Charles Auffray

Systems medicine: the future of medical genomics and healthcare

High-throughput technologies for DNA sequencing and for analyses of transcriptomes, proteomes and metabolomes have provided the foundations for deciphering the structure, variation and function of the human genome and relating them to health and disease states. The increased efficiency of DNA sequencing opens up the possibility of analyzing a large number of individual genomes and transcriptomes, and complete reference proteomes and metabolomes are within reach using powerful analytical techniques based on chromatography, mass spectrometry and nuclear magnetic resonance. Computational and mathematical tools have enabled the development of systems approaches for deciphering the functional and regulatory networks underlying the behavior of complex biological systems. Further conceptual and methodological developments of these tools are needed for the integration of various data types across the multiple levels of organization and time frames that are characteristic of human development, physiology and disease. Medical genomics has attempted to overcome the initial limitations of genome-wide association studies and has identified a limited number of susceptibility loci for many complex and common diseases. Iterative systems approaches are starting to provide deeper insights into the mechanisms of human diseases, and to facilitate the development of better diagnostic and prognostic biomarkers for cancer and many other diseases. Systems approaches will transform the way drugs are developed through academy-industry partnerships that will target multiple components of networks and pathways perturbed in diseases. They will enable medicine to become predictive, personalized, preventive and participatory, and, in the process, concepts and methods from Western and oriental cultures can be combined. We recommend that systems medicine should be developed through an international network of systems biology and medicine centers dedicated to inter-disciplinary training and education, to help reduce the gap in healthcare between developed and developing countries.

Dopaminergic neurons of the substantia nigra modulate preproenkephalin A gene expression in rat striatal neurons

The messenger RNA coding for preproenkephalin A (PPA) was detected by in situ hybridization in striatal neurons in normal rats and in rats having had the right substantia nigra destroyed by an injection of 6-hydroxydopamine or by electrolysis. Animals were killed 15, 30, 45 and 70 days following the lesion. A double-stranded PPA cDNA and a single-stranded PPA cRNA labeled with 32P or 35S were used as probes to detect the PPA mRNA in brain sections. The controls demonstrated the specificity of the labeling. The darkening of X-ray film in contact with the striatum was appraised, the optical density was measured, and the density of the cells expressing the PPA gene in sections was calculated using an image analyzer. The mean number of silver grains per labeled cell (reflecting the number of PPA mRNA copies per cell) was also calculated using an image analyzer. The 6-hydroxydopamine lesion which destroyed all dopaminergic neurons in the right substantia nigra, provoked a large increase in the number of PPA mRNA copies in enkephalin neurons of the right striatum, and decreased the number of cells expressing the PPA mRNA in the left striatum. These variations substantia nigra provoked similar variations, but less intense.(ABSTRACT TRUNCATED AT 250 WORDS)

Deciphering cellular states of innate tumor drug responses.

BackgroundThe molecular mechanisms underlying innate tumor drug resistance, a major obstacle to successful cancer therapy, remain poorly understood. In colorectal cancer (CRC), molecular studies have focused on drug-selected tumor cell lines or individual candidate genes using samples derived from patients already treated with drugs, so that very little data are available prior to drug treatment.ResultsTranscriptional profiles of clinical samples collected from CRC patients prior to their exposure to a combined chemotherapy of folinic acid, 5-fluorouracil and irinotecan were established using microarrays. Vigilant experimental design, power simulations and robust statistics were used to restrain the rates of false negative and false positive hybridizations, allowing successful discrimination between drug resistance and sensitivity states with restricted sampling. A list of 679 genes was established that intrinsically differentiates, for the first time prior to drug exposure, subsequently diagnosed chemo-sensitive and resistant patients. Independent biological validation performed through quantitative PCR confirmed the expression pattern on two additional patients. Careful annotation of interconnected functional networks provided a unique representation of the cellular states underlying drug responses.ConclusionMolecular interaction networks are described that provide a solid foundation on which to anchor working hypotheses about mechanisms underlying in vivo innate tumor drug responses. These broad-spectrum cellular signatures represent a starting point from which by-pass chemotherapy schemes, targeting simultaneously several of the molecular mechanisms involved, may be developed for critical therapeutic intervention in CRC patients. The demonstrated power of this research strategy makes it generally applicable to other physiological and pathological situations.

