Transcriptomic features associated with energy production in the muscles of Pacific bluefin tuna and Pacific cod.

Bluefin tuna are high-performance swimmers and top predators in the open ocean. Their swimming is grounded by unique features including an exceptional glycolytic potential in white muscle, which is supported by high enzymatic activities. Here we performed high-throughput RNA sequencing (RNA-Seq) in muscles of the Pacific bluefin tuna (Thunnus orientalis) and Pacific cod (Gadus macrocephalus) and conducted a comparative transcriptomic analysis of genes related to energy production. We found that the total expression of glycolytic genes was much higher in the white muscle of tuna than in the other muscles, and that the expression of only six genes for glycolytic enzymes accounted for 83.4% of the total. These expression patterns were in good agreement with the patterns of enzyme activity previously reported. The findings suggest that the mRNA expression of glycolytic genes may contribute directly to the enzymatic activities in the muscles of tuna.

Evolutionary changes of multiple visual pigment genes in the complete genome of Pacific bluefin tuna.

Tunas are migratory fishes in offshore habitats and top predators with unique features. Despite their ecological importance and high market values, the open-ocean lifestyle of tuna, in which effective sensing systems such as color vision are required for capture of prey, has been poorly understood. To elucidate the genetic and evolutionary basis of optic adaptation of tuna, we determined the genome sequence of the Pacific bluefin tuna (Thunnus orientalis), using next-generation sequencing technology. A total of 26,433 protein-coding genes were predicted from 16,802 assembled scaffolds. From these, we identified five common fish visual pigment genes: red-sensitive (middle/long-wavelength sensitive; M/LWS), UV-sensitive (short-wavelength sensitive 1; SWS1), blue-sensitive (SWS2), rhodopsin (RH1), and green-sensitive (RH2) opsin genes. Sequence comparison revealed that tuna's RH1 gene has an amino acid substitution that causes a short-wave shift in the absorption spectrum (i.e., blue shift). Pacific bluefin tuna has at least five RH2 paralogs, the most among studied fishes; four of the proteins encoded may be tuned to blue light at the amino acid level. Moreover, phylogenetic analysis suggested that gene conversions have occurred in each of the SWS2 and RH2 loci in a short period. Thus, Pacific bluefin tuna has undergone evolutionary changes in three genes (RH1, RH2, and SWS2), which may have contributed to detecting blue-green contrast and measuring the distance to prey in the blue-pelagic ocean. These findings provide basic information on behavioral traits of predatory fish and, thereby, could help to improve the technology to culture such fish in captivity for resource management.

Haptoglobin promoter polymorphism rs5472 as a prognostic biomarker for peptide vaccine efficacy in castration-resistant prostate cancer patients.

Personalized peptide vaccination (PPV) is an attractive approach to cancer immunotherapy with strong immune-boosting effects conferring significant clinical benefit. However, as with most therapeutic agents, there is a difference in clinical efficacy among patients receiving PPV. Therefore, a useful biomarker is urgently needed for prognosticating clinical outcomes to preselect patients who would benefit the most from PPV. In this retrospective study, to detect a molecular prognosticator of clinical outcomes for PPV, we analyzed whole-genome gene expression profiles of peripheral blood mononuclear cells (PBMCs) in castration-resistant prostate cancer (CRPC) patients before administration of PPV. Cox regression analysis revealed that mRNA expression of myeloperoxidase, haptoglobin, and neutrophil elastase was significantly associated with overall survival (OS) among vaccinated CRPC patients (adjusted P < 0.01). By promoter sequence analysis of these three genes, we found that rs5472 of haptoglobin (HP), an acute-phase plasma glycoprotein, was strongly correlated to OS of vaccinated CRPC patients (P = 0.0047, hazard ratio 0.47; 95 % confidence interval 0.28-0.80). Furthermore, both HP mRNA expression in PBMCs and protein level in plasma of CRPC patients before administration of PPV exhibited rs5472 dependence (P < 0.001 for mRNA expression and P < 0.05 for protein level). Our findings suggest that rs5472 may play an important role in the immune response to PPV via regulation of HP. Thus, we concluded that rs5472 is a potential prognostic biomarker for PPV.

Prdm12 regulates inhibitory neuron differentiation in mouse embryonal carcinoma cells.

