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PubMed2026

Genetic dissection of cardiac iron regulation using transcriptome network analysis and systems genetics in BXD mice.

Cardiac iron homeostasis is essential for myocardial energy metabolism and contractile function, yet the genetic and molecular mechanisms governing iron levels within the heart remain poorly understood. We used a systems genetics approach to dissect the transcriptional regulation of cardiac iron homeostasis. Myocardial iron level varies substantially across BXD strains (40-112 μg/g) and is under heritable genetic control (H2 = 0.38). Elevated cardiac iron is associated with reduced ventricular mass, increased ventricular ectopy, and prolonged atrioventricular conduction in the BXD population. Weighted gene co-expression network analysis of the BXD heart transcriptome identified a co-expression module that was significantly and negatively correlated with cardiac iron levels in both young and old BXD mice and enriched for pathways related to metabolic regulation, cyclic AMP (cAMP) signaling, circadian entrainment, and cardiovascular physiology. The module showed substantial overlap with a curated cardiac iron gene set, and cross-species enrichment analysis confirmed its conservation in human cardiomyopathy differentially expressed genes (enrichment ratio = 1.49; false discovery rate [FDR] = 0.0342). Quantitative trait locus (QTL) mapping of the first principal component of the overlapping module iron genes (n = 38), corroborated by individual gene mapping, identified trans-eQTL hotspots on multiple chromosomes, implicating Fcho2, Gcc2, and Rmdn1 as candidate upstream regulators operating through sequential steps of intracellular iron trafficking. Together, these findings establish a systems-level map of cardiac iron gene regulation, identify candidate genetic regulators, and provide a molecular framework linking disruption of iron-related transcriptional networks to structural and electrical cardiac dysfunction with implications for iron-related heart diseases.

BXD mouse population↗

PubMed2026

Potential Involvement of the IL-6/STAT3/MMP12 Signaling Axis in DMSO-Mediated Anti-Fibrotic Effects in Experimental Silicosis.

This study aims to investigate the anti-inflammatory and anti-fibrotic effects of dimethyl sulfoxide (DMSO) in a mouse model of silicosis, thereby exploring its potential therapeutic value. A mouse model of silicosis was established by intranasal instillation, and DMSO treatment was administered via intraperitoneal injection. The experiment was conducted over a period of 1 month. Lung tissues were collected from all mice; a subset was subjected to transcriptomic analysis, and differentially expressed genes were identified using the limma package. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses were conducted using ClusterProfiler to investigate gene functions and associated pathways. The remaining samples were subjected to histopathological assessment by hematoxylin and eosin staining (HE) and Masson's trichrome staining, while Western blot analysis was performed to validate transcriptomic results. This study suggests that DMSO may alleviate the fibrotic process in silicosis by modulating the IL-6/STAT3-MMP12 signaling axis. In the silica-induced silicosis mouse model, DMSO attenuated disease-associated weight loss and reduced collagen deposition. Transcriptomic analysis indicated that DMSO suppressed the activity of multiple fibrosis-related pathways and identified 51 key genes, including MMP12, which was significantly downregulated. Western blot analysis further confirmed reduced MMP12 expression, accompanied by markedly decreased levels of IL-6 and p-STAT3, suggesting the IL-6/STAT3 pathway may play a crucial role in regulating MMP12 expression. DMSO may attenuate inflammatory responses and pulmonary fibrosis in silicosis by inhibiting activation of the IL-6/STAT3 signaling pathway, thereby reducing MMP12 expression.

Animals↗

PubMed2004

Genome-wide transcriptome mapping analysis identifies organ-specific gene expression patterns along human chromosomes.

The Human Genome Project has revealed that there about 32,000 protein-encoding genes, which are distributed throughout the genome. It is unclear, however, whether genes are distributed on the chromosomes according to patterns linked to organ specificity. To explore the relationship between genes actively transcribed in normal tissues and their chromosomal locations, we analyzed serial analysis of gene expression libraries of normal human liver, brain, breast, and colon tissues. Transcriptome mapping analysis revealed that transcriptional activity in each tissue varied according to the chromosomal domains, and a weak positive correlation was observed between transcription density and gene density. We identified six liver-related and five colon-related chromosomal domains highly transcribed in each tissue, whereas no brain-related or breast-related chromosomal domains were identified. Representative genes located on these chromosomal domains were associated with the function of each organ and were highly conserved in both mouse and rat genomes. These data revealed that the transcriptional activities of normal human tissues are well orchestrated at chromosomal levels, suggesting that highly expressed genes may share physical proximity.

