A combined approach to data mining of textual and structured data to identify cancer-related targets

Low-Level Light Therapy Downregulates Scalp Inflammatory Biomarkers in Men With Androgenetic Alopecia and Boosts Minoxidil 2% to Bring a Sustainable Hair Regrowth Activity

BACKGROUND AND OBJECTIVES

Low-level light therapies using visible to infrared light are known to activate several cellular functions, such as adenosine triphosphate and nitric oxide synthesis. However, few clinical observations report its biological consequences for skin and scalp homeostasis. Since scalp inflammation was recognized as a potential physiological obstacle to the efficacy of the reference hair regrowth drug Minoxidil in vivo and since perifollicular inflammation is the hallmark of about 50%-70% follicular units in androgenetic alopecia, we decided to investigate whether the anti-inflammatory activity of LLLT/GentleWaves® device were assigned to L'Oréal by Light BioScience L.L.C., Virginia Beach, VA (US) could enhance hair regrowth activity of Minoxidil.

STUDY DESIGN/MATERIALS AND METHODS

We conducted a first experimental clinical study on 64 men with androgenetic alopecia using LLLT/GentleWaves®, 590-nm predominant wavelength 70 seconds, specifically pulsed once per day, for 3 days, and we performed a whole-genome analysis of treated scalp biopsies. In a second clinical study, including 135 alopecic volunteers, we evaluated the hair regrowth activity in response to the upgraded LLLT/GentleWaves® device and Minoxidil.

RESULTS

In the first clinical study, whole-genome analysis of treated scalp biopsies showed downregulation of scalp inflammatory biomarkers, such as AP1/FOSB messenger RNA (mRNA) and mir21, together with the disappearance of CD69 mRNA, specific to scalp-infiltrating T cells of about 50% of the studied volunteers prior to the LLLT/GentleWaves® treatment. In the second clinical study, we observed that LLLT/GentleWaves® was able to boost the hair regrowth activity of a Minoxidil 2% lotion to the extent of the highest concentration (5%) in terms of efficacy, number of responders, and perceived performance.

CONCLUSIONS

Altogether, these observations suggest the potential benefit of LLLT/GentleWaves® as a noninvasive adjunctive technology for skin and scalp conditions, where a mild perifollicular inflammation is involved. Lasers Surg. Med. 2021. Copyright © 2021 Wiley Periodicals LLC.

Integrative Analysis Revealing Human Heart-Specific Genes and Consolidating Heart-Related Phenotypes

Elucidating expression patterns of heart-specific genes is crucial for understanding developmental, physiological, and pathological processes of the heart. The aim of the present study is to identify functionally and pathologically important heart-specific genes by performing the Ingenuity Pathway Analysis (IPA). Through a median-based analysis of tissue-specific gene expression based on the Genotype-Tissue Expression (GTEx) data, we identified 56 genes with heart-specific or elevated expressions in the heart (heart-specific/enhanced), among which three common heart-specific/enhanced genes and four atrial appendage-specific/enhanced genes were unreported regarding the heart. Differential expression analysis further revealed 225 differentially expressed genes (DEGs) between atrial appendage and left ventricle. Our integrative analyses of those heart-specific/enhanced genes and DEGs with IPA revealed enriched heart-related traits and diseases, consolidating evidence of relationships between these genes and heart function. Our reports on comprehensive identification of heart-specific/enhanced genes and DEGs and their relation to pathways associated with heart-related traits and diseases provided molecular insights into essential regulators of cardiac physiology and pathophysiology and potential new therapeutic targets for heart diseases.

Drugena: A Fully Automated Immunoinformatics Platform for the Design of Antibody-Drug Conjugates Against Neurodegenerative Diseases

Antibodies are proteins that are the first line of defense in the adaptive immune response of vertebrates. Thereby, they are involved in a multitude of biochemical mechanisms and clinical manifestations with significant medical interest, such as autoimmunity, the regulation of infection, and cancer. An emerging field in antibody science that is of huge medicinal interest is the development of novel antibody-interacting drugs. Such entities are the antibody-drug conjugates (ADCs), which are a new type of targeted therapy, which consist of an antibody linked to a payload drug. Overall, the underlying principle of ADCs is the discerning delivery of a drug to a target, hoping to increase the potency of the original drug. Drugena suite is a pioneering platform that employs state-of-the-art computational biology methods in the fight against neurodegenerative diseases using ADCs. Drugena encompasses an up-to-date structural database of specialized antibodies for neurological disorders and the NCI database with over 96 million entities for the in silico development of ADCs. The pipeline of the Drugena suite has been divided into several steps and modules that are closely related with a synergistic fashion under a user-friendly graphical user interface.

Antibody Clustering Using a Machine Learning Pipeline that Fuses Genetic, Structural, and Physicochemical Properties

Antibody V domain clustering is of paramount importance to a repertoire of immunology-related areas. Although several approaches have been proposed for antibody clustering, still no consensus has been reached. Numerous attempts use information from genes, protein sequences, 3D structures, and 3D surfaces in an effort to elucidate unknown action mechanisms directly related to their function and to either link them directly to diseases or drive the discovery of new medicines, such as antibody drug conjugates (ADC). Herein, we describe a new V domain antibody clustering method based on the comparison of the interaction sites between each antibody and its antigen. A more specific clustering analysis of the antibody's V domain was provided using deep learning and data mining techniques. The multidimensional information was extracted from the structural resolved antibodies when they were captured to interact with other proteins. The available 3D structures of protein antigen-antibody (Ag-Ab) interfaces contain information about how antibody V domains recognize antigens as well as about which amino acids are involved in the recognition. As such, the antibody surface holds information about antigens' folding that reside with the Ab-Ag interface residues and how they interact. In order to gain insight into the nature of such interactions, we propose a new simple philosophy to transform the conserved framework (fragment regions, complementarity-determining regions) of antibody V domain in a binary form using structural features of antibody-antigen interactions, toward identifying new antibody signatures in V domain binding activity. Finally, an advanced three-level hybrid classification scheme has been set for clustering antibodies in subgroups, which can combine the information from the protein sequences, the three-dimensional structures, and specific "key patterns" of recognized interactions. The clusters provide multilevel information about antibodies and antibody-antigen complexes.

Prostate cancer cells enhance interleukin-15-mediated expansion of NK cells

OBJECTIVES

To identify cytokines that can activate and expand NK cells in the presence of prostate cancer cells in order to determine whether these agents may be useful in future intra-tumoural administration in pre-clinical and clinical prostate cancer trials.

