ALS cohorts, particularly in lipids, further supporting their importance as ALS pathomechanisms and therapeutics targets.

Hair follicle miRNAs: a novel biomarker for primary blast Induced-Mild traumatic brain injury.

CONTEXT: Due to the wide use of improvised explosive devices during modern warfare, primary blast-derived mild traumatic brain injury (mTBI) has become a major medical condition in the military. With minimal visually identifiable symptoms, an effective molecular biomarker system is desirable. OBJECTIVE: We assessed the potential of mammalian hair follicle miRNAs as an mTBI biomarker. MATERIALS AND METHODS: Due to their well-established roles in mTBI molecular pathology, the expression level of miR-183, miR-26a, miR-181c, miR-29a, miR-34a and miR-27b was determined using qRT-PCR in whisker hair follicles from rats subject to head-only exposure to a single-pulse shock wave. Based on established transcriptomics profiles, sub-network enrichment analysis (SNEA) was also conducted. RESULTS: The results revealed that molecular networks involving miR-183, miR-26a and miR-181c were enriched in multiple treatments, whereas sub-networks of miR-29a, miR-34a and miR-27b were unique to individual exposure groups. DISCUSSION: Our study showed that all six miRNAs were reflective of the mTBI signature involved in cellular responses. Noteworthy was that the decrease in the transcript levels of miR-181c was correlated with shockwave exposure severity. CONCLUSION: This study demonstrates for the first time that mammalian hair follicles are capable of housing miRNA biomarkers for TBI.

Transcriptional networks associated with 5-alpha-dihydrotestosterone in the fathead minnow (Pimephales promelas) ovary.

Androgens play a significant role in regulating oogenesis in teleost fishes. The androgen dihydrotestosterone (DHT) is a potent non-aromatizable androgen involved in sexual differentiation in mammals; however, its actions are not well understood in teleost fish. To better characterize the physiological role of DHT in the fathead minnow (FHM) ovary on a temporal scale, in vitro assays for 17ß-estradiol (E2) production were conducted in parallel with microarray analysis. Ovarian explants were incubated at different concentrations of DHT (10(-6), 10(-7), and 10(-8)M DHT) in three separate experiments conducted at 6, 9, and 12h. DHT treatment resulted in a rapid and consistent increase in E2 production from the ovary at all three time points. Therefore, DHT may act to shift the balance of metabolites in the steroidogenic pathway within the ovary. Major biological themes affected by DHT in the ovary in one or more of the time points included those related to blood (e.g. vasodilation, blood vessel contraction, clotting), lipids (e.g. lipid storage, cholesterol metabolism, lipid degradation) and reproduction (e.g. hormone and steroid metabolism). Gene networks related to immune responses and calcium signaling were also affected by DHT, suggesting that this androgen may play a role in regulating these processes in the ovary. This study detected no change in mRNA levels of steroidogenic enzymes (cyp19a1, star, 11ßhsd, 17ßhsd, srd5a isoforms), suggesting that the observed increase in E2 production is likely more dependent on the pre-existing gene or protein complement in the ovary rather than the de novo expression of transcripts. This study increases knowledge regarding the roles of DHT and androgens in general in the teleost ovary and identifies molecular signaling pathways that may be associated with increased E2 production.

Transcriptomics profiling and steroid production in mummichog (Fundulus heteroclitus) testes after treatment with 5α-dihydrotestosterone.

