Multi-omics inference of differential breast cancer-related transcriptional regulatory network gene hubs between young Black and White patients.

OBJECTIVE: Breast cancers (BrCA) are a leading cause of illness and mortality worldwide. Black women have a higher incidence rate relative to white women prior to age 40 years, and a lower incidence rate after 50 years. The objective of this study is to identify -omics differences between the two breast cancer cohorts to better understand the disparities observed in patient outcomes. MATERIALS AND METHODS: Using Standard SQL, we queried ISB-CGC hosted Google BigQuery tables storing TCGA BrCA gene expression, methylation, and somatic mutation data and analyzed the combined multi-omics results using a variety of methods. RESULTS: Among Stage II patients 50 years or younger, genes PIK3CA and CDH1 are more frequently mutated in White (W50) than in Black or African American patients (BAA50), while HUWE1, HYDIN, and FBXW7 mutations are more frequent in BAA50. Over-representation analysis (ORA) and Gene Set Enrichment Analysis (GSEA) results indicate that, among others, the Reactome Signaling by ROBO Receptors gene set is enriched in BAA50. Using the Virtual Inference of Protein-activity by Enriched Regulon analysis (VIPER) algorithm, putative top 20 master regulators identified include NUPR1, NFKBIL1, ZBTB17, TEAD1, EP300, TRAF6, CACTIN, and MID2. CACTIN and MID2 are of prognostic value. We identified driver genes, such as OTUB1, with suppressed expression whose DNA methylation status were inversely correlated with gene expression. Networks capturing microRNA and gene expression correlations identified notable microRNA hubs, such as miR-93 and miR-92a-2, expressed at higher levels in BAA50 than in W50. DISCUSSION/CONCLUSION: The results point to several driver genes as being involved in the observed differences between the cohorts. The findings here form the basis for further mechanistic exploration.

A Tale of Two Diseases: Exploring Mechanisms Linking Diabetes Mellitus with Alzheimer's Disease.

Dementias, including the type associated with Alzheimer's disease (AD), are on the rise worldwide. Similarly, type 2 diabetes mellitus (T2DM) is one of the most prevalent chronic diseases globally. Although mechanisms and treatments are well-established for T2DM, there remains much to be discovered. Recent research efforts have further investigated factors involved in the etiology of AD. Previously perceived to be unrelated diseases, commonalities between T2DM and AD have more recently been observed. As a result, AD has been labeled as "type 3 diabetes". In this review, we detail the shared processes that contribute to these two diseases. Insulin resistance, the main component of the pathogenesis of T2DM, is also present in AD, causing impaired brain glucose metabolism, neurodegeneration, and cognitive impairment. Dysregulation of insulin receptors and components of the insulin signaling pathway, including protein kinase B, glycogen synthase kinase 3ß, and mammalian target of rapamycin are reported in both diseases. T2DM and AD also show evidence of inflammation, oxidative stress, mitochondrial dysfunction, advanced glycation end products, and amyloid deposition. The impact that changes in neurovascular structure and genetics have on the development of these conditions is also being examined. With the discovery of factors contributing to AD, innovative treatment approaches are being explored. Investigators are evaluating the efficacy of various T2DM medications for possible use in AD, including but not limited to glucagon-like peptide-1 receptor agonists and peroxisome proliferator-activated receptor-gamma agonists. Furthermore, there are 136 active trials involving 121 therapeutic agents targeting novel AD biomarkers. With these efforts, we are one step closer to alleviating the ravaging impact of AD on our communities.

DOKI: Domain knowledge-driven inference method for reverse-engineering transcriptional regulatory relationships among genes in cancer.

