Gene Co-expression Clusters Research Articles

Shared gene expression signatures between visceral adipose and skeletal muscle tissues are associated with cardiometabolic traits in children with obesity

Obesity in children is related to the development of cardiometabolic complications later in life, where molecular changes of visceral adipose tissue (VAT) and skeletal muscle tissue (SMT) have been proven to be fundamental. The aim of this study is to unveil the gene expression architecture of both tissues in a cohort of Spanish boys with obesity, using a clustering method known as weighted gene co-expression network analysis. For this purpose, we have followed a multi-objective analytic pipeline consisting of three main approaches; identification of gene co-expression clusters associated with childhood obesity, individually in VAT and SMT (intra-tissue, approach I); identification of gene co-expression clusters associated with obesity-metabolic alterations, individually in VAT and SMT (intra-tissue, approach II); and identification of gene co-expression clusters associated with obesity-metabolic alterations simultaneously in VAT and SMT (inter-tissue, approach III). In both tissues, we identified independent and inter-tissue gene co-expression signatures associated with obesity and cardiovascular risk, some of which exceeded multiple-test correction filters. In these signatures, we could identify some central hub genes (e.g., NDUFB8, GUCY1B1, KCNMA1, NPR2, PPP3CC) participating in relevant metabolic pathways exceeding multiple-testing correction filters. We identified the central hub genes PIK3R2, PPP3C and PTPN5 associated with MAPK signaling and insulin resistance terms. This is the first time that these genes have been associated with childhood obesity in both tissues. Therefore, they could be potential novel molecular targets for drugs and health interventions, opening new lines of research on the personalized care in this pathology. This work generates interesting hypotheses about the transcriptomics alterations underlying metabolic health alterations in obesity in the pediatric population.

New Insights into MdSPS4-Mediated Sucrose Accumulation under Different Nitrogen Levels Revealed by Physiological and Transcriptomic Analysis.

Nitrogen nutrition participates in many physiological processes and understanding the physiological and molecular mechanisms of apple responses to nitrogen is very significant for improving apple quality. This study excavated crucial genes that regulates sugar metabolism in response to nitrogen in apples through physiology and transcriptome analysis, so as to lay a theoretical foundation for improving fruit quality. In this paper, the content of sugar and organic acid in apple fruit at different developmental periods under different nitrogen levels (0, 150, 300, and 600 kg·hm-2) were determined. Then, the transcriptomic analysis was performed in 120 days after bloom (DAB) and 150 DAB. The results showed that the fructose and glucose content were the highest at 120 DAB under 600 kg·hm-2 nitrogen level. Meanwhile, different nitrogen treatments decreased malate content in 30 and 60 DAB. RNA-seq analysis revealed a total of 4537 UniGenes were identified as differentially expressed genes (DEGs) under nitrogen treatments. Among these DEGs, 2362 (52.06%) were up-regulated and 2175 (47.94%) were down-regulated. The gene co-expression clusters revealed that most DEGs were significantly annotated in the photosynthesis, glycolysis/gluconeogenesis, pyruvate metabolism, carbon metabolism, carbon fixation in photosynthetic organisms and plant hormone signal transduction pathways. The key transcription factor genes (ERF, NAC, WRKY, and C2H2 genes) were differentially expressed in apple fruit. Sugar and acid metabolism-related genes (e.g., HXK1, SPS4, SS2, PPC16-2, and MDH2 genes) exhibited significantly up-regulated expression at 120 DAB, whereas they were down-regulated at 150 DAB. Furthermore, the MdSPS4 gene overexpression positively promoted sucrose accumulation in apple callus and fruit. In conclusion, the combinational analysis of transcriptome and the functional validation of the MdSPS4 gene provides new insights into apple responses to different nitrogen levels.

