Application of structure equation modeling for inferring a serial transcriptional regulation in yeast

Inference of Regulatory System for TAG Biosynthesis in Lipomyces starkeyi

Improving the bioproduction ability of efficient host microorganisms is a central aim in bioengineering. To control biosynthesis in living cells, the regulatory system of the whole biosynthetic pathway should be clearly understood. In this study, we applied our network modeling method to infer the regulatory system for triacylglyceride (TAG) biosynthesis in Lipomyces starkeyi, using factor analyses and structural equation modeling to construct a regulatory network model. By factor analysis, we classified 89 TAG biosynthesis-related genes into nine groups, which were considered different regulatory sub-systems. We constructed two different types of regulatory models. One is the regulatory model for oil productivity, and the other is the whole regulatory model for TAG biosynthesis. From the inferred oil productivity regulatory model, the well characterized genes DGA1 and ACL1 were detected as regulatory factors. Furthermore, we also found unknown feedback controls in oil productivity regulation. These regulation models suggest that the regulatory factor induction targets should be selected carefully. Within the whole regulatory model of TAG biosynthesis, some genes were detected as not related to TAG biosynthesis regulation. Using network modeling, we reveal that the regulatory system is helpful for the new era of bioengineering.

The Wright stuff: reimagining path analysis reveals novel components of the sex determination hierarchy in Drosophila melanogaster

BACKGROUND

The Drosophila sex determination hierarchy is a classic example of a transcriptional regulatory hierarchy, with sex-specific isoforms regulating morphology and behavior. We use a structural equation modeling approach, leveraging natural genetic variation from two studies on Drosophila female head tissues--DSPR collection (596 F1-hybrids from crosses between DSPR sub-populations) and CEGS population (75 F1-hybrids from crosses between DGRP/Winters lines to a reference strain w1118)--to expand understanding of the sex hierarchy gene regulatory network (GRN). This approach is completely generalizable to any natural population, including humans.

RESULTS

We expanded the sex hierarchy GRN adding novel links among genes, including a link from fruitless (fru) to Sex-lethal (Sxl) identified in both populations. This link is further supported by the presence of fru binding sites in the Sxl locus. 754 candidate genes were added to the pathway, including the splicing factors male-specific lethal 2 and Rm62 as downstream targets of Sxl which are well-supported links in males. Independent studies of doublesex and transformer mutants support many additions, including evidence for a link between the sex hierarchy and metabolism, via Insulin-like receptor.

CONCLUSIONS

The genes added in the CEGS population were enriched for genes with sex-biased splicing and components of the spliceosome. A common goal of molecular biologists is to expand understanding about regulatory interactions among genes. Using natural alleles we can not only identify novel relationships, but using supervised approaches can order genes into a regulatory hierarchy. Combining these results with independent large effect mutation studies, allows clear candidates for detailed molecular follow-up to emerge.

Investigating perturbed pathway modules from gene expression data via structural equation models

Background

It is currently accepted that the perturbation of complex intracellular networks, rather than the dysregulation of a single gene, is the basis for phenotypical diversity. High-throughput gene expression data allow to investigate changes in gene expression profiles among different conditions. Recently, many efforts have been made to individuate which biological pathways are perturbed, given a list of differentially expressed genes (DEGs). In order to understand these mechanisms, it is necessary to unveil the variation of genes in relation to each other, considering the different phenotypes. In this paper, we illustrate a pipeline, based on Structural Equation Modeling (SEM) that allowed to investigate pathway modules, considering not only deregulated genes but also the connections between the perturbed ones.

Results

The procedure was tested on microarray experiments relative to two neurological diseases: frontotemporal lobar degeneration with ubiquitinated inclusions (FTLD-U) and multiple sclerosis (MS). Starting from DEGs and dysregulated biological pathways, a model for each pathway was generated using databases information biological databases, in order to design how DEGs were connected in a causal structure. Successively, SEM analysis proved if pathways differ globally, between groups, and for specific path relationships. The results confirmed the importance of certain genes in the analyzed diseases, and unveiled which connections are modified among them.

Conclusions

We propose a framework to perform differential gene expression analysis on microarray data based on SEM, which is able to: 1) find relevant genes and perturbed biological pathways, investigating putative sub-pathway models based on the concept of disease module; 2) test and improve the generated models; 3) detect a differential expression level of one gene, and differential connection between two genes. This could shed light, not only on the mechanisms affecting variations in gene expression, but also on the causes of gene-gene relationship modifications in diseased phenotypes.