MATERIALS AND METHODS

Lymphocytes isolated from normal donor blood were set up in co-cultures with either cancer or non-cancerous prostate cell lines, together with each of the cytokines interleukin (IL)-2, IL-12, IL-15, interferon (IFN)-γ or IL-21 for a period of 7 days. Then, expansion of NK cells, NKT cells and CD8 T cells was measured by flow cytometry and compared with the expansion of the same cells in the absence of prostate cells. The cytotoxic activity of NK cells, as measured by perforin and tumour cell killing, was also assessed. NK cell receptors and their corresponding ligands on prostate tumour cells were analysed to determine whether any of these were modulated by co-culture. The role of the tumour-secreted heat shock proteins HSP90 and HSP70 in the expansion of NK cells in the co-cultures was also investigated because of their effects on NK and CD8 T-cell activation.

RESULTS

We showed that, among a panel of cytokines known to cause NK cell activation and expansion, only IL-15 could actively induce expansion of NK, NKT and CD8 T cells in the presence of prostate cancer cell lines. Furthermore, the expansion of NK cells was far greater (up to 50% greater) in the presence of the cancer cells (LNCaP, PC3) than when lymphocytes were incubated alone. In contrast, non-cancerous cell lines (PNT2 and WPMY-1) did not exert any expansion of NK cells. The cytolytic activity of the NK cells, as measured by perforin, CD107a and killing of tumour cells, was also greatest in co-cultures with IL-15. Examination of NK cell receptors shows that NKG2D is upregulated to a greater degree in the presence of prostate cancer cells, compared with the upregulation with IL-15 in lymphocytes alone. However, blocking of NKG2D does not inhibit the enhanced expansion of NK cells in the presence of tumour cells.

CONCLUSIONS

Among a panel of NK cell-activating cytokines, IL-15 was the only cytokine that could stimulate expansion of NK cells in the presence of prostate cancer cells; therefore IL-15 may be a good candidate for novel future intra-tumoural therapy of the disease.

Transcriptome analysis revealed anti-obesity effects of the Sodium Alginate in high-fat diet -induced obese mice

Human obesity and overweight, caused by accumulated of fat, is the most commonly phenomenon from all over the world, especially in Western countries and Chinese mainland during the past three decades. Sodium Alginate, a polysaccharide extracted from brown seaweeds, has been proved its strong ability on body weight loss and anti-inflammatory response. However, no studies have been explored the effects of Sodium Alginate on colonic transcriptome, especially in obese individuals. Therefore, the current study was designed to detect whether Sodium Alginate could remit obesity and ease chronic metabolism disease through strengthening the bio-functionality of the lower intestine, particularly in colon. The data showed after Sodium Alginate gavaged for four weeks, the body weight, fat accumulation, triglyceride and total cholesterol were ameliorated in high fat diet induced obese mice. Sodium Alginate also improved the blood glucose level and lipopolysaccharides in serum. Furthermore, data from RNA sequence indicated that there were significantly changes in several genes, which involved in lipid metabolism and carbohydrate metabolism. In conclusion, these results suggested that Sodium Alginate could effectively suppress obesity and obesity related metabolic syndromes, due to the colonic transcriptome changes.

Identification of breast cancer candidate genes using gene co-expression and protein-protein interaction information

Breast cancer (BC) is one of the most common malignancies that could threaten female health. As the molecular mechanism of BC has not yet been completely discovered, identification of related genes of this disease is an important area of research that could provide new insights into gene function as well as potential treatment targets. Here we used subnetwork extraction algorithms to identify novel BC related genes based on the known BC genes (seed genes), gene co-expression profiles and protein-protein interaction network. We computationally predicted seven key genes (EPHX2, GHRH, PPYR1, ALPP, KNG1, GSK3A and TRIT1) as putative genes of BC. Further analysis shows that six of these have been reported as breast cancer associated genes, and one (PPYR1) as cancer associated gene. Lastly, we developed an expression signature using these seven key genes which significantly stratified 1660 BC patients according to relapse free survival (hazard ratio [HR], 0.55; 95% confidence interval [CI], 0.46–0.65; Logrank p = 5.5e−13). The 7-genes signature could be established as a useful predictor of disease prognosis in BC patients. Overall, the identified seven genes might be useful prognostic and predictive molecular markers to predict the clinical outcome of BC patients.

Transcriptome analysis of mRNA and microRNAs in intramuscular fat tissues of castrated and intact male Chinese Qinchuan cattle

Intramuscular fat (IMF) is known to enhance beef palatability and can be markedly increased by castration. However, there is little understanding of the molecular mechanism underlying the IMF deposition after castration of beef cattle. We hypothesize that genetic regulators function differently in IMF from bulls and steers. Therefore, after detecting serum testosterone and lipid parameter, as well as the contents of IMF at 6, 12, 18 and 24 months, we have investigated differentially expressed (DE) microRNAs (miRNAs) and mRNAs in IMF of bulls and steers at 24 months of age in Qinchuan cattle using next-generation sequencing, and then explored the possible biopathways regulating IMF deposition. Serum testosterone levels were significantly decreased in steers, whereas IMF content, serum total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and triglycerides (TGs) were markedly increased in steers. Comparing the results of steers and bulls, 580 upregulated genes and 1,120 downregulated genes in IMF tissues were identified as DE genes correlated with IMF deposition. The upregulated genes were mainly associated with lipid metabolism, lipogenesis and fatty acid transportation signalling pathways, and the downregulated genes were correlated with immune response and intracellular signal transduction. Concurrently, the DE miRNAs—important players in adipose tissue accumulation induced by castration—were also examined in IMF tissues; 52 DE miRNAs were identified. The expression profiles of selected genes and miRNAs were also confirmed by quantitative real-time PCR (qRT-PCR) assays. Using integrated analysis, we constructed the microRNA-target regulatory network which was supported by target validation using the dual luciferase reporter system. Moreover, Ingenuity Pathway Analysis (IPA) software was used to construct a molecular interaction network that could be involved in regulating IMF after castration. The detected molecular network is closely associated with lipid metabolism and adipocyte differentiation, which is supported by functional identification results of bta-let-7i on bovine preadipocytes. These results provided valuable insights into the molecular mechanisms of the IMF phenotype differences between steers and bulls.