Dual-Stream Subspace Clustering Network for revealing gene targets in Alzheimer's disease

The rapid development of scRNA-seq technology in recent years has enabled us to capture high-throughput gene expression profiles at single-cell resolution, reveal the heterogeneity of complex cell populations, and greatly advance our understanding of the underlying mechanisms in human diseases. Traditional methods for gene co-expression clustering are limited to discovering effective gene groups in scRNA-seq data. In this paper, we propose a novel gene clustering method based on convolutional neural networks called Dual-Stream Subspace Clustering Network (DS-SCNet). DS-SCNet can accurately identify important gene clusters from large scales of single-cell RNA-seq data and provide useful information for downstream analysis. Based on the simulated datasets, DS-SCNet successfully clusters genes into different groups and outperforms mainstream gene clustering methods, such as DBSCAN and DESC, across different evaluation metrics. To explore the biological insights of our proposed method, we applied it to real scRNA-seq data of patients with Alzheimer's disease (AD). DS-SCNet analyzed the single-cell RNA-seq data with 10,850 genes, and accurately identified 8 optimal clusters from 6673 cells. Enrichment analysis of these gene clusters revealed functional signaling pathways including the ILS signaling, the Rho GTPase signaling, and hemostasis pathways. Further analysis of gene regulatory networks identified new hub genes such as ELF4 as important regulators of AD, which indicates that DS-SCNet contributes to the discovery and understanding of the pathogenesis in Alzheimer's disease.

The Iron Deficiency-Regulated Small Protein Effector FEP3/IRON MAN1 Modulates Interaction of BRUTUS-LIKE1 With bHLH Subgroup IVc and POPEYE Transcription Factors.

In light of climate change and human population growth one of the most challenging tasks is to generate plants that are Fe-efficient, resilient to low Fe supply and Fe-biofortified. For such endeavors, it is crucial to understand the regulation of Fe acquisition and allocation in plants. One open question is how identified Fe-regulatory proteins comprising positive and negative regulators act together to steer Fe homeostasis. bHLH transcription factors (TFs) belonging to the subgroups IVb and IVc can initiate a bHLH cascade controlling the –Fe response in roots. In Arabidopsis thaliana, the –Fe-induced genes are sub-divided into several gene co-expression clusters controlled by different sets of TFs. Some of the co-expressed genes encode regulatory E3 ligase proteins BRUTUS (BTS)/BTS-LIKE (BTSL) and small proteins belonging to the group of FE UPTAKE-INDUCING PEPTIDE/IRON MAN (FEP/IMA). Recently, it was described that FEP1/IMA3 and FEP3/IMA1 proteins inhibit the repression of bHLH factors by BTS. We had postulated that –Fe-regulated co-expression clusters provide new information about regulatory protein interaction complexes. Here, we report a targeted yeast two-hybrid screen among 23 proteins of the –Fe response. This identified a novel protein interactome involving another E3 ligase, namely BTSL1, basic helix-loop-helix (bHLH) protein POPEYE (PYE) and transcription factors of the subgroup IVc as well as FEP3/IMA1. Because of the difficulty in stable BTSL1 protein expression in plant cells, we used a yeast two hybrid-based deletion mapping, homology modeling and molecular docking, to pinpoint interaction sites in BTSL1 and FEP3/IMA1. bHLH IVc TFs have similar residues at their C-terminus as FEP3/IMA1 interacting sites. FEP3/IMA1 attenuated interaction of BTSL1 and bHLH proteins in a yeast three-hybrid assay, in line with physiological data pointing to enhanced Fe acquisition and allocation in FEP3/IMA1 overexpression and btsl1 btsl2 mutant plants. Hence, exploiting –Fe-induced gene co-expression networks identified FEP3/IMA1 as a small effector protein that binds and inhibits the BTSL1 complex with PYE and bHLH subgroup IVc proteins. Structural analysis resolved interaction sites. This information helps improving models of Fe regulation and identifying novel targets for breeding of Fe-efficient crops.

Multiomics Identification of Potential Targets for Alzheimer Disease and Antrocin as a Therapeutic Candidate.

