Reverse Engineering of Gene Regulatory Networks for Discovery of Novel Interactions in Pathways Using Gene Expression Data

A variety of chemicals in the environment have the potential to adversely affect the biological systems. We examined the responses of Rat (Rattus norvegicus) to the RDX exposure and female fathead minnows (FHM, Pimephales promelas) to a model aromatase inhibitor, fadrozole, using a transcriptional network inference approach. Rats were exposed to RDX and fish were exposed to 0 or 30mg/L fadrozole for 8 days. We analyzed gene expression changes using 8000 probes microarrays for rat experiment and 15,000 probe microarrays for fish. We used these changes to infer a transcriptional network. The central nervous system is remarkably plastic in its ability to recover from trauma. We examined recovery from chemicals in rats and fish through changes in transcriptional networks. Transcriptional networks from time series experiments provide a good basis for organizing and studying the dynamic behavior of biological processes. The goal of this work was to identify networks affected by chemical exposure and track changes in these networks as animals recover. The top 1254 significantly changed genes based upon 1.5-fold change and P\u3c 0.05 across all the time points from the fish data and 937 significantly changed genes from rat data were chosen for network modeling using either a Mutual Information network (MIN) or a Graphical Gaussian Model (GGM) or a Dynamic Bayesian Network (DBN) approach. The top interacting genes were queried to find sub-networks, possible biological networks, biochemical pathways, and network topologies impacted after exposure to fadrozole. The methods were able to reconstruct transcriptional networks with few hub structures, some of which were found to be involved in major biological process and molecular function. The resulting network from rat experiment exhibited a clear hub (central in terms of connections and direction) connectivity structure. Genes such as Ania-7, Hnrpdl, Alad, Gapdh, etc. (all CNS related), GAT-2, Gabra6, Gabbrl, Gabbr2 (GABA, neurotransmitter transporters and receptors), SLC2A1 (glucose transporter), NCX3 (Na-Ca exchanger), Gnal (Olfactory related), skn-la were showed up in our network as the \u27hub\u27 genes while some of the known transcription factors Msx3, Cacngl, Brs3, NGF1 etc. were also matched with our network model. Aromatase in the fish experiment was a highly connected gene in a sub-network along with other genes involved in steroidogenesis. Many of the sub-networks were involved in fatty acid metabolism, gamma-hexachlorocyclohexane degradation, and phospholipase activating pathways. Aromatase was a highly connected gene in a sub-network along with the genes LDLR, StAR, KRT18, HER1, CEBPB, ESR2A, and ACVRL1. Many of the subnetworks were involved in fatty acid metabolism, gamma-hexachlorocyclohexane degradation, and phospholipase activating pathways. A credible transcriptional network was recovered from both the time series data and the static data. The network included transcription factors and genes with roles in brain function, neurotransmission and sex hormone synthesis. Examination of the dynamic changes in expression within this network over time provided insight into recovery from traumas and chemical exposures

Supervised Learning Method for the Prediction of Subcellular Localization of Proteins Using Amino Acid and Amino Acid Pair Composition

Background Occurrence of protein in the cell is an important step in understanding its function. It is highly desirable to predict a protein\u27s subcellular locations automatically from its sequence. Most studied methods for prediction of subcellular localization of proteins are signal peptides, the location by sequence homology, and the correlation between the total amino acid compositions of proteins. Taking amino-acid composition and amino acid pair composition into consideration helps improving the prediction accuracy. Results We constructed a dataset of protein sequences from SWISS-PROT database and segmented them into 12 classes based on their subcellular locations. SVM modules were trained to predict the subcellular location based on amino acid composition and amino acid pair composition. Results were calculated after 10-fold cross validation. Radial Basis Function (RBF) outperformed polynomial and linear kernel functions. Total prediction accuracy reached to 71.8% for amino acid composition and 77.0% for amino acid pair composition. In order to observe the impact of number of subcellular locations we constructed two more datasets of nine and five subcellular locations. Total accuracy was further improved to 79.9% and 85.66%. Conclusions A new SVM based approach is presented based on amino acid and amino acid pair composition. Result shows that data simulation and taking more protein features into consideration improves the accuracy to a great extent. It was also noticed that the data set needs to be crafted to take account of the distribution of data in all the classes