Enzyme-Instructed Self-Assembly for Spatiotemporal Profiling of the Activities of Alkaline Phosphatases on Live Cells

Alkaline phosphatase (ALP), an ectoenzyme, plays important roles in biology. But there is no activity probes for imaging ALPs in live cell environment due to the diffusion and cytotoxicity of current probes. Here we report the profiling of the activities of ALPs on live cells by enzyme-instructed self-assembly (EISA) of a D-peptidic derivative that forms fluorescent, non-diffusive nanofibrils. Our study reveals the significantly higher activities of ALP on cancer cells than on stromal cells in their co-culture and shows an inherent and dynamic difference in ALP activities between drug sensitive and resistant cancer cells or between cancer cells with and without hormonal stimulation. Being complementary to genomic profiling of cells, EISA, as a reaction-diffusion controlled process, achieves high spatiotemporal resolution for profiling activities of ALPs of live cells at single cell level. The activity probes of ALP contribute to understanding the reversible phosphorylation/dephosphorylation in the extracellular domains that is an emerging frontier in biomedicine.

Polypharmacology in Drug Development: A Minireview of Current Technologies

Polypharmacology, the process in which a single drug is able to bind to multiple targets specifically and simultaneously, is an emerging paradigm in drug development. The potency of a given drug can be increased through the engagement of multiple targets involved in a certain disease. Polypharmacology may also help identify novel applications of existing drugs through drug repositioning. However, many problems and challenges remain in this field. Rather than covering all aspects of polypharmacology, this Minireview is focused primarily on recently reported techniques, from bioinformatics technologies to cheminformatics approaches as well as text-mining-based methods, all of which have made significant contributions to the research of polypharmacology.

Condensin I and II behaviour in interphase nuclei and cells undergoing premature chromosome condensation

Condensin is an integral component of the mitotic chromosome condensation machinery, which ensures orderly segregation of chromosomes during cell division. In metazoans, condensin exists as two complexes, condensin I and II. It is not yet clear what roles these complexes may play outside mitosis, and so we have examined their behaviour both in normal interphase and in premature chromosome condensation (PCC). We find that a small fraction of condensin I is retained in interphase nuclei, and our data suggests that this interphase nuclear condensin I is active in both gene regulation and chromosome condensation. Furthermore, live cell imaging demonstrates condensin II dramatically increases on G1 nuclei following completion of mitosis. Our PCC studies show condensins I and II and topoisomerase II localise to the chromosome axis in G1-PCC and G2/M-PCC, while KIF4 binding is altered. Individually, condensins I and II are dispensable for PCC. However, when both are knocked out, G1-PCC chromatids are less well structured. Our results define new roles for the condensins during interphase and provide new information about the mechanism of PCC.

Transcriptomic Sequencing Reveals a Set of Unique Genes Activated by Butyrate-Induced Histone Modification

Butyrate is a nutritional element with strong epigenetic regulatory activity as a histone deacetylase inhibitor. Based on the analysis of differentially expressed genes in the bovine epithelial cells using RNA sequencing technology, a set of unique genes that are activated only after butyrate treatment were revealed. A complementary bioinformatics analysis of the functional category, pathway, and integrated network, using Ingenuity Pathways Analysis, indicated that these genes activated by butyrate treatment are related to major cellular functions, including cell morphological changes, cell cycle arrest, and apoptosis. Our results offered insight into the butyrate-induced transcriptomic changes and will accelerate our discerning of the molecular fundamentals of epigenomic regulation.

Supramolecular Hydrogelators and Hydrogels: From Soft Matter to Molecular Biomaterials

In this review we intend to provide a relatively comprehensive summary of the work of supramolecular hydrogelators after 2004 and to put emphasis particularly on the applications of supramolecular hydrogels/hydrogelators as molecular biomaterials. After a brief introduction of methods for generating supramolecular hydrogels, we discuss supramolecular hydrogelators on the basis of their categories, such as small organic molecules, coordination complexes, peptides, nucleobases, and saccharides. Following molecular design, we focus on various potential applications of supramolecular hydrogels as molecular biomaterials, classified by their applications in cell cultures, tissue engineering, cell behavior, imaging, and unique applications of hydrogelators. Particularly, we discuss the applications of supramolecular hydrogelators after they form supramolecular assemblies but prior to reaching the critical gelation concentration because this subject is less explored but may hold equally great promise for helping address fundamental questions about the mechanisms or the consequences of the self-assembly of molecules, including low molecular weight ones. Finally, we provide a perspective on supramolecular hydrogelators. We hope that this review will serve as an updated introduction and reference for researchers who are interested in exploring supramolecular hydrogelators as molecular biomaterials for addressing the societal needs at various frontiers.

Transcriptomic Profiling of Spleen in Grass-Fed and Grain-Fed Angus Cattle

The grass-fed cattle obtain nutrients directly from pastures containing limited assimilable energy but abundant amount of fiber; by contrast, grain-fed steers receive a diet that is comprised mainly of grains and serves as an efficient source of high-digestible energy. Besides energy, these two types of diet differ in a large number of nutritional components. Additionally, animals maintained on rich-energy regimen are more likely to develop metabolic disorders and infectious diseases than pasture raised individuals. Thus, we hypothesize that spleen–a relevant immune organ–may function differently under disparate regimes. The objective of this study was to find the differentially expressed genes in the spleen of grass-fed and grain-fed steers, and furtherly explore the potential involved biopathways. Through RNA sequencing (RNA-Seq), we detected 123 differentially expressed genes. Based on these genes, we performed an Ingenuity Pathway Analysis (IPA) and identified 9 significant molecular networks and 13 enriched biological pathways. Two of the pathways, Nur77 signaling in T lymphocytes and calcium-induced T lymphocyte apoptosis which are immune related, contain a pair of genes HLA-DRA and NR4A1 with dramatically altered expression level. Collectively, our results provided valuable insights into understanding the molecular mechanism of spleen under varied feeding regimens.

Ruminal Transcriptomic Analysis of Grass-Fed and Grain-Fed Angus Beef Cattle

Beef represents a major diet component and one of the major sources of protein in human. The beef industry in the United States is currently undergoing changes and is facing increased demands especially for natural grass-fed beef. The grass-fed beef obtained their nutrients directly from pastures, which contained limited assimilable energy but abundant amount of fiber. On the contrary, the grain-fed steers received a grain-based regime that served as an efficient source of high-digestible energy. Lately, ruminant animals have been accused to be a substantial contributor for the green house effect. Therefore, the concerns from environmentalism, animal welfare and public health have driven consumers to choose grass-fed beef. Rumen is one of the key workshops to digest forage constituting a critical step to supply enough nutrients for animals' growth and production. We hypothesize that rumen may function differently in grass- and grain-fed regimes. The objective of this study was to find the differentially expressed genes in the ruminal wall of grass-fed and grain-fed steers, and then explore the potential biopathways. In this study, the RNA Sequencing (RNA-Seq) method was used to measure the gene expression level in the ruminal wall. The total number of reads per sample ranged from 24,697,373 to 36,714,704. The analysis detected 342 differentially expressed genes between ruminal wall samples of animals raised under different regimens. The Fisher's exact test performed in the Ingenuity Pathway Analysis (IPA) software found 16 significant molecular networks. Additionally, 13 significantly enriched pathways were identified, most of which were related to cell development and biosynthesis. Our analysis demonstrated that most of the pathways enriched with the differentially expressed genes were related to cell development and biosynthesis. Our results provided valuable insights into the molecular mechanisms resulting in the phenotype difference between grass-fed and grain-fed cattle.