Alzheimer’s disease (AD) is the most frequent cause of neurodegenerative dementia and affects nearly 50 million people worldwide. Early stage diagnosis of AD is challenging, and there is presently no effective treatment for AD. The specific genetic alterations and pathological mechanisms of the development and progression of dementia remain poorly understood. Therefore, identifying essential genes and molecular pathways that are associated with this disease’s pathogenesis will help uncover potential treatments. In an attempt to achieve a more comprehensive understanding of the molecular pathogenesis of AD, we integrated the differentially expressed genes (DEGs) from six microarray datasets of AD patients and controls. We identified ATPase H+ transporting V1 subunit A (ATP6V1A), BCL2 interacting protein 3 (BNIP3), calmodulin-dependent protein kinase IV (CAMK4), TOR signaling pathway regulator-like (TIPRL), and the translocase of outer mitochondrial membrane 70 (TOMM70) as upregulated DEGs common to the five datasets. Our analyses revealed that these genes exhibited brain-specific gene co-expression clustering with OPA1, ITFG1, OXCT1, ATP2A2, MAPK1, CDK14, MAP2K4, YWHAB, PARK2, CMAS, HSPA12A, and RGS17. Taking the mean relative expression levels of this geneset in different brain regions into account, we found that the frontal cortex (BA9) exhibited significantly (p < 0.05) higher expression levels of these DEGs, while the hippocampus exhibited the lowest levels. These DEGs are associated with mitochondrial dysfunction, inflammation processes, and various pathways involved in the pathogenesis of AD. Finally, our blood–brain barrier (BBB) predictions using the support vector machine (SVM) and LiCABEDS algorithm and molecular docking analysis suggested that antrocin is permeable to the BBB and exhibits robust ligand–receptor interactions with high binding affinities to CAMK4, TOMM70, and T1PRL. Our results also revealed good predictions for ADMET properties, drug-likeness, adherence to Lipinskís rules, and no alerts for pan-assay interference compounds (PAINS) Conclusions: These results suggest a new molecular signature for AD parthenogenesis and antrocin as a potential therapeutic agent. Further investigation is warranted.

Potential Arabidopsis thaliana glucosinolate genes identified from the co-expression modules using graph clustering approach.

BackgroundGlucosinolates (GSLs) are plant secondary metabolites that contain nitrogen-containing compounds. They are important in the plant defense system and known to provide protection against cancer in humans. Currently, increasing the amount of data generated from various omics technologies serves as a hotspot for new gene discovery. However, sometimes sequence similarity searching approach is not sufficiently effective to find these genes; hence, we adapted a network clustering approach to search for potential GSLs genes from the Arabidopsis thaliana co-expression dataset.MethodsWe used known GSL genes to construct a comprehensive GSL co-expression network. This network was analyzed with the DPClusOST algorithm using a density of 0.5. 0.6. 0.7, 0.8, and 0.9. Generating clusters were evaluated using Fisher’s exact test to identify GSL gene co-expression clusters. A significance score (SScore) was calculated for each gene based on the generated p-value of Fisher’s exact test. SScore was used to perform a receiver operating characteristic (ROC) study to classify possible GSL genes using the ROCR package. ROCR was used in determining the AUC that measured the suitable density value of the cluster for further analysis. Finally, pathway enrichment analysis was conducted using ClueGO to identify significant pathways associated with the GSL clusters.ResultsThe density value of 0.8 showed the highest area under the curve (AUC) leading to the selection of thirteen potential GSL genes from the top six significant clusters that include IMDH3, MVP1, T19K24.17, MRSA2, SIR, ASP4, MTO1, At1g21440, HMT3, At3g47420, PS1, SAL1, and At3g14220. A total of Four potential genes (MTO1, SIR, SAL1, and IMDH3) were identified from the pathway enrichment analysis on the significant clusters. These genes are directly related to GSL-associated pathways such as sulfur metabolism and valine, leucine, and isoleucine biosynthesis. This approach demonstrates the ability of the network clustering approach in identifying potential GSL genes which cannot be found from the standard similarity search.

Emergence of co-expression in gene regulatory networks.

