Modeling molecular networks: a systems biology approach to gene function

A report on the European Science Foundation Workshop on Modeling of Molecular Networks, Granada, Spain, 11-14 June 2002.     

Application of a systems approach to study developmental gene regulation

All cells in a multicellular organism contain the same genome, yet different cell types express different sets of genes. Recent advances in high throughput genomic technologies have opened up new opportunities to understand the gene regulatory network in diverse cell types in a genome-wide manner. Here, I discuss recent advances in experimental and computational approaches for the study of gene regulation in embryonic development from a systems perspective. This review is written for computational biologists who have an interest in studying developmental gene regulation through integrative analysis of gene expression, chromatin landscape, and signaling pathways. I highlight the utility of publicly available data and tools, as well as some common analysis approaches.     

Ranking candidate disease genes from gene expression and protein interaction: a Katz-centrality based approach

Many diseases have complex genetic causes, where a set of alleles can affect the propensity of getting the disease. The identification of such disease genes is important to understand the mechanistic and evolutionary aspects of pathogenesis, improve diagnosis and treatment of the disease, and aid in drug discovery. Current genetic studies typically identify chromosomal regions associated specific diseases. But picking out an unknown disease gene from hundreds of candidates located on the same genomic interval is still challenging. In this study, we propose an approach to prioritize candidate genes by integrating data of gene expression level, protein-protein interaction strength and known disease genes. Our method is based only on two, simple, biologically motivated assumptions--that a gene is a good disease-gene candidate if it is differentially expressed in cases and controls, or that it is close to other disease-gene candidates in its protein interaction network. We tested our method on 40 diseases in 58 gene expression datasets of the NCBI Gene Expression Omnibus database. On these datasets our method is able to predict unknown disease genes as well as identifying pleiotropic genes involved in the physiological cellular processes of many diseases. Our study not only provides an effective algorithm for prioritizing candidate disease genes but is also a way to discover phenotypic interdependency, cooccurrence and shared pathophysiology between different disorders.     

Meta-analysis of gene expression data: a predictor-based approach

MOTIVATION: With the increasing availability of cancer microarray data sets there is a growing need for integrative computational methods that evaluate multiple independent microarray data sets investigating a common theme or disorder. Meta-analysis techniques are designed to overcome the low sample size typical to microarray experiments and yield more valid and informative results than each experiment separately. RESULTS: We propose a new meta-analysis technique that aims at finding a set of classifying genes, whose expression level may be used to answering the classification question in hand. Specifically, we apply our method to two independent lung cancer microarray data sets and identify a joint core subset of genes which putatively play an important role in tumor genesis of the lung. The robustness of the identified joint core set is demonstrated on a third unseen lung cancer data set, where it leads to successful classification using very few top-ranked genes. Identifying such a set of genes is of significant importance when searching for biologically meaningful biomarkers. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Detecting intergene correlation changes in microarray analysis: a new approach to gene selection

Background  

Microarray technology is commonly used as a simple screening tool with a focus on selecting genes that exhibit extremely large differential expressions between different phenotypes. It lacks the ability to select genes that change their relationships with other genes in different biological conditions (differentially correlated genes). We intend to enrich the above procedure by proposing a nonparametric selection procedure that selects differentially correlated genes.     

Parallel gene cloning and protein production in multiple expression systems

To quickly find an optimal expression system for recombinant protein production, a set of vectors with the same restriction sites were constructed for parallel cloning of a target gene and recombinant protein production in prokaryotic and eukaryotic expression systems, simultaneously. These vectors include nucleotide sequences encoding protein tags and protease recognition sites for tag removal, followed by the cloning sites 5′‐EcoRI/3′‐XhoI identical in these vectors for ligating with the sticky‐end PCR product of a target gene. Our vectors allow parallel gene cloning and protein production in multiple expression systems with minimal cloning effort. © 2009 American Institute of Chemical Engineers Biotechnol. Prog., 2009     

Collagen-related gene and protein expression changes in the lung in response to chronic hypoxia

Collagen accumulation likely contributes to increased vascular and airway impedance in hypoxia-induced pulmonary hypertension (HPH). Collagen exists in multiple subtypes and can accumulate via increased synthesis or decreased degradation. To better understand the individual contributions of fibrillar (FB) and basement membrane (BM) collagen, matrix metalloproteinases (MMPs) and tissue inhibitors of MMPs (TIMPs) to pulmonary vascular and airway remodeling in HPH, we investigated the temporal changes in gene and protein expression in the lungs of mice exposed to hypoxia for 0, 3, 6, 10 and 15 days. The earliest and largest change in gene expression was of type I FB collagen, which was significantly increased over control levels at 6, 10 and 15 days of hypoxia (p  <  0.05). Type III FB and type IV BM collagen were increased at 10 and 15 days of hypoxia (p <  0.05); MMP and TIMP gene expression levels were typically higher but sometimes lower than control levels at various time points. Collagen protein content was increased in whole lungs as early as 6 days of hypoxia and increased monotonically with longer exposures. However, neither qualitative nor semi-quantitative analysis of immunohistochemistry demonstrated accumulation of type I FB collagen in compartments of the lung other than large airways, suggesting that other collagen subtypes may be important contributors to collagen protein accumulation. These results provide insight into the patterns of gene and protein expression relevant to collagen accumulation in the lung in response to chronic hypoxia, through which we can develop a better understanding of the time course of changes in matrix biology and biomechanics that occur in HPH.     