The Use of Chemical-Chemical Interaction and Chemical Structure to Identify New Candidate Chemicals Related to Lung Cancer

Lung cancer causes over one million deaths every year worldwide. However, prevention and treatment methods for this serious disease are limited. The identification of new chemicals related to lung cancer may aid in disease prevention and the design of more effective treatments. This study employed a weighted network, constructed using chemical-chemical interaction information, to identify new chemicals related to two types of lung cancer: non-small lung cancer and small-cell lung cancer. Then, a randomization test as well as chemical-chemical interaction and chemical structure information were utilized to make further selections. A final analysis of these new chemicals in the context of the current literature indicates that several chemicals are strongly linked to lung cancer.

Ectoenzyme switches the surface of magnetic nanoparticles for selective binding of cancer cells

Enzymatic switch, such as phosphorylation and dephosphorylation of proteins, is the most important mechanism for cellular signal transductions. Inspired by Nature and encouraged by our recent unexpected observation of the dephosphorylation of d-tyrosine phosphate-contain small peptides, we modify the surface of magnetic nanoparticles (MNP) with d-tyrosine phosphate that is a substrate of alkaline phosphatase (ALP). Our studies find that ALP is able to remove the phosphate groups from the magnetic nanoparticles. Most importantly, placental alkaline phosphatase (ALPP), an ectoenzyme that locates on cell surface with catalytic domains outside the plasma membrane and is overexpressed on many cancer cells, dephosphorylate the d-tyrosine phosphates on the surface of the magnetic nanoparticle and enable the magnetic nanoparticles to adhere selectively to the cancer cells, such as HeLa cells. Unlikely commonly used antibodies, the selectivity of the magnetic nanoparticles to cancer cells originates from the enzymatic reaction catalyzed by ALPP. The use of enzymatic reaction to modulate the surface of various nanostructures may lead to a general method to broadly target cancer cells without relying on specific ligand-receptor interactions (e.g., antibodies). This work, thus, illustrates a fundamentally new concept to allow cells to actively engineer the surface of colloids materials, such as magnetic nanoparticles, for various applications.

Efficacy-oriented compatibility for component-based Chinese medicine

Single-target drugs have not achieved satisfactory therapeutic effects for complex diseases involving multiple factors. Instead, innovations in recent drug research and development have revealed the emergence of compound drugs, such as cocktail therapies and "polypills", as the frontier in new drug development. A traditional Chinese medicine (TCM) prescription that is usually composed of several medicinal herbs can serve a typical representative of compound medicines. Although the traditional compatibility theory of TCM cannot be well expressed using modern scientific language nowadays, the fundamental purpose of TCM compatibility can be understood as promoting efficacy and reducing toxicity. This paper introduces the theory and methods of efficacy-oriented compatibility for developing component-based Chinese medicines.

Synthesis of amphiphilic resveratrol lipoconjugates and evaluation of their anticancer activity towards neuroblastoma SH-SY5Y cell line

Resveratrol, a polyphenol present in grapes and other edible plants, possesses several important pharmacological activities, including anticancer activity. Nevertheless, its therapeutic use is still limited because of some unfavourable physicochemical and pharmacokinetic properties, mainly, poor cellular uptake and too rapid metabolism resulting in elimination from the body. To meet these drawbacks, some resveratrol conjugates would be useful, which would possess improved stability, uptake and bioavailability than the lead compound, and the ability to release it once it is internalized into the cell. In this paper we report a synthetic strategy which allowed us to obtain new amphiphilic resveratrol derivatives starting from different selectively protected resveratrol phosphoramidites or even from the resveratrol triphosphoramidite. Specifically, resveratrol was conjugated through phosphate bridge(s) to different lipophilic groups related to membrane lipids, such as cholesteryl or diacylglycero moieties. All the new lipoconjugates were tested towards human neuroblastoma SH-SY5Y cells and proved to be significantly more active than resveratrol, with a concentration-dependent activity.

Enzymatic transformation of phosphate decorated magnetic nanoparticles for selectively sorting and inhibiting cancer cells

As an important and necessary step of sampling biological specimens, the separation of malignant cells from a mixed population of cells usually requires sophisticated instruments and/or expensive reagents. For health care in the developing regions, there is a need for an inexpensive sampling method to capture tumor cells for rapid and accurate diagnosis. Here we show that an underexplored generic difference-overexpression of ectophosphatases-between cancer and normal cells triggers the d-tyrosine phosphate decorated magnetic nanoparticles (Fe3O4-p(d-Tyr)) to adhere selectively on cancer cells upon catalytic dephosphorylation, which enables magnetic separation of cancer cells from mixed population of cells (e.g., cocultured cancer cell (HeLa-GFP) and stromal cells (HS-5)). Moreover, the Fe3O4-p(d-Tyr) nanoparticles also selectively inhibit cancer cells in the coculture. As a general method to broadly target cancer cells without highly specific ligand-receptor interactions (e.g., antibodies), the use of an enzymatic reaction to spatiotemporally modulate the state of various nanostructures in cellular environments will ultimately lead to the development of new theranostic applications of nanomaterials.

Bioinformatic Dissecting of TP53 Regulation Pathway Underlying Butyrate-induced Histone Modification in Epigenetic Regulation

Butyrate affects cell proliferation, differentiation, and motility. Butyrate inhibits histone deacetylase (HDAC) activities and induces cell-cycle arrest and apoptosis. TP53 is one of the most active upstream regulators discovered by ingenuity pathways analysis (IPA) in our RNA-sequencing data set. TP53 signaling pathway plays key role in many cellular processes. TP53 pathway and their involvement in cellular functions modified by butyrate treatment were scrutinized in this report by data mining the RNA-sequencing data using IPA (Ingenuity System^®). The TP53 mechanistic pathway targets more than 600 genes. Downstream analysis predicted the activation of the TP53 pathway after butyrate treatment. The data mining also revealed that nine transcription factors are downstream regulators in TP53 signaling pathways. The analysis results also indicated that butyrate not only inhibits the HDAC activities, but also regulates genes encoding the HDAC enzymes through modification of histones and epigenomic landscape.