Transcriptomes are known to organize themselves into gene co-expression clusters or modules where groups of genes display distinct patterns of coordinated or synchronous expression across independent biological samples. The functional significance of these co-expression clusters is suggested by the fact that highly coexpressed groups of genes tend to be enriched in genes involved in common functions and biological processes. While gene co-expression is widely assumed to reflect close regulatory proximity, the validity of this assumption remains unclear. Here we use a simple synthetic gene regulatory network (GRN) model and contrast the resulting co-expression structure produced by these networks with their known regulatory architecture and with the co-expression structure measured in available human expression data. Using randomization tests, we found that the levels of co-expression observed in simulated expression data were, just as with empirical data, significantly higher than expected by chance. When examining the source of correlated expression, we found that individual regulators, both in simulated and experimental data, fail, on average, to display correlated expression with their immediate targets. However, highly correlated gene pairs tend to share at least one common regulator, while most gene pairs sharing common regulators do not necessarily display correlated expression. Our results demonstrate that widespread co-expression naturally emerges in regulatory networks, and that it is a reliable and direct indicator of active co-regulation in a given cellular context.

The Contribution of Y Chromosome Genes to Spontaneous Differentiation of Human Embryonic Stem Cells into Embryoid Bodies In Vitro.

Objective Sexual dimorphism in mammals can be described as subsequent transcriptional differences from their distinct sex chromosome complements. Following X inactivation in females, the Y chromosome is the major genetic difference between sexes. In this study, we used a male embryonic stem cell line (Royan H6) to identify the potential role of the male-specific region of the Y chromosome (MSY) during spontaneous differentiation into embryoid bodies (EBs) as a model of early embryonic development. Materials and Methods In this experimental study, RH6 cells were cultured on inactivated feeder layers and Matrigel. In a dynamic suspension system, aggregates were generated in the same size and were spontaneously differentiated into EBs. During differentiation, expression patterns of specific markers for three germ layers were compared with MSY genes. Results Spontaneous differentiation was determined by downregulation of pluripotent markers and upregulation of fourteen differentiation markers. Upregulation of the ectoderm markers was observed on days 4 and 16, whereas mesoderm markers were upregulated on the 8th day and endodermic markers on days 12-16. Mesoderm markers correlated with 8 MSY genes namely DDX3Y, RPS4Y1, KDM5D, TBL1Y, BCORP1, PRY, DAZ, and AMELY, which were classified as a mesoderm cluster. Endoderm markers were co-expressed with 7 MSY genes, i.e. ZFY, TSPY, PRORY, VCY, EIF1AY, USP9Y, and RPKY, which were grouped as an endoderm cluster. Finally, the ectoderm markers correlated with TXLNGY, NLGN4Y, PCDH11Y, TMSB4Y, UTY, RBMY1, and HSFY genes of the MSY, which were categorized as an ectoderm cluster. In contrast, 2 MSY genes, SRY and TGIF2LY, were more highly expressed in RH6 cells compared to EBs. ConclusionWe found a significant correlation between spontaneous differentiation and upregulation of specific MSY genes. The expression alterations of MSY genes implied the potential responsibility of their gene co-expression clusters for EB differentiation. We suggest that these genes may play important roles in early embryonic development.

ScLM: Automatic Detection of Consensus Gene Clusters Across Multiple Single-cell Datasets

In gene expression profiling studies, including single-cell RNAsequencing (scRNA-seq) analyses, the identification and characterization of co-expressed genes provides critical information on cell identity and function. Gene co-expression clustering in scRNA-seq data presents certain challenges. We show that commonly used methods for single-cell data are not capable of identifying co-expressed genes accurately, and produce results that substantially limit biological expectations of co-expressed genes. Herein, we present single-cell Latent-variable Model (scLM), a gene co-clustering algorithm tailored to single-cell data that performs well at detecting gene clusters with significant biologic context. Importantly, scLM can simultaneously cluster multiple single-cell datasets, i.e., consensus clustering, enabling users to leverage single-cell data from multiple sources for novel comparative analysis. scLM takes raw count data as input and preserves biological variation without being influenced by batch effects from multiple datasets. Results from both simulation data and experimental data demonstrate that scLM outperforms the existing methods with considerably improved accuracy. To illustrate the biological insights of scLM, we apply it to our in-house and public experimental scRNA-seq datasets. scLM identifies novel functional gene modules and refines cell states, which facilitates mechanism discovery and understanding of complex biosystems such as cancers. A user-friendly R package with all the key features of the scLM method is available at https://github.com/QSong-github/scLM.