5α-Dihydrotestosterone (DHT) is a potent androgen in mammals with multiple roles; however the physiological actions of DHT in male fishes are not well known. To address this knowledge gap, male mummichog (Fundulus heteroclitus) were continuously exposed to 0, 5, and 50 µg/L DHT for 21 days. Following exposure, testes were separated for histology, ex vivo incubation to measure steroidogenic capacity, and gene expression analyses (real-time PCR and microarray). DHT significantly decreased ex vivo 11-ketotestosterone (11KT) production in males exposed to 50 µg/L DHT but not 5 µg/L DHT, and DHT exposure did not affect ex vivo testosterone production. Histological examination revealed that the amount of interlobular and connective tissue present in the testes was increased in the 50 µg/L DHT treatment. Despite reductions in the production of 11KT, DHT did not affect the expression of targeted genes in the steroidogenic pathway such as steroidogenic acute regulatory protein (star), P450 side chain cleavage (cyp11a1) and 11ß-hydroxysteroid dehydrogenase (hsd11b3). Microarray analysis in the testes of individuals from control and 50 µg/L DHT revealed that males exposed to 50 µg/L DHT showed regulated transcriptional sub-networks that were related to immunity, regulation of blood flow, lipids and xenobiotic clearance, suggesting that DHT may be involved in the physiological regulation of these processes in the fish testes. A second objective of this study was to determine the feasibility of measuring mRNA levels in tissues used for ex vivo steroid production by comparing RNA integrity and transcript levels in testes of both immediately flash frozen tissue and incubated tissue. There was no significant difference in RNA quality between the two time points, indicating RNA integrity can remain intact for at least 18 h in ex vivo assays, thereby providing a viable option for researchers assessing multi-level biological reproductive endpoints when limited tissue is available. While the gene expression levels of actb, efla, rps12, rps18, star, and hsd11b3 remained unchanged, esr2a (esrba), esr2b (esrbb) and cyp11a1 were significantly lower in incubated tissue compared to flash frozen tissue. Therefore caution must be used as the steady-state levels of select genes may change over time. This study improves our understanding of DHT action in the teleostean testis and generates new hypotheses regarding cell processes that are regulated by this underexplored and potent androgen.

Methylmercury-induced changes in gene transcription associated with neuroendocrine disruption in largemouth bass (Micropterus salmoides).

Methyl-mercury (MeHg) is a potent neuroendocrine disruptor that impairs reproductive processes in fish. The objectives of this study were to (1) characterize transcriptomic changes induced by MeHg exposure in the female largemouth bass (LMB) hypothalamus under controlled laboratory conditions, (2) investigate the health and reproductive impacts of MeHg exposure on male and female largemouth bass (LMB) in the natural environment, and (3) identify MeHg-associated gene expression patterns in whole brain of female LMB from MeHg-contaminated habitats. The laboratory experiment was a single injection of 2.5 µg MeHg/g body weight for 96 h exposure. The field survey compared river systems in Florida, USA with comparably lower concentrations of MeHg (Wekiva, Santa Fe, and St. Johns Rivers) in fish and one river system with LMB that contained elevated concentrations of MeHg (St. Marys River). Microarray analysis was used to quantify transcriptomic responses to MeHg exposure. Although fish at the high-MeHg site did not show overt health or reproductive impairment, there were MeHg-responsive genes and pathways identified in the laboratory study that were also altered in fish from the high-MeHg site relative to fish at the low-MeHg sites. Gene network analysis suggested that MeHg regulated the expression targets of neuropeptide receptor and steroid signaling, as well as structural components of the cell. Disease-associated gene networks related to MeHg exposure, based upon expression data, included cerebellum ataxia, movement disorders, and hypercalcemia. Gene responses in the CNS are consistent with the documented neurotoxicological and neuroendocrine disrupting effects of MeHg in vertebrates.

Transcriptomic profiling of progesterone in the male fathead minnow (Pimephales promelas) testis.

P4 is a hormone with diverse functions that include roles in reproduction, growth, and development. The objectives of this study were to examine the effects of P4 on androgen production in the mature teleost testis and to identify molecular signaling cascades regulated by P4 to improve understanding of its role in male reproduction. Fathead minnow (FHM) testis explants were treated in vitro with two concentrations of P4 (10(-8) and 10(-6) M) for 6 and 12 h. P4 significantly increased testosterone (T) production in the FHM testis but did not affect 11-ketotestosterone. Gene network analysis revealed that insulin growth factor (Igf1) and tumor necrosis factor receptor (Tnfr) signaling was significantly depressed with P4 treatment after 12h. There was also a 20% increase in a gene network for follicle-stimulating hormone secretion and an 18% decrease in genes involved in vasopressin signaling. Genes in steroid metabolism (e.g. star, cyp19a, 11bhsd) were not significantly affected by P4 treatments in this study, and it is hypothesized that pre-existing molecular machinery may be more involved in the increased production of T rather than the de novo expression of steroid-related transcripts and receptors. There was a significant decrease in prostaglandin E synthase 3b (cytosolic) (ptges3b) after treatment with P4, suggesting that there is cross talk between P4 and prostaglandin pathways in the reproductive testis. P4 has a role in regulating steroid production in the male testis and may do so by modulating gene networks related to endocrine pathways, such as Igf1, Tnfr, and vasopressin.