Prediction of food protein allergenicity: a bioinformatic learning systems approach

Food hypersensitivity is constantly increasing in Western societies with a prevalence of about 1-2% in Europe and in the USA. Among children, the incidence is even higher. Because of the introduction of foods derived from genetically modified crops on the marketplace, the scientific community, regulatory bodies and international associations have intensified discussions on risk assessment procedures to identify potential food allergenicity of the newly introduced proteins. In this work, we present a novel biocomputational methodology for the classification of amino acid sequences with regard to food allergenicity and non-allergenicity. This method relies on a computerised learning system trained using selected excerpts of amino acid sequences. One example of such a successful learning system is presented which consists of feature extraction from sequence alignments performed with the FASTA3 algorithm (employing the BLOSUM50 substitution matrix) combined with the k-Nearest-Neighbour (kNN) classification algorithm. Briefly, the two features extracted are the alignment score and the alignment length and the kNN algorithm assigns the pair of extracted features from an unknown sequence to the prevalent class among its k nearest neighbours in the training (prototype) set available. 91 food allergens from several specialised public repositories of food allergy and the SWALL database were identified, pre-processed, and stored, yielding one of the most extensively characterised repositories of allergenic sequences known today. All allergenic sequences were classified using a standard one-leave-out cross validation procedure yielding about 81% correctly classified allergens and the classification of 367 non-allergens in an independent test set resulted in about 98% correct classifications. The biocomputational approach presented should be regarded as a significant extension and refinement of earlier attempts suggested for in silico food safety assessment. Our results show that the framework described here is powerful enough to become useful as part of a multiple-procedure test scheme that also depicts other evaluation approaches such as solid phase immunoassay and tests for stability to digestions.     

From gene to protein: a review of new and enabling technologies for multi-parallel protein expression

In the post-genomic era, increasingly greater demands and expectations are being placed on protein production laboratories to produce more proteins and in faster timelines. This has been coupled with an exponential increase in the number of requests for the production of proteins which lack structural and functional information. No longer can groups use literature available in the public domain solely to drive their expression strategy, and moreover current expression and concomitant purification strategies clearly do not meet modern-day demands for protein production. This review will therefore attempt to provide a definitive review of current 'best in class' cloning, expression and purification systems, and the adaptations and developments that have been made by laboratories, both academic and industrial, to enhance protein production throughput.     

Computing gene expression data with a knowledge-based gene clustering approach

Computational analysis methods for gene expression data gathered in microarray experiments can be used to identify the functions of previously unstudied genes. While obtaining the expression data is not a difficult task, interpreting and extracting the information from the datasets is challenging. In this study, a knowledge-based approach which identifies and saves important functional genes before filtering based on variability and fold change differences was utilized to study light regulation. Two clustering methods were used to cluster the filtered datasets, and clusters containing a key light regulatory gene were located. The common genes to both of these clusters were identified, and the genes in the common cluster were ranked based on their coexpression to the key gene. This process was repeated for 11 key genes in 3 treatment combinations. The initial filtering method reduced the dataset size from 22,814 probes to an average of 1134 genes, and the resulting common cluster lists contained an average of only 14 genes. These common cluster lists scored higher gene enrichment scores than two individual clustering methods. In addition, the filtering method increased the proportion of light responsive genes in the dataset from 1.8% to 15.2%, and the cluster lists increased this proportion to 18.4%. The relatively short length of these common cluster lists compared to gene groups generated through typical clustering methods or coexpression networks narrows the search for novel functional genes while increasing the likelihood that they are biologically relevant.     

Use of protein microarray to identify gene expression changes of Yersinia pestis at different temperatures

Yersinia pestis is a bacterium that is transmitted between fleas, which have a body temperature of 26 °C, and mammalian hosts, which have a body temperature of 37 °C. To adapt to the temperature shift, phenotype variations, including virulence, occur. In this study, an antigen microarray including 218 proteins of Y. pestis was used to evaluate antibody responses in a pooled plague serum that was unadsorbed, adsorbed by Y. pestis cultivated at 26 °C, or adsorbed by Y. pestis cultivated at 26 and 37 °C to identify protein expression changes during the temperature shift. We identified 12 proteins as being expressed at 37 °C but not at 26 °C, or expressed at significantly higher levels at 37 °C than at 26 °C. The antibodies against 7 proteins in the serum adsorbed by Y. pestis cultivated at 26 and 37 °C remained positive, suggesting that they were not expressed on the surface of Y. pestis in LB broth in vitro or specifically expressed in vivo. This study proved that protein microarray and antibody profiling comprise a promising technique for monitoring gene expression at the protein level and for better understanding pathogenicity, to find new vaccine targets against plague.     

A proteomics approach to identify protein expression changes in rat liver following administration of 3,5,3'-triiodo-L-thyronine

We analyzed whole cell protein content of rat liver following T3 administration. Fourteen differentially expressed proteins were unambiguously identified and were involved in substrates and lipid metabolism, energy metabolism, detoxification of cytotoxic products, calcium homeostasis, amino acid catabolism, and the urea cycle. This study represents the first systematic identification of T3-induced changes in liver protein expression profile and provides novel information at the molecular, cellular, and tissue level of T3 action.