Novel integrative genomics strategies to identify genes for complex traits

Forward genetics is a common approach to dissecting complex traits like common human diseases. The ultimate aim of this approach was the identification of genes that are causal for disease or other phenotypes of interest. However, the forward genetics approach is by definition restricted to the identification of genes that have incurred mutations over the course of evolution or that incurred mutations as a result of chemical mutagenesis, and that as a result lead to disease or to variations in other phenotypes of interest. Genes that harbour no such mutations, but that play key roles in parts of the biological network that lead to disease, are systematically missed by this class of approaches. Recently, a class of novel integrative genomics approaches has been devised to elucidate the complexity of common human diseases by intersecting genotypic, molecular profiling, and clinical data in segregating populations. These novel approaches take a more holistic view of biological systems and leverage the vast network of gene–gene interactions, in combination with DNA variation data, to establish causal relationships among molecular profiling traits and Fbetween molecular profiling and disease (or other classic phenotypes). A number of novel genes for disease phenotypes have been identified as a result of these approaches, highlighting the utility of integrating orthogonal sources of data to get at the underlying causes of disease.     

Mass spectrometry-based expression profiling of clinical prostate cancer

The maturation of MS technologies has provided a rich opportunity to interrogate protein expression patterns in normal and disease states by applying expression protein profiling methods. Major goals of this research strategy include the identification of protein biomarkers that demarcate normal and disease populations, and the identification of therapeutic biomarkers for the treatment of diseases such as cancer (Celis, J. E., and Gromov, P. (2003) Proteomics in translational cancer research: Toward an integrated approach. Cancer Cell 3, 9-151). Prostate cancer is one disease that would greatly benefit from implementing MS-based expression profiling methods because of the need to stratify the disease based on molecular markers. In this review, we will summarize the current MS-based methods to identify and validate biomarkers in human prostate cancer. Lastly, we propose a reverse proteomic approach implementing a quantitative MS research strategy to identify and quantify biomarkers implicated in prostate cancer development. With this approach, the absolute levels of prostate cancer biomarkers will be identified and quantified in normal and diseased samples by measuring the levels of native peptide biomarkers in relation to a chemically identical but isotopically labeled reference peptide. Ultimately, a centralized prostate cancer peptide biomarker expression database could function as a repository for the identification, quantification, and validation of protein biomarker(s) during prostate cancer progression in men.     

Proteomics in clinical interventions: achievements and limitations in biomarker development

Development of toxicological and clinical biomarkers for disease diagnosis, quantification of toxicant/drug responses and rapid patient care are major concerns in modern biology. Even after human genome sequencing, identification of specific molecular signatures for unambiguous correlation with toxicity and clinical interventions is a challenging task. Differential protein expression patterns and protein-protein interaction studies have started unraveling rigorous molecular explanation of multi-factorial and toxicant borne diseases. Proteome profiling is extensively used to investigate etiology of diseases, develop predictive biomarkers for toxicity and therapeutic interventions and potential strategies for treatment of complex and toxicant mediated diseases. In this review, achievements and limitations of proteomics in developing predictive biomarkers for toxicological and clinical interventions have been discussed.     

High-throughput protein arrays: prospects for molecular diagnostics

High-throughput protein arrays allow the miniaturized and parallel analysis of large numbers of diagnostic markers in complex samples. Using automated colony picking and gridding, cDNA or antibody libraries can be expressed and screened as clone arrays. Protein microarrays are constructed from recombinantly expressed, purified, and yet functional proteins, entailing a range of optimized expression systems. Antibody microarrays are becoming a robust format for expression profiling of whole genomes. Alternative systems, such as aptamer, PROfusion, nano- and microfluidic arrays are all at proof-of-concept stage. Differential protein profiles have been used as molecular diagnostics for cancer and autoimmune diseases and might ultimately be applied to screening of high-risk and general populations.     

Development of multigene expression signature maps at the protein level from digitized immunohistochemistry slides

Molecular classification of diseases based on multigene expression signatures is increasingly used for diagnosis, prognosis, and prediction of response to therapy. Immunohistochemistry (IHC) is an optimal method for validating expression signatures obtained using high-throughput genomics techniques since IHC allows a pathologist to examine gene expression at the protein level within the context of histologically interpretable tissue sections. Additionally, validated IHC assays may be readily implemented as clinical tests since IHC is performed on routinely processed clinical tissue samples. However, methods have not been available for automated n-gene expression profiling at the protein level using IHC data. We have developed methods to compute expression level maps (signature maps) of multiple genes from IHC data digitized on a commercial whole slide imaging system. Areas of cancer for these expression level maps are defined by a pathologist on adjacent, co-registered H&E slides, allowing assessment of IHC statistics and heterogeneity within the diseased tissue. This novel way of representing multiple IHC assays as signature maps will allow the development of n-gene expression profiling databases in three dimensions throughout virtual whole organ reconstructions.     