Serum Mass Spectrometry Proteomics and Protein Set Identification in Response to FOLFOX-4 in Drug-Resistant Ovarian Carcinoma.

Ovarian cancer is a highly lethal gynecological malignancy. Drug resistance rapidly occurs, and different therapeutic approaches are needed. So far, no biomarkers have been discovered to predict early response to therapies in the case of multi-treated ovarian cancer patients. The aim of our investigation was to identify a protein panel and the molecular pathways involved in chemotherapy response through a combination of studying proteomics and network enrichment analysis by considering a subset of samples from a clinical setting. Differential mass spectrometry studies were performed on 14 serum samples from patients with heavily pretreated platinum-resistant ovarian cancer who received the FOLFOX-4 regimen as a salvage therapy. The serum was analyzed at baseline time (T0) before FOLFOX-4 treatment, and before the second cycle of treatment (T1), with the aim of understanding if it was possible, after a first treatment cycle, to detect significant proteome changes that could be associated with patients responses to therapy. A total of 291 shared expressed proteins was identified and 12 proteins were finally selected between patients who attained partial response or no-response to chemotherapy when both response to therapy and time dependence (T0, T1) were considered in the statistical analysis. The protein panel included APOL1, GSN, GFI1, LCATL, MNA, LYVE1, ROR1, SHBG, SOD3, TEC, VPS18, and ZNF573. Using a bioinformatics network enrichment approach and metanalysis study, relationships between serum and cellular proteins were identified. An analysis of protein networks was conducted and identified at least three biological processes with functional and therapeutic significance in ovarian cancer, including lipoproteins metabolic process, structural component modulation in relation to cellular apoptosis and autophagy, and cellular oxidative stress response. Five proteins were almost independent from the network (LYVE1, ROR1, TEC, GFI1, and ZNF573). All proteins were associated with response to drug-resistant ovarian cancer resistant and were mechanistically connected to the pathways associated with cancer arrest. These results can be the basis for extending a biomarker discovery process to a clinical trial, as an early predictive tool of chemo-response to FOLFOX-4 of heavily treated ovarian cancer patients and for supporting the oncologist to continue or to interrupt the therapy.

EviCor: Interactive Web Platform for Exploration of Molecular Features and Response to Anti-cancer Drugs.

Experimental biologists are often left alone with the task to download, process, and analyze big datasets in order to perform correlation or other simpler analyses. To address these issues, we introduce EviCor, a handy toolbox for exploration of data from large public resources such as The Cancer Genome Atlas and The Cancer Cell Line Encyclopedia, complemented with follow-up information on same samples, which couples omics datasets with drug response profiles (https://www.evicor.org/). The data was processed for easy retrieval from the server-side database and includes pre-computed drug-feature correlation tables. Using information from multiple independent sources, the task-oriented web interface presents relations between phenotype, single-molecule, and pathway variables with graphical, statistical, and network analysis tools. Building custom multivariate models is enabled via user-friendly web interface and programmatic access via RESTinterface. Project code is available at https://github.com/aveviort/HyperSet.

Integrated Network Discovery Using Multi-Proteomic Data.

A fundamental goal of systems biology is to seek a better understanding of the cell's molecular mechanisms. Experimentalists most frequently rely upon reductionist methods to isolate and analyze discrete signaling compartments, including subcellular domains, organelles, and protein-protein interactions. Among the systems-biology community, there is a growing need to integrate multiple datasets to resolve complex cellular networks. In this chapter, we share our procedures for the discovery of integrated signaling networks, across multi-proteomic data. Demonstrating these procedures, we provide an integrated analysis of the cellular proteome and extracellular (secretome) of human glioma LN229.

Morphometric and proteomic responses of early-life stage rainbow trout (Oncorhynchus mykiss) to the aquatic herbicide diquat dibromide.

