Aldh2 knockout mice were more sensitive to DNA damage in leukocytes due to ethyl tertiary butyl ether exposure

To clarify the genotoxicity of ethyl tertiary butyl ether (ETBE), a gasoline additive, male and female C57BL/6 mice of Aldh2+/+ and Aldh2-/- genotypes, aged 8 wk, were exposed to 0, 500, 1,750, or 5,000 ppm ETBE for 6 h/day, 5 d per week for 13 wk. DNA damage in leukocytes was measured by the alkaline comet assay and expressed quantitatively as Tail Intensity (TI). For male mice, TI was significantly higher in all three groups exposed to ETBE than in those without exposure within Aldh2-/- mice, whereas within Aldh2+/+ mice, TI increased only in those exposed to 5,000 ppm of ETBE as compared with mice without exposure. For female mice, a significant increase in TI values was observed in the group exposed to 5,000 ppm of ETBE as compared with those without exposure within Aldh2-/- mice; TI in Aldh2-/- mice exposed to 1,750 and 5,000 ppm was significantly higher than in Aldh2+/+ mice without exposure. TI did not significantly increase in any of the groups exposed to ETBE within female Aldh2+/+ mice. Based on the results we suggest that Aldh2-/- mice are more sensitive to DNA damage caused by ETBE than Aldh2+/+ mice and that males seem more susceptible to this effect than females.

Boolean modeling of transcriptome data reveals novel modes of heterotrimeric G-protein action

Classical mechanisms of heterotrimeric G-protein signaling are observed to function in regulation of the transcriptome. Conversely, many theoretical regulatory modes of the G-protein are not manifested in the transcriptomes we investigate.

A new mechanism of G-protein signaling is revealed, in which the β subunit regulates gene expression identically in the presence or absence of the α subunit.

We find evidence of cross-talk between G-protein-mediated and hormone-mediated transcriptional regulation.

We find evidence of system specificity in G-protein signaling.

Heterotrimeric G-proteins, composed of α, β, and γ subunits, participate in a wide range of signaling pathways in eukaryotes (Morris and Malbon, 1999). According to the typical, mammalian paradigm, in its inactive state, the G-protein exists as an associated heterotrimer. G-protein signaling begins with ligand binding that results in a conformational change in a G-protein-coupled receptor (GPCR). Once activated by the GPCR, the Gα separates from the associated Gβγ dimer and the freed Gα and Gβγ proteins can then interact with downstream effector molecules, alone or in combination, to transduce the signal. Subsequent to signal propagation, Gα re-associates with the Gβγ dimer to reform the G-protein complex.

There are several classical routes for signal propagation through heterotrimeric G-proteins that have been categorized in mammalian systems (Marrari et al, 2007; Dupre et al, 2009). One route, which we designate classical I, requires the presence of both subunits, and can invoke one of two distinct mechanisms. In one mechanism, on GPCR activation, freed Gα and Gβγ each interact with downstream effectors to elicit the downstream response. In a related mechanism, Gα but not Gβγ interacts with downstream effectors, but the Gβγ dimer is nevertheless required to facilitate coupling of Gα with the relevant GPCR (Marrari et al, 2007). In a second route, which we designate classical II, it is solely the Gβγ dimer that interacts with downstream effectors; in this case, sequestration of Gβγ within the heterotrimer prevents signal propagation. In addition, a few non-classical G-protein regulatory modes have also been implicated in some systems, for example signaling by the intact heterotrimer in yeast (Klein et al, 2000; Frank et al, 2005). Observations such as these lead to a fundamental question, namely, which of all the theoretical regulatory modes of G-protein signaling are realized biologically. Our study answers this question in the context of the model plant Arabidopsis thaliana, and in addition analyzes the manner in which G-protein signaling couples with signaling by the plant hormone abscisic acid. The Arabidopsis genome encodes only one canonical Gα subunit, GPA1, and one canonical Gβ subunit, AGB1, and knockout mutants are available for each of these, allowing clear dissection of Gα- and Gβ-related phenotypes.