Repair of methyl lesions in RNA by oxidative demethylation

Nucleic acid methylation is one of the most important epigenetic modifications that have been studied intensively for the past several decades.

Butyrate Induced IGF2 Activation Correlated with Distinct Chromatin Signatures Due to Histone Modification

Histone modification has emerged as a very important mechanism regulating the transcriptional status of the genome. Insulin-like growth factor 2 (IGF2) is a peptide hormone controlling various cellular processes, including proliferation and apoptosis. H19 gene is closely linked to IGF2 gene, and IGF2 and H19 are reciprocally regulated imprinted genes. The epigenetic signature of H19 promoter (hypermethylation) on the paternal allele plays a vital role in allowing the expression of the paternal allele of IGF2.46 Our previous studies demonstrate that butyrate regulates the expression of IGF2 as well as genes encoding IGF Binding proteins. To obtain further understanding of histone modification and its regulatory potentials in controlling IGF2/H19 gene expression, we investigated the histone modification status of some key histones associated with the expression of IGF2/H19 genes in bovine cells using RNA-seq in combination with Chip-seq technology. A high-resolution map of the major chromatin modification at the IGF2/H19 locus induced by butyrate was constructed to illustrate the fundamental association of the chromatin modification landscape that may play a role in the activation of the IGF2 gene. High-definition epigenomic landscape mapping revealed that IGF2 and H19 have distinct chromatin modification patterns at their coding and promoter regions, such as TSSs and TTSs. Moreover, the correlation between the differentially methylated regions (DMRs) of IGF2/H19 locus and histone modification (acetylation and methylation) indicated that epigenetic signatures/markers of DNA methylation, histone methylation and histone acetylation were differentially distributed on the expressed IGF2 and silenced H19 genes. Our evidence also suggests that butyrate-induced regional changes of histone acetylation statusin the upstream regulation domain of H19 may be related to the reduced expression of H19 and strong activation of IGF2. Our results provided insights into the mechanism of butyrate-induced loss of imprinting (LOI) of IGF2 and regulation of gene expression by histone modification.

Transcriptome characterization by RNA-seq unravels the mechanisms of butyrate-induced epigenomic regulation in bovine cells

Short-chain fatty acids (SCFAs), especially butyrate, affect cell differentiation, proliferation, and motility. Butyrate also induces cell cycle arrest and apoptosis through its inhibition of histone deacetylases (HDACs). In addition, butyrate is a potent inducer of histone hyper-acetylation in cells. Therefore, this SCFA provides an excellent in vitro model for studying the epigenomic regulation of gene expression induced by histone acetylation. In this study, we analyzed the differential in vitro expression of genes induced by butyrate in bovine epithelial cells by using deep RNA-sequencing technology (RNA-seq). The number of sequences read, ranging from 57,303,693 to 78,933,744, were generated per sample. Approximately 11,408 genes were significantly impacted by butyrate, with a false discovery rate (FDR) <0.05. The predominant cellular processes affected by butyrate included cell morphological changes, cell cycle arrest, and apoptosis. Our results provided insight into the transcriptome alterations induced by butyrate, which will undoubtedly facilitate our understanding of the molecular mechanisms underlying butyrate-induced epigenomic regulation in bovine cells.

Computational and biological evaluation of quinazolinone prodrug for targeting pancreatic cancer

Our concept of enzyme-mediated cancer imaging and therapy aims to use radiolabeled compounds to target hydrolases over-expressed on the extracellular surface of solid tumors. A data mining approach identified extracellular sulfatase 1 (SULF1) as an enzyme expressed on the surface of pancreatic cancer cells. We designed, synthesized, and characterized 2-(2'-sulfooxyphenyl)-6-iodo-4-(3H)-quinazolinone (IQ(2-S)) as well as its radioiodinated form ((125) IQ(2-S)) as a prodrug with potential for hydrolysis by SULF1. IQ(2-S) was successfully docked in silico into three enzymes - homolog of SULF1, alkaline phosphatase, and prostatic acid phosphatase. The incubation of (125) IQ(2-S) and (125) IQ(2-P) with the three enzymes in solution confirms the docking results and enzyme selectivity for the analogs. The hydrolysis of both radioactive compounds produces the water-insoluble, fluorescent product 2-(2'-hydroxyphenyl)-6-[(125) I]iodo-4-(3H)-quinazolinone ((125) IQ(2-OH)). The in vitro incubation of (127) IQ(2-S) and (127) IQ(2-P) with pancreatic, ovarian, and prostate cancer cells expressing studied hydrolases also results in their hydrolysis and the precipitation of (127) IQ(2-OH) fluorescent crystals on the cell surface. To our knowledge, these findings are the first to report the targeting of a radioactive substrate to SULF1 and that this prodrug may be potentially useful in the imaging ((123) I/(124) I/(131) I) and radiotherapy ((131) I) of pancreatic cancer.

Alpha-Tocopherol Alters Transcription Activities that Modulates Tumor Necrosis Factor Alpha (TNF-α) Induced Inflammatory Response in Bovine Cells

To further investigate the potential role of α-tocopherol in maintaining immuno-homeostasis in bovine cells (Madin-Darby bovine kidney epithelial cell line), we undertook in vitro experiments using recombinant TNF-α as an immuno-stimulant to simulate inflammation response in cells with or without α-tocopherol pre-treatment. Using microarray global-profiling and IPA (Ingenuity Pathways Analysis, Ingenuity^® Systems, http://www.ingenuity.com) data analysis on TNF-α-induced gene perturbation in those cells, we focused on determining whether α-tocopherol treatment of normal bovine cells in a standard cell culture condition can modify cell’s immune response induced by TNF-α challenge. When three datasets were filtered and compared using IPA, there were a total of 1750 genes in all three datasets for comparison, 97 genes were common in all three sets; 615 genes were common in at least two datasets; there were 261 genes unique in TNF-α challenge, 399 genes were unique in α-tocopherol treatment, and 378 genes were unique in the α-tocopherol plus TNF-α treatment. TNF-α challenge induced significant change in gene expression. Many of those genes induced by TNF-α are related to the cells immune and inflammatory responses. The results of IPA data analysis showed that α-tocopherol-pretreatment of cells modulated cell’s response to TNF-α challenge. In most of the canonical pathways, α-tocopherol pretreatment showed the antagonistic effect against the TNF-α-induced pro-inflammatory responses. We concluded that α-tocopherol pre-treatment has a significant antagonistic effect that modulates the cell’s response to the TNF-α challenge by altering the gene expression activities of some important signaling molecules.