Efficient reverse-engineering methods are important for identifying transcriptional regulatory relationships among genes in cancer. These methods are becoming increasingly useful in this era where huge volumes of data are generated through the use of high-throughput technologies such as next-generation sequencing technologies and microarrays. However, it is important to improve current methods because of complications involved in modelling complex biological systems. In this paper, we present a novel approach, Domain Knowledge-driven Inference (DOKI), for identification of transcriptional regulatory relationships among genes, given a biological context such as cancer. Combining data normalization, the use of a probability distribution function and Kullback-Leibler Divergence, DOKI incorporates a domain knowledge-driven criterion to make determinations of the existence of regulatory relationships between given transcription factors and given specific gene targets. Characteristics of DOKI enable it to adequately handle complexities inherent in data, and accurately unearth linear and higher-order dependent relationships among genes. DOKI performed equally well with one established high-performing method and better than three other high-performing methods on relatively small data sets. However, it remarkably outperformed these methods on larger data sets to demonstrate its utility. Furthermore, we demonstrate the relevance of such inference algorithms for identifying novel relationships among genes in breast cancer, as some of the consensus results representing novel relationships were confirmed in previously published experimental results. Thus, DOKI will facilitate current efforts to gain etiological insights and help uncover new targeted therapies for various diseases.

High-fat diet induces fibrosis in mice lacking CYP2A5 and PPARα: a new model for steatohepatitis-associated fibrosis.

Obesity is linked to nonalcoholic steatohepatitis. Peroxisome proliferator-activated receptor-α (PPARα) regulates lipid metabolism. Cytochrome P-450 2A5 (CYP2A5) is a potential antioxidant and CYP2A5 induction by ethanol is CYP2E1 dependent. High-fat diet (HFD)-induced obesity and steatosis are more severe in CYP2A5 knockout (cyp2a5-/-) mice than in wild-type mice although PPARα is elevated in cyp2a5-/- mice. To examine why the upregulated PPARα failed to prevent the enhanced steatosis in cyp2a5-/- mice, we abrogate the upregulated PPARα in cyp2a5-/- mice by cross-breeding cyp2a5-/- mice with PPARα knockout (pparα-/-) mice to create pparα-/-/cyp2a5-/- mice. The pparα-/-/cyp2a5-/- mice, pparα-/- mice, and cyp2a5-/- mice were fed HFD to induce steatosis. After HFD feeding, more severe steatosis was developed in pparα-/-/cyp2a5-/- mice than in pparα-/- mice and cyp2a5-/- mice. The pparα-/-/cyp2a5-/- mice and pparα-/- mice exhibited comparable and impaired lipid metabolism. Elevated serum alanine transaminase and liver interleukin-1ß, liver inflammatory cell infiltration, and foci of hepatocellular ballooning were observed in pparα-/-/cyp2a5-/- mice but not in pparα-/- mice and cyp2a5-/- mice. In pparα-/-/cyp2a5-/- mice, although redox-sensitive transcription factor nuclear factor erythroid 2-related factor 2 and its target antioxidant genes were upregulated as a compensation, thioredoxin was suppressed, and phosphorylation of JNK and formation of nitrotyrosine adduct were increased. Liver glutathione was decreased, and lipid peroxidation was increased. Interestingly, inflammation and fibrosis were all observed within the clusters of lipid droplets, and these lipid droplet clusters were all located inside the area with CYP2E1-positive staining. These results suggest that HFD-induced fibrosis in pparα-/-/cyp2a5-/- mice is associated with steatosis, and CYP2A5 interacts with PPARα to participate in regulating steatohepatitis-associated fibrosis.

Enhanced immortalization, HUWE1 mutations and other biological drivers of breast invasive carcinoma in Black/African American patients.