The epigenetic regulatory system significant influences the fate determination of cells during developmental processes. Prdm12 is a transcriptional regulator that modulates gene expression epigenetically. The Prdm12 gene has been shown to be expressed in neural tissues, specifically during development, but its detailed function is not fully understood. This study investigated the function of the Prdm12 gene in P19 mouse embryonic tumor cells as a model for neural differentiation. A decrease in the expression of neuron-specific genes and the alterations of dendrites and axons morphology was confirmed in Prdm12-knockout P19 cells. In addition, almost no astrocytes were generated in Prdm12-knockout P19 cells. Comprehensive gene expression analysis revealed that there was a reduction in the expression of the inhibitory neuron-specific genes Gad1/2 and Glyt2, but not the excitatory neuron-specific gene VGLUT2, in Prdm12-knockout P19 cells. Furthermore, the expression of inhibitory neuron-related factors, Ptf1a, Dbx1, and Gsx1/2, decreased in Prdm12-knockout P19 cells. Gene expression analysis also revealed that the Ptf1a, Hic1, and Foxa1 genes were candidate targets of Prdm12 during neurogenesis. These results suggest that Prdm12 regulates the differentiation of inhibitory neurons and astrocytes by controlling the expression of these genes during the neural differentiation of P19 cells.

Identification and classification of a two-component system based on domain structures in bacteria and differences in domain structure between Gram-positive and Gram-negative bacteria.

Genome sequencing has revealed many pairs of proteins termed two-component systems (TCSs) in bacteria. Each pair consists of a sensor or histidine kinase (HK) and an effector or response regulator (RR). The HK is usually a membrane-spanning protein that senses specific environmental parameters and communicates this information to the cytoplasmic RR protein through phosphotransfer reactions to cope with a variety of environmental stresses, including osmotic pressure, nitrogen lack, phosphoric acid lack, and the presence of oxygen. Furthermore, some proteins have been identified as hybrid kinases composed of HK and RR. We identified the domain structures of 360 bacteria and 43 archaea by domain search against the PFAM database using HMMER. We then classified 8,573 HK, 10,807 RR, and 2,477 hybrid kinases. In addition, we identified specific domains among phylogenic clusters based on differences in domain structure of TCS genes applying the Signal-to-Noise ratio.

Construction of enzyme-substrate complexes between hen egg-white lysozyme and N-acetyl-D-glucosamine hexamer by systematic conformational search and molecular dynamics simulation.

We constructed the complexes between HEWL and (GlcNAc)6 oligomer in order to investigate the amino acid residues related to substrate binding in the productive and nonproductive complexes, and the relationship between the distortion of the GlcNAc residue D and the formation of the productive complexes. We obtained 49 HEWL-(GlcNAc)6 complexes by a systematic conformational search and classified the each one to the three binding modes; left side, center, or right side. Furthermore we performed the molecular dynamics simulation against 20 HEWL-(GlcNAc)6 complexes (8: chair model, 12 : half-chair model) selected from the 49 complexes to investigate the interaction between HEWL and (GlcNAc)6. As results, we confirmed that it is necessary for GlcNAc residue D to be half-chaired form to bind toward the right side to form productive complexes. We found newly that eight amino acid residues interact with the (GlcNAc)6 oligomer, as follows, Arg73, Gly102, Asn103 for GlcNAc residue A; Asn103 for GlcNAc residues B and C; Leu56, Ala107, Val109 for GlcNAc residue D; Ala110 for GlcNAc residue E; and Lys33 for GlcNAc residue F. We also indicated that GlcNAc residue F does not interact with Thr47 and rarely interacts with Phe34 and Asn37.

Detection and normalization of biases present in spotted cDNA microarray data: a composite method addressing dye, intensity-dependent, spatially-dependent, and print-order biases.

Microarrays are often used to identify target genes that trigger specific diseases, to elucidate the mechanisms of drug effects, and to check SNPs. However, data from microarray experiments are well known to contain biases resulting from the experimental protocols. Therefore, in order to elucidate biological knowledge from the data, systematic biases arising from their protocols must be removed prior to any data analysis. To remove these biases, many normalization methods are used by researchers. However, not all biases are eliminated from the microarray data because not all types of errors from experimental protocols are known. In this paper, we report an effective way of removing various types of biases by treating each microarray dataset independently to detect biases present in the dataset. After the biases contained in each dataset were identified, a combination of normalization methods specifically made for each dataset was applied to remove biases one at a time.