Human placental alkaline phosphatase-mediated hydrolysis correlates tightly with the electrostatic contribution from tail group

Human placental alkaline phosphatase has been identified as a hydrolase that is significantly overexpressed on the surface of various solid tumor cells, and is therefore a suitable prodrug design target for non-invasive cancer imaging and therapy. Structure-based prediction of enzymatic activities is essential for rational prodrug design. We have been probing the catalytic proficiency--(k(cat) /K(M) )/k(w)--of placental alkaline phosphatase toward several widely diverse substrate structures experimentally and correlating these results to in silico predictions that are based on the free energy estimates obtained from docking of each substrate structure with placental alkaline phosphatase. We have found that electrostatic contribution from the tail group is the most crucial factor to determine the catalytic efficiencies of the substrates. The electrostatic contribution and the total binding energy of the tail group are well correlated with catalytic efficiencies (R² = 0.79 and 0.89, respectively). However, hydrophobic contribution from the tail group does not correlate with the catalytic efficiencies (negative correlation, R² = 0.27). This supports the prior hypothesis stating that alkaline phosphatase-mediated differential hydrolysis of its substrates is attributable to the differential interactions with the tail group, determined by the electrostatic contributions from the non-bridging oxygen atoms. Calculation of the electrostatic potentials within the active site of human placental alkaline phosphatase also suggests that the local positive electrostatic environment may account for its capability to distinguish various substrates. Our study is likely to have immediate implications in the design of prodrugs against human placental alkaline phosphatase and other esterases overexpressed by human tumor cells.

Integrated bioinformatics analysis for cancer target identification

The exponential growth of high-throughput Omics data has provided an unprecedented opportunity for new target identification to fuel the dried-up drug discovery pipeline. However, the bioinformatics analysis of large amount and heterogeneous Omics data has posed a great deal of technical challenges for experimentalists who lack statistical skills. Moreover, due to the complexity of human diseases, it is essential to analyze the Omics data in the context of molecular networks to detect meaningful biological targets and understand disease processes. Here, we describe an integrated bioinformatics analysis strategy and provide a running example to identify suitable targets for our in-house Enzyme-Mediated Cancer Imaging and Therapy (EMCIT) technology. In addition, we go through a few key concepts in the process, including corrected false discovery rate (FDR), Gene Ontology (GO), pathway analysis, and tissue specificity. We also describe popular programs and databases which allow the convenient annotation and network analysis of Omics data. We provide a practical guideline for researchers to quickly follow the protocol described and identify those targets that are pertinent to their work.

Preliminary evidence of ubiquitin proteasome system dysregulation in schizophrenia and bipolar disorder: convergent pathway analysis findings from two independent samples

Schizophrenia (SCZ) and bipolar disorder (BPD) are polygenic disorders with many genes contributing to their etiologies. The aim of this investigation was to search for dysregulated molecular and cellular pathways for these disorders as well as psychosis. We conducted a blood-based microarray investigation in two independent samples with SCZ and BPD from San Diego (SCZ = 13, BPD = 9, control = 8) and Taiwan (SCZ = 11, BPD = 14, control = 16). Diagnostic groups were compared to controls, and subjects with a history of psychosis [PSYCH(+): San Diego (n = 6), Taiwan (n = 14)] were compared to subjects without such history [PSYCH(-): San Diego (n = 11), Taiwan (n = 14)]. Analyses of covariance comparing mean expression levels on a gene-by-gene basis were conducted to generate the top 100 significantly dysregulated gene lists for both samples by each diagnostic group. Gene lists were imported into Ingenuity Pathway Analysis (IPA) software. Results showed the ubiquitin proteasome pathway (UPS) was listed in the top ten canonical pathways for BPD and psychosis diagnostic groups across both samples with a considerably low likelihood of a chance occurrence (P = 0.001). No overlap in dysregulated genes populating these pathways was observed between the two independent samples. Findings provide preliminary evidence of UPS dysregulation in BPD and psychosis as well as support further investigation of the UPS and other molecular and cellular pathways for potential biomarkers for SCZ, BPD, and/or psychosis.

The power of the web in cancer drug discovery and clinical trial design: research without a laboratory?

The discovery of effective cancer treatments is a key goal for pharmaceutical companies. However, the current costs of bringing a cancer drug to the market in the USA is now estimated at $1 billion per FDA approved drug, with many months of research at the bench and costly clinical trials. A growing number of papers highlight the use of data mining tools to determine associations between drugs, genes or protein targets, and possible mechanism of actions or therapeutic efficacy which could be harnessed to provide information that can refine or direct new clinical cancer studies and lower costs. This report reviews the paper by R.J. Epstein, which illustrates the potential of text mining using Boolean parameters in cancer drug discovery, and other studies which use alternative data mining approaches to aid cancer research.

Analysis of biological processes and diseases using text mining approaches

A number of biomedical text mining systems have been developed to extract biologically relevant information directly from the literature, complementing bioinformatics methods in the analysis of experimentally generated data. We provide a short overview of the general characteristics of natural language data, existing biomedical literature databases, and lexical resources relevant in the context of biomedical text mining. A selected number of practically useful systems are introduced together with the type of user queries supported and the results they generate. The extraction of biological relationships, such as protein-protein interactions as well as metabolic and signaling pathways using information extraction systems, will be discussed through example cases of cancer-relevant proteins. Basic strategies for detecting associations of genes to diseases together with literature mining of mutations, SNPs, and epigenetic information (methylation) are described. We provide an overview of disease-centric and gene-centric literature mining methods for linking genes to phenotypic and genotypic aspects. Moreover, we discuss recent efforts for finding biomarkers through text mining and for gene list analysis and prioritization. Some relevant issues for implementing a customized biomedical text mining system will be pointed out. To demonstrate the usefulness of literature mining for the molecular oncology domain, we implemented two cancer-related applications. The first tool consists of a literature mining system for retrieving human mutations together with supporting articles. Specific gene mutations are linked to a set of predefined cancer types. The second application consists of a text categorization system supporting breast cancer-specific literature search and document-based breast cancer gene ranking. Future trends in text mining emphasize the importance of community efforts such as the BioCreative challenge for the development and integration of multiple systems into a common platform provided by the BioCreative Metaserver.