Abscisic acid (ABA) is a major plant hormone, which inhibits growth and promotes tolerance of abiotic stresses such as drought, salinity, and cold. ABA signaling is known to interact with heterotrimeric G-protein signaling in both developmental and stress responses in a complex manner, causing, for example, ABA hyposensitivity of guard cell stomatal opening in gpa1 and agb1 single mutants as well as agb1 gpa1 double mutants (Fan et al, 2008), but ABA hypersensitivity of the inhibition of seed germination and post-germination seedling development in the same mutants (Pandey et al, 2006). These experimental observations implicate G-proteins as one of the components of ABA signaling, but to date no systematic study has been conducted in either plant or metazoan systems to define the co-regulatory modes of a G-protein and a hormone.

In this study, we conduct genome-wide gene expression profiling in G-protein subunit mutants of A. thaliana guard cells and leaves, with or without treatment with ABA. By introducing one or more mediators acting downstream of the G-protein and ABA to control transcript levels, we propose nine G-protein/ABA signaling pathways including ABA-independent G-protein signaling pathways, G-protein-independent ABA signaling pathways, and seven distinct ABA–G-protein-coupled signaling pathways (Figure 1). We develop a Boolean modeling framework to systematically enumerate 14 possible theoretical regulatory modes of the G-protein and 142 co-regulatory modes of the G-protein and ABA, and then use a pattern matching approach to associate target genes with theoretical regulatory modes.

Our analysis shows that the G-protein regulatory mode that requires the presence of both Gα and Gβγ subunits (consistent with classical I mechanisms), is well represented in both guard cells and leaves. The G-protein regulatory mode that requires a freed Gβγ subunit (classical II G-protein regulatory mechanism) is well supported in guard cells and somewhat less so in leaves. In addition, a G-protein regulatory mode representing a non-classical regulatory mechanism is prevalent in guard cells but less so in leaves (Figure 5). In this regulatory mode, signaling by Gβ(γ) occurs, and this signaling is not regulated in any way by Gα.

By relating the target genes with the nine proposed G-protein/ABA signaling pathways, we are able to gauge the plausibility of regulatory modes of the G-protein and ABA at the pathway level. We find that G-protein-independent ABA signaling pathways are prevalent in both guard cells and leaves. The existence of an ABA-independent regulatory activity of the G-protein is well supported in guard cells, but not supported in leaves. Additive regulation by G-protein signaling plus G-protein-independent ABA signaling is rare in both guard cells and leaves. In addition, combinatorial cross-talk between G-protein signaling and ABA signaling and additive cross-talk between ABA–G-protein signaling and G-protein-independent ABA signaling are observed in both guard cells and leaves. Our transcriptome analysis indicates that in some cases, ABA definitely does not influence G-protein signaling, though it may do so in some other cases.

To investigate whether previously observed hypersensitivity or hyposensitivity of developmental and dynamic transient responses to ABA in G-protein mutants is recapitulated at the level of transcriptional regulation, we compare gene regulation by ABA in guard cells and leaves of the G-protein mutants versus wild type. We find that in guard cells, equal ABA hyposensitivity of all mutants combined is significant, although hyposensitivity in individual mutants is not. There is also a separate group of genes in guard cells that show ABA hypersensitivity in the gpa1 mutant, suggesting complex interactions between ABA and G-protein signaling in gene regulation in this cell type. In leaves, ABA hyposensitivity of gene expression in the three individual mutants and equal hyposensitivity in all mutants are strongly supported. In addition, several of the functional categories identified by our analysis of G-protein regulatory modes have been implicated in previous physiological analyses of G-protein mutants, providing validation to the biological interpretation of our results.

In summary, by conducting a genome-wide gene expression profiling study in G-protein subunit mutants of A. thaliana guard cells and leaves and developing a Boolean modeling framework, we systematically evaluate the biological utilization of mechanisms of G-protein regulatory action and reveal novel regulatory modes of the G-protein. The results generate empirical evidence and insights regarding molecular events of G-protein signaling and response at the physiological level in both plants and mammals.