Transcriptome profiling of Saccharomyces cerevisiae mutants lacking C2H2 zinc finger proteins

<p>Abstract</p> <p>Background</p> <p>The budding yeast <it>Saccharomyces cerevisiae</it> is a eukaryotic organism with extensive genetic redundancy. Large-scale gene deletion analysis has shown that over 80% of the ~6200 predicted genes are nonessential and that the functions of 30% of all ORFs remain unclassified, implying that yeast cells can tolerate deletion of a substantial number of individual genes. For example, a class of zinc finger proteins containing C2H2 zinc fingers in tandem arrays of two or three is predicted to be transcription factors; however, seven of the thirty-one predicted genes of this class are nonessential, and their functions are poorly understood. In this study we completed a transcriptomic profiling of three mutants lacking C2H2 zinc finger proteins, <it>ypr013cΔ,</it><it>ypr015cΔ</it> and <it>ypr013cΔypr015cΔ</it>.</p> <p>Results</p> <p>Gene expression patterns were remarkably different between wild type and the mutants. The results indicate altered expression of 79 genes in<it> ypr013</it>cΔ, 185 genes in <it>ypr015</it>cΔ and 426 genes in the double mutant when compared with that of the wild type strain. More than 80% of the alterations in the double mutants were not observed in either one of the single deletion mutants. Functional categorization based on Munich Information Center for Protein Sequences (MIPS) revealed up-regulation of genes related to transcription and down-regulation of genes involving cell rescue and defense, suggesting a decreased response to stress conditions. Genes related to cell cycle and DNA processing whose expression was affected by single or double deletions were also identified.</p> <p>Conclusion</p> <p>Our results suggest that microarray analysis can define the biological roles of zinc finger proteins with unknown functions and identify target genes that are regulated by these putative transcriptional factors. These findings also suggest that both YPR013C and YPR015C have biological processes in common, in addition to their own regulatory pathways.</p

Transcriptome Profiling of \u3ci\u3eSaccharomyces cerevisiae\u3c/i\u3e Mutants Lacking C2H2 Zinc Finger Proteins

BackgroundThe budding yeast Saccharomyces cerevisiae is a eukaryotic organism with extensive genetic redundancy. Large-scale gene deletion analysis has shown that over 80% of the ~6200 predicted genes are nonessential and that the functions of 30% of all ORFs remain unclassified, implying that yeast cells can tolerate deletion of a substantial number of individual genes. For example, a class of zinc finger proteins containing C2H2 zinc fingers in tandem arrays of two or three is predicted to be transcription factors; however, seven of the thirty-one predicted genes of this class are nonessential, and their functions are poorly understood. In this study we completed a transcriptomic profiling of three mutants lacking C2H2 zinc finger proteins, ypr013cΔ, ypr015cΔ and ypr013cΔypr015cΔ. ResultsGene expression patterns were remarkably different between wild type and the mutants. The results indicate altered expression of 79 genes in ypr013 cΔ, 185 genes in ypr015 cΔ and 426 genes in the double mutant when compared with that of the wild type strain. More than 80% of the alterations in the double mutants were not observed in either one of the single deletion mutants. Functional categorization based on Munich Information Center for Protein Sequences (MIPS) revealed up-regulation of genes related to transcription and down-regulation of genes involving cell rescue and defense, suggesting a decreased response to stress conditions. Genes related to cell cycle and DNA processing whose expression was affected by single or double deletions were also identified. ConclusionOur results suggest that microarray analysis can define the biological roles of zinc finger proteins with unknown functions and identify target genes that are regulated by these putative transcriptional factors. These findings also suggest that both YPR013C and YPR015C have biological processes in common, in addition to their own regulatory pathways

