Genetic variants regulating immune cell levels in health and disease.

The complex network of specialized cells and molecules in the immune system has evolved to defend against pathogens, but inadvertent immune system attacks on "self" result in autoimmune disease. Both genetic regulation of immune cell levels and their relationships with autoimmunity are largely undetermined. Here, we report genetic contributions to quantitative levels of 95 cell types encompassing 272 immune traits, in a cohort of 1,629 individuals from four clustered Sardinian villages. We first estimated trait heritability, showing that it can be substantial, accounting for up to 87% of the variance (mean 41%). Next, by assessing ∼8.2 million variants that we identified and confirmed in an extended set of 2,870 individuals, 23 independent variants at 13 loci associated with at least one trait. Notably, variants at three loci (HLA, IL2RA, and SH2B3/ATXN2) overlap with known autoimmune disease associations. These results connect specific cellular phenotypes to specific genetic variants, helping to explicate their involvement in disease.

Modulation of the Arabidopsis Starch Metabolic Network by the Cytosolic Acetyl-CoA Pathway in the Context of the Diurnal Illumination Cycle.

The starch metabolic network was investigated in relation to other metabolic processes by examining a mutant with altered single-gene expression of ATP citrate lyase (ACL), an enzyme responsible for generating cytosolic acetyl-CoA pool from citrate. Previous research has shown that transgenic antisense plants with reduced ACL activity accumulate abnormally enlarged starch granules. In this study, we explored the underlying molecular mechanisms linking cytosolic acetyl-CoA generation and starch metabolism under short-day photoperiods. We performed transcriptome and quantification of starch accumulation in the leaves of wild-type and antisense seedlings with reduced ACL activity. The antisense-ACLA mutant accumulated more starch than the wild type under short-day conditions. Zymogram analyses were conducted to compare the activities of starch-metabolizing enzymes with transcriptomic changes in the seedling. Differential expression between wild-type and antisense-ACLA plants was detected in genes implicated in starch and acetyl-CoA metabolism, and cell wall metabolism. These analyses revealed a strong correlation between the transcript levels of genes responsible for starch synthesis and degradation, reflecting coordinated regulation at the transcriptomic level. Furthermore, our data provide novel insights into the regulatory links between cytosolic acetyl-CoA metabolism and starch metabolic pathways.

Dissecting the dynamics of dysregulation of cellular processes in mouse mammary gland tumor.

BACKGROUND: Elucidating the sequence of molecular events underlying breast cancer formation is of enormous value for understanding this disease and for design of an effective treatment. Gene expression measurements have enabled the study of transcriptome-wide changes involved in tumorigenesis. This usually occurs through identification of differentially expressed genes or pathways. RESULTS: We propose a novel approach that is able to delineate new cancer-related cellular processes and the nature of their involvement in tumorigenesis. First, we define modules as densely interconnected and functionally enriched areas of a Protein Interaction Network. Second, 'differential expression' and 'differential co-expression' analyses are applied to the genes in these network modules, allowing for identification of processes that are up- or down-regulated, as well as processes disrupted (low co-expression) or invoked (high co-expression) in different tumor stages. Finally, we propose a strategy to identify regulatory miRNAs potentially responsible for the observed changes in module activities. We demonstrate the potential of this analysis on expression data from a mouse model of mammary gland tumor, monitored over three stages of tumorigenesis. Network modules enriched in adhesion and metabolic processes were found to be inactivated in tumor cells through the combination of dysregulation and down-regulation, whereas the activation of the integrin complex and immune system response modules is achieved through increased co-regulation and up-regulation. Additionally, we confirmed a known miRNA involved in mammary gland tumorigenesis, and present several new candidates for this function. CONCLUSIONS: Understanding complex diseases requires studying them by integrative approaches that combine data sources and different analysis methods. The integration of methods and data sources proposed here yields a sensitive tool, able to pinpoint new processes with a role in cancer, dissect modulation of their activity and detect the varying assignments of genes to functional modules over the course of a disease.

Towards functional phosphoproteomics by mapping differential phosphorylation events in signaling networks.

