Prediction of Body Fluids where Proteins are Secreted into Based on Protein Interaction Network

Determining the body fluids where secreted proteins can be secreted into is important for protein function annotation and disease biomarker discovery. In this study, we developed a network-based method to predict which kind of body fluids human proteins can be secreted into. For a newly constructed benchmark dataset that consists of 529 human-secreted proteins, the prediction accuracy for the most possible body fluid location predicted by our method via the jackknife test was 79.02%, significantly higher than the success rate by a random guess (29.36%). The likelihood that the predicted body fluids of the first four orders contain all the true body fluids where the proteins can be secreted into is 62.94%. Our method was further demonstrated with two independent datasets: one contains 57 proteins that can be secreted into blood; while the other contains 61 proteins that can be secreted into plasma/serum and were possible biomarkers associated with various cancers. For the 57 proteins in first dataset, 55 were correctly predicted as blood-secrete proteins. For the 61 proteins in the second dataset, 58 were predicted to be most possible in plasma/serum. These encouraging results indicate that the network-based prediction method is quite promising. It is anticipated that the method will benefit the relevant areas for both basic research and drug development

Prediction of Phenotype-Associated Genes via a Cellular Network Approach: A Candida albicans Infection Case Study

Candida albicans is the most prevalent opportunistic fungal pathogen in humans causing superficial and serious systemic infections. The infection process can be divided into three stages: adhesion, invasion, and host cell damage. To enhance our understanding of these C. albicans infection stages, this study aimed to predict phenotype-associated genes involved during these three infection stages and their roles in C. albicans–host interactions. In light of the principles that proteins that lie closer to one another in a protein interaction network are more likely to have similar functions, and that genes regulated by the same transcription factors tend to have similar functions, a cellular network approach was proposed to predict the phenotype-associated genes in this study. A total of 4, 12, and 3 genes were predicted as adhesion-, invasion-, and damage-associated genes during C. albicans infection, respectively. These predicted genes highlight the facts that cell surface components are critical for cell adhesion, and that morphogenesis is crucial for cell invasion. In addition, they provide targets for further investigations into the mechanisms of the three C. albicans infection stages. These results give insights into the responses elicited in C. albicans during interaction with the host, possibly instrumental in identifying novel therapies to treat C. albicans infection

Modelling biochemical networks with intrinsic time delays: a hybrid semi-parametric approach

<p>Abstract</p> <p>Background</p> <p>This paper presents a method for modelling dynamical biochemical networks with intrinsic time delays. Since the fundamental mechanisms leading to such delays are many times unknown, non conventional modelling approaches become necessary. Herein, a hybrid semi-parametric identification methodology is proposed in which discrete time series are incorporated into fundamental material balance models. This integration results in hybrid delay differential equations which can be applied to identify unknown cellular dynamics.</p> <p>Results</p> <p>The proposed hybrid modelling methodology was evaluated using two case studies. The first of these deals with dynamic modelling of transcriptional factor A in mammalian cells. The protein transport from the cytosol to the nucleus introduced a delay that was accounted for by discrete time series formulation. The second case study focused on a simple network with distributed time delays that demonstrated that the discrete time delay formalism has broad applicability to both discrete and distributed delay problems.</p> <p>Conclusions</p> <p>Significantly better prediction qualities of the novel hybrid model were obtained when compared to dynamical structures without time delays, being the more distinctive the more significant the underlying system delay is. The identification of the system delays by studies of different discrete modelling delays was enabled by the proposed structure. Further, it was shown that the hybrid discrete delay methodology is not limited to discrete delay systems. The proposed method is a powerful tool to identify time delays in ill-defined biochemical networks.</p

A network-based biomarker approach for molecular investigation and diagnosis of lung cancer