Black/African-American (B/AA) breast cancer patients tend to have more aggressive tumor biology compared to White/Caucasians. In this study, a variety of breast tumor molecular expression profiles of patients derived from the two racial groupings were investigated. Breast invasive carcinoma sample data (RNASeq version 2, Reverse Phase Protein Array, mutation, and miRSeq data) from the Cancer Genome Atlas were examined. The results affirm that B/AA patients are more likely than Caucasian patients to harbor the aggressive basal-like or the poor prognosis-associated HER2-enriched molecular subtypes of breast cancer. There is also a higher incidence of the triple-negative breast cancer (TNBC) among B/AA patients than the general population, a fact reflected in the mutation patterns of genes such as PIK3CA and TP53. Furthermore, an immortalization signature gene set, is enriched in samples from B/AA patients. Among stage III patients, TERT, DRAP1, and PQBP1, all members of the immortalization gene signature set, are among master-regulators with increased activity in B/AA patients. Master-regulators driving differences in expression profiles between the two groups include immortalization markers, senescence markers, and immune response and redox gene products. Differences in expression, between B/AA and Caucasian patients, of RB1, hsa-let-7a, E2F1, c-MYC, TERT, and other biomolecules appear to cooperate to enhance entry into the S-phase of the cell cycle in B/AA patients. Higher expression of miR-221, an oncomiR that facilitates entry into the cell cycle S-phase, is regulated by c-MYC, which is expressed more in breast cancer samples from B/AA patients. Furthermore, the cell migration- and invasion-promoting miRNA, miR-135b, has increased relative expression in B/AA patients. Knock down of the immortalization marker TERT inhibited triple-negative breast cancer cell lines (MDA-MB-231 and MDA-MB-468) cell viability and decreased expression of TERT, MYC and WNT11. For those patients with available survival data, prognosis of stage II patients 50 years of age or younger at diagnosis, was distinctly poorer in B/AA patients. Also associated with this subset of B/AA patients are missense mutations in HUWE1 and PTEN expression loss. Relative to Caucasian non-responders to endocrine therapy, B/AA non-responders show suppressed expression of a signature gene set on which biological processes including signaling by interleukins, circadian clock, regulation of lipid metabolism by PPARα, FOXO-mediated transcription, and regulation of TP53 degradation are over-represented. Thus, we identify molecular expression patterns suggesting diminished response to oxidative stress, changes in regulation of tumor suppressors/facilitators, and enhanced immortalization in B/AA patients are likely important in defining the more aggressive molecular tumor phenotype reported in B/AA patients.

Enhanced immortalization, HUWE1 mutations and other biological drivers of breast invasive carcinoma in Black/African American patients.

Black/African-American (B/AA) breast cancer patients tend to have more aggressive tumor biology compared to White/Caucasians. In this study, a variety of breast tumor molecular expression profiles of patients derived from the two racial groupings were investigated. Breast invasive carcinoma sample data (RNASeq version 2, Reverse Phase Protein Array, mutation, and miRSeq data) from the Cancer Genome Atlas were examined. The results affirm that B/AA patients are more likely than Caucasian patients to harbor the aggressive basal-like or the poor prognosis-associated HER2-enriched molecular subtypes of breast cancer. There is also a higher incidence of the triple-negative breast cancer (TNBC) among B/AA patients than the general population, a fact reflected in the mutation patterns of genes such as PIK3CA and TP53. Furthermore, an immortalization signature gene set, is enriched in samples from B/AA patients. Among stage III patients, TERT, DRAP1, and PQBP1, all members of the immortalization gene signature set, are among master-regulators with increased activity in B/AA patients. Master-regulators driving differences in expression profiles between the two groups include immortalization markers, senescence markers, and immune response and redox gene products. Differences in expression, between B/AA and Caucasian patients, of RB1, hsa-let-7a, E2F1, c-MYC, TERT, and other biomolecules appear to cooperate to enhance entry into the S-phase of the cell cycle in B/AA patients. Higher expression of miR-221, an oncomiR that facilitates entry into the cell cycle S-phase, is regulated by c-MYC, which is expressed more in breast cancer samples from B/AA patients. Furthermore, the cell migration- and invasion-promoting miRNA, miR-135b, has increased relative expression in B/AA patients. Knock down of the immortalization marker TERT inhibited triple-negative breast cancer cell lines (MDA-MB-231 and MDA-MB-468) cell viability and decreased expression of TERT, MYC and WNT11. For those patients with available survival data, prognosis of stage II patients 50 years of age or younger at diagnosis, was distinctly poorer in B/AA patients. Also associated with this subset of B/AA patients are missense mutations in HUWE1 and PTEN expression loss. Relative to Caucasian non-responders to endocrine therapy, B/AA non-responders show suppressed expression of a signature gene set on which biological processes including signaling by interleukins, circadian clock, regulation of lipid metabolism by PPARα, FOXO-mediated transcription, and regulation of TP53 degradation are over-represented. Thus, we identify molecular expression patterns suggesting diminished response to oxidative stress, changes in regulation of tumor suppressors/facilitators, and enhanced immortalization in B/AA patients are likely important in defining the more aggressive molecular tumor phenotype reported in B/AA patients.

