Sachiyo Aburatani

Oil Accumulation by the Oleaginous Diatom Fistulifera solaris as Revealed by the Genome and Transcriptome

F. solaris has an allodiploid genome structure, and activation of lipid accumulation and degradation metabolism pathways at the same time might underlie its simultaneous growth and oil accumulation. Oleaginous photosynthetic organisms such as microalgae are promising sources for biofuel production through the generation of carbon-neutral sustainable energy. However, the metabolic mechanisms driving high-rate lipid production in these oleaginous organisms remain unclear, thus impeding efforts to improve productivity through genetic modifications. We analyzed the genome and transcriptome of the oleaginous diatom Fistulifera solaris JPCC DA0580. Next-generation sequencing technology provided evidence of an allodiploid genome structure, suggesting unorthodox molecular evolutionary and genetic regulatory systems for reinforcing metabolic efficiencies. Although major metabolic pathways were shared with nonoleaginous diatoms, transcriptome analysis revealed unique expression patterns, such as concomitant upregulation of fatty acid/triacylglycerol biosynthesis and fatty acid degradation (β-oxidation) in concert with ATP production. This peculiar pattern of gene expression may account for the simultaneous growth and oil accumulation phenotype and may inspire novel biofuel production technology based on this oleaginous microalga.

ASIAN: a web server for inferring a regulatory network framework from gene expression profiles.

The standard workflow in gene expression profile analysis to identify gene function is the clustering by various metrics and techniques, and the following analyses, such as sequence analyses of upstream regions. A further challenging analysis is the inference of a gene regulatory network, and some computational methods have been intensively developed to deduce the gene regulatory network. Here, we describe our web server for inferring a framework of regulatory networks from a large number of gene expression profiles, based on graphical Gaussian modeling (GGM) in combination with hierarchical clustering (). GGM is based on a simple mathematical structure, which is the calculation of the inverse of the correlation coefficient matrix between variables, and therefore, our server can analyze a wide variety of data within a reasonable computational time. The server allows users to input the expression profiles, and it outputs the dendrogram of genes by several hierarchical clustering techniques, the cluster number estimated by a stopping rule for hierarchical clustering and the network between the clusters by GGM, with the respective graphical presentations. Thus, the ASIAN (Automatic System for Inferring A Network) web server provides an initial basis for inferring regulatory relationships, in that the clustering serves as the first step toward identifying the gene function.

Network evaluation from the consistency of the graph structure with the measured data

BackgroundA knowledge-based network, which is constructed by extracting as many relationships identified by experimental studies as possible and then superimposing them, is one of the promising approaches to investigate the associations between biological molecules. However, the molecular relationships change dynamically, depending on the conditions in a living cell, which suggests implicitly that all of the relationships in the knowledge-based network do not always exist. Here, we propose a novel method to estimate the consistency of a given network with the measured data: i) the network is quantified into a log-likelihood from the measured data, based on the Gaussian network, and ii) the probability of the likelihood corresponding to the measured data, named the graph consistency probability (GCP), is estimated based on the generalized extreme value distribution.ResultsThe plausibility and the performance of the present procedure are illustrated by various graphs with simulated data, and with two types of actual gene regulatory networks in Escherichia coli: the SOS DNA repair system with the corresponding data measured by fluorescence, and a set of 29 networks with data measured under anaerobic conditions by microarray. In the simulation study, the procedure for estimating GCP is illustrated by a simple network, and the robustness of the method is scrutinized in terms of various aspects: dimensions of sampling data, parameters in the simulation study, magnitudes of data noise, and variations of network structures.In the actual networks, the former example revealed that our method operates well for an actual network with a size similar to those of the simulated networks, and the latter example illustrated that our method can select the activated network candidates consistent with the actual data measured under specific conditions, among the many network candidates.ConclusionThe present method shows the possibility of bridging between the static network from the literature and the corresponding measurements, and thus will shed light on the network structure variations in terms of the changes in molecular interaction mechanisms that occur in response to the environment in a living cell.

Prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells

Predictive toxicology using stem cells or their derived tissues has gained increasing importance in biomedical and pharmaceutical research. Here, we show that toxicity category prediction by support vector machines (SVMs), which uses qRT-PCR data from 20 categorized chemicals based on a human embryonic stem cell (hESC) system, is improved by the adoption of gene networks, in which network edge weights are added as feature vectors when noisy qRT-PCR data fail to make accurate predictions. The accuracies of our system were 97.5–100% for three toxicity categories: neurotoxins (NTs), genotoxic carcinogens (GCs) and non-genotoxic carcinogens (NGCs). For two uncategorized chemicals, bisphenol-A and permethrin, our system yielded reasonable results: bisphenol-A was categorized as an NGC, and permethrin was categorized as an NT; both predictions were supported by recently published papers. Our study has two important features: (i) as the first study to employ gene networks without using conventional quantitative structure-activity relationships (QSARs) as input data for SVMs to analyze toxicogenomics data in an hESC validation system, it uses additional information of gene-to-gene interactions to significantly increase prediction accuracies for noisy gene expression data; and (ii) using only undifferentiated hESCs, our study has considerable potential to predict late-onset chemical toxicities, including abnormalities that occur during embryonic development.

Application of Structure Equation Modeling for Inferring a Serial Transcriptional Regulation in Yeast

Revealing the gene regulatory systems among DNA and proteins in living cells is one of the central aims of systems biology. In this study, I used Structural Equation Modeling (SEM) in combination with stepwise factor analysis to infer the protein-DNA interactions for gene expression control from only gene expression profiles, in the absence of protein information. I applied my approach to infer the causalities within the well-studied serial transcriptional regulation composed of GAL-related genes in yeast. This allowed me to reveal the hierarchy of serial transcriptional regulation, including previously unclear protein-DNA interactions. The validity of the constructed model was demonstrated by comparing the results with previous reports describing the regulation of the transcription factors. Furthermore, the model revealed combinatory regulation by Gal4p and Gal80p. In this study, the target genes were divided into three types: those regulated by one factor and those controlled by a combination of two factors.

Gene systems network inferred from expression profiles in hepatocellular carcinogenesis by graphical Gaussian model

Hepatocellular carcinoma (HCC) in a liver with advanced-stage chronic hepatitis C (CHC) is induced by hepatitis C virus, which chronically infects about 170 million people worldwide. To elucidate the associations between gene groups in hepatocellular carcinogenesis, we analyzed the profiles of the genes characteristically expressed in the CHC and HCC cell stages by a statistical method for inferring the network between gene systems based on the graphical Gaussian model. A systematic evaluation of the inferred network in terms of the biological knowledge revealed that the inferred network was strongly involved in the known gene-gene interactions with high significance , and that the clusters characterized by different cancer-related responses were associated with those of the gene groups related to metabolic pathways and morphological events. Although some relationships in the network remain to be interpreted, the analyses revealed a snapshot of the orchestrated expression of cancer-related groups and some pathways related with metabolisms and morphological events in hepatocellular carcinogenesis, and thus provide possible clues on the disease mechanism and insights that address the gap between molecular and clinical assessments.

Synthetic PAMPS gel activates BMP/Smad signaling pathway in ATDC5 cells, which plays a significant role in the gel-induced chondrogenic differentiation

The purposes of this study were to identify signaling pathways that were specifically activated in ATDC5 cells cultured on poly (2-acrylamido-2-methylpropanesulfonic acid) (PAMPS) gel in insulin-free maintenance medium and to evaluate the significance of the determined signaling pathways in the chondrogenic differentiation induced by this gel. In this study, ATDC5 cells cultured on PAMPS gel using the maintenance medium without insulin (PAMPS Culture) were compared with cells cultured on polystyrene using the differentiation medium containing insulin (PS-I Culture). The microarray analysis, Western blot analysis, and real-time PCR analysis demonstrated that the TGF-β/BMP signaling pathway was significantly enhanced at Days 1, 2, and 3 in the PAMPS Culture when compared with the PS-I Culture. Inhibition of the BMP type-I receptor reduced the phosphorylation level of Smad1/5 and expression of type-2 collagen and aggrecan mRNA in the cells accompanied by a reduction in cell aggregation at Day 13 in the PAMPS Culture. The inhibition of the TGF-β/BMP signaling pathway significantly inhibited the chondrogenic differentiation induced by the PAMPS gel. The present study demonstrated that synthetic PAMPS gel activates the TGF-β/BMP/Smad signaling pathway in the ATDC5 cells in the absence of insulin, and that this activation plays a significant role in the chondrogenic differentiation induced by PAMPS gel.