<p>Abstract</p> <p>Background</p> <p>Lung cancer is the leading cause of cancer deaths worldwide. Many studies have investigated the carcinogenic process and identified the biomarkers for signature classification. However, based on the research dedicated to this field, there is no highly sensitive network-based method for carcinogenesis characterization and diagnosis from the systems perspective.</p> <p>Methods</p> <p>In this study, a systems biology approach integrating microarray gene expression profiles and protein-protein interaction information was proposed to develop a network-based biomarker for molecular investigation into the network mechanism of lung carcinogenesis and diagnosis of lung cancer. The network-based biomarker consists of two protein association networks constructed for cancer samples and non-cancer samples.</p> <p>Results</p> <p>Based on the network-based biomarker, a total of 40 significant proteins in lung carcinogenesis were identified with carcinogenesis relevance values (CRVs). In addition, the network-based biomarker, acting as the screening test, proved to be effective in diagnosing smokers with signs of lung cancer.</p> <p>Conclusions</p> <p>A network-based biomarker using constructed protein association networks is a useful tool to highlight the pathways and mechanisms of the lung carcinogenic process and, more importantly, provides potential therapeutic targets to combat cancer.</p

Constructing gene regulatory networks for long term photosynthetic light acclimation in Arabidopsis thaliana

Abstract Background Photosynthetic light acclimation is an important process that allows plants to optimize the efficiency of photosynthesis, which is the core technology for green energy. However, currently little is known about the molecular mechanisms behind the regulation of the photosynthetic light acclimation response. In this study, a systematic method is proposed to investigate this mechanism by constructing gene regulatory networks from microarray data of Arabidopsis thaliana. Methods The potential TF-gene regulatory pairs of photosynthetic light acclimation have been obtained by data mining of literature and databases. Following the identification of these potential TF-gene pairs, they have been refined using Pearson's correlation, allowing the construction of a rough gene regulatory network. This rough gene regulatory network is then pruned using time series microarray data of Arabidopsis thaliana via the maximum likelihood system identification method and Akaike's system order detection method to approach the real gene regulatory network of photosynthetic light acclimation. Results By comparing the gene regulatory networks under the PSI-to-PSII light shift and the PSII-to-PSI light shift, it is possible to identify important transcription factors for the different light shift conditions. Furthermore, the robustness of the gene network, in particular the hubs and weak linkage points, are also discussed under the different light conditions to gain further insight into the mechanisms of photosynthesis. Conclusions This study investigates the molecular mechanisms of photosynthetic light acclimation for Arabidopsis thaliana from the physiological level. This has been achieved through the construction of gene regulatory networks from the limited data sources and literature via an efficient computation method. If more experimental data for whole-genome ChIP-chip data and microarray data with multiple sampling points becomes available in the future, the proposed method will be improved on by constructing the whole-genome gene regulatory network. These advances will greatly improve our understanding of the mechanisms of the photosynthetic system.</p

Global screening of potential Candida albicans biofilm-related transcription factors via network comparison

[[abstract]]Background: Candida albicans is a commonly encountered fungal pathogen in humans. The formation of biofilm is a major virulence factor in C. albicans pathogenesis and is related to antidrug resistance of this organism. Although many factors affecting biofilm have been analyzed, molecular mechanisms that regulate biofilm formation still await to be elucidated. Results: In this study, from the gene regulatory network perspective, we developed an efficient computational framework, which integrates different kinds of data from genome-scale analysis, for global screening of potential transcription factors (TFs) controlling C. albicans biofilm formation. S. cerevisiae information and ortholog data were used to infer the possible TF-gene regulatory associations in C. albicans. Based on TF-gene regulatory associations and gene expression profiles, a stochastic dynamic model was employed to reconstruct the gene regulatory networks of C. albicans biofilm and planktonic cells. The two networks were then compared and a score of relevance value (RV) was proposed to determine and assign the quantity of correlation of each potential TF with biofilm formation. A total of twenty-three TFs are identified to be related to the biofilm formation; ten of them are previously reported by literature evidences. Conclusions: The results indicate that the proposed screening method can successfully identify most known biofilm-related TFs and also identify many others that have not been previously reported. Together, this method can be employed as a pre-experiment screening approach that reveals new target genes for further characterization to understand the regulatory mechanisms in biofilm formation, which can serve as the starting point for therapeutic intervention of C. albicans infections.[[fileno]]2050120010008[[department]]生科

