Functional Gene Networks Research Articles

Planting increases the abundance and structure complexity of soil core functional genes relevant to carbon and nitrogen cycling.

Plants have an important impact on soil microbial communities and their functions. However, how plants determine the microbial composition and network interactions is still poorly understood. During a four-year field experiment, we investigated the functional gene composition of three types of soils (Phaeozem, Cambisols and Acrisol) under maize planting and bare fallow regimes located in cold temperate, warm temperate and subtropical regions, respectively. The core genes were identified using high-throughput functional gene microarray (GeoChip 3.0), and functional molecular ecological networks (fMENs) were subsequently developed with the random matrix theory (RMT)-based conceptual framework. Our results demonstrated that planting significantly (P < 0.05) increased the gene alpha-diversity in terms of richness and Shannon – Simpson’s indexes for all three types of soils and 83.5% of microbial alpha-diversity can be explained by the plant factor. Moreover, planting had significant impacts on the microbial community structure and the network interactions of the microbial communities. The calculated network complexity was higher under maize planting than under bare fallow regimes. The increase of the functional genes led to an increase in both soil respiration and nitrification potential with maize planting, indicating that changes in the soil microbial communities and network interactions influenced ecological functioning.

Co-Inheritance Analysis within the Domains of Life Substantially Improves Network Inference by Phylogenetic Profiling.

Phylogenetic profiling, a network inference method based on gene inheritance profiles, has been widely used to construct functional gene networks in microbes. However, its utility for network inference in higher eukaryotes has been limited. An improved algorithm with an in-depth understanding of pathway evolution may overcome this limitation. In this study, we investigated the effects of taxonomic structures on co-inheritance analysis using 2,144 reference species in four query species: Escherichia coli, Saccharomyces cerevisiae, Arabidopsis thaliana, and Homo sapiens. We observed three clusters of reference species based on a principal component analysis of the phylogenetic profiles, which correspond to the three domains of life—Archaea, Bacteria, and Eukaryota—suggesting that pathways inherit primarily within specific domains or lower-ranked taxonomic groups during speciation. Hence, the co-inheritance pattern within a taxonomic group may be eroded by confounding inheritance patterns from irrelevant taxonomic groups. We demonstrated that co-inheritance analysis within domains substantially improved network inference not only in microbe species but also in the higher eukaryotes, including humans. Although we observed two sub-domain clusters of reference species within Eukaryota, co-inheritance analysis within these sub-domain taxonomic groups only marginally improved network inference. Therefore, we conclude that co-inheritance analysis within domains is the optimal approach to network inference with the given reference species. The construction of a series of human gene networks with increasing sample sizes of the reference species for each domain revealed that the size of the high-accuracy networks increased as additional reference species genomes were included, suggesting that within-domain co-inheritance analysis will continue to expand human gene networks as genomes of additional species are sequenced. Taken together, we propose that co-inheritance analysis within the domains of life will greatly potentiate the use of the expected onslaught of sequenced genomes in the study of molecular pathways in higher eukaryotes.

Integrative Analysis of Transcriptomic and Epigenomic Data to Reveal Regulation Patterns for BMD Variation

Integration of multiple profiling data and construction of functional gene networks may provide additional insights into the molecular mechanisms of complex diseases. Osteoporosis is a worldwide public health problem, but the complex gene-gene interactions, post-transcriptional modifications and regulation of functional networks are still unclear. To gain a comprehensive understanding of osteoporosis etiology, transcriptome gene expression microarray, epigenomic miRNA microarray and methylome sequencing were performed simultaneously in 5 high hip BMD (Bone Mineral Density) subjects and 5 low hip BMD subjects. SPIA (Signaling Pathway Impact Analysis) and PCST (Prize Collecting Steiner Tree) algorithm were used to perform pathway-enrichment analysis and construct the interaction networks. Through integrating the transcriptomic and epigenomic data, firstly we identified 3 genes (FAM50A, ZNF473 and TMEM55B) and one miRNA (hsa-mir-4291) which showed the consistent association evidence from both gene expression and methylation data; secondly in network analysis we identified an interaction network module with 12 genes and 11 miRNAs including AKT1, STAT3, STAT5A, FLT3, hsa-mir-141 and hsa-mir-34a which have been associated with BMD in previous studies. This module revealed the crosstalk among miRNAs, mRNAs and DNA methylation and showed four potential regulatory patterns of gene expression to influence the BMD status. In conclusion, the integration of multiple layers of omics can yield in-depth results than analysis of individual omics data respectively. Integrative analysis from transcriptomics and epigenomic data improves our ability to identify causal genetic factors, and more importantly uncover functional regulation pattern of multi-omics for osteoporosis etiology.

