Dynamic covariation between gene expression and proteome characteristics

Comparative proteomic analysis reveals novel insights into the interaction between rice and Xanthomonas oryzae pv. oryzae

BACKGROUND

Bacterial blight, which is caused by Xanthomonas oryzae pv. oryzae (Xoo), is a devastating rice disease worldwide. Rice introgression line H471, derived from the recurrent parent Huang-Hua-Zhan (HHZ) and the donor parent PSBRC28, exhibits broad-spectrum resistance to Xoo, including to the highly virulent Xoo strain PXO99^A, whereas its parents are susceptible to PXO99^A. To characterize the responses to Xoo, we compared the proteome profiles of the host and pathogen in the incompatible interaction (H471 inoculated with PXO99^A) and the compatible interaction (HHZ inoculated with PXO99^A).

RESULTS

In this study, a total of 374 rice differentially abundant proteins (DAPs) and 117 Xoo DAPs were detected in the comparison between H471 + PXO99^A and HHZ + PXO99^A. Most of the Xoo DAPs related to pathogen virulence, including the outer member proteins, type III secretion system proteins, TonB-dependent receptors, and transcription activator-like effectors, were less abundant in the incompatible interaction than in the compatible interaction. The rice DAPs were mainly involved in secondary metabolic processes, including phenylalanine metabolism and the biosynthesis of flavonoids and phenylpropanoids. Additionally, some DAPs involved in the phenolic phytoalexin and salicylic acid (SA) biosynthetic pathways accumulated much more in H471 than in HHZ after the inoculation with PXO99^A, suggesting that phytoalexin and SA productions were induced faster in H471 than in HHZ. Further analyses revealed that the SA content increased much more rapidly in H471 than in HHZ after the inoculation, suggesting that the SA signaling pathway was activated faster in the incompatible interaction than in the compatible interaction.

CONCLUSIONS

Overall, our results indicate that during an incompatible interaction between H471 and PXO99^A, rice plants prevent pathogen invasion and also initiate multi-component defense responses that inhibit disease development.

Determining the Transcription Rates Yielding Steady-State Production of mRNA in the Lac Genetic Switch of Escherichia coli

To elucidate the regulatory dynamics of the gene expression activation and inactivation, an in silico biochemical model of the lac circuit in Escherichia coli was used to evaluate the transcription rates that yield the steady-state mRNA production in active and inactive states of the lac circuit. This result can be used in synthetic biology applications to understand the limits of the genetic synthesis. Since most genetic networks involve many interconnected components with positive and negative feedback control, intuitive understanding of their dynamics is often difficult to obtain. Although the kinetic model of the lac circuit considered involves only a single positive feedback, the developed computational framework can be used to evaluate supported ranges of other reaction rates in genetic circuits with more complex regulatory networks. More specifically, the inducible lac gene switch in E. coli is regulated by unbinding and binding of the inducer-repressor complexes to or from the DNA operator to switch the gene expression on and off. The dependency of mRNA production at steady state on different transcription rates and the repressor complexes has been studied by computer simulations in the Lattice Microbe software. Provided that the lac circuit is in active state, the transcription rate is independent of the inducer-repressor complexes present in the cell. In inactive state, the transcription rate is dependent on the specific inducer-repressor complex bound to the operator that inactivates the gene expression. We found that the repressor complex with the largest affinity to the operator yields the smallest range of the feasible transcription rates to yield the steady state while the lac circuit is in inactive state. In contrast, the steady state in active state can be obtained for any value of the transcription rate.