MSclassifier: median-supplement model-based classification tool for automated knowledge discovery.

High-throughput technologies have resulted in an exponential growth of publicly available and accessible datasets for biomedical research. Efficient computational models, algorithms and tools are required to exploit the datasets for knowledge discovery to aid medical decisions. Here, we introduce a new tool, MSclassifier, based on median-supplement approaches to machine learning to enable an automated and effective binary classification for optimal decision making. The MSclassifier package estimates medians of features (attributes) to deduce supplementary data, which is subsequently introduced into the training set for balancing and building superior models for classification. To test our approach, it is used to determine HER2 receptor expression status phenotypes in breast cancer and also predict protein subcellular localization (plasma membrane and nucleus). Using independent sample and cross-validation tests, the performance of MSclassifier is evaluated and compared with well established tools that could perform such tasks. In the HER2 receptor expression status phenotype identification tasks, MSclassifier achieved statistically significant higher classification rates than the best performing existing tool (90.30% versus 89.83%, p=8.62e-3). In the subcellular localization prediction tasks, MSclassifier and one other existing tool achieved equally high performances (93.42% versus 93.19%, p=0.06) although they both outperformed tools based on Naive Bayes classifiers. Overall, the application and evaluation of MSclassifier reveal its potential to be applied to varieties of binary classification problems. The MSclassifier package provides an R-portable and user-friendly application to a broad audience, enabling experienced end-users as well as non-programmers to perform an effective classification in biomedical and other fields of study.

Restricted-derestricted dynamic Bayesian Network inference of transcriptional regulatory relationships among genes in cancer.

Understanding transcriptional regulatory relationships among genes is important for gaining etiological insights into diseases such as cancer. To this end, high-throughput biological data have been generated through advancements in a variety of technologies. These rely on computational approaches to discover underlying structures in such data. Among these computational approaches, Bayesian networks (BNs) stand out because their probabilistic nature enables them to manage randomness in the dynamics of gene regulation and experimental data. Feedback loops inherent in networks of regulatory relationships are more tractable when enhancements to BNs are applied to them. Here, we propose Restricted-Derestricted dynamic BNs with a novel search technique, Restricted-Derestricted Greedy Method, for such tasks. This approach relies on the Restricted-Derestricted Greedy search technique to infer transcriptional regulatory networks in two phases: restricted inference and derestricted inference. An application of this approach to real data sets reveals it performs favourably well compared to other existing well performing dynamic BN approaches in terms of recovering true relationships among genes. In addition, it provides a balance between searching for optimal networks and keeping biologically relevant regulatory interactions among variables.

Machine learning approaches to decipher hormone and HER2 receptor status phenotypes in breast cancer.

Breast cancer prognosis and administration of therapies are aided by knowledge of hormonal and HER2 receptor status. Breast cancer lacking estrogen receptors, progesterone receptors and HER2 receptors are difficult to treat. Regarding large data repositories such as The Cancer Genome Atlas, available wet-lab methods for establishing the presence of these receptors do not always conclusively cover all available samples. To this end, we introduce median-supplement methods to identify hormonal and HER2 receptor status phenotypes of breast cancer patients using gene expression profiles. In these approaches, supplementary instances based on median patient gene expression are introduced to balance a training set from which we build simple models to identify the receptor expression status of patients. In addition, for the purpose of benchmarking, we examine major machine learning approaches that are also applicable to the problem of finding receptor status in breast cancer. We show that our methods are robust and have high sensitivity with extremely low false-positive rates compared with the well-established methods. A successful application of these methods will permit the simultaneous study of large collections of samples of breast cancer patients as well as save time and cost while standardizing interpretation of outcomes of such studies.