Functional Genomics of PCOS

Polycystic ovary syndrome (PCOS) is a common endocrinopathy characterized by increased ovarian androgen biosynthesis, anovulation, and infertility. PCOS has a strong heritable component based on familial clustering and twin studies. Genome-wide association studies (GWAS) identified several PCOS candidate loci including LHCGR, FSHR, ZNF217, YAP1, INSR, RAB5B, and C9orf3. We review the functional roles of strong PCOS candidate loci focusing on FSHR, LHCGR, INSR, and DENND1A. We propose that these candidates comprise a hierarchical signaling network by which DENND1A, LHCGR, INSR, RAB5B, adapter proteins, and associated downstream signaling cascades converge to regulate theca cell androgen biosynthesis. Future elucidation of the functional gene networks predicted by the PCOS GWAS will result in new diagnostic and therapeutic approaches for women with PCOS.

Systems toxicology identifies mechanistic impacts of 2-amino-4,6-dinitrotoluene (2A-DNT) exposure in Northern Bobwhite.

BackgroundA systems toxicology investigation comparing and integrating transcriptomic and proteomic results was conducted to develop holistic effects characterizations for the wildlife bird model, Northern bobwhite (Colinus virginianus) dosed with the explosives degradation product 2-amino-4,6-dinitrotoluene (2A-DNT). A subchronic 60d toxicology bioassay was leveraged where both sexes were dosed via daily gavage with 0, 3, 14, or 30 mg/kg-d 2A-DNT. Effects on global transcript expression were investigated in liver and kidney tissue using custom microarrays for C. virginianus in both sexes at all doses, while effects on proteome expression were investigated in liver for both sexes and kidney in males, at 30 mg/kg-d.ResultsAs expected, transcript expression was not directly indicative of protein expression in response to 2A-DNT. However, a high degree of correspondence was observed among gene and protein expression when investigating higher-order functional responses including statistically enriched gene networks and canonical pathways, especially when connected to toxicological outcomes of 2A-DNT exposure. Analysis of networks statistically enriched for both transcripts and proteins demonstrated common responses including inhibition of programmed cell death and arrest of cell cycle in liver tissues at 2A-DNT doses that caused liver necrosis and death in females. Additionally, both transcript and protein expression in liver tissue was indicative of induced phase I and II xenobiotic metabolism potentially as a mechanism to detoxify and excrete 2A-DNT. Nuclear signaling assays, transcript expression and protein expression each implicated peroxisome proliferator-activated receptor (PPAR) nuclear signaling as a primary molecular target in the 2A-DNT exposure with significant downstream enrichment of PPAR-regulated pathways including lipid metabolic pathways and gluconeogenesis suggesting impaired bioenergetic potential.ConclusionAlthough the differential expression of transcripts and proteins was largely unique, the consensus of functional pathways and gene networks enriched among transcriptomic and proteomic datasets provided the identification of many critical metabolic functions underlying 2A-DNT toxicity as well as impaired PPAR signaling, a key molecular initiating event known to be affected in di- and trinitrotoluene exposures.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1798-4) contains supplementary material, which is available to authorized users.

Gene expression and functional brain networks

Gene expression and functional brain networks

Fine-tuning of ecaA and pepc gene expression increases succinic acid production in Escherichia coli.