Heterotrimeric G-proteins mediate crucial and diverse signaling pathways in eukaryotes. Here, we generate and analyze microarray data from guard cells and leaves of G-protein subunit mutants of the model plant Arabidopsis thaliana, with or without treatment with the stress hormone, abscisic acid. Although G-protein control of the transcriptome has received little attention to date in any system, transcriptome analysis allows us to search for potentially uncommon yet significant signaling mechanisms. We describe the theoretical Boolean mechanisms of G-protein × hormone regulation, and then apply a pattern matching approach to associate gene expression profiles with Boolean models. We find that (1) classical mechanisms of G-protein signaling are well represented. Conversely, some theoretical regulatory modes of the G-protein are not supported; (2) a new mechanism of G-protein signaling is revealed, in which Gβ regulates gene expression identically in the presence or absence of Gα; (3) guard cells and leaves favor different G-protein modes in transcriptome regulation, supporting system specificity of G-protein signaling. Our method holds significant promise for analyzing analogous ‘switch-like' signal transduction events in any organism.

Mutagenicity of acrylamide and glycidamide in the testes of big blue mice

Acrylamide (AA) is an industrial chemical, a by-product of fried starchy foods, and a mutagen and rodent carcinogen. It can also cause damage during spermatogenesis. In this study, we investigated whether AA and its metabolite glycidamide (GA) induce mutagenic effects in the germ cells of male mice. Male Big Blue transgenic mice were administered 1.4 or 7.0mM of AA or GA in the drinking water for up to 4 weeks. Testicular cII mutant frequency (MF) was determined 3 weeks after the last treatment, and the types of the mutations in the cII gene were analyzed by DNA sequencing. The testes cII MFs in mice treated with either the low or high exposure concentrations of AA and GA were increased significantly. There was no significant difference in the cII MFs between AA and GA at the low exposure concentration. The mutation spectra in mice treated with AA (1.4mM) or GA (both 1.4 and 7.0mM) differed significantly from those of controls, but there were no significant differences in mutation patterns between AA and GA treatments. Comparison of the mutation spectra between testes and livers showed that the spectra differed significantly between the two tissues following treatment with AA or GA, whereas the mutation spectra in the two tissues from control mice were similar. These results suggest that AA possesses mutagenic effects on testes by virtue of its metabolism to GA, possibly targeting spermatogonial stem cells, but possibly via different pathways when compared mutations in liver.

Acyclovir and transmission of HIV-1 from persons infected with HIV-1 and HSV-2

BACKGROUND

Most persons who are infected with human immunodeficiency virus type 1 (HIV-1) are also infected with herpes simplex virus type 2 (HSV-2), which is frequently reactivated and is associated with increased plasma and genital levels of HIV-1. Therapy to suppress HSV-2 reduces the frequency of reactivation of HSV-2 as well as HIV-1 levels, suggesting that suppression of HSV-2 may reduce the risk of transmission of HIV-1.

METHODS

We conducted a randomized, placebo-controlled trial of suppressive therapy for HSV-2 (acyclovir at a dose of 400 mg orally twice daily) in couples in which only one of the partners was seropositive for HIV-1 (CD4 count, > or = 250 cells per cubic millimeter) and that partner was also infected with HSV-2 and was not taking antiretroviral therapy at the time of enrollment. The primary end point was transmission of HIV-1 to the partner who was not initially infected with HIV-1; linkage of transmissions was assessed by means of genetic sequencing of viruses.

RESULTS

A total of 3408 couples were enrolled at 14 sites in Africa. Of the partners who were infected with HIV-1, 68% were women, and the baseline median CD4 count was 462 cells per cubic millimeter. Of 132 HIV-1 seroconversions that occurred after randomization (an incidence of 2.7 per 100 person-years), 84 were linked within couples by viral sequencing: 41 in the acyclovir group and 43 in the placebo group (hazard ratio with acyclovir, 0.92, 95% confidence interval [CI], 0.60 to 1.41; P=0.69). Suppression with acyclovir reduced the mean plasma concentration of HIV-1 by 0.25 log(10) copies per milliliter (95% CI, 0.22 to 0.29; P<0.001) and the occurrence of HSV-2-positive genital ulcers by 73% (risk ratio, 0.27; 95% CI, 0.20 to 0.36; P<0.001). A total of 92% of the partners infected with HIV-1 and 84% of the partners not infected with HIV-1 remained in the study for 24 months. The level of adherence to the dispensed study drug was 96%. No serious adverse events related to acyclovir were observed.