Weighted gene coexpression network analysis identifies a new biomarker of CENPF for prediction disease prognosis and progression in nonmuscle invasive bladder cancer.

BackgroundThe dreadful prognosis of nonmuscle invasive bladder cancer mainly results from the delay in recognition of individuals with a high risk of progression. Thus, the emphasis of this work lies in developing valuable biomarkers that is conducive to accurately predicting the progression of NMIBC.MethodsMicroarray data from GSE32894 including 209 NMIBC samples were performed by weighted gene coexpression network analysis (WGCNA), which could find modules of highly correlated genes and relate modules to external sample traits. Besides, we constructed a protein–protein interaction to facilitate screening the hub gene. At last, we used RNA‐seq and microarray data and clinical information from ArrayExpress (E‐MTAB‐4321) and GSE13507 to select and validate the candidate gene.ResultsIn current paper, blue module of 13 gene coexpression clusters we identified was selected as the key modules. Seven genes namely: CDCA8, CENPF, MCM6, MELK, PRC1, STIL, and TPX2 have been identified as candidate genes. Notably, among them, only elevated CENPF in NIMBC tissue was closely associated with low progression‐free survival (PFS) and overall survival (OS) rate in three datasets and had a large area under receiver operating characteristic (ROC) curve. Finally, CENPF was identified as an effective biomarker in NMIBC.ConclusionTherefore, our findings submit a new progressive and prognostic molecular marker and therapeutic target for NMIBC. Moreover, these genes that deserve to be further researched may improve the comprehension about the occurrence and development of superficial bladder cancer.

Seed desiccation mechanisms co‐opted for vegetative desiccation in the resurrection grass Oropetium thomaeum

Resurrection plants desiccate during periods of prolonged drought stress, then resume normal cellular metabolism upon water availability. Desiccation tolerance has multiple origins in flowering plants, and it likely evolved through rewiring seed desiccation pathways. Oropetium thomaeum is an emerging model for extreme drought tolerance, and its genome, which is the smallest among surveyed grasses, was recently sequenced. Combining RNA-seq, targeted metabolite analysis and comparative genomics, we show evidence for co-option of seed-specific pathways during vegetative desiccation. Desiccation-related gene co-expression clusters are enriched in functions related to seed development including several seed-specific transcription factors. Across the metabolic network, pathways involved in programmed cell death inhibition, ABA signalling and others are activated during dehydration. Oleosins and oil bodies that typically function in seed storage are highly abundant in desiccated leaves and may function for membrane stability and storage. Orthologs to seed-specific LEA proteins from rice and maize have neofunctionalized in Oropetium with high expression during desiccation. Accumulation of sucrose, raffinose and stachyose in drying leaves mirrors sugar accumulation patterns in maturing seeds. Together, these results connect vegetative desiccation with existing seed desiccation and drought responsive pathways and provide some key candidate genes for engineering improved drought tolerance in crop plants.

Temporal and spatial transcriptomic and microRNA dynamics of CAM photosynthesis in pineapple.