For making a complex synthetic gene network function as designed, the parameters in the network have to be extensively tuned. In this study, a simple and general approach to rapidly tune gene networks in Escherichia coli was developed, which uses the hypermutable simple sequence repeats embedded in the spacer region between the ribosome binding site and the initiation codon. It was found that the change of sequence length and compositions of the repeated base pairs in 5'UTR contributes together to the changeable expression levels of the target gene. The mechanism of this phenomenon is that the transcriptional process makes greater impact on the expression level when compared to the translational process, which is utilized to successfully predict sample gene expression levels over a 50-fold range. The utility of the approach to regulate heterologous ecaA and pepc gene expression in the engineered E. coli for improving succinic acid yield and production has also been demonstrated. When the expression level of ecaA gene was 3.53-fold of the control and the expression level of pepc gene was 1.06-fold of the control, the highest yield of succinic acid and productivity were achieved, which was 0.87 g g(-1) and 2.01 g L(-1) h(-1), respectively.

Synthetic biology: Novel approaches for microbiology.

In the past twenty years, molecular genetics has created powerful tools for genetic manipulation of living organisms. Whole genome sequencing has provided necessary information to assess knowledge on gene function and protein networks. In addition, new tools permit to modify organisms to perform desired tasks. Gene function analysis is speed up by novel approaches that couple both high throughput data generation and mining. Synthetic biology is an emerging field that uses tools for generating novel gene networks, whole genome synthesis and engineering. New applications in biotechnological, pharmaceutical and biomedical research are envisioned for synthetic biology. In recent years these new strategies have opened up the possibilities to study gene and genome editing, creation of novel tools for functional studies in virus, parasites and pathogenic bacteria. There is also the possibility to re-design organisms to generate vaccine subunits or produce new pharmaceuticals to combat multi-drug resistant pathogens. In this review we provide our opinion on the applicability of synthetic biology strategies for functional studies of pathogenic organisms and some applications such as genome editing and gene network studies to further comprehend virulence factors and determinants in pathogenic organisms. We also discuss what we consider important ethical issues for this field of molecular biology, especially for potential misuse of the new technologies.

Abundant genetic overlap between blood lipids and immune-mediated diseases indicates shared molecular genetic mechanisms.

Epidemiological studies suggest a relationship between blood lipids and immune-mediated diseases, but the nature of these associations is not well understood. We used genome-wide association studies (GWAS) to investigate shared single nucleotide polymorphisms (SNPs) between blood lipids and immune-mediated diseases. We analyzed data from GWAS (n~200,000 individuals), applying new False Discovery Rate (FDR) methods, to investigate genetic overlap between blood lipid levels [triglycerides (TG), low density lipoproteins (LDL), high density lipoproteins (HDL)] and a selection of archetypal immune-mediated diseases (Crohn’s disease, ulcerative colitis, rheumatoid arthritis, type 1 diabetes, celiac disease, psoriasis and sarcoidosis). We found significant polygenic pleiotropy between the blood lipids and all the investigated immune-mediated diseases. We discovered several shared risk loci between the immune-mediated diseases and TG (n = 88), LDL (n = 87) and HDL (n = 52). Three-way analyses differentiated the pattern of pleiotropy among the immune-mediated diseases. The new pleiotropic loci increased the number of functional gene network nodes representing blood lipid loci by 40%. Pathway analyses implicated several novel shared mechanisms for immune pathogenesis and lipid biology, including glycosphingolipid synthesis (e.g. FUT2) and intestinal host-microbe interactions (e.g. ATG16L1). We demonstrate a shared genetic basis for blood lipids and immune-mediated diseases independent of environmental factors. Our findings provide novel mechanistic insights into dyslipidemia and immune-mediated diseases and may have implications for therapeutic trials involving lipid-lowering and anti-inflammatory agents.

Integration of somatic mutation, expression and functional data reveals potential driver genes predictive of breast cancer survival.