CONCLUSIONS

Daily acyclovir therapy did not reduce the risk of transmission of HIV-1, despite a reduction in plasma HIV-1 RNA of 0.25 log(10) copies per milliliter and a 73% reduction in the occurrence of genital ulcers due to HSV-2. (ClinicalTrials.gov number, NCT00194519.)

The modulation of electronic and optical properties of OXD-X through introduction of the electron-withdrawing groups: a DFT study

A comparative study using density functional theory (DFT) on the molecular structure, electronic structure and relative properties of 1,3,4-oxadiazole dimers 1,3-bis[2-(4-methylphenyl)-1,3,4-oxadiazol-5-yl]benzene (OXD-X) and its derivatives with alkoxy substituents -O(CH(2))(n-1)CH(3) (OXD-An, n=1, 2) and electron-withdrawing substituents -CN (OXD-C), -CF(3) (OXD-TFM), -NO(2) (OXD-N) added at meta-substitution in the phenyl ring are presented. The ground state structures of the title complexes are optimized at B3LYP/6-31G level of theory. In addition, the time dependent density functional theory (TDDFT) method is applied to calculate the absorption and emission spectra of the derivatives based on the ground state geometries. Comparing with the alkoxy substituents, the results show that the electron-withdrawing substituents have remarkable influences on the energy levels of the frontier molecular orbitals, the spectra and the transition compositions. Especially, -NO(2) group plays the prominent role and the fluorine improves the energy level of LUMO further. The experimental absorption wavelengths for OXD-X, OXD-A3 and OXD-A7 are well reproduced by the TDDFT technique. Moreover the absorption wavelengths and the transition compositions can be effectively adjusted through introducing electro-withdrawing groups in the phenyl ring. The reorganization energies for hole and electron are smaller than that of typical hole and electron transport materials.

Automatic modeling of signaling pathways by network flow model

Signal transduction is an important process that controls cell proliferation, metabolism, differentiation, and so on. Effective computational models which unravel such a process by taking advantage of high-throughput genomic and proteomic data are highly demanded to understand the essential mechanisms underlying signal transduction. Since protein-protein interaction (PPI) plays an important role in signal transduction, in this paper, we present a novel method for modeling signaling pathways from PPI networks automatically. Given an undirected weighted protein interaction network, finding signaling pathways is treated as searching for optimal subnetworks according to some cost function. To cope with this optimization problem, a network flow model is proposed in this work to extract signaling pathways from protein interaction networks. In particular, the network flow model is formalized and solved as a mixed integer linear programming (MILP) model, which is simple in algorithm and efficient in computation. The numerical results on two known yeast MAPK signaling pathways demonstrate the efficiency and effectiveness of the proposed method.

Modeling post-transcriptional regulation activity of small non-coding RNAs in Escherichia coli

BACKGROUND

Transcriptional regulation is a fundamental process in biological systems, where transcription factors (TFs) have been revealed to play crucial roles. In recent years, in addition to TFs, an increasing number of non-coding RNAs (ncRNAs) have been shown to mediate post-transcriptional processes and regulate many critical pathways in both prokaryotes and eukaryotes. On the other hand, with more and more high-throughput biological data becoming available, it is possible and imperative to quantitatively study gene regulation in a systematic and detailed manner.

RESULTS

Most existing studies for inferring transcriptional regulatory interactions and the activity of TFs ignore the possible post-transcriptional effects of ncRNAs. In this work, we propose a novel framework to infer the activity of regulators including both TFs and ncRNAs by exploring the expression profiles of target genes and (post)transcriptional regulatory relationships. We model the integrated regulatory system by a set of biochemical reactions which lead to a log-bilinear problem. The inference process is achieved by an iterative algorithm, in which two linear programming models are efficiently solved. In contrast to available related studies, the effects of ncRNAs on transcription process are considered in this work, and thus more reasonable and accurate reconstruction can be expected. In addition, the approach is suitable for large-scale problems from the viewpoint of computation. Experiments on two synthesized data sets and a model system of Escherichia coli (E. coli) carbon source transition from glucose to acetate illustrate the effectiveness of our model and algorithm.