Protein phosphorylation plays a central role in many signal transduction pathways that mediate biological processes. Novel quantitative mass spectrometry-based methods have recently revealed phosphorylation dynamics in animals, yeast, and plants. These methods are important for our understanding of how differential phosphorylation participates in translating distinct signals into proper physiological responses, and shifted research towards screening for potential cancer therapies and in-depth analysis of phosphoproteomes. In this review, we aim to describe current progress in quantitative phosphoproteomics. This emerging field has changed numerous static pathways into dynamic signaling networks, and revealed protein kinase networks that underlie adaptation to environmental stimuli. Mass spectrometry enables high-throughput and high-quality analysis of differential phosphorylation at a site-specific level. Although determination of differential phosphorylation between treatments is analogous to detecting differential gene expression, the large body of statistical techniques that has been developed for analysis of differential gene expression is not generally applied for detecting differential phosphorylation. We suggest possible improvements for analysis of quantitative phosphorylation by increasing the number of biological replicates and adapting statistical tests used for gene expression profiling and widely implemented in freely available software tools.

Regulon organization of Arabidopsis.

BACKGROUND: Despite the mounting research on Arabidopsis transcriptome and the powerful tools to explore biology of this model plant, the organization of expression of Arabidopsis genome is only partially understood. Here, we create a coexpression network from a 22,746 Affymetrix probes dataset derived from 963 microarray chips that query the transcriptome in response to a wide variety of environmentally, genetically, and developmentally induced perturbations. RESULTS: Markov chain graph clustering of the coexpression network delineates 998 regulons ranging from one to 1623 genes in size. To assess the significance of the clustering results, the statistical over-representation of GO terms is averaged over this set of regulons and compared to the analogous values for 100 randomly-generated sets of clusters. The set of regulons derived from the experimental data scores significantly better than any of the randomly-generated sets. Most regulons correspond to identifiable biological processes and include a combination of genes encoding related developmental, metabolic pathway, and regulatory functions. In addition, nearly 3000 genes of unknown molecular function or process are assigned to a regulon. Only five regulons contain plastomic genes; four of these are exclusively plastomic. In contrast, expression of the mitochondrial genome is highly integrated with that of nuclear genes; each of the seven regulons containing mitochondrial genes also incorporates nuclear genes. The network of regulons reveals a higher-level organization, with dense local neighborhoods articulated for photosynthetic function, genetic information processing, and stress response. CONCLUSION: This analysis creates a framework for generation of experimentally testable hypotheses, gives insight into the concerted functions of Arabidopsis at the transcript level, and provides a test bed for comparative systems analysis.

Articulation of three core metabolic processes in Arabidopsis: fatty acid biosynthesis, leucine catabolism and starch metabolism.

BACKGROUND: Elucidating metabolic network structures and functions in multicellular organisms is an emerging goal of functional genomics. We describe the co-expression network of three core metabolic processes in the genetic model plant Arabidopsis thaliana: fatty acid biosynthesis, starch metabolism and amino acid (leucine) catabolism. RESULTS: These co-expression networks form modules populated by genes coding for enzymes that represent the reactions generally considered to define each pathway. However, the modules also incorporate a wider set of genes that encode transporters, cofactor biosynthetic enzymes, precursor-producing enzymes, and regulatory molecules. We tested experimentally the hypothesis that one of the genes tightly co-expressed with starch metabolism module, a putative kinase AtPERK10, will have a role in this process. Indeed, knockout lines of AtPERK10 have an altered starch accumulation. In addition, the co-expression data define a novel hierarchical transcript-level structure associated with catabolism, in which genes performing smaller, more specific tasks appear to be recruited into higher-order modules with a broader catabolic function. CONCLUSION: Each of these core metabolic pathways is structured as a module of co-expressed transcripts that co-accumulate over a wide range of environmental and genetic perturbations and developmental stages, and represent an expanded set of macromolecules associated with the common task of supporting the functionality of each metabolic pathway. As experimentally demonstrated, co-expression analysis can provide a rich approach towards understanding gene function.

Genotypic and phenotypic versatility of Aspergillus flavus during maize exploitation.

Aspergillus flavus is a cosmopolitan fungus able to respond to external stimuli and to shift both its trophic behaviour and the production of secondary metabolites, including that of the carcinogen aflatoxin (AF). To better understand the adaptability of this fungus, we examined genetic and phenotypic responses within the fungus when grown under four conditions that mimic different ecological niches ranging from saprophytic growth to parasitism. Global transcription changes were observed in both primary and secondary metabolism in response to these conditions, particularly in secondary metabolism where transcription of nearly half of the predicted secondary metabolite clusters changed in response to the trophic states of the fungus. The greatest transcriptional change was found between saprophytic and parasitic growth, which resulted in expression changes in over 800 genes in A. flavus. The fungus also responded to growth conditions, putatively by adaptive changes in conidia, resulting in differences in their ability to utilize carbon sources. We also examined tolerance of A. flavus to oxidative stress and found that growth and secondary metabolism were altered in a superoxide dismutase (sod) mutant and an alkyl-hydroperoxide reductase (ahp) mutant of A. flavus. Data presented in this study show a multifaceted response of A. flavus to its environment and suggest that oxidative stress and secondary metabolism are important in the ecology of this fungus, notably in its interaction with host plant and in relation to changes in its lifestyle (i.e. saprobic to pathogenic).