Discovery of novel serum biomarkers for prenatal Down syndrome screening by integrative data mining

Background

To facilitate the experimental search for novel maternal serum biomarkers in prenatal Down Syndrome screening, we aimed to create a set of candidate biomarkers using a data mining approach.

Methodology/Principal Findings

Because current screening markers are derived from either fetal liver or placental trophoblasts, we reasoned that new biomarkers can primarily be found to be derived from these two tissues. By applying a three-stage filtering strategy on publicly available data from different sources, we identified 49 potential blood-detectable protein biomarkers. Our set contains three biomarkers that are currently widely used in either first- or second-trimester screening (AFP, PAPP-A and fβ-hCG), as well as ten other proteins that are or have been examined as prenatal serum markers. This supports the effectiveness of our strategy and indicates the set contains other markers potentially applicable for screening.

Conclusions/Significance

We anticipate the set will help support further experimental studies for the identification of new Down Syndrome screening markers in maternal blood.

Target discovery from data mining approaches

Data mining of available biomedical data and information has greatly boosted target discovery in the 'omics' era. Target discovery is the key step in the biomarker and drug discovery pipeline to diagnose and fight human diseases. In biomedical science, the 'target' is a broad concept ranging from molecular entities (such as genes, proteins and miRNAs) to biological phenomena (such as molecular functions, pathways and phenotypes). Within the context of biomedical science, data mining refers to a bioinformatics approach that combines biological concepts with computer tools or statistical methods that are mainly used to discover, select and prioritize targets. In response to the huge demand of data mining for target discovery in the 'omics' era, this review explicates various data mining approaches and their applications to target discovery with emphasis on text and microarray data analysis. Two emerging data mining approaches, chemogenomic data mining and proteomic data mining, are briefly introduced. Also discussed are the limitations of various data mining approaches found in the level of database integration, the quality of data annotation, sample heterogeneity and the performance of analytical and mining tools. Tentative strategies of integrating different data sources for target discovery, such as integrated text mining with high-throughput data analysis and integrated mining with pathway databases, are introduced.

Integrative genomic data mining for discovery of potential blood-borne biomarkers for early diagnosis of cancer

Background

With the arrival of the postgenomic era, there is increasing interest in the discovery of biomarkers for the accurate diagnosis, prognosis, and early detection of cancer. Blood-borne cancer markers are favored by clinicians, because blood samples can be obtained and analyzed with relative ease. We have used a combined mining strategy based on an integrated cancer microarray platform, Oncomine, and the biomarker module of the Ingenuity Pathways Analysis (IPA) program to identify potential blood-based markers for six common human cancer types.

Methodology/Principal Findings

In the Oncomine platform, the genes overexpressed in cancer tissues relative to their corresponding normal tissues were filtered by Gene Ontology keywords, with the extracellular environment stipulated and a corrected Q value (false discovery rate) cut-off implemented. The identified genes were imported to the IPA biomarker module to separate out those genes encoding putative secreted or cell-surface proteins as blood-borne (blood/serum/plasma) cancer markers. The filtered potential indicators were ranked and prioritized according to normalized absolute Student t values. The retrieval of numerous marker genes that are already clinically useful or under active investigation confirmed the effectiveness of our mining strategy. To identify the biomarkers that are unique for each cancer type, the upregulated marker genes that are in common between each two tumor types across the six human tumors were also analyzed by the IPA biomarker comparison function.

Conclusion/Significance

The upregulated marker genes shared among the six cancer types may serve as a molecular tool to complement histopathologic examination, and the combination of the commonly upregulated and unique biomarkers may serve as differentiating markers for a specific cancer. This approach will be increasingly useful to discover diagnostic signatures as the mass of microarray data continues to grow in the ‘omics’ era.

Novel prodrugs for targeting diagnostic and therapeutic radionuclides to solid tumors

Most cancer therapeutics (chemo, radiation, antibody-based, anti-angiogenic) are at best partially and/or temporarily effective. In general, the causes for failure can be summarized as: (i) poor diffusion and/or nonuniform distribution of drug/prodrug molecules in solid tumors; (ii) high drug concentration and retention in normal tissues (leading to side effects); (iii) requirement for plasma-membrane permeability and/or internalization of drug/prodrug molecules; (iv) low uptake of drug by tumor; (v) lack of retention of drug within tumor (most have gradient-driven reversible binding); and (vi) multidrug resistance. We are developing an innovative technology that aims to surmount these problems by actively concentrating and permanently entrapping radioimaging and radiotherapeutic prodrugs specifically within solid tumors. The approach will enable noninvasive sensing (imaging) and effective therapy of solid tumors, allowing tumor detection, diagnosis, and treatment to be closely coupled (personalized medicine).

Transcriptional networks inferred from molecular signatures of breast cancer

Global genomic approaches in cancer research have provided new and innovative strategies for the identification of signatures that differentiate various types of human cancers. Computational analysis of the promoter composition of the genes within these signatures may provide a powerful method for deducing the regulatory transcriptional networks that mediate their collective function. In this study we have systematically analyzed the promoter composition of gene classes derived from previously established genetic signatures that recently have been shown to reliably and reproducibly distinguish five molecular subtypes of breast cancer associated with distinct clinical outcomes. Inferences made from the trends of transcription factor binding site enrichment in the promoters of these gene groups led to the identification of regulatory pathways that implicate discrete transcriptional networks associated with specific molecular subtypes of breast cancer. One of these inferred pathways predicted a role for nuclear factor-kappaB in a novel feed-forward, self-amplifying, autoregulatory module regulated by the ERBB family of growth factor receptors. The existence of this pathway was verified in vivo by chromatin immunoprecipitation and shown to be deregulated in breast cancer cells overexpressing ERBB2. This analysis indicates that approaches of this type can provide unique insights into the differential regulatory molecular programs associated with breast cancer and will aid in identifying specific transcriptional networks and pathways as potential targets for tumor subtype-specific therapeutic intervention.