Peripheral blood gene expression profiling for cardiovascular disease assessment

Whole blood gene expression profiling has the potential to be informative about dynamic changes in disease states and to provide information on underlying disease mechanisms. Having demonstrated proof of concept in animal models, a number of studies have now tried to tackle the complexity of cardiovascular disease in human hosts to develop better diagnostic and prognostic indicators. These studies show that genomic signatures are capable of classifying patients with cardiovascular diseases into finer categories based on the molecular architecture of a patient's disease and more accurately predict the likelihood of a cardiovascular event than current techniques. To highlight the spectrum of potential applications of whole blood gene expression profiling approach in cardiovascular science, we have chosen to review the findings in a number of complex cardiovascular diseases such as atherosclerosis, hypertension and myocardial infarction as well as thromboembolism, aortic aneurysm, and heart transplant.     

Advances in functional protein microarray technology

Numerous innovations in high-throughput protein production and microarray surface technologies have enabled the development of addressable formats for proteins ordered at high spatial density. Protein array implementations have largely focused on antibody arrays for high-throughput protein profiling. However, it is also possible to construct arrays of full-length, functional proteins from a library of expression clones. The advent of protein-based microarrays allows the global observation of biochemical activities on an unprecedented scale, where hundreds or thousands of proteins can be simultaneously screened for protein-protein, protein-nucleic acid, and small molecule interactions. This technology holds great potential for basic molecular biology research, disease marker identification, toxicological response profiling and pharmaceutical target screening.     

Gene expression signatures in primary immunodeficiencies: the experience from human disease and mouse models

Extensive research on molecular genetics in recent decades has provided a wealth of information regarding the underlying mechanisms of primary immunodeficiency diseases. The microarray technology has made its entry into the molecular biology research area and hereby enabled signature expression profiling of whole species genomes. Perhaps no other methodological approach has transformed molecular biology more in recent years than the use of microarrays. Microarray technology has led the way from studies of the individual biological functions of a few related genes, proteins or, at best, pathways towards more global investigations of cellular activity. The development of this technology immediately yielded new and interesting information, and has produced more data than can be currently dealt with. It has also helped to realize that even a 'horizontally exhaustive' molecular analysis is insufficient. Applications of this tool in primary immunodeficiency studies have generated new information, which has led to a better understanding of the underlying basic biology of the diseases. Also, the technology has been used as an exploratory tool to disease genes in immunodeficiency diseases of unknown cause as in the case of the CD3Delta-chain and the MAPBPIP deficiency. For X-linked agammaglobulinemia, the technique has provided better understanding of the genes influenced by Btk. There is considerable hope that the microarray technology will lead to a better understanding of disease processes and the molecular phenotypes obtained from microarray experiments may represent a new tool for diagnosis of the disease.     

Integrative Network Analysis Unveils Convergent Molecular Pathways in Parkinson's Disease and Diabetes

Background

Shared dysregulated pathways may contribute to Parkinson''s disease and type 2 diabetes, chronic diseases that afflict millions of people worldwide. Despite the evidence provided by epidemiological and gene profiling studies, the molecular and functional networks implicated in both diseases, have not been fully explored. In this study, we used an integrated network approach to investigate the extent to which Parkinson''s disease and type 2 diabetes are linked at the molecular level.

Methods and Findings

Using a random walk algorithm within the human functional linkage network we identified a molecular cluster of 478 neighboring genes closely associated with confirmed Parkinson''s disease and type 2 diabetes genes. Biological and functional analysis identified the protein serine-threonine kinase activity, MAPK cascade, activation of the immune response, and insulin receptor and lipid signaling as convergent pathways. Integration of results from microarrays studies identified a blood signature comprising seven genes whose expression is dysregulated in Parkinson''s disease and type 2 diabetes. Among this group of genes, is the amyloid precursor protein (APP), previously associated with neurodegeneration and insulin regulation. Quantification of RNA from whole blood of 192 samples from two independent clinical trials, the Harvard Biomarker Study (HBS) and the Prognostic Biomarker Study (PROBE), revealed that expression of APP is significantly upregulated in Parkinson''s disease patients compared to healthy controls. Assessment of biomarker performance revealed that expression of APP could distinguish Parkinson''s disease from healthy individuals with a diagnostic accuracy of 80% in both cohorts of patients.

Conclusions

These results provide the first evidence that Parkinson''s disease and diabetes are strongly linked at the molecular level and that shared molecular networks provide an additional source for identifying highly sensitive biomarkers. Further, these results suggest for the first time that increased expression of APP in blood may modulate the neurodegenerative phenotype in type 2 diabetes patients.     