The objective of this study was to examine the acute toxicity and sub-lethal effects of the commercial formulation of diquat dibromide, Reward® Landscape and Aquatic Herbicide, on multiple life stages of rainbow trout. The continuous exposure 96 h LC50 derived for juvenile feeding fry aged 85 d post-hatch was 9.8 mg/L. Rainbow trout eyed embryos and juvenile feeding fry were also exposed to concentrations of Reward® ranging from 0.12 to 10 mg/L during two 24 h pulse exposures separated by 14 d of rearing in fresh water to mimic the manufacturers instructions for direct applications to water bodies. Decreased survival and body morphometrics were evident at 9.3 mg/L during the embryo/alevin exposures, but not in feeding juveniles, indicating a higher sensitivity of the early life stage fish. Quantitative proteomics and subnetwork enrichment analyses were conducted in the livers for both life stages to evaluate protein profiles after exposure to 0.37 mg/L diquat via Reward® exposure. Unique protein profiles were revealed for pre-feeding swim-up fry and for feeding juvenile fish, reflecting differences between the two life stages in sub-cellular responses after diquat dibromide exposure. Hepatic proteome effects were more dramatic in the pre-feeding swim-up fry with 315 proteins differentially expressed between the control and exposed fish while in the later life stage feeding fry, only 84 proteins were different after Reward® exposure. Exposure to Reward® significantly increased RNA/mRNA processes, induced activation of Atk/mTOR and caspase activity, and altered energy homeostasis. Proteomic alterations are associated with reduced growth observed in embryo/alevin at higher exposure concentrations, offering insight into key events underlying growth impairment within the adverse outcome pathway framework. This study is the first to report the sub-cellular and whole organism level effects of diquat dibromide in a commercial formulation and demonstrates that concentrations based on aquatic application rates alter the hepatic proteome.

Network-Based Approaches for Multi-omics Integration.

Network-based approach is rapidly emerging as a promising strategy to integrate and interpret different -omics datasets, including metabolomics. The first section of this chapter introduces the current progresses and main concepts in multi-omics integration. The second section provides an overview of the public resources available for creation of biological networks. The third section describes three common application scenarios including subnetwork identification, network-based enrichment analysis, and systems metabolomics. The section four introduces the concept of hierarchical community network analysis. The section five discusses different tools for network visualization. The chapter ends with a future perspective on multi-omics integration.

Integrative computational evaluation of genetic markers for Alzheimer's disease.

Recent studies have reported hundreds of genes linked to Alzheimer's Disease (AD). However, many of these candidate genes may be not identified in different studies when analyses were replicated. Moreover, results could be controversial. Here, we proposed a computational workflow to curate and evaluate AD related genes. The method integrates large scale literature knowledge data and gene expression data that were acquired from postmortem human brain regions (AD case/control: 31/32 and 22/8). Pathway Enrichment, Sub-Network Enrichment, and Gene-Gene Interaction analysis were conducted to study the pathogenic profile of the candidate genes, with 4 metrics proposed and validated for each gene. By using our approach, a scalable AD genetic database was developed, including AD related genes, pathways, diseases and info of supporting references. The AD case/control classification supported the effectiveness of the 4 proposed metrics, which successfully identified 21 well-studied AD genes (i.g. TGFB1, CTNNB1, APP, IL1B, PSEN1, PTGS2, IL6, VEGFA, SOD1, AKT1, CDK5, TNF, GSK3B, TP53, CCL2, BDNF, NGF, IGF1, SIRT1, AGER and TLR) and highlighted one recently reported AD gene (i.g. ITGB1). The computational biology approach and the AD database developed in this study provide a valuable resource which may facilitate the understanding of the AD genetic profile.

An Integrative Computational Approach to Evaluate Genetic Markers for Chronic Lymphocytic Leukemia.

Recent studies reported hundreds of genes linked to chronic lymphocytic leukemia (CLL). However, many of these candidate genes were lack of replication and results were not always consistent. Here, we proposed a computational workflow to curate and evaluate CLL-related genes. The method integrates large-scale literature knowledge data, gene expression data, and related pathways/network information for quantitative marker evaluation. Pathway Enrichment, Sub-Network Enrichment, and Gene-Gene Interaction analysis were conducted to study the pathogenic profile of the candidate genes, with four metrics proposed and validated for each gene. By using our approach, a scalable CLL genetic database was developed including CLL-related genes, pathways, diseases and information of supporting references. The CLL case/control classification supported the effectiveness of the four proposed metrics, which successfully identified nine well-studied CLL genes (i.e., TNF, BCL2, TP53, VEGFA, P2RX7, AKT1, SYK, IL4, and MDM2) and highlighted two newly reported CLL genes (i.e., PDGFRA and CSF1R). The computational biology approach and the CLL database developed in this study provide a valuable resource that may facilitate the understanding of the genetic profile of CLL.

