Ant colony optimization for the identification of dysregulated gene subnetworks from expression data.

BACKGROUND: High-throughput experimental technologies can provide deeper insights into pathway perturbations in biomedical studies. Accordingly, their usage is central to the identification of molecular targets and the subsequent development of suitable treatments for various diseases. Classical interpretations of generated data, such as differential gene expression and pathway analyses, disregard interconnections between studied genes when looking for gene-disease associations. Given that these interconnections are central to cellular processes, there has been a recent interest in incorporating them in such studies. The latter allows the detection of gene modules that underlie complex phenotypes in gene interaction networks. Existing methods either impose radius-based restrictions or freely grow modules at the expense of a statistical bias towards large modules. We propose a heuristic method, inspired by Ant Colony Optimization, to apply gene-level scoring and module identification with distance-based search constraints and penalties, rather than radius-based constraints. RESULTS: We test and compare our results to other approaches using three datasets of different neurodegenerative diseases, namely Alzheimer's, Parkinson's, and Huntington's, over three independent experiments. We report the outcomes of enrichment analyses and concordance of gene-level scores for each disease. Results indicate that the proposed approach generally shows superior stability in comparison to existing methods. It produces stable and meaningful enrichment results in all three datasets which have different case to control proportions and sample sizes. CONCLUSION: The presented network-based gene expression analysis approach successfully identifies dysregulated gene modules associated with a certain disease. Using a heuristic based on Ant Colony Optimization, we perform a distance-based search with no radius constraints. Experimental results support the effectiveness and stability of our method in prioritizing modules of high relevance. Our tool is publicly available at github.com/GhadiElHasbani/ACOxGS.git.

Detecting protein complexes in protein interaction networks using a ranking algorithm with a refined merging procedure.

BACKGROUND: Developing suitable methods for the identification of protein complexes remains an active research area. It is important since it allows better understanding of cellular functions as well as malfunctions and it consequently leads to producing more effective cures for diseases. In this context, various computational approaches were introduced to complement high-throughput experimental methods which typically involve large datasets, are expensive in terms of time and cost, and are usually subject to spurious interactions. RESULTS: In this paper, we propose ProRank+, a method which detects protein complexes in protein interaction networks. The presented approach is mainly based on a ranking algorithm which sorts proteins according to their importance in the interaction network, and a merging procedure which refines the detected complexes in terms of their protein members. ProRank + was compared to several state-of-the-art approaches in order to show its effectiveness. It was able to detect more protein complexes with higher quality scores. CONCLUSIONS: The experimental results achieved by ProRank + show its ability to detect protein complexes in protein interaction networks. Eventually, the method could potentially identify previously-undiscovered protein complexes.The datasets and source codes are freely available for academic purposes at http://faculty.uaeu.ac.ae/nzaki/Research.htm.

Application of quantitative transcriptomics in evaluating the ex vivo effects of per- and polyfluoroalkyl substances on Atlantic cod (Gadus morhua) ovarian physiology.

Because of their global consumption and persistence, per- and polyfluoroalkyl substances (PFASs), are ubiquitously distributed in the environment, as well as in wildlife and humans. In the present study, we have employed an ex vivo organ culture technique, based on the floating agarose method, of Atlantic cod ovarian tissue to investigate the effects of three different concentrations of PFOS, PFOA (1, 5 and 25 µM) and PFNA (0.5, 5 and 50 µM), used singly and in also in combination (1×, 20× and 100×). In the 1× exposure mixture, concentrations were decided based on their proportional levels (in molar equivalents) relative to PFOS, which is the most abundant PFAS in cod liver from a 2013 screening project. To investigate the detailed underlying mechanisms and biological processes, transcriptome sequencing was performed on exposed ovarian tissue. The number of differentially expressed genes (DEGs) having at least 0.75 log2-fold change was elevated in high, compared to low and medium concentration exposures. The highest PFNA, PFOA and PFOS concentrations, and the highest (100×) mixture exposure, showed 40, 68, 1295, and 802 DEGs, respectively. The latter two exposure groups shared a maximum of 438 DEGs. In addition, they both shared the majority of functionally enriched pathways belonging to biological processes such as cellular signaling, cell adhesion, lipid metabolism, immunological responses, cancer, reproduction and metabolism. Shortlisted DEGs that were specifically annotated to reproduction associated gene ontology (GO) terms were observed only in the highest PFOS and mixture exposure groups. These transcripts contributed to ovarian key events such as steroidogenesis (star, cyp19a1a), oocyte growth (amh), maturation (igfbp5b, tgfß2, tgfß3), and ovulation (pgr, mmp2). Contrary to other PFAS congeners, the highest PFOS concentration showed almost similar transcript expression patterns compared to the highest mixture exposure group. This indicates that PFOS is the active component of the mixture that significantly altered the normal functioning of female gonads, and possibly leading to serious reproductive consequences in teleosts.

ReCodLiver0.9: Overcoming Challenges in Genome-Scale Metabolic Reconstruction of a Non-model Species.