Differential transcriptome analysis reveals genes related to cold tolerance in seabuckthorn carpenter moth, Eogystia hippophaecolus

Seabuckthorn carpenter moth, Eogystia hippophaecolus (Lepidoptera: Cossidae), is an important pest of sea buckthorn (Hippophae rhamnoides), which is a shrub that has significant ecological and economic value in China. E. hippophaecolus is highly cold tolerant, but limited studies have been conducted to elucidate the molecular mechanisms underlying its cold resistance. Here we sequenced the E. hippophaecolus transcriptome using RNA-Seq technology and performed de novo assembly from the short paired-end reads. We investigated the larval response to cold stress by comparing gene expression profiles between treatments. We obtained 118,034 unigenes, of which 22,161 were annotated with gene descriptions, conserved domains, gene ontology terms, and metabolic pathways. These resulted in 57 GO terms and 193 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. By comparing transcriptome profiles for differential gene expression, we identified many differentially expressed proteins and genes, including heat shock proteins and cuticular proteins which have previously been reported to be involved in cold resistance of insects. This study provides a global transcriptome analysis and an assessment of differential gene expression in E. hippophaecolus under cold stress. We found seven differential expressed genes in common between developmental stages, which were verified with qPCR. Our findings facilitate future genomic studies aimed at improving our understanding of the molecular mechanisms underlying the response of insects to low temperatures.

On predicting regulatory genes by analysis of functional networks in C. elegans

Background

Connectivity networks, which reflect multiple interactions between genes and proteins, possess not only a descriptive but also a predictive value, as new connections can be extrapolated and tested by means of computational analysis. Integration of different types of connectivity data (such as co-expression and genetic interactions) in one network has proven to benefit ‘guilt by association’ analysis. However predictive values of connectives of different types, that had their specific functional meaning and topological characteristics were not obvious, and have been addressed in this analysis.

Methods

eQTL data for 3 experimental C.elegans age groups were retrieved from WormQTL. WormNet has been used to obtain pair-wise gene interactions. The Shortest Path Function (SPF) has been adopted for statistical validation of the co-expressed gene clusters and for computational prediction of their potential gene expression regulators from a network context. A new SPF-based algorithm has been applied to genetic interactions sub-networks adjacent to the clusters of co-expressed genes for ranking the most likely gene expression regulators causal to eQTLs.

Results

We have demonstrated that known co-expression and genetic interactions between C. elegans genes can be complementary in predicting gene expression regulators. Several algorithms were compared in respect to their predictive potential in different network connectivity contexts. We found that genes associated with eQTLs are highly clustered in a C. elegans co-expression sub-network, and their adjacent genetic interactions provide the optimal functional connectivity environment for application of the new SPF-based algorithm. It was successfully tested in the reverse-prediction analysis on groups of genes with known regulators and applied to co-expressed genes and experimentally observed expression quantitative trait loci (eQTLs).

Conclusions

This analysis demonstrates differences in topology and connectivity of co-expression and genetic interactions sub-networks in WormNet. The modularity of less continuous genetic interaction network does not correspond to modularity of the dense network comprised by gene co-expression interactions. However the genetic interaction network can be used much more efficiently with the SPF method in prediction of potential regulators of gene expression. The developed method can be used for validation of functional significance of suggested eQTLs and a discovery of new regulatory modules.

Chemotherapy sorting can be used to identify cancer stem cell populations

Cancer stem cells (CSCs) of bladder transitional cell cancers (BTCC) had not been identified by the reported common methods. According to the phenomenon that CSCs were resistant to chemotherapy, BTCC cell lines T24 and 5637 were cultured with mitomycin C respectively. Cell inhibition assay revealed an increased population of drug resistant cancer cells with a concentration gradient of mitomycin C. The maximal and minimal cell inhibition rate in cell line T24 was 92.5 % ± 1.0 versus 64.1 % ± 1.4 (P < 0.001), and in cell line 5637 was 90.2 % ± 2.5 versus 55.1 % ± 1.8 (P < 0.001), respectively. There is no significant difference between these two groups. Drug resistant cells just comprised approximately 7.5 % (T24) versus 9.8 % (5637) of the total cells. Compared with control cells, cell cycle analysis demonstrated that more drug resistant cells were at G0G1 phase and fewer were at S phase with the concentration gradient of mitomycin C in both cell lines, which is in accord with the stem cell theory that most stem cells maintain in a quiescent condition. Importantly, we found that embryonic stem cell markers (OCT-4 and NANOG) were highly expressed in both gene and protein level in BTCC cell line T24 and 5637 after 24-h chemotherapy exposure. Interestingly, the drug concentration gradient was in accord with OCT-4 and NANOG expression, suggesting that chemotherapy sorting might be a feasible method for BTCC CSCs identification.