Life-threatening influenza pneumonitis in a child with inherited IRF9 deficiency

Life-threatening pulmonary influenza can be caused by inborn errors of type I and III IFN immunity. We report a 5-yr-old child with severe pulmonary influenza at 2 yr. She is homozygous for a loss-of-function IRF9 allele. Her cells activate gamma-activated factor (GAF) STAT1 homodimers but not IFN-stimulated gene factor 3 (ISGF3) trimers (STAT1/STAT2/IRF9) in response to IFN-α2b. The transcriptome induced by IFN-α2b in the patient's cells is much narrower than that of control cells; however, induction of a subset of IFN-stimulated gene transcripts remains detectable. In vitro, the patient's cells do not control three respiratory viruses, influenza A virus (IAV), parainfluenza virus (PIV), and respiratory syncytial virus (RSV). These phenotypes are rescued by wild-type IRF9, whereas silencing IRF9 expression in control cells increases viral replication. However, the child has controlled various common viruses in vivo, including respiratory viruses other than IAV. Our findings show that human IRF9- and ISGF3-dependent type I and III IFN responsive pathways are essential for controlling IAV

Pengaruh Pendekatan Pembelajaran Humanistik Terhadap Hasil Belajar Siswa Pada Mata Pelajaran Fiqih Kelas V MI NU Istiqlal Ploso Jati Kudus Tahun Pelajaran 2020/2021

Penelitian ini bertujuan untuk mengetahui pendekatan pembelajaran humanistik pada mata pelajaran Fiqih kelas V MI NU Istiqlal Ploso Jati Kudus. Mengetahui hasil belajar Fiqih kelas V MI NU Istiqlal Ploso Jati Kudus. Mengetahui pengaruh pendekatan pembelajaran humanistik terhadap hasil belajar Fiqih kelas V MI NU Istiqlal Ploso Jati Kudus. Jenis penelitian ini adalah penelitian lapangan (field research) yang termasuk jenis penelitian kuantitatif. Teknik pengumpulan data menggunakan metodeItes. Sampel penelitian ini adalah siswa kelas V MI NU Istiqlal Ploso Jati Kudus yang berjumlah 32 siswa. Teknik analisis data menggunakan analisis regresi sederhana. Hasil penelitian menunjukkan bahwa terdapat pengaruh pendekatan pembelajaran humanistik terhadap hasil belajar siswa pada mata pelajaran Fiqih Kelas V MI NU Istiqlal Ploso Jati Kudus Tahun Pelajaran 2020/2021. Berdasarkan nilai t hitung > t tabel yaitu sebesar (2,492>2,04227). Didukung dengan nilai signifikansi yang lebih kecil dari 0,05 yaitu menunjukkan nilai sebesar 0,026. Hasil penelitian menunjukkan bahwa pembelajaran humanistik berpengaruh terhadap hasil belajar siswa pada mata pelajaran Fiqih Kelas V MI NU Istiqlal Ploso Jati Kudus Tahun Pelajaran 2020/2021 sebesar 47,6%, hal tersebut sesuai dengan hasil penyebaran angket menyatakan bahwa pembelajaran humanistik siswa pada mata pelajaran Fiqih Kelas V MI NU Istiqlal Ploso Jati Kudus Tahun Pelajaran 2020/2021 tergolong sangat baik. Setiap orang memiliki kecepatan belajar yang berbeda-beda sehingga keberhasilan belajar akan tercapai jika seseorang mampu memahami diri dan lingkungannya

GOfetcher: A Database with Complex Searching Facility for Gene Ontology

Motivation: An important contribution to the Gene Ontology (GO) project is to develop tools that facilitate the creation, maintenance and use of ontologies. Several tools have been created for communicating and using the GO project. However, a limitation with most of these tools is that they suffer from lack of a comprehensive search facility. We developed a web application, GOfetcher, with a very comprehensive search facility for the GO project and a variety of output formats for the results. GOfetcher has three different levels for searching the GO: Quick Search, Advanced Search and Upload Files for searching. The application includes a unique search option which generates gene information given a nucleotide or protein accession number which can then be used in generating GO information. The output data in GOfetcher can be saved into several different formats; including spreadsheet, comma-separated values and the extensible markup language (XML) format. The database is available at http://mcbc.usm.edu/gofetcher/