‘The 39 steps’ in gene expression profiling: critical issues and proposed best practices for microarray experiments

Gene expression microarrays have been used widely to address increasingly complex biological questions and to produce an unprecedented amount of data, but have yet to realize their full potential. The interpretation of microarray data remains a major challenge because of the complexity of the underlying biological networks. To gather meaningful expression data, it is crucial to develop standardized approaches for vigilant study design, controlled annotation of resources, careful quality control of experiments, robust statistics, and data registration and storage. This article reviews the steps needed in the design and execution of valid microarray experiments so that global gene expression data can play a major role in the pursuit of future biological discoveries that will impact drug development.

Protein subnetwork markers improve prediction of cancer outcome

Mol Syst Biol. 3: 141nnThe reliability of gene predictors of cancer outcome has been recently questioned, pointing to deficiencies in experimental design, insufficient statistical power due to small sample size, and flaws in predictor generation and performance assessment, with proposed guidelines to overcome these limitations (Ntzani and Ioannidis, 2003; Michiels et al , 2005, Dupuy and Simon, 2007). Now, in a recent article published in Molecular Systems Biology (Chuang et al , 2007), a complementary strategy has been proposed based on integration of expression profiles with protein interactions, demonstrating that more reproducible and robust predictors can be generated with the additional benefit of including mutated genes which are excluded in the classical analyses, and also providing models for the molecular mechanisms involved in metastasis formation. This is achieved through combination of mRNA expression profiles with curated protein–protein interaction data, which became recently available (Rual et al , 2005), leveraging methods for modular subnetwork identification and biological validation (Segal et al , 2003; Poyatos and Hurst, 2004).nnDuring the past decade, transcriptome analysis has been used increasingly to monitor expression profiles of extensive collections of genes in cancer samples, providing insights into the molecular mechanisms underlying cancer development …

Computational disease modeling – fact or fiction?

BackgroundBiomedical research is changing due to the rapid accumulation of experimental data at an unprecedented scale, revealing increasing degrees of complexity of biological processes. Life Sciences are facing a transition from a descriptive to a mechanistic approach that reveals principles of cells, cellular networks, organs, and their interactions across several spatial and temporal scales. There are two conceptual traditions in biological computational-modeling. The bottom-up approach emphasizes complex intracellular molecular models and is well represented within the systems biology community. On the other hand, the physics-inspired top-down modeling strategy identifies and selects features of (presumably) essential relevance to the phenomena of interest and combines available data in models of modest complexity.ResultsThe workshop, ESF Exploratory Workshop on Computational disease Modeling, examined the challenges that computational modeling faces in contributing to the understanding and treatment of complex multi-factorial diseases. Participants at the meeting agreed on two general conclusions. First, we identified the critical importance of developing analytical tools for dealing with model and parameter uncertainty. Second, the development of predictive hierarchical models spanning several scales beyond intracellular molecular networks was identified as a major objective. This contrasts with the current focus within the systems biology community on complex molecular modeling.ConclusionDuring the workshop it became obvious that diverse scientific modeling cultures (from computational neuroscience, theory, data-driven machine-learning approaches, agent-based modeling, network modeling and stochastic-molecular simulations) would benefit from intense cross-talk on shared theoretical issues in order to make progress on clinically relevant problems.