Evidence for the adaptation of protein pH-dependence to subcellular pH

BACKGROUND

The availability of genome sequences, and inferred protein coding genes, has led to several proteome-wide studies of isoelectric points. Generally, isoelectric points are distributed following variations on a biomodal theme that originates from the predominant acid and base amino acid sidechain pKas. The relative populations of the peaks in such distributions may correlate with environment, either for a whole organism or for subcellular compartments. There is also a tendency for isoelectric points averaged over a subcellular location to not coincide with the local pH, which could be related to solubility. We now calculate the correlation of other pH-dependent properties, calculated from 3D structure, with subcellular pH.

RESULTS

For proteins with known structure and subcellular annotation, the predicted pH at which a protein is most stable, averaged over a location, gives a significantly better correlation with subcellular pH than does isoelectric point. This observation relates to the cumulative properties of proteins, since maximal stability for individual proteins follows the bimodal isoelectric point distribution. Histidine residue location underlies the correlation, a conclusion that is tested against a background of proteins randomised with respect to this feature, and for which the observed correlation drops substantially.

CONCLUSION

There exists a constraint on protein pH-dependence, in relation to the local pH, that is manifested in the pKa distribution of histidine sub-proteomes. This is discussed in terms of protein stability, pH homeostasis, and fluctuations in proton concentration.

Dynamic covariation between gene expression and genome characteristics

Gene and protein expression is controlled so that cells can react to changing intra- and extracellular signals by modulating biochemical networks and pathways. We have previously shown that gene expression and the properties of expressed proteins are dynamically correlated. Here we investigated correlations between gene related parameters and gene expression patterns, and found statistically significant correlations in microarray datasets for different cell types, organisms and processes, including human B and T cell stimulation, cell cycle in HeLa cells, infection in intestinal epithelial cells, Drosophila melanogaster life span, and Saccharomyces cerevisiae cell cycle. Our method was applied to time course datasets individually for each time point. We derived from sequence information numerous parameters for nucleotide composition, two-base composition, codon usage, skew parameters, and codon bias. In addition to coding regions, we also investigated correlations for complete genes and introns. Significant dynamic correlations were identified for each of the analyses. Our method also proved useful for detecting dynamic shifts in gene expression profiles, such as in the D. melanogaster dataset. Detection of changes in the properties of expressed genes and proteins might be useful for predicting or following biological processes, responses, growth, differentiation and possibly in related disorders.

Multilectin Affinity Chromatography for Characterization of Multiple Glycoprotein Biomarker Candidates in Serum from Breast Cancer Patients

Background: Glycoproteins are often associated with cancer and are important in serum studies, for which glycosylation is a common posttranslational modification.

Methods: We used multilectin affinity chromatography (M-LAC) to isolate glycoproteins from the sera of breast cancer patients and controls. The proteins were identified by HPLC–tandem mass spectrometry (MS/MS) analysis of the corresponding tryptic digests. We used the FuncAssociate Gene Ontology program for association analysis of the identified proteins. Biomarker candidates in these groups were comparatively quantitated by use of peak area measurements, with inclusion of an internal standard. We analyzed data for concordance within the ontology association groups for vector of change with the development of breast cancer.

Results: Detection of the known low-concentration biomarker HER-2 (8–24 μg/L) enabled us to establish a dynamic range of 106, relative to the amount of albumin, for the depletion step. We then used ELISA to confirm this range. Proteins associated with lipid transport and metabolism, cell growth and maintenance, ion homeostasis, and protease inhibition were found to be differentially regulated in serum from women with breast cancer compared with serum from women without breast cancer.

Conclusions: M-LAC for isolation of the serum glycoproteome, coupled with liquid chromatography–MS/MS and the use of gene ontology associations, can be used to characterize large panels of candidate markers, which can then be evaluated in a particular patient population.