High contaminant loads in Lake Apopka's riparian wetland disrupt gene networks involved in reproduction and immune function in largemouth bass.

Lake Apopka (FL, USA) has elevated levels of some organochlorine pesticides in its sediments and a portion of its watershed has been designated a US Environmental Protection Agency Superfund site. This study assessed reproductive endpoints in Florida largemouth bass (LMB) (Micropterus salmoides floridanus) after placement into experimental ponds adjacent to Lake Apopka. LMB collected from a clean reference site (DeLeon Springs) were stocked at two periods of time into ponds constructed in former farm fields on the north shore of the lake. LMB were stocked during early and late oogenesis to determine if there were different effects of contamination on LMB that may be attributed to their reproductive stage. LMB inhabiting the ponds for ~4months had anywhere from 2 to 800 times higher contaminant load for a number of organochlorine pesticides (e.g. p, p'-DDE, methoxychlor) compared to control animals. Gonadosomatic index and plasma vitellogenin were not different between reproductively-stage matched LMB collected at reference sites compared to those inhabiting the ponds. However, plasma 17ß-estradiol was lower in LMB inhabiting the Apopka ponds compared to ovary stage-matched LMB from the St. Johns River, a site used as a reference site. Sub-network enrichment analysis revealed that genes related to reproduction (granulosa function, oocyte development), endocrine function (steroid metabolism, hormone biosynthesis), and immune function (T cell suppression, leukocyte accumulation) were differentially expressed in the ovaries of LMB placed into the ponds. These data suggest that (1) LMB inhabiting the Apopka ponds showed disrupted reproduction and immune responses and that (2) gene expression profiles provided site-specific information by discriminating LMB from different macro-habitats.

Gene expression profiling for targeted cancer treatment.

INTRODUCTION: There is certain degree of frustration and discontent in the area of microarray gene expression data analysis of cancer datasets. It arises from the mathematical problem called 'curse of dimensionality,' which is due to the small number of samples available in training sets, used for calculating transcriptional signatures from the large number of differentially expressed (DE) genes, measured by microarrays. The new generation of causal reasoning algorithms can provide solutions to the curse of dimensionality by transforming microarray data into activity of a small number of cancer hallmark pathways. This new approach can make feature space dimensionality optimal for mathematical signature calculations. AREAS COVERED: The author reviews the reasons behind the current frustration with transcriptional signatures derived from DE genes in cancer. He also provides an overview of the novel methods for signature calculations based on differentially variable genes and expression regulators. Furthermore, the authors provide perspectives on causal reasoning algorithms that use prior knowledge about regulatory events described in scientific literature to identify expression regulators responsible for the differential expression observed in cancer samples. EXPERT OPINION: The author advocates causal reasoning methods to calculate cancer pathway activity signatures. The current challenge for these algorithms is in ensuring quality of the knowledgebase. Indeed, the development of cancer hallmark pathway collections, together with statistical algorithms to transform activity of expression regulators into pathway activity, are necessary for causal reasoning to be used in cancer research.

Assessing gene network stability and individual variability in the fathead minnow (Pimephales promelas) transcriptome.