CONCLUSION

Our results show that incorporating the post-transcriptional regulation of ncRNAs into system model can mine the hidden effects from the regulation activity of TFs in transcription processes and thus can uncover the biological mechanisms in gene regulation in a more accurate manner. The software for the algorithm in this paper is available upon request.

Effects of in utero exposure to 2,2',4,4',5,5'-hexachlorobiphenyl on postnatal development and thyroid function in rat offspring

Exposure to polychlorobiphenyls (PCBs) has been reported to affect endocrine glands; however, little is known about the precise toxicological properties of individual PCBs. We determined whether prenatal exposure to 2,2',4,4',5,5'-hexachlorobiphenyl (PCB 153), a di-ortho-substituted non-coplanar congener, affects postnatal development in rat offspring. Pregnant Sprague-Dawley rats were given PCB 153 (0, 1, or 4 mg/kg/d) orally from gestational day (GD) 10 to 16, and somatic parameters and thyroid functions in offspring were examined. We found no dose-dependent changes in body weight, body length, tail length, or weight of liver, kidney, testis, seminal vesicle, prostate, ovary, relative organ weight, anogenital distance (AGD), or AGD index in offspring at 1, 3 or 9 wk of age. We observed no compound-related changes in the plasma concentrations of thyroxine (T(4)), tri-iodothyronine (T(3)) or thyroid-stimulating hormone (TSH), although there was a significant difference in T(3) only in 1-wk-old males. In addition, thyroid glands from PCB 153 groups had normal T(4) responses to exogenous TSH in vivo. These findings suggest that low doses of PCB 153 given prenatally (GD 10-16, 1-4 mg/kg/d) might have little effect on postnatal somatic growth or thyroid development of male and female rat offspring under the experimental conditions of the present study.

Discrete dynamic modeling of cellular signaling networks

Understanding signal transduction in cellular systems is a central issue in systems biology. Numerous experiments from different laboratories generate an abundance of individual components and causal interactions mediating environmental and developmental signals. However, for many signal transduction systems there is insufficient information on the overall structure and the molecular mechanisms involved in the signaling network. Moreover, lack of kinetic and temporal information makes it difficult to construct quantitative models of signal transduction pathways. Discrete dynamic modeling, combined with network analysis, provides an effective way to integrate fragmentary knowledge of regulatory interactions into a predictive mathematical model which is able to describe the time evolution of the system without the requirement for kinetic parameters. This chapter introduces the fundamental concepts of discrete dynamic modeling, particularly focusing on Boolean dynamic models. We describe this method step-by-step in the context of cellular signaling networks. Several variants of Boolean dynamic models including threshold Boolean networks and piecewise linear systems are also covered, followed by two examples of successful application of discrete dynamic modeling in cell biology.

Alteration of brain neurotransmitters in female rat offspring induced by prenatal administration of 16 and 64 mg/kg of 2,2',4,4',5,5'-hexachlorobiphenyl (PCB153)

PCB153 (2,2',4,4',5,5'-hexachlorobiphenyl), a non-coplanar PCB and the congener most widely distributed in the environment, was orally administered to pregnant Sprague-Dawley (Crj: CD (SD) IGS) rats from gestation day 10 through 16 at doses of 0 (control), 16 and 64 mg/kg body weight. Female pups were sacrificed at 1, 3, 6, and 9 wk, and at 1 yr of age to evaluate the differences in brain neurotransmitters and their metabolites between PCB153-exposed and control groups. Brain levels of norepinephrine (NE), 3-methoxy-4-hydroxyphenylglycol (MHPG), dopamine (DA), 3,4-dihydroxyphenylacetic acid (DOPAC), homovanillic acid (HVA), serotonin (5HT), 5-hydroxyindoleacetic acid (5HIAA), acetylcholine (ACh), and choline (Ch) in discrete brain regions or in whole brain were measured. At 1 to 3 wk after birth, brain levels of DA, DOPAC, HVA, 5HT and 5HIAA in PCB-exposed groups were higher than those of the control group. At 9 wk after birth, DA turnover was reduced in half of the four brain areas examined (forebrain and hindbrain), and 5HIAA levels were increased in all brain areas in the PCB-treated group compared to those of the control group. At 1 yr after birth, the levels of DA, DOPAC, and HVA in the hippocampus, hypothalamus, and medulla oblongata were lower in the PCB-exposed groups than in the control group. Prenatal exposure to PCB153 stimulated the turnover of 5HT neurons in the brain of female offspring at early stages (1 to 9 wk) of development. On the other hand, the turnover of DA neurons in the PCB-exposed groups was reduced in late stages (9 wk to 1 yr) of development compared with that of the control group. The brain neurotransmitters of dams treated with PCB were assayed at 3 wk after delivery (15 wk old), and decreases in DA, DOPAC, and HVA were observed. PCB153 reduced the activity of DA neurons in the brain of dams. These results are discussed in relation to health effects observed in humans exposed to PCBs.