O efeito modelador da tradução errada de genes em Candida albicans na interação hospedeiro-patogénio e na patogénese

Candida albicans is the major fungal pathogen in humans, causing diseases ranging from mild skin infections to severe systemic infections in immunocompromised individuals. The pathogenic nature of this organism is mostly due to its capacity to proliferate in numerous body sites and to its ability to adapt to drastic changes in the environment, such as pH, oxidative stress, temperature and osmolarity. C.albicans exhibit a unique translational system, decoding the leucine-CUG codon ambiguously as leucine (3%) and serine (97%), using a novel serine tRNA (tRNACAG Ser). That is aminoacylated by Seryl-tRNA synthetase (SerRS) and the LeucyltRNA synthetase (LeuRS). Exposure of C. albicans to macrophages, oxidative and pH stress and antifungals increases Leu misincorporation levels from 3% to 15%, suggesting that C. albicans could regulate mistranslation levels. The aim of this study was to identify molecules and pathways involved in the regulation of this genetic code ambiguity, and ultimately contribute to better understand this unique translational process. To accomplish this, the levels of the SerRS and LeuRS were determined under different physiological conditions, using a GFP sensor where the GFP opening reading frame was fused to the promoters of the SerRS and LeuRS genes (caSES1 and caCDC60 respectively). The data revealed that increased Leu incorporation at CUG codons is associated with higher LeuRS/SerRS ratio. To identify putative regulators (kinases and transcription factors (TF)) of SerRS and LeuRS expression, C. albicans kinase and TF knockout (KO) strains were transformed with the sensor and the expression of both aaRSs was quantified. Our data allowed us to identify the Transcription Factors Rph2 and Hap31 and Cbk1 kinase as putative regulators of the expression of both SerRS and LeuRS.Candida albicans é o principal fungo patogénico humano, causador de doenças, que vão desde pequenas infeções superficiais até infeções sistémicas graves, principalmente em indivíduos com deficit imunitário. A natureza patogénica deste organismo deve-se principalmente à sua capacidade de proliferar em numerosos orgãos e à sua capacidade de adesão às mucosas, resistindo a variações de pH, stress oxidativo e à temperatura. A C. albicans exibe um sistema de tradução único, descodificando o codão CUG de forma ambígua como leucina (3% dos codões) e serina (97%dos codões), usando um tRNA híbrido (tRNACAG Ser) que é aminoacilado pelas aminoacil-tRNA sintetases de Leucina (LeuRS) e Serina (SerRS). A exposição de C. albicans a macrófagos, stress oxidativo, variações de pH/osmolaridade e a antifúngicos aumenta os níveis de incorporação de leucina dos “normais” 3% para valores superiores a 15%, não se conhecendo, contudo, a relevância biológica de tal ambiguidade nem o seu mecanismo de regulação. Os objetivos desta tese foram estudar os mecanismos de controlo desta ambiguidade do código genético e contribuir para o melhor conhecimento deste processo em C. albicans. Para tal, os níveis de SerRS e LeuRS foram determinados em diferentes condições fisiológicas, usando um sensor fluorescente constituído pela fusão do gene da GFP com os promotores dos genes da SerRS e da LeuRS (caSES1 e caCDC60, respetivamente). Os dados revelaram que um aumento da incorporação de leucina nos codões CUG está associado ao aumento da razão LeuRS/SerRS em C. albicans. Para identificar as cinases e os fatores de transcrição (TF), que regulam a transcrição dos genes da SerRS e LeuRS (caSES1 e caCDC60), foram usadas estirpes de C. albicans com deleções nos genes das várias cinases e TFs. Estas estirpes mutantes foram transformadas com o sensor fluorescente e a expressão das sintetases foi quantificada. Os nossos dados permitiram identificar os TFs Rph2 e Hap31 e ainda a cinase Cbk1 como reguladores da expressão da LeuRS e SerRS.Programa Doutoral em Biomedicin