Brain in situ hybridization maps as a source for reverse-engineering transcriptional regulatory networks: Alzheimer's disease insights.

Microarray data have been a valuable resource for identifying transcriptional regulatory relationships among genes. As an example, brain region-specific transcriptional regulatory events have the potential of providing etiological insights into Alzheimer Disease (AD). However, there is often a paucity of suitable brain-region specific expression data obtained via microarrays or other high throughput means. The Allen Brain Atlas in situ hybridization (ISH) data sets (Jones et al., 2009) represent a potentially valuable alternative source of high-throughput brain region-specific gene expression data for such purposes. In this study, Allen Brain Atlas mouse ISH data in the hippocampal fields were extracted, focusing on 508 genes relevant to neurodegeneration. Transcriptional regulatory networks were learned using three high-performing network inference algorithms. Only 17% of regulatory edges from a network reverse-engineered based on brain region-specific ISH data were also found in a network constructed upon gene expression correlations in mouse whole brain microarrays, thus showing the specificity of gene expression within brain sub-regions. Furthermore, the ISH data-based networks were used to identify instructive transcriptional regulatory relationships. Ncor2, Sp3 and Usf2 form a unique three-party regulatory motif, potentially affecting memory formation pathways. Nfe2l1, Egr1 and Usf2 emerge among regulators of genes involved in AD (e.g. Dhcr24, Aplp2, Tia1, Pdrx1, Vdac1, and Syn2). Further, Nfe2l1, Egr1 and Usf2 are sensitive to dietary factors and could be among links between dietary influences and genes in the AD etiology. Thus, this approach of harnessing brain region-specific ISH data represents a rare opportunity for gleaning unique etiological insights for diseases such as AD.

A regulatory role for the insulin- and BDNF-linked RORA in the hippocampus: implications for Alzheimer's disease.

Alzheimer's disease (AD) is the leading cause of dementia. The etiology of AD remains, in large part, unresolved. In this study, gene expression (microarray) data from postmortem brains in normal aged as well as AD-affected brains in conjunction with transcriptional regulatory networks were explored for etiological insights. The focus was on the hippocampus, a brain region key to memory and learning. The transcriptional regulatory networks were inferred using a trees-based (random forests or extra-trees) as well as a mutual information-based algorithm applied to compendia of adult mouse whole brain and hippocampus microarray data. Network nodes representing human orthologs of the mouse networks were used in the subsequent analysis. Among the potential transcriptional regulators tied to insulin or brain-derived neurotrophic factor (INS1, INS2, BDNF), whose peptide products have been linked to AD, is the Retinoic Acid Receptor-Related Orphan Receptor (RORA). RORA is a nuclear receptor transcription factor whose expression is distinctly upregulated in the AD hippocampus. A notable cross-section of genes differentially expressed in the AD hippocampus was found to be linked to RORA in the networks. Furthermore, several genes associated with RORA in the networks, such as APP, DNM1L, and TIA1 have been implicated in AD. Computationally-derived clusters and modules within the networks indicated strong ties between RORA and genes involved in the AD etiology. In addition, a functional mapping scheme using activity and interaction data affirmed the same network links to RORA. Thus, RORA emerges as a gene with a probable central role in the AD pathology/etiology.

SAGA: a hybrid search algorithm for Bayesian Network structure learning of transcriptional regulatory networks.