Effects of in utero exposure to 2,2',4,4',5,5'-hexachlorobiphenyl (PCB 153) on somatic growth and endocrine status in rat offspring

Exposure to polychlorobiphenyl (PCB) mixtures at an early stage of development has been reported to affect endocrine glands; however, little is known about the precise toxicological properties of individual PCB. The present study was undertaken to determine whether prenatal exposure to 2,2',4,4',5,5'-hexachlorobiphenyl (PCB 153), a di-ortho-substituted non-coplanar congener, affects postnatal development in rat offspring. Pregnant Sprague-Dawley rats (Crj: CD (SD) IGS) were given PCB 153 (0, 16, or 64 mg/kg/day) orally from gestational day (GD) 10 through GD 16, and developmental parameters in the male and female offspring were examined. We found no dose-dependent changes in body weight, body length (nose-anus length), tail length, or the weights of kidneys, testes, ovaries and uterus in offspring at 1 or 3 weeks of age. Liver weights were increased in the PCB 153-treated groups, although we observed a significant difference only in males. Anogenital distance was unaffected in the PCB 153-treated groups. We observed a significant dose-dependent decrease in the plasma concentrations of thyroxine and tri-iodothyronine, whereas those of thyroid-stimulating hormone were not significantly changed. In addition, there were no dose-dependent changes in plasma concentrations of growth hormone and insulin-like growth factor-I in any dose group. These findings suggest that prenatal exposure to PCB 153 (GD 10-16, 16-64 mg/kg/day) may alter the thyroid status in rat offspring to some extent without affecting somatic growth or its related hormonal parameters.

Alteration of brain levels of neurotransmitters and amino acids in male F344 rats induced by three-week repeated inhalation exposure to 1-bromopropane

The present study investigated the effects of 1-bromopropane (1BP) on brain neuroactive substances of rats to determine the extent of its toxicity to the central nervous system (CNS). We measured the changes in neurotransmitters (acetylcholine, catecholamine, serotonin and amino acids) and their metabolites or precursors in eight brain regions after inhalation exposure to 1BP at 50 to 1,000 ppm for 8 h per day for 7 d per week for 3 wk. Rats were sacrificed at 2 h (Case 1), or at 19 h (Case 2) after the end of exposure. In Case 1, the level of 5-hydroxyindoleacetic acid (5HIAA) was lowered in some brain regions by 1BP exposure. The decrease of 5HIAA in the frontal cortex was statistically significant at 50 ppm 1BP exposure. In Case 2, gamma-amino butyric acid (GABA) and taurine were decreased in many brain regions of exposed rats, and a significant decrease of taurine in the midbrain occurred at 50 ppm 1BP exposure. In both cases of 2-h and 19-h intervals from the end of exposure to sacrifice, aspartate and glutamine levels were elevated in many brain regions, but the acetylcholine level did not change in any brain region. Three-week repeated exposure to 1BP produced significantly changes in amino acid contents of rat brains, particularly at 1,000 ppm.

Uncovering signal transduction networks from high-throughput data by integer linear programming

Signal transduction is an important process that transmits signals from the outside of a cell to the inside to mediate sophisticated biological responses. Effective computational models to unravel such a process by taking advantage of high-throughput genomic and proteomic data are needed to understand the essential mechanisms underlying the signaling pathways. In this article, we propose a novel method for uncovering signal transduction networks (STNs) by integrating protein interaction with gene expression data. Specifically, we formulate STN identification problem as an integer linear programming (ILP) model, which can be actually solved by a relaxed linear programming algorithm and is flexible for handling various prior information without any restriction on the network structures. The numerical results on yeast MAPK signaling pathways demonstrate that the proposed ILP model is able to uncover STNs or pathways in an efficient and accurate manner. In particular, the prediction results are found to be in high agreement with current biological knowledge and available information in literature. In addition, the proposed model is simple to be interpreted and easy to be implemented even for a large-scale system.