Chromosome Mapping↗

PubMed2026

A Practical Workflow for Spatial Transcriptomics Data Analysis: From Data Acquisition to Advanced Analyses.

Spatial transcriptomics (ST) profiles genome-wide gene expression while preserving the two-dimensional spatial context of mRNA molecules within tissue sections, enabling studies of tissue architecture and microenvironment-associated biology. However, ST analysis remains challenging because data import, quality control, integration, deconvolution, spatial statistics, and visualization often require multiple software environments and reproducible parameter choices. This protocol presents a practical computational workflow for public ST datasets in R, beginning with data acquisition and software setup and proceeding through Seurat-based data loading, quality control, normalization, multi-sample integration, clustering, and spatially variable gene analysis. The workflow then applies complementary deconvolution strategies, including reference-guided SPOTlight analysis and unsupervised STdeconvolve topic modeling, followed by Giotto-based spatial cell-cell communication analysis and interactive region-of-interest (ROI) selection using a custom Python Dash application. By emphasizing script-based execution, explicit parameter rationales, expected outputs, and troubleshooting checkpoints, the protocol provides an adaptable framework for standard array-based ST datasets and related platforms after dataset- and platform-specific parameter evaluation.

Spatial Transcriptomics↗

PubMed2026

Uncovering hub genes and key pathways responsive to drought stress in rice via meta-analysis of transcriptomic data.

Drought stress presents a formidable threat to global rice cultivation, triggering complex molecular responses that impact plant growth and productivity. To decipher the underlying gene expression dynamics, we performed a comprehensive meta-analysis of transcriptomic datasets derived from drought-tolerant rice genotypes. Via microarray data from three independent studies, we identified a set of consistently expressed differentially expressed genes (DEGs) under drought conditions. Integration of functional annotation tools, including GO and KEGG pathway enrichment, revealed key biological processes and signaling cascades involved in stress mitigation, such as ABA signaling, protein folding, and photosynthesis suppression. Protein-protein interaction (PPI) network construction, followed by hub gene identification via maximal clique centrality (MCC), highlighted pivotal regulators including LEA proteins, dehydrins, HSP70, and several transcription factors. Machine learning approaches further prioritize potential biomarkers, with Random Forest models achieving high classification accuracy and pinpointing key predictive genes. Chromosomal localization analysis provided spatial insights into the distribution of these hub genes, whose expression patterns were further compared against qRT-PCR data from previously published studies. This integrative approach identifies candidate genomic markers and mechanistic insights that may support future breeding strategies for drought-tolerant rice, pending experimental validation.

Cytoscape↗

PubMed2026

transFusion: a novel comprehensive platform for integration analysis of single-cell and spatial transcriptomics.

MOTIVATION: Understanding spatial organization, intercellular interactions, and regulatory networks within the spatial context of tissues is crucial for uncovering complex biological processes and disease mechanisms. Spatial transcriptomics technologies have revolutionized this field by enabling the spatially resolved profiling of gene expression. 10× Visium has emerged as the predominant spatial technology, but its low resolution and the complexity of integrating multimodal datasets present significant analytical challenges, particularly for researchers with limited computational and statistical expertise. Current spatial transcriptomics analysis platforms generally fall short of effectively integrating multimodal data and maximizing the utility of spatial information-such as uncovering complex cellular spatial dependencies, multimodal gradient patterns, and spatial coexpression of ligand-receptor pairs and regulatory networks related to disease or biological states-thereby limiting their ability to provide comprehensive end-to-end analytical workflows when analyzing 10× Visium data. RESULTS: To address these limitations, we developed transFusion, a novel, advanced web-based platform specializing in the most comprehensive and effective integration analysis of scRNA-seq and 10× Visium spatial transcriptomics data. transFusion offers 12 key functions, from basic visualization to advanced analyses, including intercellular dependency analysis, ligand-receptor coexpression identification and visualization, and spatial multimodal gradient variation patterns. Two case studies were used to demonstrate transFusion's capabilities in exploring tissue architecture, intercellular communication, dependency networks, and multimodal gradient variation patterns with minimal computational skills and statistical expertise. transFusion provides a flexible and powerful framework for multimodal data integration analysis. AVAILABILITY AND IMPLEMENTATION: transFusion is freely available at https://github.com/WQLin8/transFusion.