Genome and transcriptome analyses can be used to explore cancers comprehensively, and it is increasingly common to have multiple omics data measured from each individual. Furthermore, there are rich functional data such as predicted impact of mutations on protein coding and gene/protein networks. However, integration of the complex information across the different omics and functional data is still challenging. Clinical validation, particularly based on patient outcomes such as survival, is important for assessing the relevance of the integrated information and for comparing different procedures. An analysis pipeline is built for integrating genomic and transcriptomic alterations from whole-exome and RNA sequence data and functional data from protein function prediction and gene interaction networks. The method accumulates evidence for the functional implications of mutated potential driver genes found within and across patients. A driver-gene score (DGscore) is developed to capture the cumulative effect of such genes. To contribute to the score, a gene has to be frequently mutated, with high or moderate mutational impact at protein level, exhibiting an extreme expression and functionally linked to many differentially expressed neighbors in the functional gene network. The pipeline is applied to 60 matched tumor and normal samples of the same patient from The Cancer Genome Atlas breast-cancer project. In clinical validation, patients with high DGscores have worse survival than those with low scores (P = 0.001). Furthermore, the DGscore outperforms the established expression-based signatures MammaPrint and PAM50 in predicting patient survival. In conclusion, integration of mutation, expression and functional data allows identification of clinically relevant potential driver genes in cancer. The documented pipeline including annotated sample scripts can be found in http://fafner.meb.ki.se/biostatwiki/driver-genes/. yudi.pawitan@ki.se Supplementary data are available at Bioinformatics online.

Psychopathology and cognitive performance in individuals with membrane-associated guanylate kinase mutations: a functional network phenotyping study.

BackgroundRare pathogenic variants in membrane-associated guanylate kinase (MAGUK) genes cause intellectual disability (ID) and have recently been associated with neuropsychiatric risk in the non-ID population. However, it is not known whether risk for psychiatric symptoms amongst individuals with ID due to MAGUK gene mutations is higher than expected for the degree of general intellectual impairment, nor whether specific cognitive differences are associated with disruption to this gene functional network.MethodsThis study addresses these two questions via behavioural questionnaires and cognitive testing, applying quantitative methods previously validated in populations with ID. We compared males with X-linked ID caused by mutations in three MAGUK genes (PAK3, DLG3, OPHN1; n = 9) to males with ID caused by mutations in other X chromosome genes (n = 17). Non-parametric and parametric analyses were applied as appropriate to data.ResultsGroups did not differ in age, global cognitive impairment, adaptive function or epilepsy prevalence. However, individuals with MAGUK gene mutations demonstrated significantly higher psychopathology risks, comprising elevated total problem behaviours, prominent hyperactivity and elevated scores on an autism screening checklist. Despite these overt difficulties, individuals in the MAGUK group performed more accurately than expected for age and intelligence quotient (IQ) on computerised tests of visual attention, convergent with mouse models of MAGUK loss-of-function.ConclusionsOur findings support a role for MAGUK genes in influencing cognitive parameters relevant to psychiatric risk. In addition to establishing clear patterns of impairment for this group, our findings highlight the importance of careful phenotyping after genetic diagnosis, showing that gene functional network disruptions can be associated with specific psychopathological risks and cognitive differences within the context of ID.Electronic supplementary materialThe online version of this article (doi:10.1186/s11689-015-9105-x) contains supplementary material, which is available to authorized users.

Functional Gene Networks: R/Bioc package to generate and analyse gene networks derived from functional enrichment and clustering.

Summary: Functional Gene Networks (FGNet) is an R/Bioconductor package that generates gene networks derived from the results of functional enrichment analysis (FEA) and annotation clustering. The sets of genes enriched with specific biological terms (obtained from a FEA platform) are transformed into a network by establishing links between genes based on common functional annotations and common clusters. The network provides a new view of FEA results revealing gene modules with similar functions and genes that are related to multiple functions. In addition to building the functional network, FGNet analyses the similarity between the groups of genes and provides a distance heatmap and a bipartite network of functionally overlapping genes. The application includes an interface to directly perform FEA queries using different external tools: DAVID, GeneTerm Linker, TopGO or GAGE; and a graphical interface to facilitate the use.Availability and implementation: FGNet is available in Bioconductor, including a tutorial. URL: http://bioconductor.org/packages/release/bioc/html/FGNet.htmlContact: jrivas@usal.esSupplementary information: Supplementary data are available at Bioinformatics online.