A pilot study of gene expression analysis in workers with hand-arm vibration syndrome

The purpose of this pilot study was to examine differences in gene expressions by cDNA microarray analysis of hand-arm vibration syndrome (HAVS) patients. Vein blood samples were collected and total RNA was extracted. All blood samples were obtained in the morning in one visit after a standard light breakfast. We performed microarray analysis with the labeled cDNA prepared by reverse transcription from RNA samples, using the Human CHIP version 1 (DNA Chip Research Inc, Yokohama, Japan). There are 2,976 genes on the chip, and these genes were selected from a cDNA library prepared with human peripheral white blood cells (WBC). Different gene levels between the HAVS patients and controls, and between groups of HAVS with different levels of symptoms, were indicated by the randomized variance model. The most up-regulated genes were analyzed for their possible functions and association with the occurrence of HAVS. From the results of this pilot study, although the results were obtained a limited number of subjects, it would appear that cDNA microarray analysis of HAVS patients has potential as a new objective method of HAVS diagnosis. Further research is needed to examine the gene expression with increased numbers of patients at different stages of HAVS.

Quantitative function for community detection

We propose a quantitative function for community partition -- i.e., modularity density or D value. We demonstrate that this quantitative function is superior to the widely used modularity Q and also prove its equivalence with the objective function of the kernel k means. Both theoretical and numerical results show that optimizing the new criterion not only can resolve detailed modules that existing approaches cannot achieve, but also can correctly identify the number of communities.

Inferring transcriptional interactions and regulator activities from experimental data

Gene regulation is a fundamental process in biological systems, where transcription factors (TFs) play crucial roles. Inferring transcriptional interactions between TFs and their target genes has utmost importance for understanding the complex regulatory mechanisms in cellular systems. On one hand, with the rapid progress of various high-throughput experiment techniques, more and more biological data become available, which makes it possible to quantitatively study gene regulation in a systematic manner. On the other hand, transcription regulation is a complex biological process mediated by many events such as post-translational modifications, degradation, and competitive binding of multiple TFs. In this review, with a particular emphasis on computational methods, we report the recent advances of the research topics related to transcriptional regulatory networks, including how to infer transcriptional interactions, reveal combinatorial regulation mechanisms, and reconstruct TF activity profiles.

Analysis on multi-domain cooperation for predicting protein-protein interactions

Background

Domains are the basic functional units of proteins. It is believed that protein-protein interactions are realized through domain interactions. Revealing multi-domain cooperation can provide deep insights into the essential mechanism of protein-protein interactions at the domain level and be further exploited to improve the accuracy of protein interaction prediction.

Results

In this paper, we aim to identify cooperative domains for protein interactions by extending two-domain interactions to multi-domain interactions. Based on the high-throughput experimental data from multiple organisms with different reliabilities, the interactions of domains were inferred by a Linear Programming algorithm with Multi-domain pairs (LPM) and an Association Probabilistic Method with Multi-domain pairs (APMM). Experimental results demonstrate that our approach not only can find cooperative domains effectively but also has a higher accuracy for predicting protein interaction than the existing methods. Cooperative domains, including strongly cooperative domains and superdomains, were detected from major interaction databases MIPS and DIP, and many of them were verified by physical interactions from the crystal structures of protein complexes in PDB which provide intuitive evidences for such cooperation. Comparison experiments in terms of protein/domain interaction prediction justified the benefit of considering multi-domain cooperation.

Conclusion

From the computational viewpoint, this paper gives a general framework to predict protein interactions in a more accurate manner by considering the information of both multi-domains and multiple organisms, which can also be applied to identify cooperative domains, to reconstruct large complexes and further to annotate functions of domains. Supplementary information and software are provided in and .