High throughput molecular diagnostics in bladder cancer - on the brink of clinical utility

An enormous body of high-throughput genome-wide data, in particular gene expression data, has been gathered from roughly all human cancer forms in the past 10 years. This has widely increased our understanding of the cancer disease and its molecular changes and pathways, with a large contribution from studies of cancer cell lines and functional genomics. In the last three years, the focus has been moved to clinical outcome parameters as recurrence, progression, metastasis and treatment response. The huge variability of molecular changes and poor availability of samples have hampered progress in the field of epithelial cancer (carcinoma). However, independent validation of molecular profiles across high-throughput platforms, methods, laboratories and cancer populations has recently been successfully performed for several carcinomas, including bladder cancer. Application of advanced bioinformatics to identify interrelated pathways has revealed common signatures predictive of molecular subgroups, improving histopathological diagnosis, and ultimately outcome prediction. With breast cancer leading the field, colorectal, bladder and renal cell carcinomas well on their way, and many others soon to join, the era of clinical applications of high-throughput molecular methods in cancer lies closely ahead. This review illustrates in detail the perspectives for the management of bladder cancer.

Frontiers of biomedical text mining: current progress

It is now almost 15 years since the publication of the first paper on text mining in the genomics domain, and decades since the first paper on text mining in the medical domain. Enormous progress has been made in the areas of information retrieval, evaluation methodologies and resource construction. Some problems, such as abbreviation-handling, can essentially be considered solved problems, and others, such as identification of gene mentions in text, seem likely to be solved soon. However, a number of problems at the frontiers of biomedical text mining continue to present interesting challenges and opportunities for great improvements and interesting research. In this article we review the current state of the art in biomedical text mining or 'BioNLP' in general, focusing primarily on papers published within the past year.

Structural templates predict novel protein interactions and targets from pancreas tumour gene expression data

MOTIVATION

Pancreatic ductal adenocarcinoma (PDAC) eludes early detection and is characterized by its aggressiveness and resistance to current therapies. A number of gene expression screens have been carried out to identify genes differentially expressed in cancerous tissue. To identify molecular markers and suitable targets, these genes have been mapped to protein interactions to gain an understanding at systems level.

RESULTS

Here, we take such a network-centric approach to pancreas cancer by re-constructing networks from known interactions and by predicting novel protein interactions from structural templates. The pathways we find to be largely affected are signal transduction, actin cytoskeleton regulation, cell growth and cell communication. Our analysis indicates that the alteration of the calcium pathway plays an important role in pancreas-specific tumorigenesis. Furthermore, our structural prediction method identifies 40 novel interactions including the tissue factor pathway inhibitor 2 (TFPI2) interacting with the transmembrane protease serine 4 (TMPRSS4). Since TMPRSS4 is involved in metastasis formation, we hypothesize that the upregulation of TMPRSS4 and the downregulation of its predicted inhibitor TFPI2 plays an important role in this process. Moreover, we examine the potential role of BVDU (RP101) as an inhibitor of TMPRSS4. BDVU is known to support apoptosis and prevent the acquisition of chemoresistance. Our results suggest that BVDU might bind to the active site of TMPRSS4, thus reducing its assistance in metastasis.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

High-throughput electronic biology: mining information for drug discovery

The vast range of in silico resources that are available in life sciences research hold much promise towards aiding the drug discovery process. To fully realize this opportunity, computational scientists must consider the practical issues of data integration and identify how best to apply these resources scientifically. In this article we describe in silico approaches that are driven towards the identification of testable laboratory hypotheses; we also address common challenges in the field. We focus on flexible, high-throughput techniques, which may be initiated independently of 'wet-lab' experimentation, and which may be applied to multiple disease areas. The utility of these approaches in drug discovery highlights the contribution that in silico techniques can make and emphasizes the need for collaboration between the areas of disease research and computational science.

Computational Modeling and Experimental Evaluation of a Novel Prodrug for Targeting the Extracellular Space of Prostate Tumors

We are developing a noninvasive approach for targeting imaging and therapeutic radionuclides to prostate cancer. Our method, Enzyme-Mediated Cancer Imaging and Therapy (EMCIT), aims to use enzyme-dependent, site-specific, in vivo precipitation of a radioactive molecule within the extracellular space of solid tumors. Advanced methods for data mining of the literature, protein databases, and knowledge bases (IT. Omics LSGraph and Ingenuity Systems) identified prostatic acid phosphatase (PAP) as an enzyme overexpressed in prostate cancer and secreted in the extracellular space. Using AutoDock 3.0 software, the prodrug ammonium 2-(2'-phosphoryloxyphenyl)-6-iodo-4-(3H)-quinazolinone (IQ(2-P)) was docked in silico into the X-ray structure of PAP. The data indicate that IQ(2-P) docked into the PAP active site with a calculated inhibition constant (K(i)) more favorable than that of the PAP inhibitor alpha-benzylaminobenzylphosphonic acid. When (125)IQ(2-P), the radioiodinated form of the water-soluble prodrug, was incubated with PAP, rapid hydrolysis of the compound was observed as exemplified by formation of the water-insoluble 2-(2'-hydroxyphenyl)-6-[(125)I]iodo-4-(3H)-quinazolinone ((125)IQ(2-OH)). Similarly, the incubation of IQ(2-P) with human LNCaP, PC-3, and 22Rv1 prostate tumor cells resulted in the formation of large fluorescent IQ(2-OH) crystals. No hydrolysis was seen in the presence of normal human cells. Autoradiography of tumor cells incubated with (125)IQ(2-P) showed accumulation of radioactive grains ((125)IQ(2-OH)) around the cells. We anticipate that the EMCIT approach will enable the active in vivo entrapment of radioimaging and radiotherapeutic compounds within the extracellular spaces of primary prostate tumors and their metastases.

Pathway analysis identifies perturbation of genetic networks induced by butyrate in a bovine kidney epithelial cell line

Ruminant species have evolved to metabolize the short-chain volatile fatty acids (VFA), acetate, propionate, and butyrate, to fulfill up to 70% of their nutrient energy requirements. The inherent VFA dependence of ruminant cells was exploited to add a level of increased sensitivity to the study of the role of butyrate gene-response elements in regulatory biochemical pathways. Global gene expression profiles of the bovine kidney epithelial cells regulated by sodium butyrate were investigated with high-density oligonucleotide microarrays. The detailed mechanisms by which butyrate induces cell growth arrest and apoptosis were analyzed using the Ingenuity Pathways Knowledge Base. The functional category and pathway analyses of the microarray data revealed that four canonical pathways (Cell cycles: G2/M DNA damage checkpoint, and pyrimidine metabolism; G1/S checkpoint regulation and purine metabolism) were significantly perturbed. The biologically relevant networks and pathways of these genes were also identified. IGF2, TGFB1, TP53, E2F4, and CDC2 were established as being centered in these genomic networks. The present findings provide a basis for understanding the full range of the biological roles and the molecular mechanisms that butyrate may play in animal cell growth, proliferation, and energy metabolisms.