Bayesian Networks have been used for the inference of transcriptional regulatory relationships among genes, and are valuable for obtaining biological insights. However, finding optimal Bayesian Network (BN) is NP-hard. Thus, heuristic approaches have sought to effectively solve this problem. In this work, we develop a hybrid search method combining Simulated Annealing with a Greedy Algorithm (SAGA). SAGA explores most of the search space by undergoing a two-phase search: first with a Simulated Annealing search and then with a Greedy search. Three sets of background-corrected and normalized microarray datasets were used to test the algorithm. BN structure learning was also conducted using the datasets, and other established search methods as implemented in BANJO (Bayesian Network Inference with Java Objects). The Bayesian Dirichlet Equivalence (BDe) metric was used to score the networks produced with SAGA. SAGA predicted transcriptional regulatory relationships among genes in networks that evaluated to higher BDe scores with high sensitivities and specificities. Thus, the proposed method competes well with existing search algorithms for Bayesian Network structure learning of transcriptional regulatory networks.

Pharmacokinetics of artemisinin delivered by oral consumption of Artemisia annua dried leaves in healthy vs. Plasmodium chabaudi-infected mice.

ETHNOPHARMACOLOGICAL RELEVANCE: The Chinese have used Artemisia annua as a tea infusion to treat fever for >2000 years. The active component is artemisinin. Previously we showed that when compared to mice fed an equal amount of pure artemisinin, a single oral dose of dried leaves of Artemisia annua (pACT) delivered to Plasmodium chabaudi-infected mice reduced parasitemia at least fivefold. Dried leaves also delivered >40 times more artemisinin in the blood with no toxicity. The pharmacokinetics (PK) of artemisinin delivered from dried plant material has not been adequately studied. MATERIALS AND METHODS: Healthy and Plasmodium chabaudi-infected mice were oral gavaged with pACT to deliver a 100 mg kg(-1) body weight dose of artemisinin. Concentrations of serum artemisinin and one of its liver metabolites, deoxyartemisinin, were measured over two hours by GCMS. RESULTS: The first order elimination rate constant for artemisinin in pACT-treated healthy mice was estimated to be 0.80 h(-1) with an elimination half-life (T½) of 51.6 min. The first order absorption rate constant was estimated at 1.39 h(-1). Cmax and Tmax were 4.33 mg L(-1) and 60 min, respectively. The area under the curve (AUC) was 299.5 mg min L(-1). In contrast, the AUC for pACT-treated infected mice was significantly greater at 435.6 mg min L(-1). Metabolism of artemisinin to deoxyartemisinin was suppressed in infected mice over the period of observation. Serum levels of artemisinin in the infected mice continued to rise over the 120 min of the study period, and as a result, the T½ was not determined; the Cmax and Tmax were estimated at ≥6.64 mgL(-1) and ≥120 min, respectively. Groups of healthy mice were also fed either artemisinin or artemisinin mixed in mouse chow. When compared at 60 min, artemisinin was undetectable in the serum of mice fed 100 mg AN kg(-1) body weight. When plant material was present either as mouse chow or Artemisia annua pACT, artemisinin levels in the serum rose to 2.44 and 4.32 mg L(-1), respectively, indicating that the presence of the plant matrix, even that of mouse chow, had a positive impact on the appearance of artemisinin in the blood. CONCLUSIONS: These results showed that artemisinin and one of its drug metabolites were processed differently in healthy and infected mice. The results have implications for possible therapeutic use of pACT in treating malaria and other artemisinin-susceptible diseases.

Suppressed expression of T-box transcription factors is involved in senescence in chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a major global health problem. The etiology of COPD has been associated with apoptosis, oxidative stress, and inflammation. However, understanding of the molecular interactions that modulate COPD pathogenesis remains only partly resolved. We conducted an exploratory study on COPD etiology to identify the key molecular participants. We used information-theoretic algorithms including Context Likelihood of Relatedness (CLR), Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNE), and Inferelator. We captured direct functional associations among genes, given a compendium of gene expression profiles of human lung epithelial cells. A set of genes differentially expressed in COPD, as reported in a previous study were superposed with the resulting transcriptional regulatory networks. After factoring in the properties of the networks, an established COPD susceptibility locus and domain-domain interactions involving protein products of genes in the generated networks, several molecular candidates were predicted to be involved in the etiology of COPD. These include COL4A3, CFLAR, GULP1, PDCD1, CASP10, PAX3, BOK, HSPD1, PITX2, and PML. Furthermore, T-box (TBX) genes and cyclin-dependent kinase inhibitor 2A (CDKN2A), which are in a direct transcriptional regulatory relationship, emerged as preeminent participants in the etiology of COPD by means of senescence. Contrary to observations in neoplasms, our study reveals that the expression of genes and proteins in the lung samples from patients with COPD indicate an increased tendency towards cellular senescence. The expression of the anti-senescence mediators TBX transcription factors, chromatin modifiers histone deacetylases, and sirtuins was suppressed; while the expression of TBX-regulated cellular senescence markers such as CDKN2A, CDKN1A, and CAV1 was elevated in the peripheral lung tissue samples from patients with COPD. The critical balance between senescence and anti-senescence factors is disrupted towards senescence in COPD lungs.