Systematic Analysis of Integrated Gene Functional Network of Four Chronic Stress-related Lifestyle Disorders.

Background:Stress is a term used to define factors involved in changes in the physiological balances resulting in disease conditions. Chronic exposure to stress conditions in modern lifestyles has resulted in a group of disorders called lifestyle disorders. Genetic background and environmental factors are interrelated to lifestyle in determining the health status of individuals. Hence, identification of disease-associated genes is the primary step toward explanations of pathogenesis of these diseases. In functional genomics, large-scale molecular and physiological data are used for the identification of causative genes associated with a disease.Aim:The objective of our study was to find a common set of genes involved in chronic stress-related lifestyle diseases such as cardiovascular diseases (CVDs), type 2 diabetes (T2D), hypertension (HTN), and obesity.Materials and Methods:In our study, we have performed a systematic analysis of the functional gene network of four chronic stress-related lifestyle diseases by retrieving genes from published databases. We have tried to systematically construct a functional protein-protein interaction (PPI) network. The goals of establishing this network were the functional enrichment study of interacting partners as well as functional disease ontology annotation (FunDO) of the enriched genes.Results:This study enabled the identification of key genes involved in these stress-related lifestyle diseases by prioritizing candidate genes based on their degree of involvement. In this systematic analysis, we have found key genes for these diseases based on their involvement and association at the gene network level and PPI.Conclusion:We have deciphered a group of genes that in combination play a crucial role and may impact the function of the whole genome in the four lifestyle disorders mentioned.

Transcriptional profiling defines dynamics of parasite tissue sequestration during malaria infection.

BackgroundDuring intra-erythrocytic development, late asexually replicating Plasmodium falciparum parasites sequester from peripheral circulation. This facilitates chronic infection and is linked to severe disease and organ-specific pathology including cerebral and placental malaria. Immature gametocytes - sexual stage precursor cells - likewise disappear from circulation. Recent work has demonstrated that these sexual stage parasites are located in the hematopoietic system of the bone marrow before mature gametocytes are released into the bloodstream to facilitate mosquito transmission. However, as sequestration occurs only in vivo and not during in vitro culture, the mechanisms by which it is regulated and enacted (particularly by the gametocyte stage) remain poorly understood.ResultsWe generated the most comprehensive P. falciparum functional gene network to date by integrating global transcriptional data from a large set of asexual and sexual in vitro samples, patient-derived in vivo samples, and a new set of in vitro samples profiling sexual commitment. We defined more than 250 functional modules (clusters) of genes that are co-expressed primarily during the intra-erythrocytic parasite cycle, including 35 during sexual commitment and gametocyte development. Comparing the in vivo and in vitro datasets allowed us, for the first time, to map the time point of asexual parasite sequestration in patients to 22 hours post-invasion, confirming previous in vitro observations on the dynamics of host cell modification and cytoadherence. Moreover, we were able to define the properties of gametocyte sequestration, demonstrating the presence of two circulating gametocyte populations: gametocyte rings between 0 and approximately 30 hours post-invasion and mature gametocytes after around 7 days post-invasion.ConclusionsThis study provides a bioinformatics resource for the functional elucidation of parasite life cycle dynamics and specifically demonstrates the presence of the gametocyte ring stages in circulation, adding significantly to our understanding of the dynamics of gametocyte sequestration in vivo.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-015-0133-7) contains supplementary material, which is available to authorized users.

Glutamate Networks Implicate Cognitive Impairments in Schizophrenia: Genome-Wide Association Studies of 52 Cognitive Phenotypes.