Uncovering fuzzy community structure in complex networks

There has been an increasing interest in properties of complex networks, such as small-world property, power-law degree distribution, and network transitivity which seem to be common to many real world networks. In this study, a useful community detection method based on non-negative matrix factorization (NMF) technique is presented. Based on a popular modular function, a proper feature matrix from diffusion kernel and NMF algorithm, the presented method can detect an appropriate number of fuzzy communities in which a node may belong to more than one community. The distinguished characteristic of the method is its capability of quantifying how much a node belongs to a community. The quantification provides an absolute membership degree for each node to each community which can be employed to uncover fuzzy community structure. The computational results of the method on artificial and real networks confirm its ability.

Inferring transcriptional regulatory networks from high-throughput data

MOTIVATION

Inferring the relationships between transcription factors (TFs) and their targets has utmost importance for understanding the complex regulatory mechanisms in cellular systems. However, the transcription factor activities (TFAs) cannot be measured directly by standard microarray experiment owing to various post-translational modifications. In particular, cooperative mechanism and combinatorial control are common in gene regulation, e.g. TFs usually recruit other proteins cooperatively to facilitate transcriptional reaction processes.

RESULTS

In this article, we propose a novel method for inferring transcriptional regulatory networks (TRN) from gene expression data based on protein transcription complexes and mass action law. With gene expression data and TFAs estimated from transcription complex information, the inference of TRN is formulated as a linear programming (LP) problem which has a globally optimal solution in terms of L(1) norm error. The proposed method not only can easily incorporate ChIP-Chip data as prior knowledge, but also can integrate multiple gene expression datasets from different experiments simultaneously. A unique feature of our method is to take into account protein cooperation in transcription process. We tested our method by using both synthetic data and several experimental datasets in yeast. The extensive results illustrate the effectiveness of the proposed method for predicting transcription regulatory relationships between TFs with co-regulators and target genes.

Reproductive toxicity of ethylene glycol monoethyl ether in Aldh2 knockout mice

Ethylene glycol monoethyl ether (EGEE) can cause damage to testes and sperm, and its metabolites are believed to play an important role in its toxicity. Aldehyde dehydrogenase 2 (ALDH2) is involved in the metabolism of this chemical. To investigate whether and how the enzyme affects the toxicity of EGEE, we conducted experiments comparing Aldh2 knockout mice with wild-type mice. Administration of EGEE at 100 and 600 mg/kg/day for one week did not induce any significant change in the weight and body weight ratios of testes, prostate and epididymides in either Aldh2 knockout or wild-type mice. However, motion of sperm from the spermaduct, as analyzed with a Hamilton-Thorne Sperm analyzer, was slightly decreased in the low dose group, and significantly lower in the high dose group; and the percentage of progressive sperm was also reduced in the two EGEE groups. This effect of EGEE treatment was observed in the wild-type, but not in the Aldh2 knockout mice. Sperm motion from the cauda epididymides was not affected. On the other hand, the concentration of ethoxyacetic acid, a metabolite of EGEE, in 24 h pooled urine of EGEE-treated Aldh2 knockout mice was not significantly lower than that of the wild-type mice on most days of urine sampling. These results suggest that inactivation of the ALDH2 enzyme due to gene mutation may be linked to differences in the susceptibility to EGEE-induced sperm toxicity.

Minimum conflict individual haplotyping from SNP fragments and related genotype

The Minimum Error Correction (MEC) is an important model for haplotype reconstruction from SNP fragments. However, this model is effective only when the error rate of SNP fragments is low. In this paper, we propose a new computational model called Minimum Conflict Individual Haplotyping (MCIH) as an extension to MEC. In contrast to the conventional approaches, the new model employs SNP fragment information and also related genotype information, thereby a high accurate inference can be expected. We first prove the MCIH problem to be NP-hard. To evaluate the practicality of the new model we design an exact algorithm (a dynamic programming procedure) to implement MCIH on a special data structure. The numerical experience indicates that it is fairly effective to use MCIH at the cost of related genotype information, especially in the case of SNP fragments with a high error rate. Moreover, we present a feed-forward neural network algorithm to solve MCIH for general data structure and large size instances. Numerical results on real biological data and simulation data show that the algorithm works well and MCIH is a potential alternative in individual haplotyping.