Spatial Transcriptomics↗

PubMed2006

Initial transcriptome and proteome analyses of low culture temperature-induced expression in CHO cells producing erythropoietin.

Low culture temperature is known to enhance the specific productivity of Chinese hamster ovary (CHO) cells expressing erythropoietin (EPO) (LGE10-9-27). Genomic and proteomic approaches were taken to better understand the intracellular responses of these CHO cells resulting from use of low culture temperature (33 degrees C). For transcriptome analysis, commercially available rat and mouse cDNA microarrays were used. The data obtained from the rat and mouse cDNA chips were only somewhat informative in understanding the gene expression profile of CHO cells because of their different sequence homologies with CHO transcriptomes. Overall, transcriptome analysis revealed that low culture temperature could lead to changes in gene expression in various cellular processes such as metabolism, transport, and signaling pathways. Proteome analysis was carried out using 2-D PAGE. Based on spot intensity, 60 high intensity protein spots, from a total of more than 800, were chosen for MS analysis. Forty of the 60 protein spots, which represent 26 different kinds of proteins, were identified by MALDI-TOF-MS and validated by MS/MS. Compared to the reference temperature (37 degrees C), the expression levels of seven proteins (PDI, vimentin, NDK B, ERp57, RIKEN cDNA, phosphoglycerate kinase, and heat shock cognate 71 kDa protein) were increased over twofold at 33 degrees C and those of two proteins (HSP90-beta and EF2) were decreased over twofold at 33 degrees C. Taken together, the results demonstrate the potential of combined analysis of transcriptome and proteome analyses as a tool for the systematic comprehension of cellular mechanisms in CHO cells.

Animals↗

PubMed2004

Plasticity of mouse renal collecting duct in response to potassium depletion.

Plasticity of mouse renal collecting duct in response to potassium depletion.--Renal collecting ducts are the main sites for regulation of whole body potassium balance. Changes in dietary intake of potassium induce pleiotropic adaptations of collecting duct cells, which include alterations of ion and water transport properties along with an hypertrophic response. To study the pleiotropic adaptation of the outer medullary collecting duct (OMCD) to dietary potassium depletion, we combined functional studies of renal function (ion, water, and acid/base handling), analysis of OMCD hypertrophy (electron microscopy) and hyperplasia (PCNA labeling), and large scale analysis of gene expression (transcriptome analysis). The transcriptome of OMCD was compared in mice fed either a normal or a potassium-depleted diet for 3 days using serial analysis of gene expression (SAGE) adapted for downsized extracts. SAGE is based on the generation of transcript-specific tag libraries. Approximately 20,000 tags corresponding to 10,000 different molecular species were sequenced in each library. Among the 186 tags differentially expressed (P < 0.05) between the two libraries, 120 were overexpressed and 66 were downregulated. The SAGE expression profile obtained in the control library was representative of different functional classes of proteins and of the two cell types (principal and alpha-intercalated cells) constituting the OMCD. Combined with gene expression analysis, results of functional and morphological studies allowed us to identify candidate genes for distinct physiological processes modified by potassium depletion: sodium, potassium, and water handling, hyperplasia and hypertrophy. Finally, comparison of mouse and human OMCD transcriptomes allowed us to address the question of the relevance of the mouse as a model for human physiology and pathophysiology.

Acid-Base Equilibrium↗

PubMed2025

Multiomics Analysis Reveals Therapeutic Targets for Chronic Kidney Disease With Sarcopenia.