Identification of Risk Pathways and Functional Modules for Coronary Artery Disease Based on Genome-wide SNP Data

Coronary artery disease(CAD) is a complex human disease, involving multiple genes and their nonlinear interactions, which often act in a modular fashion. Genome-wide single nucleotide polymorphism(SNP) profiling provides an effective technique to unravel these underlying genetic interplays or their functional involvements for CAD. This study aimed to identify the susceptible pathways and modules for CAD based on SNP omics. First, the Wellcome Trust Case Control Consortium(WTCCC) SNP datasets of CAD and control samples were used to assess the jointeffect of multiple genetic variants at the pathway level, using logistic kernel machine regression model. Then, an expanded genetic network was constructed by integrating statistical gene–gene interactions involved in these susceptible pathways with their protein–protein interaction(PPI)knowledge. Finally, risk functional modules were identified by decomposition of the network. Of 276 KEGG pathways analyzed, 6 pathways were found to have a significant effect on CAD. Other than glycerolipid metabolism, glycosaminoglycan biosynthesis, and cardiac muscle contraction pathways, three pathways related to other diseases were also revealed, including Alzheimer's disease, non-alcoholic fatty liver disease, and Huntington's disease. A genetic epistatic network of 95 genes was further constructed using the abovementioned integrative approach. Of 10 functional modules derived from the network, 6 have been annotated to phospholipase C activity and cell adhesion molecule binding, which also have known functional involvement in Alzheimer's disease.These findings indicate an overlap of the underlying molecular mechanisms between CAD and Alzheimer's disease, thus providing new insights into the molecular basis for CAD and its molecular relationships with other diseases.     

Giardia: an under-reported foodborne parasite

Foodborne zoonotic pathogens are a serious public health issue and result in significant global economic losses. Despite their importance to public health, epidemiological data on foodborne diseases including giardiasis caused by the enteric parasite, Giardia duodenalis, are lacking. This parasite is estimated to cause ～28.2 million cases of diarrhoea each year due to contamination of food, but very few foodborne outbreaks have been documented due to the limitations of current detection as well as surveillance methods. The current method for the recovery of Giardia cysts from food matrices using immunomagnetic separation requires further standardisation and cost reduction before it can be widely used. It also should incorporate downstream molecular procedures for genotyping, and traceback and viability analyses. Foodborne giardiasis can be potentially controlled through improvements in national disease surveillance systems and the establishment of Hazard Analysis and Critical Control Point interventions across the food chain. Studies are needed to assess the true prevalence and public health impact of foodborne giardiasis.     

Gene expression profiling of human diseases by serial analysis of gene expression

Until recently, the approach to understanding the molecular basis of complex syndromes such as cancer, coronary artery disease, and diabetes was to study the behavior of individual genes. However, it is generally recognized that expression of a number of genes is coordinated both spatially and temporally and that this coordination changes during the development and progression of diseases. Newly developed functional genomic approaches, such as serial analysis of gene expression (SAGE) and DNA microarrays have enabled researchers to determine the expression pattern of thousands of genes simultaneously. One attractive feature of SAGE compared to microarrays is its ability to quantify gene expression without prior sequence information or information about genes that are thought to be expressed. SAGE has been successfully applied to the gene expression profiling of a number of human diseases. In this review, we will first discuss SAGE technique and contrast it to microarray. We will then highlight new biological insights that have emerged from its application to the study of human diseases.     

Clinical proteomics: present and future prospects

Advances in proteomics technology offer great promise in the understanding and treatment of the molecular basis of disease. The past decade of proteomics research, the study of dynamic protein expression, post-translational modifications, cellular and sub-cellular protein distribution, and protein-protein interactions, has culminated in the identification of many disease-related biomarkers and potential new drug targets. While proteomics remains the tool of choice for discovery research, new innovations in proteomic technology now offer the potential for proteomic profiling to become standard practice in the clinical laboratory. Indeed, protein profiles can serve as powerful diagnostic markers, and can predict treatment outcome in many diseases, in particular cancer. A number of technical obstacles remain before routine proteomic analysis can be achieved in the clinic; however the standardisation of methodologies and dissemination of proteomic data into publicly available databases is starting to overcome these hurdles. At present the most promising application for proteomics is in the screening of specific subsets of protein biomarkers for certain diseases, rather than large scale full protein profiling. Armed with these technologies the impending era of individualised patient-tailored therapy is imminent. This review summarises the advances in proteomics that has propelled us to this exciting age of clinical proteomics, and highlights the future work that is required for this to become a reality.     

Protein profiling in respiratory disease: techniques and impact

Multifactorial diseases such as respiratory disease call for a global analysis of such disorders. Recent advances in protein profiling techniques may allow for early diagnosis of respiratory disease, which is crucial for intervention and treatment. In order to reduce false-positive rates, clinical diagnosis requires a high degree of sensitivity and specificity to be an effective screening tool. Protein profiles identified by ProteinChip (Ciphergen Biosystems) technology coupled with mass spectrometry affords a global analysis of clinical samples and is beginning to reach acceptable levels of sensitivity and specificity. Combining the profile with another diagnostic tool enhances the effectiveness of protein profiles to classify disease. Although current efforts have centered on serum protein profiling, the local environment of the lung may be better reflected in proteins of bronchoalveolar lavage or sputum. Identification of biomarkers of disease by protein profiling analyses may lead to an understanding of the mechanisms of this disease and contribute to the discovery of new therapeutics for the prevention and treatment of disease. Advancing these analyses are techniques such as ProteinChip mass spectrometry, laser capture microdissection, tissue microarrays and fluorescently labeled antibody bead arrays, which enable the direct global analysis of complex mixtures. Effective high-throughput and ease of use of clinical testing will arrive with improvements in bioinformatics and decreases in instrumentation costs.