Cognitive impairments are a core feature in patients with schizophrenia. These deficits could serve as effective tools for understanding the genetic architecture of schizophrenia. This study investigated whether genetic variants associated with cognitive impairments aggregate in functional gene networks related to the pathogenesis of schizophrenia. Here, genome-wide association studies (GWAS) of a range of cognitive phenotypes relevant to schizophrenia were performed in 411 healthy subjects. We attempted to replicate the GWAS data using 257 patients with schizophrenia and performed a meta-analysis of the GWAS findings and the replicated results. Because gene networks, rather than a single gene or genetic variant, may be strongly associated with the susceptibility to schizophrenia and cognitive impairments, gene-network analysis for genes in close proximity to the replicated variants was performed. We observed nominal associations between 3054 variants and cognitive phenotypes at a threshold of P < 1.0 × 10− 4. Of the 3054 variants, the associations of 191 variants were replicated in the replication samples (P < .05). However, no variants achieved genome-wide significance in a meta-analysis (P > 5.0 × 10− 8). Additionally, 115 of 191 replicated single nucleotide polymorphisms (SNPs) have genes located within 10 kb of the SNPs (60.2%). These variants were moderately associated with cognitive phenotypes that ranged from P = 2.50 × 10− 5 to P = 9.40 × 10− 8. The genes located within 10 kb from the replicated SNPs were significantly grouped in terms of glutamate receptor activity (false discovery rate (FDR) q = 4.49 × 10− 17) and the immune system related to major histocompatibility complex class I (FDR q = 8.76 × 10− 11) networks. Our findings demonstrate that genetic variants related to cognitive trait impairment in schizophrenia are involved in the N-methyl-d-aspartate glutamate network.

System-level insights into the cellular interactome of a non-model organism: inferring, modelling and analysing functional gene network of soybean (Glycine max).

Cellular interactome, in which genes and/or their products interact on several levels, forming transcriptional regulatory-, protein interaction-, metabolic-, signal transduction networks, etc., has attracted decades of research focuses. However, such a specific type of network alone can hardly explain the various interactive activities among genes. These networks characterize different interaction relationships, implying their unique intrinsic properties and defects, and covering different slices of biological information. Functional gene network (FGN), a consolidated interaction network that models fuzzy and more generalized notion of gene-gene relations, have been proposed to combine heterogeneous networks with the goal of identifying functional modules supported by multiple interaction types. There are yet no successful precedents of FGNs on sparsely studied non-model organisms, such as soybean (Glycine max), due to the absence of sufficient heterogeneous interaction data. We present an alternative solution for inferring the FGNs of soybean (SoyFGNs), in a pioneering study on the soybean interactome, which is also applicable to other organisms. SoyFGNs exhibit the typical characteristics of biological networks: scale-free, small-world architecture and modularization. Verified by co-expression and KEGG pathways, SoyFGNs are more extensive and accurate than an orthology network derived from Arabidopsis. As a case study, network-guided disease-resistance gene discovery indicates that SoyFGNs can provide system-level studies on gene functions and interactions. This work suggests that inferring and modelling the interactome of a non-model plant are feasible. It will speed up the discovery and definition of the functions and interactions of other genes that control important functions, such as nitrogen fixation and protein or lipid synthesis. The efforts of the study are the basis of our further comprehensive studies on the soybean functional interactome at the genome and microRNome levels. Additionally, a web tool for information retrieval and analysis of SoyFGNs can be accessed at SoyFN: http://nclab.hit.edu.cn/SoyFN.

Gene and Protein Network Analysis of AmpC β Lactamase.

AmpC β-lactamase is a cephalosporinase, which exhibits resistance against all existing β-lactam antibiotics except carbapenems. Their occurrence in many bacterial pathogens poses a threat to public health and is a growing concern in the medical world. The ampC gene is highly inducible in the presence of β-lactam antibiotics and can be expressed in high levels due to mutation. This inducible expression is regulated by several functional genes. Several studies on functional relationship of these genes and its resistance mechanisms are carried out but it still lacks comprehensible evidences. Thus, in our current study, we used computational gene networks to analyze ampC gene. Based on its interaction type, co-expression, Gene Ontology, and text mining, a functional interaction network is constructed. Around 247 functional genes in 15 different bacterial genus have a functional association with ampC gene. It is predicted that 19.8% ampD, 13.3% frdD, 8.5% gcvA, 2.4% ampR, and 55.7% of other functional partners are associated with ampC gene. Our present study provides a glimpse about the functional gene network of ampC gene and also provides the integrated evidence for ampC gene in regulating the β-lactamase production and its role in antibiotic resistance.