Transcriptomics is increasingly used to assess biological responses to environmental stimuli and stressors such as aquatic pollutants. However, fundamental studies characterizing individual variability in mRNA levels are lacking, which currently limits the use of transcriptomics in environmental monitoring assessments. To address individual variability in transcript abundance, we performed a meta-analysis on 231 microarrays that were conducted in the fathead minnow (FHM), a widely used toxicological model. The mean variability for gene probes was ranked from most to least variable based upon the coefficient of variation. Transcripts that were the most variable in individual tissues included NADH dehydrogenase flavoprotein 1, GTPase IMAP family member 7-like and v-set domain-containing T-cell activation inhibitor 1-like while genes encoding ribosomal proteins (rpl24 and rpl36), basic transcription factor 3, and nascent polypeptide-associated complex alpha subunit were the least variable in individuals across a range of microarray experiments. Gene networks that showed high variability (based upon the variation in expression of individual members within the network) included cell proliferation, metabolism (steroid, lipids, and glucose), cell adhesion, vascularization, and regeneration while those that showed low variability (more stability) included mRNA and rRNA processing, regulation of translational fidelity, RNA splicing, and ribosome biogenesis. Real-time PCR was conducted on a subset of genes for comparison of variability collected from the microarrays. There was a significant positive relationship between the two methods when measuring individual variability, suggesting that variability detected in microarray data can be used to guide decisions on sample sizes for measuring transcripts in real-time PCR experiments. A power analysis revealed that measuring estrogen receptor ba (esrba) requires fewer biological replicates than that of estrogen receptor bb (esrbb) in the gonad and samples sizes required to detect a 50% change for reproductive-related transcripts is between 12 and 20. Characterizing individual variability at the molecular level will prove necessary as efforts are made toward integrating molecular tools into environmental risk assessments.

Classifying chemical mode of action using gene networks and machine learning: a case study with the herbicide linuron.

The herbicide linuron (LIN) is an endocrine disruptor with an anti-androgenic mode of action. The objectives of this study were to (1) improve knowledge of androgen and anti-androgen signaling in the teleostean ovary and to (2) assess the ability of gene networks and machine learning to classify LIN as an anti-androgen using transcriptomic data. Ovarian explants from vitellogenic fathead minnows (FHMs) were exposed to three concentrations of either 5α-dihydrotestosterone (DHT), flutamide (FLUT), or LIN for 12h. Ovaries exposed to DHT showed a significant increase in 17ß-estradiol (E2) production while FLUT and LIN had no effect on E2. To improve understanding of androgen receptor signaling in the ovary, a reciprocal gene expression network was constructed for DHT and FLUT using pathway analysis and these data suggested that steroid metabolism, translation, and DNA replication are processes regulated through AR signaling in the ovary. Sub-network enrichment analysis revealed that FLUT and LIN shared more regulated gene networks in common compared to DHT. Using transcriptomic datasets from different fish species, machine learning algorithms classified LIN successfully with other anti-androgens. This study advances knowledge regarding molecular signaling cascades in the ovary that are responsive to androgens and anti-androgens and provides proof of concept that gene network analysis and machine learning can classify priority chemicals using experimental transcriptomic data collected from different fish species.

Meta-type analysis of dopaminergic effects on gene expression in the neuroendocrine brain of female goldfish.

Dopamine (DA) is a major neurotransmitter important for neuroendocrine control and recent studies have described genomic signaling pathways activated and inhibited by DA agonists and antagonists in the goldfish brain. Here we perform a meta-type analysis using microarray datasets from experiments conducted with female goldfish to characterize the gene expression responses that underlie dopaminergic signaling. Sexually mature, pre-spawning [gonadosomatic index (GSI) = 4.5 ± 1.3%] or sexually regressing (GSI = 3 ± 0.4%) female goldfish (15-40 g) injected intraperitoneally with either SKF 38393, LY 171555, SCH 23390, sulpiride, or a combination of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine and α-methyl-p-tyrosine. Microarray meta-type analysis identified 268 genes in the telencephalon and hypothalamus as having reciprocal (i.e., opposite between agonism and antagonism/depletion) fold change responses, suggesting that these transcripts are likely targets for DA-mediated regulation. Noteworthy genes included ependymin, vimentin, and aromatase, genes that support the significance of DA in neuronal plasticity and tissue remodeling. Sub-network enrichment analysis (SNEA) was used to identify common gene regulators and binding proteins associated with the differentially expressed genes mediated by DA. SNEA analysis identified gene expression targets that were related to three major categories that included cell signaling (STAT3, SP1, SMAD, Jun/Fos), immune response (IL-6, IL-1ß, TNFs, cytokine, NF-κB), and cell proliferation and growth (IGF1, TGFß1). These gene networks are also known to be associated with neurodegenerative disorders such as Parkinsons' disease, well-known to be associated with loss of dopaminergic neurons. This study identifies genes and networks that underlie DA signaling in the vertebrate CNS and provides targets that may be key neuroendocrine regulators. The results provide a foundation for future work on dopaminergic regulation of gene expression in fish model systems.