Principles of Genomic Robustness Inspire Fault-Tolerant WSN Topologies: A Network Science Based Case Study

Wireless sensor networks (WSNs) are frameworks for modern pervasive computing infrastructures, and are often subject to operational difficulties, such as the inability to effectively mitigate signal noise or sensor failure. Natural systems, such as gene regulatory networks (GRNs), participate in similar information transport and are often subject to similar operational disruptions (noise, damage, etc.). Moreover, they self-adapt to maintain system function under adverse conditions. Using a PBN-type model valid in the operational and functional overlap between GRNs and WSNs, we study how attractors in the GRN-the target state of an evolving network-behave under selective gene or sensor failure. For larger networks, attractors are robust, in the sense that gene failures (or selective sensor failures in the WSN) conditionally increase their total number; the distance between initial states and their attractors (interpreted as the end-to-end packet delay) simultaneously decreases. Moreover, the number of attractors is conserved if the receiving sensor returns packets to the transmitting node; however, the distance to the attractors increases under similar conditions and sensor failures. Interpreting network state-transitions as packet transmission scenarios may allow for trade-offs between network topology and attractor robustness to be exploited to design novel fault-tolerant routing protocols, or other damage-mitigation strategies. © 2011 IEEE

Investigations of transcript expression in fathead minnow (\u3ci\u3ePimephales promelas\u3c/i\u3e) brain tissue reveal toxicological impacts of RDX exposure

Production, usage and disposal of the munitions constituent (MC) cyclotrimethylenetrinitramine (RDX) has led to environmental releases on military facilities. The chemical attributes of RDX are conducive for leaching to surface water which may put aquatic organisms at risk of exposure. Because RDX has been observed to cause aberrant neuromuscular effects across a wide range of animal phyla, we assessed the effects of RDX on central nervous system (CNS) functions in the representative aquatic ecotoxicological model species, fathead minnow (Pimephales promelas). We developed a fathead minnow brain-tissue cDNA library enriched for transcripts differentially expressed in response to RDX and trinitrotoluene (TNT) exposure. All 4,128 cDNAs were sequenced, quality filtered and assembled yielding 2230 unique sequences and 945 significant blastx matches (E≤10−5). The cDNA library was leveraged to create custom-spotted microarrays for use in transcript expression assays. The impact of RDX on transcript expression in brain tissue was examined in fathead minnows exposed to RDX at 0.625, 2.5, 5, 10 mg/L or an acetone-spike control for 10 days. Overt toxicity of RDX in fathead minnow occurred only at the highest exposure concentration resulting in 50% mortality and weight loss. Conversely, Bayesian analysis of microarray data indicated significant changes in transcript expression at concentrations as low as 0.625 mg/L. In total, 154 cDNAs representing 44 unique transcripts were differentially expressed in RDX exposures, the majority of which were validated by reverse transcriptase-quantitative PCR (RT-qPCR). Investigation of molecular pathways, gene ontology (GO) and individual gene functions affected by RDX exposures indicated changes in metabolic processes involved in: oxygen transport, neurological function, calcium binding/signaling, energy metabolism, cell growth/division, oxidative stress and ubiquitination. In total, our study indicated that RDX exposure affected molecular processes critical to CNS function in fathead minnow

Supervised learning method for the prediction of subcellular localization of proteins using amino acid and amino acid pair composition-2

<p><b>Copyright information:</b></p><p>Taken from "Supervised learning method for the prediction of subcellular localization of proteins using amino acid and amino acid pair composition"</p><p>http://www.biomedcentral.com/1471-2164/9/S1/S16</p><p>BMC Genomics 2008;9(Suppl 1):S16-S16.</p><p>Published online 20 Mar 2008</p><p>PMCID:PMC2386058.</p><p></p