Use of gene networks from full genome microarray libraries to identify functionally relevant drug-affected genes and gene regulation cascades.

We developed an extensive yeast gene expression library consisting of full-genome cDNA array data for over 500 yeast strains, each with a single-gene disruption. Using this data, combined with dose and time course expression experiments with the oral antifungal agent griseofulvin, whose exact molecular targets were previously unknown, we used Boolean and Bayesian network discovery techniques to determine the gene expression regulatory cascades affected directly by this drug. Using this method we identified CIK1 as an important affected target gene related to the functional phenotype induced by griseofulvin. Cellular functional analysis of griseofulvin showed similar tubulin-specific morphological effects on mitotic spindle formation to those of the drug, in agreement with the known function of CIK1p. Further, using the nonparametric, nonlinear Bayesian gene networks we were able to identify alternative ligand-dependant transcription factors and G protein homologues upstream of CIK1 that regulate CIK1 expression and might therefore serve as alternative molecular targets to induce the same molecular response as griseofulvin.

Discovery of novel transcription control relationships with gene regulatory networks generated from multiple-disruption full genome expression libraries.

Gene regulatory networks elucidated from strategic, genome-wide experimental data can aid in the discovery of novel gene function information and expression regulation events from observation of transcriptional regulation among genes of known and unknown biological function. To create a reliable and comprehensive data set for the elucidation of transcription regulation networks, we conducted systematic genome-wide disruption expression experiments of yeast on 118 genes with known involvement in transcription regulation. We report several novel regulatory relationships between known transcription factors and other genes with previously unknown biological function discovered with this expression library. Here we report the downstream regulatory subnetworks for UME6 and MET28. The elucidated network topology among these genes demonstrates MET28's role as a nodal point between genes involved in cell division and those involved in DNA repair mechanisms.

Deciphering cellular functions of protein phosphatases by comparison of gene expression profiles in Saccharomyces cerevisiae.

Expression profiles of protein phosphatase (PPase) disruptants were analyzed by use of Pearson's correlation coefficient to find profiles that correlated with those of 316 Reference Gene (RG) disruptants harboring deletions in genes with known functions. Twenty-six Deltappase disruptants exhibited either a positive or negative correlation with 94 RG disruptants when the p value for Pearson's correlation coefficient was >0.2. Some of the predictions that arose from this analysis were tested experimentally and several new Delta ppase phenotypes were found. Notably, Delta sit4 and Delta siw14 disruptants exhibited hygromycin B sensitivity, Delta sit4 and Delta ptc1 disruptants grew slowly on glycerol medium, the Delta ptc1 disruptant was found to be sensitive to calcofluor white and congo red, while the Delta ppg1 disruptant was found to be sensitive to congo red. Because on-going analysis of expression profiles of Saccharomyces cerevisiae disruptants is rapidly generating new data, we suggest that the approach used in the present study to explore PPase function is also applicable to other genes.

Properties of a trifluoroleucine-resistant mutant of Saccharomyces cerevisiae.

We characterized a trifluoroleucine-resistant mutant of Saccharomyces cerevisiae, TFL20, that has a mutation in the LEU4 gene. We monitored the concentration of extracellular i-AmOH and intracellular amino acids, and compared the ratios of gene expression in TFL20 with the wild-type strain, K30. We found that the LEU1, LEU2, and BAT1 genes were up-regulated in TFL20 for metabolism, and that TFL20 simultaneously produced as much i-AmOH and leucine as K30 does.

[Drug discovery based on microarray].

Gene regulatory networks developed from full genome expression libraries from gene perturbation variant cell lines can be used to quickly and efficiently identify the molecular mechanism of action of drugs or lead compound molecules. We developed an extensive yeast gene expression library consisting of full-genome cDNA array data for over 500 yeast strains each with a single gene disruption. Using this data, combined with dose and time course expression experiments with the oral antifungal agent, we used Boolean network discovery techniques to determine the genes whose expression was most profoundly affected by this drug. Our system identified the gene as the most significantly suppressed target molecule due to exposure to the antifungal agent. This process for network based drug discovery can significantly decrease the time and resources necessary to make rational drug targeting decisions.