Inferring gene networks: dream or nightmare?

Inferring gene networks is a daunting task. We here describe several algorithms we used in the Dialogue for Reverse Engineering Assessments and Methods (DREAM2) Reverse Engineering Competition 2007: an algorithm based on first-order partial correlation for discovering BCL6 targets in Challenge 1 and an algorithm using nonlinear optimization with winning performance in Challenge 3. After the gold standards for the challenges were released, the performance of alternative variants of the algorithms could be evaluated. The DREAM competition taught us some strong lessons. Amazingly, simpler methods performed in general better than more advanced, theoretically motivated approaches. Also, the challenges strongly showed that inferring gene networks requires controlled experimentation using a well-defined experimental design. Analyzing data obtained through merging many unrelated datasets indeed resulted in weak performances of all algorithms, while algorithms that explicitly took the experimental design into account performed best.

Inferring gene networks: dream or nightmare?

We describe several algorithms with winning performance in the Dialogue for Reverse Engineering Assessments and Methods (DREAM2) Reverse Engineering Competition 2007. After the gold standards for the challenges were released, the performance of the algorithms could be thoroughly evaluated under different parameters or alternative ways of solving systems of equations. For the analysis of Challenge 4, the "In-silico" challenges, we employed methods to explicitly deal with perturbation data and time-series data. We show that original methods used to produce winning submissions could easily be altered to substantially improve performance. For Challenge 5, the genome-scale Escherichia coli network, we evaluated a variety of measures of association. These data are troublesome, and no good solutions could be produced, either by us or by any other teams. Our best results were obtained when analyzing subdatasets instead of considering the dataset as a whole.

Molecular characterization of a heteromeric ATP-citrate lyase that generates cytosolic acetyl-coenzyme A in Arabidopsis.

Acetyl-coenzyme A (CoA) is used in the cytosol of plant cells for the synthesis of a diverse set of phytochemicals including waxes, isoprenoids, stilbenes, and flavonoids. The source of cytosolic acetyl-CoA is unclear. We identified two Arabidopsis cDNAs that encode proteins similar to the amino and carboxy portions of human ATP-citrate lyase (ACL). Coexpression of these cDNAs in yeast (Saccharomyces cerevisiae) confers ACL activity, indicating that both the Arabidopsis genes are required for ACL activity. Arabidopsis ACL is a heteromeric enzyme composed of two distinct subunits, ACLA (45 kD) and ACLB (65 kD). The holoprotein has a molecular mass of 500 kD, which corresponds to a heterooctomer with an A(4)B(4) configuration. ACL activity and the ACLA and ACLB polypeptides are located in the cytosol, consistent with the lack of targeting peptides in the ACLA and ACLB sequences. In the Arabidopsis genome, three genes encode for the ACLA subunit (ACLA-1, At1g10670; ACLA-2, At1g60810; and ACLA-3, At1g09430), and two genes encode the ACLB subunit (ACLB-1, At3g06650 and ACLB-2, At5g49460). The ACLA and ACLB mRNAs accumulate in coordinated spatial and temporal patterns during plant development. This complex accumulation pattern is consistent with the predicted physiological needs for cytosolic acetyl-CoA, and is closely coordinated with the accumulation pattern of cytosolic acetyl-CoA carboxylase, an enzyme using cytosolic acetyl-CoA as a substrate. Taken together, these results indicate that ACL, encoded by the ACLA and ACLB genes of Arabidopsis, generates cytosolic acetyl-CoA. The heteromeric organization of this enzyme is common to green plants (including Chlorophyceae, Marchantimorpha, Bryopsida, Pinaceae, monocotyledons, and eudicots), species of fungi, Glaucophytes, Chlamydomonas, and prokaryotes. In contrast, all known animal ACL enzymes have a homomeric structure, indicating that a evolutionary fusion of the ACLA and ACLB genes probably occurred early in the evolutionary history of this kingdom.