The altered carbon assimilation pathway of crassulacean acid metabolism (CAM) photosynthesis results in an up to 80% higher water-use efficiency than C3 photosynthesis in plants making it a potentially useful pathway for engineering crop plants with improved drought tolerance. Here we surveyed detailed temporal (diel time course) and spatial (across a leaf gradient) gene and microRNA (miRNA) expression patterns in the obligate CAM plant pineapple [Ananas comosus (L.) Merr.]. The high-resolution transcriptome atlas allowed us to distinguish between CAM-related and non-CAM gene copies. A differential gene co-expression network across green and white leaf diel datasets identified genes with circadian oscillation, CAM-related functions, and source-sink relations. Gene co-expression clusters containing CAM pathway genes are enriched with clock-associated cis-elements, suggesting circadian regulation of CAM. About 20% of pineapple microRNAs have diel expression patterns, with several that target key CAM-related genes. Expression and physiology data provide a model for CAM-specific carbohydrate flux and long-distance hexose transport. Together these resources provide a list of candidate genes for targeted engineering of CAM into C3 photosynthesis crop species.

De novo Transcriptome Assembly and Dynamic Spatial Gene Expression Analysis in Red Clover.

Red clover ( L.) is a cool-season forage legume grown throughout the northeastern United States and is the most widely planted forage legume after alfalfa ( L.). Red clover provides high-value feed to the livestock because of high protein content and easy digestibility. To date, genomic resources for red clover are scarce. In the current study, a de novo transcriptome assembly of red clover was constructed representing different tissue types. The draft assembly consists of 37,565 contigs with N50 and average contig length of 1707 and 1262 bp, respectively. A comparative study with three other legume species displayed a high degree of sequence conservation between red clover and other legumes. The assembled transcriptome was annotated to allow identification of desirable genes. In particular, a genome-wide identification of red clover transcripts encoding putative transcription factors was performed. A comparative gene expression analysis between different tissue types was performed using the assembled transcriptome as the reference, which revealed dynamic gene expression patterns across different tissue types and also identified spatially dynamic gene coexpression clusters. Genes representing tissue-enriched clusters were subjected to gene ontology (GO) enrichment analysis to identify over-represented functional groups. Identification of these tissue-enriched gene coexpression clusters can help in future research focusing on developmental studies across tissues or in biotechnological improvement of red clover.

Identification of gene co-expression clusters in liver tissues from multiple porcine populations with high and low backfat androstenone phenotype.

BackgroundBoar taint is principally caused by accumulation of androstenone and skatole in adipose tissues. Studies have shown high heritability estimates for androstenone whereas skatole production is mainly dependent on nutritional factors. Androstenone is a lipophilic steroid mainly metabolized in liver. Majority of the studies on hepatic androstenone metabolism focus only on a single breed and very few studies account for population similarities/differences in gene expression patterns. In this work, we concentrated on population similarities in gene expression to identify the common genes involved in hepatic androstenone metabolism of multiple pig populations. Based on androstenone measurements, publicly available gene expression datasets from three porcine populations were compiled into either low or high androstenone dataset. Gene expression correlation coefficients from these datasets were converted to rank ratios and joint probabilities of these rank ratios were used to generate dataset specific co-expression clusters. Finally, these networks were clustered using a graph clustering technique.ResultsCluster analysis identified a number of statistically significant co-expression clusters in the dataset. Further enrichment analysis of these clusters showed that one of the clusters from low androstenone dataset was highly enriched for xenobiotic, drug, cholesterol and lipid metabolism and cytochrome P450 associated metabolism of drugs and xenobiotics. Literature references revealed that a number of genes in this cluster were involved in phase I and phase II metabolism. Physical and functional similarity assessment showed that the members of this cluster were dispersed across multiple clusters in high androstenone dataset, possibly indicating a weak co-expression of these genes in high androstenone dataset.ConclusionsBased on these results we hypothesize that majority of the genes in this cluster forms a signature co-expression cluster in low androstenone dataset in our experiment and that majority of the members of this cluster might be responsible for hepatic androstenone metabolism across all the three populations used in our study. We propose these results as a background work towards understanding breed similarities in hepatic androstenone metabolism. Additional large scale experiments using data from multiple porcine breeds are necessary to validate these findings.Electronic supplementary materialThe online version of this article (doi:10.1186/s12863-014-0158-8) contains supplementary material, which is available to authorized users.