A Functional and Regulatory Network Associated with PIP Expression in Human Breast Cancer

Background The PIP (prolactin-inducible protein) gene has been shown to be expressed in breast cancers, with contradictory results concerning its implication. As both the physiological role and the molecular pathways in which PIP is involved are poorly understood, we conducted combined gene expression profiling and network analysis studies on selected breast cancer cell lines presenting distinct PIP expression levels and hormonal receptor status, to explore the functional and regulatory network of PIP co-modulated genes. Principal Findings Microarray analysis allowed identification of genes co-modulated with PIP independently of modulations resulting from hormonal treatment or cell line heterogeneity. Relevant clusters of genes that can discriminate between [PIP+] and [PIP−] cells were identified. Functional and regulatory network analyses based on a knowledge database revealed a master network of PIP co-modulated genes, including many interconnecting oncogenes and tumor suppressor genes, half of which were detected as differentially expressed through high-precision measurements. The network identified appears associated with an inhibition of proliferation coupled with an increase of apoptosis and an enhancement of cell adhesion in breast cancer cell lines, and contains many genes with a STAT5 regulatory motif in their promoters. Conclusions Our global exploratory approach identified biological pathways modulated along with PIP expression, providing further support for its good prognostic value of disease-free survival in breast cancer. Moreover, our data pointed to the importance of a regulatory subnetwork associated with PIP expression in which STAT5 appears as a potential transcriptional regulator.

A prioritization analysis of disease association by data-mining of functional annotation of human genes

Complex diseases result from contributions of multiple genes that act in concert through pathways. Here we present a method to prioritize novel candidates of disease-susceptibility genes depending on the biological similarities to the known disease-related genes. The extent of disease-susceptibility of a gene is prioritized by analyzing seven features of human genes captured in H-InvDB. Taking rheumatoid arthritis (RA) and prostate cancer (PC) as two examples, we evaluated the efficiency of our method. Highly scored genes obtained included TNFSF12 and OSM as candidate disease genes for RA and PC, respectively. Subsequent characterization of these genes based upon an extensive literature survey reinforced the validity of these highly scored genes as possible disease-susceptibility genes. Our approach, Prioritization ANalysis of Disease Association (PANDA), is an efficient and cost-effective method to narrow down a large set of genes into smaller subsets that are most likely to be involved in the disease pathogenesis.

A transgenic mouse model engineered to investigate human brain-derived neurotrophic factor in vivo

Brain-derived neurotrophic factor (BDNF) is an attractive component for the treatment of various neurodegenerative diseases such as Alzheimer’s or Parkinson’s disease. Innovative non-invasive therapeutic approaches involve appropriate pharmacological induction of endogenous BDNF synthesis in brain. A transgenic mouse model has been established to study human BDNF gene expression and permit the screening of compounds capable of stimulating its activity. A 145-kb yeast artificial chromosome carrying the human BDNF gene has been engineered to produce the transgene which contains the extended BDNF promoter and 3′ flanking regions and has integrated the enhanced green fluorescent protein (E-GFP) coding sequence in place of the BDNF coding exon. Five transgenic lines have been obtained through microinjection of the YAC into fertilized mouse oocytes. From the three lines expressing the transgene, one displays the specific pattern of BDNF expression. Faithful tissue-restricted transcription of BDNF 5′ exons and localization of the fluorescent reporter gene product in the expected brain subregions are reported. This line constitutes an exploitable system for investigating human BDNF gene regulation in vivo.

Functional Annotation: Extracting functional and regulatory order from microarrays

Mol Syst Biol. 1: 2005.0009nnA discussion of recent advances and limitations in functional annotation and network reconstruction based on gene expression microarray datannSystems approaches for understanding biological complexity and studying diseases rely on iterative and extensive characterization of genes, transcripts, proteins and their interactions, generation of hypotheses about how they functionally inter‐relate within subsystems, conversion of these hypotheses into formal mathematical models and their experimental testing (Auffray et al , 2003a). In this context, modeling of gene regulatory networks from functional annotations is currently performed top‐down, studying global network architecture and performance (Bray, 2003), and bottom‐up, identifying modular subsystems from functional genomics data (Alon, 2003).nnBecause transcription is the first step of gene expression subjected to extensive regulations by internal and external factors, systems approaches rely heavily on gene expression data. Microarray technology has developed steadily for three decades to allow measurements of expression levels for thousands of genes in different biological contexts, and a wealth of such data is now available in public repositories (Ball et al , 2004). The expectation is that microarray analysis will help elucidating what the genes do, when, where and how they are expressed as elements of an orchestrated system under the effects of perturbations, and thus reveal …