PACAP interactions in the mouse brain: implications for behavioral and other disorders.

As an activator of adenylate cyclase, the neuropeptide Pituitary Adenylate Cyclase Activating Peptide (PACAP) impacts levels of cyclic AMP, a key second messenger available in brain cells. PACAP is involved in certain adult behaviors. To elucidate PACAP interactions, a compendium of microarrays representing mRNA expression in the adult mouse whole brain was pooled from the Phenogen database for analysis. A regulatory network was computed based on mutual information between gene pairs using gene expression data across the compendium. Clusters among genes directly linked to PACAP, and probable interactions between corresponding proteins were computed. Database "experts" affirmed some of the inferred relationships. The findings suggest ADCY7 is probably the adenylate cyclase isoform most relevant to PACAP's action. They also support intervening roles for kinases including GSK3B, PI 3-kinase, SGK3 and AMPK. Other high-confidence interactions are hypothesized for future testing. This new information has implications for certain behavioral and other disorders.

Subcellular location and molecular mobility of human cytosolic sulfotransferase 1C1 in living human embryonic kidney 293 cells.

Cytosolic sulfotransferases were first isolated from the hepatic cytosol, and they have been localized in the cytoplasm of formaldehyde-fixed human cell samples. The current work was carried out to determine the subcellular localization and molecular mobility of cytosolic sulfotransferases in living human embryonic kidney (HEK) 293 cells. In this work, the subcellular location of human cytosolic sulfotransferase 1C1 (SULT1C1) was studied in cultured HEK293 cells using confocal laser-scanning microscopy. A green fluorescent protein (GFP)-tagged SULT1C1 protein was localized in the cytoplasm of living HEK293 cells. This is consistent with results from previous studies on several other cytosolic sulfotransferase isoforms. Fluorescence recovery after photobleaching microscopy was performed to assess the molecular mobility of the expressed GFP-SULT1C1 molecules. The results suggested that the expressed recombinant GFP-SULT1C1 molecules in living HEK293 cells may include both mobile and immobile populations. To obtain additional insights into the subcellular location of SULT1C1, two machine learning algorithms, Sequential Minimal Optimization and Multilayer Perceptron, were used to compute the probability distribution for the localization of SULT1C1 in nine selected cellular compartments. The resulting probability distribution suggested that the most likely subcellular location of SULT1C1 is the cytosol.

Ethanol sensitivity: a central role for CREB transcription regulation in the cerebellum.