Quantitatively analyze composition principle of Ma Huang Tang by structural equation modeling

ETHNOPHARMACOLOGICAL RELEVANCE

In Chinese classic formulas, Ma Huang Tang (MHT), composed of Ephedra, Cassia twig, Bitter apricot kernel and Prepared licorice, has been widely used to treat cold, influenza, acute bronchitis, bronchial asthma and other pulmonary diseases. However, there is no quantitative interpretation about composition principle of MHT as well as other Chinese compound prescriptions. This study was aimed using structural equation modeling (SEM) to decipher 'monarch, minister, assistant and guide' which is the unique and integrated composition principle of Chinese compound recipes, by taking MHT for instance.

MATERIALS AND METHODS

Sixteen prescriptions of different dose ratios were combined orthogonally from four herbal drugs of MHT, then their diaphoretic, antispasmodic and analgesic effects were assessed by the indicators of the rat sweating point number, the spasmolysis percentage of guinea pig trachea and the murine writhing number, respectively. Basing on SME, the systematology analysis method to complex causality, path diagrams for herbal drugs were drawn with the Amos software and the relationships of the four herbal ingredients and therapeutic effects were measured.

RESULTS

Sixteen recipes induced SD rats sweating, remitted spasm of guinea pig trachea smooth muscle, and relieved ICR mouse pain due to acetic acid in comparison with animal model group or normal control groups. Three different SME models were specified and the relevant relationship was analyzed. According to the results of measured standardized path coefficients, Ephedra exerts the greatest contribution to the integral potency, so it acts as the monarch drug in MHT; Cassia twig is slightly weakly effective than Ephedra, and has the most significant interaction with Ephedra, which shows that it is the minister drug; the direct effects of Bitter apricot kernel and Prepared licorice on the integral potency are non-significant, while these two drugs have very significant synergetic effect with Ephedra or Cassia twig, thus they can be interpreted as subordinate drugs to strengthen the therapeutical effects of the monarch and minister drugs; the higher interaction values of Bitter apricot kernel suggest that it is the assistant drug, and Prepared licorice is the guide drug with lower values.

CONCLUSION

SME can be used to quantitatively analyze the composition principle of Chinese compound prescriptions like MHT, which demystifies the ancient and classical system theory of traditional Chinese medicine from a totally new viewpoint.

Systems biology in physiology: the vasopressin signaling network in kidney

Over the past 80 years, physiological research has moved progressively in a reductionist direction, providing mechanistic information on a smaller and smaller scale. This trend has culminated in the present focus on "molecular physiology," which deals with the function of single molecules responsible for cellular function. There is a need to assemble the information from the molecular level into models that explain physiological function at cellular, tissue, organ, and whole organism levels. Such integration is the major focus of an approach called "systems biology." The genome sequencing projects provide a basis for a new kind of systems biology called "data-rich" systems biology that is based on large-scale data acquisition methods including protein mass spectrometry, DNA microarrays, and deep sequencing of nucleic acids. These techniques allow investigators to measure thousands of variables simultaneously in response to an external stimulus. My laboratory is applying such an approach to the question: "How does the peptide hormone vasopressin regulate water permeability in the renal collecting duct?" We are using protein mass spectrometry to identify and quantify the phosphoproteome of collecting duct cells. The response to vasopressin, presented in the form of a network model, includes a general downregulation of proline-directed kinases (MAP kinases and cyclin-dependent kinases) and upregulation of basophilic kinases (ACG kinases and calmodulin-dependent kinases). Further progress depends on characterization and localization of candidate protein kinases in these families. The ultimate goal is to use multivariate statistical techniques and differential equations to obtain predictive models describing vasopressin signaling in the renal collecting duct.

Network inference of pal-1 lineage-specific regulation in the C. elegans embryo by structural equation modeling

The elucidation of spatial and temporal control during developmental stages is one of the central tasks for systems biology, and a variety of intracellular factors are known as regulators for specific gene expression. The activity information of those various factors is not directly reflected in their gene expression profiles. Hence, a method based on Structural Equation Modeling (SEM) is described. SEM can include the latent variables within the constructed model and infer the relationships among latent and observed variables, as a network model. An improved SEM approach for the construction of an optimal model is applied to infer the regulatory network for the determination of C lineage fate in C. elegans development. The inferred network model shows that the 13 analysed transcription factor genes were regulated by several other factors in addition to pal-1 expression. The other regulatory factors are those involved in protein accumulation and localization as important regulatory factors for normal development. Those regulatory factors were regulated sequentially in the network model. The regulation of the known pal-1 regulated genes was dependent on this sequential control of the regulatory factors. The interpretation of the network model shows insights to the complex regulation occurring during the C lineage determination by pal-1.