AraNet v2: an improved database of co-functional gene networks for the study of Arabidopsis thaliana and 27 other nonmodel plant species.

Arabidopsis thaliana is a reference plant that has been studied intensively for several decades. Recent advances in high-throughput experimental technology have enabled the generation of an unprecedented amount of data from A. thaliana, which has facilitated data-driven approaches to unravel the genetic organization of plant phenotypes. We previously published a description of a genome-scale functional gene network for A. thaliana, AraNet, which was constructed by integrating multiple co-functional gene networks inferred from diverse data types, and we demonstrated the predictive power of this network for complex phenotypes. More recently, we have observed significant growth in the availability of omics data for A. thaliana as well as improvements in data analysis methods that we anticipate will further enhance the integrated database of co-functional networks. Here, we present an updated co-functional gene network for A. thaliana, AraNet v2 (available at http://www.inetbio.org/aranet), which covers approximately 84% of the coding genome. We demonstrate significant improvements in both genome coverage and accuracy. To enhance the usability of the network, we implemented an AraNet v2 web server, which generates functional predictions for A. thaliana and 27 nonmodel plant species using an orthology-based projection of nonmodel plant genes on the A. thaliana gene network.

A systematic view of the rice calcineurin B-like protein interacting protein kinase family

Calcium (Ca2+), as a universal second messenger, plays an important role in various signal transduction pathway networks in plants. There are several components of Ca2+ mediating signaling pathways including calcium-dependent protein kinases, calmodulin and calcineurin B-like proteins (CBLs), and CBL-interacting protein kinases (CIPK). Here, we provide a systematic view of 33 rice CIPK (OsCIPK) family members, which are further classified into seven subgroups. The integration of various meta-expression data such as anatomy, diurnal regulation, and phylogenomic tree context of the OsCIPK family reveals that 11 of the family members show preferred expression in leaves during vegetative growth. In addition, 20 OsCIPK genes show peak expression during the day, while none of the genes peaked expression in the dark. Regarding abiotic stress, 16 OsCIPK genes were induced by drought stress in leaf organs; of these, 14 OsCIPK genes showed up-regulation and two OsCIPK genes were down-regulated based on quantitative RT-PCR analyses. We then developed a functional gene network that consists of 15 out of the 16 genes. This network proposes a useful hypothetical model to understand the molecular mechanism of drought response and circadian regulation associated with OsCIPK family genes.

From experimental design to functional gene networks: DNA microarray contribution to skin ageing research.

There is no doubt that the DNA microarray-based technology contributed to increase our knowledge of a wide range of processes. However, integrating genes into functional networks, rather than terms describing generic characteristics, remains an important challenge. The highly context-dependent function of a given gene and feedback mechanisms complexify greatly the interpretation of the data. Moreover, it is difficult to determine whether changes in gene expression are the result or the cause of pathologies or physiological events. In both cases, the difficulty relies on the involvement of processes that, at an early stage, can be protective and later on, deleterious because of their runaway. Each individual cell has its own transcription profile that determines its behaviour and its relationships with its neighbours. This is particularly true when a mechanism such as cell cycle is concerned. Another issue concerns the analyses from samples of different donors. Whereas the statistical tools lead to determine common features among groups, they tend to smooth the overall data and consequently, the selected values represent the 'tip of the iceberg'. There is a significant overlap in the set of genes identified in the different studies on skin ageing processes described in the present review. The reason of this overlap is because most of these genes belong to the basic machinery controlling cell growth and arrest. To get a more full picture of these processes, a hard work has still to be done to determine the precise mechanisms conferring the cell type specificity of ageing. Integrative biology applied to the huge amount of existing microarray data should fulfil gaps, through the characterization of additional actors accounting for the activation of specific signalling pathways at crossing points. Furthermore, computational tools have to be developed taking into account that expression values among similar groups may not vary 'by chance' but may reflect, along with other subtle changes, specific features of one given donor. Through a better stratification, these tools will allow to recover genes from the 'bottom of the iceberg'. Identifying these genes should contribute to understand how skin ages among individuals, thus paving the way for personalized skin care.