Integer programming-based approach to allocation of reporter genes for cell array analysis.

In this paper, we consider the problem of selecting the most effective set of reporter genes for analysis of biological networks using cell microarrays. We propose two graph theoretic formulations of the reporter gene allocation problem, and show that both problems are hard to approximate. We propose integer programming-based methods for solving practical instances of these problems optimally. We apply them to apoptosis pathway maps, and discuss the biological significance of the result. We also apply them to artificial networks, the result of which shows that optimal solutions can be obtained within several seconds for networks with 10,000 nodes.

Tracking Difference in Gene Expression in a Time-Course Experiment Using Gene Set Enrichment Analysis

Fistulifera sp. strain JPCC DA0580 is a newly sequenced pennate diatom that is capable of simultaneously growing and accumulating lipids. This is a unique trait, not found in other related microalgae so far. It is able to accumulate between 40 to 60% of its cell weight in lipids, making it a strong candidate for the production of biofuel. To investigate this characteristic, we used RNA-Seq data gathered at four different times while Fistulifera sp. strain JPCC DA0580 was grown in oil accumulating and non-oil accumulating conditions. We then adapted gene set enrichment analysis (GSEA) to investigate the relationship between the difference in gene expression of 7,822 genes and metabolic functions in our data. We utilized information in the KEGG pathway database to create the gene sets and changed GSEA to use re-sampling so that data from the different time points could be included in the analysis. Our GSEA method identified photosynthesis, lipid synthesis and amino acid synthesis related pathways as processes that play a significant role in oil production and growth in Fistulifera sp. strain JPCC DA0580. In addition to GSEA, we visualized the results by creating a network of compounds and reactions, and plotted the expression data on top of the network. This made existing graph algorithms available to us which we then used to calculate a path that metabolizes glucose into triacylglycerol (TAG) in the smallest number of steps. By visualizing the data this way, we observed a separate up-regulation of genes at different times instead of a concerted response. We also identified two metabolic paths that used less reactions than the one shown in KEGG and showed that the reactions were up-regulated during the experiment. The combination of analysis and visualization methods successfully analyzed time-course data, identified important metabolic pathways and provided new hypotheses for further research.

Construction of a High-precision Chemical Prediction System Using Human ESCs

u3000Toxicity prediction based on stem cells and tissue derived from stem cells plays a very important role in the fields of biomedicine and pharmacology. Here we report on qRT-PCR data obtained by exposing 20 compounds to human embryonic stem (ES) cells. The data are intended to improve toxicity prediction, per category, of various compounds through the use of support vector machines, and by applying gene networks. The accuracy of our system was 97.5-100% in three toxicity categories: neurotoxins (NTs), genotoxic carcinogens (GCs), and non-genotoxic carcinogens (NGCs). We predicted that two uncategorized compounds (bisphenol-A and permethrin) should be classified as follows: bisphenol-A as a non-genotoxic carcinogen, and permethrin as a neurotoxin. These predictions are supported by recent reports, and as such constitute a good outcome. Our results include two important features: 1) The accuracy of prediction was higher when machine learning was carried out using gene networks and activity, rather than the normal quantitative structure-activity relationship (QSAR); and 2) By using undifferentiated ES cells, the late effect of chemical substances was predicted. From these results, we succeeded in constructing a highly effective and highly accurate system to predict the toxicity of compounds using stem cells.