BACKGROUND: Lowered sensitivity to the effects of ethanol increases the risk of developing alcoholism. Inbred mouse strains have been useful for the study of the genetic basis of various drug addiction-related phenotypes. Inbred Long-Sleep (ILS) and Inbred Short-Sleep (ISS) mice differentially express a number of genes thought to be implicated in sensitivity to the effects of ethanol. Concomitantly, there is evidence for a mediating role of cAMP/PKA/CREB signalling in aspects of alcoholism modelled in animals. In this report, the extent to which CREB signalling impacts the differential expression of genes in ILS and ISS mouse cerebella is examined. RESULTS: A training dataset for Machine Learning (ML) and Exploratory Data Analyses (EDA) was generated from promoter region sequences of a set of genes known to be targets of CREB transcription regulation and a set of genes whose transcription regulations are potentially CREB-independent. For each promoter sequence, a vector of size 132, with elements characterizing nucleotide composition features was generated. Genes whose expressions have been previously determined to be increased in ILS or ISS cerebella were identified, and their CREB regulation status predicted using the ML scheme C4.5. The C4.5 learning scheme was used because, of four ML schemes evaluated, it had the lowest predicted error rate. On an independent evaluation set of 21 genes of known CREB regulation status, C4.5 correctly classified 81% of instances with F-measures of 0.87 and 0.67 respectively for the CREB-regulated and CREB-independent classes. Additionally, six out of eight genes previously determined by two independent microarray platforms to be up-regulated in the ILS or ISS cerebellum were predicted by C4.5 to be transcriptionally regulated by CREB. Furthermore, 64% and 52% of a cross-section of other up-regulated cerebellar genes in ILS and ISS mice, respectively, were deemed to be CREB-regulated. CONCLUSION: These observations collectively suggest that ethanol sensitivity, as it relates to the cerebellum, may be associated with CREB transcription activity.

Predicting the subcellular localization of human proteins using machine learning and exploratory data analysis.

Identifying the subcellular localization of proteins is particularly helpful in the functional annotation of gene products. In this study, we use Machine Learning and Exploratory Data Analysis (EDA) techniques to examine and characterize amino acid sequences of human proteins localized in nine cellular compartments. A dataset of 3,749 protein sequences representing human proteins was extracted from the SWISS-PROT database. Feature vectors were created to capture specific amino acid sequence characteristics. Relative to a Support Vector Machine, a Multi-layer Perceptron, and a Naive Bayes classifier, the C4.5 Decision Tree algorithm was the most consistent performer across all nine compartments in reliably predicting the subcellular localization of proteins based on their amino acid sequences (average Precision=0.88; average Sensitivity=0.86). Furthermore, EDA graphics characterized essential features of proteins in each compartment. As examples, proteins localized on the plasma membrane had higher proportions of hydrophobic amino acids; cytoplasmic proteins had higher proportions of neutral amino acids; and mitochondrial proteins had higher proportions of neutral amino acids and lower proportions of polar amino acids. These data showed that the C4.5 classifier and EDA tools can be effective for characterizing and predicting the subcellular localization of human proteins based on their amino acid sequences.

In utero ethanol suppresses cerebellar activator protein-1 and nuclear factor-kappa B transcriptional activation in a rat fetal alcohol syndrome model.

A model of fetal alcohol syndrome was used to investigate prenatal ethanol effects on cerebellar transcription factors. Pregnant Sprague-Dawley rats were divided into three treatment groups: ethanol-exposed (E), calorically matched pair-fed (PF), and freely fed ad libitum (AL) groups. Ethanol exposure was stopped 2 days before parturition. The DNA binding in neonatal cerebella of the redox-sensitive transcription factors nuclear factor-kappa B (NF-kappa B) and activator protein-1 (AP-1) were determined by electrophoretic mobility shift assays. On the first postnatal day (PD1), there was decreased activation of these transcription factors in the E group relative to the control groups. The PD1 transcriptional effects were reversed as the neonate underwent development without further ethanol exposure. Western blot studies showed no corresponding decreases in protein amounts of both AP-1 and NF-kappa B components on PD1. Postnatal glutathione levels and catalase activity, as measures of oxidative stress hypothesized to be a probable cause of the transcriptional effects, showed no statistically significant effects attributable to ethanol. Examination of prenatal cerebella on embryonic day 20 (EM20), a time during ethanol exposure, showed DNA-binding trends similar to those of PD1. EM20 Western blot studies showed decreases in the levels of the active form of glycogen synthase kinase-3 (GSK-3). GSK-3 inhibition was reversed by PD1. Blocking of GSK-3 activity with gestational dietary lithium diminished both AP-1 and NF-kappa B DNA binding. Thus, prenatal ethanol exposure has the effect of diminishing pro-survival transcriptional activation, an effect possibly mediated by changes in GSK-3 activity.