Gene coexpression clusters and putative regulatory elements underlying seed storage reserve accumulation in Arabidopsis

BackgroundIn Arabidopsis, a large number of genes involved in the accumulation of seed storage reserves during seed development have been characterized, but the relationship of gene expression and regulation underlying this physiological process remains poorly understood. A more holistic view of this molecular interplay will help in the further study of the regulatory mechanisms controlling seed storage compound accumulation.ResultsWe identified gene coexpression networks in the transcriptome of developing Arabidopsis (Arabidopsis thaliana) seeds from the globular to mature embryo stages by analyzing publicly accessible microarray datasets. Genes encoding the known enzymes in the fatty acid biosynthesis pathway were found in one coexpression subnetwork (or cluster), while genes encoding oleosins and seed storage proteins were identified in another subnetwork with a distinct expression profile. In the triacylglycerol assembly pathway, only the genes encoding diacylglycerol acyltransferase 1 (DGAT1) and a putative cytosolic "type 3" DGAT exhibited a similar expression pattern with genes encoding oleosins. We also detected a large number of putative cis-acting regulatory elements in the promoter regions of these genes, and promoter motifs for LEC1 (LEAFY COTYLEDON 1), DOF (DNA-binding-with-One-Finger), GATA, and MYB transcription factors (TF), as well as SORLIP5 (Sequences Over-Represented in Light-Induced Promoters 5), are overrepresented in the promoter regions of fatty acid biosynthetic genes. The conserved CCAAT motifs for B3-domain TFs and binding sites for bZIP (basic-leucine zipper) TFs are enriched in the promoters of genes encoding oleosins and seed storage proteins.ConclusionsGenes involved in the accumulation of seed storage reserves are expressed in distinct patterns and regulated by different TFs. The gene coexpression clusters and putative regulatory elements presented here provide a useful resource for further experimental characterization of protein interactions and regulatory networks in this process.

Coexpression, coregulation, and cofunctionality of neighboring genes in eukaryotic genomes

Accumulating evidence indicates that gene order in eukaryotic genomes is not completely random and that genes with similar expression levels tend to be clustered within the same genomic neighborhoods. The mechanism behind these gene coexpression clusters is as yet unclear. In this article, plausible biochemical, genetic, evolutionary, and technological determinants of this pattern are briefly reviewed.

Nine 3-ketoacyl-CoA thiolases (KATs) and acetoacetyl-CoA thiolases (ACATs) encoded by five genes in Arabidopsis thaliana are targeted either to peroxisomes or cytosol but not to mitochondria

The sub-cellular location of enzymes of fatty acid beta-oxidation in plants is controversial. In the current debate the role and location of particular thiolases in fatty acid degradation, fatty acid synthesis and isoleucine degradation are important. The aim of this research was to determine the sub-cellular location and hence provide information about possible functions of all the putative 3-ketoacyl-CoA thiolases (KAT) and acetoacetyl-CoA thiolases (ACAT) in Arabidopsis. Arabidopsis has three genes predicted to encode KATs, one of which encodes two polypeptides that differ at the N-terminal end. Expression in Arabidopsis cells of cDNAs encoding each of these KATs fused to green fluorescent protein (GFP) at their C-termini showed that three are targeted to peroxisomes while the fourth is apparently cytosolic. The four KATs are also predicted to have mitochondrial targeting sequences, but purified mitochondria were unable to import any of the proteins in vitro. Arabidopsis also has two genes encoding a total of five different putative ACATs. One isoform is targeted to peroxisomes as a fusion with GFP, while the others display no targeting in vivo as GFP fusions, or import into isolated mitochondria. Analysis of gene co-expression clusters in Arabidopsis suggests a role for peroxisomal KAT2 in beta-oxidation, while KAT5 co-expresses with genes of the flavonoid biosynthesis pathway and cytosolic ACAT2 clearly co-expresses with genes of the cytosolic mevalonate biosynthesis pathway. We conclude that KATs and ACATs are present in the cytosol and peroxisome, but are not found in mitochondria. The implications for fatty acid beta-oxidation and for isoleucine degradation in mitochondria are discussed.