The availability of genome sequences, annotations, and knowledge of the biochemistry underlying metabolic transformations has led to the generation of metabolic network reconstructions for a wide range of organisms in bacteria, archaea, and eukaryotes. When modeled using mathematical representations, a reconstruction can simulate underlying genotype-phenotype relationships. Accordingly, genome-scale metabolic models (GEMs) can be used to predict the response of organisms to genetic and environmental variations. A bottom-up reconstruction procedure typically starts by generating a draft model from existing annotation data on a target organism. For model species, this part of the process can be straightforward, due to the abundant organism-specific biochemical data. However, the process becomes complicated for non-model less-annotated species. In this paper, we present a draft liver reconstruction, ReCodLiver0.9, of Atlantic cod (Gadus morhua), a non-model teleost fish, as a practicable guide for cases with comparably few resources. Although the reconstruction is considered a draft version, we show that it already has utility in elucidating metabolic response mechanisms to environmental toxicants by mapping gene expression data of exposure experiments to the resulting model.

RNA-Seq analysis of transcriptome responses in Atlantic cod (Gadus morhua) precision-cut liver slices exposed to benzo[a]pyrene and 17α-ethynylestradiol.

Polycyclic aromatic hydrocarbons such as benzo[a]pyrene (BaP) that activate the aryl hydrocarbon receptor (Ahr) pathway, and endocrine disruptors acting through the estrogen receptor pathway are among environmental pollutants of major concern. In this work, we exposed Atlantic cod (Gadus morhua) precision-cut liver slices (PCLS) to BaP (10 nM and 1000 nM), ethynylestradiol (EE2) (10 nM and 1000 nM), and equimolar mixtures of BaP and EE2 (10 nM and 1000 nM) for 48 h, and performed RNA-Seq based transcriptome mapping followed by systematic bioinformatics analyses. Our gene expression analysis showed that several genes were differentially expressed in response to BaP and EE2 treatments in PCLS. Strong up-regulation of genes coding for the cytochrome P450 1a (Cyp1a) enzyme and the Ahr repressor (Ahrrb) was observed in BaP treated PCLS. EE2 treatment of liver slices strongly up-regulated genes coding for precursors of vitellogenin (Vtg) and eggshell zona pellucida (Zp) proteins. As expected, pathway enrichment and network analysis showed that the Ahr and estrogen receptor pathways are among the top affected by BaP and EE2 treatments, respectively. Interestingly, two genes coding for fibroblast growth factor 3 (Fgf3) and fibroblast growth factor 4 (Fgf4) were up-regulated by EE2 in this study. To our knowledge, the fgf3 and fgf4 genes have not previously been described in relation to estrogen signaling in fish liver, and these results suggest the modulation of the FGF signaling pathway by estrogens in fish. The signature expression profiles of top differentially expressed genes in response to the single compound (BaP or EE2) treatment were generally maintained in the expression responses to the equimolar binary mixtures. However, in the mixture-treated groups, BaP appeared to have anti-estrogenic effects as observed by lower number of differentially expressed putative EE2 responsive genes. Our in-depth quantitative analysis of changes in liver transcriptome in response to BaP and EE2, using PCLS tissue culture provides further mechanistic insights into effects of the compounds. Moreover, the analyses demonstrate the usefulness of PCLS in cod for omics experiments.

Detecting Protein Complexes in Protein Interaction Networks Modeled as Gene Expression Biclusters.

Developing suitable methods for the detection of protein complexes in protein interaction networks continues to be an intriguing area of research. The importance of this objective originates from the fact that protein complexes are key players in most cellular processes. The more complexes we identify, the better we can understand normal as well as abnormal molecular events. Up till now, various computational methods were designed for this purpose. However, despite their notable performance, questions arise regarding potential ways to improve them, in addition to ameliorative guidelines to introduce novel approaches. A close interpretation leads to the assent that the way in which protein interaction networks are initially viewed should be adjusted. These networks are dynamic in reality and it is necessary to consider this fact to enhance the detection of protein complexes. In this paper, we present "DyCluster", a framework to model the dynamic aspect of protein interaction networks by incorporating gene expression data, through biclustering techniques, prior to applying complex-detection algorithms. The experimental results show that DyCluster leads to higher numbers of correctly-detected complexes with better evaluation scores. The high accuracy achieved by DyCluster in detecting protein complexes is a valid argument in favor of the proposed method. DyCluster is also able to detect biologically meaningful protein groups. The code and datasets used in the study are downloadable from https://github.com/emhanna/DyCluster.

Ant colony optimization algorithm for interpretable Bayesian classifiers combination: application to medical predictions.

UNLABELLED: Prediction and classification techniques have been well studied by machine learning researchers and developed for several real-word problems. However, the level of acceptance and success of prediction models are still below expectation due to some difficulties such as the low performance of prediction models when they are applied in different environments. Such a problem has been addressed by many researchers, mainly from the machine learning community. A second problem, principally raised by model users in different communities, such as managers, economists, engineers, biologists, and medical practitioners, etc., is the prediction models' interpretability. The latter is the ability of a model to explain its predictions and exhibit the causality relationships between the inputs and the outputs. In the case of classification, a successful way to alleviate the low performance is to use ensemble classiers. It is an intuitive strategy to activate collaboration between different classifiers towards a better performance than individual classier. Unfortunately, ensemble classifiers method do not take into account the interpretability of the final classification outcome. It even worsens the original interpretability of the individual classifiers. In this paper we propose a novel implementation of classifiers combination approach that does not only promote the overall performance but also preserves the interpretability of the resulting model. We propose a solution based on Ant Colony Optimization and tailored for the case of Bayesian classifiers. We validate our proposed solution with case studies from medical domain namely, heart disease and Cardiotography-based predictions, problems where interpretability is critical to make appropriate clinical decisions. AVAILABILITY: The datasets, Prediction Models and software tool together with supplementary materials are available at http://faculty.uaeu.ac.ae/salahb/ACO4BC.htm.