BACKGROUND: The presence of sarcopenia in patients with chronic kidney disease (CKD) is associated with poor prognosis. The mechanism underlying CKD-induced muscle wasting has not yet been fully explored. This study investigates the influence of renal secretions on muscles using multiomics sequencing. METHODS: The kidney transcriptome analysis by RNA-seq and protein profiling by tandem mass tag (TMT), serum TMT and muscle TMT were performed in CKD established using 0.2% adenine and control mice. Spp1 recombinant protein was used to study its effect on myotube atrophy in vitro. In animal experiments on CKD, pharmacological inhibition of Spp1 was used to explore the role of Spp1 in skeletal muscle wasting. Transcriptome analysis was performed to identify differentially expressed genes (DEGs) in the gastrocnemius muscle following Spp1 pharmacological inhibition. RESULTS: In the renal transcriptome and TMT, 503 and 377 proteins/genes respectively were co-upregulated and co-downregulated. In the serum TMT of CKD and normal control (NC) mice, 22 upregulated and 7 downregulated differentially expressed proteins (DEPs) showed the same expression patterns as those in the kidney transcriptome and TMT analysis. Based on bioinformatics analysis and reported studies, we selected Spp1 for further validation. Spp1 recombinant protein was added to C2C12 myotubes in vitro, and the results indicated that Spp1 significantly increased the protein levels of the muscle atrophy marker (Murf-1) and promoted the smaller myotubes (all p < 0.05). Compared with NC mice, Spp1 mRNA and protein levels were significantly upregulated in the kidneys of CKD mice, and the serum concentration of Spp1 was also markedly increased (all p < 0.05). In animal experiments, pharmacological inhibition of Spp1 increased the weights of gastrocnemius and tibialis anterior muscles (p < 0.05) and improved muscle atrophy phenotype. Transcriptome analysis showed that DEGs in the gastrocnemius muscle following Spp1 pharmacological inhibition were enriched in protein digestion and absorption, glucagon signalling pathway, apelin signalling pathway and ECM-receptor interaction pathway. CONCLUSIONS: Our study is the first to establish a regulatory network of kidney-muscle crosstalk to explore the potential mechanism of CKD-related sarcopenia. Employing multiomics analysis, cellular assessment and animal experiments, we have identified that Spp1 could potentialy serve as a promising therapeutic target for CKD patients with sarcopenia.

Sarcopenia↗

PubMed2006

Integrated analysis of transcriptomic and proteomic data of Desulfovibrio vulgaris: zero-inflated Poisson regression models to predict abundance of undetected proteins.

MOTIVATION: Integrated analysis of global scale transcriptomic and proteomic data can provide important insights into the metabolic mechanisms underlying complex biological systems. However, because the relationship between protein abundance and mRNA expression level is complicated by many cellular and physical processes, sophisticated statistical models need to be developed to capture their relationship. RESULTS: In this study, we describe a novel data-driven statistical model to integrate whole-genome microarray and proteomic data collected from Desulfovibrio vulgaris grown under three different conditions. Based on the Poisson distribution pattern of proteomic data and the fact that a large number of proteins were undetected (excess zeros), zero-inflated Poisson (ZIP)-based models were proposed to define the correlation pattern between mRNA and protein abundance. In addition, by assuming that there is a probability mass at zero representing unexpressed genes and expressed proteins that were undetected owing to technical limitations, a Potential ZIP model was established. Two significant improvements introduced by this approach are (1) the predicted protein abundance level values for experimentally detected proteins are corrected by considering their mRNA levels and (2) protein abundance values can be predicted for undetected proteins (in the case of this study, approximately 83% of the proteins in the D.vulgaris genome) for better biological interpretation. We demonstrated the use of these statistical models by comparatively analyzing proteomic and microarray results from D.vulgaris grown on lactate-based versus formate-based media. These models correctly predicted increased expression of Ech hydrogenase and decreased expression of Coo hydrogenase for D.vulgaris grown on formate.

Adenosine Triphosphate↗

PubMed2003

SAGE analysis of transcriptome responses in Arabidopsis roots exposed to 2,4,6-trinitrotoluene.

Serial analysis of gene expression was used to profile transcript levels in Arabidopsis roots and assess their responses to 2,4,6-trinitrotoluene (TNT) exposure. SAGE libraries representing control and TNT-exposed seedling root transcripts were constructed, and each was sequenced to a depth of roughly 32,000 tags. More than 19,000 unique tags were identified overall. The second most highly induced tag (27-fold increase) represented a glutathione S-transferase. Cytochrome P450 enzymes, as well as an ABC transporter and a probable nitroreductase, were highly induced by TNT exposure. Analyses also revealed an oxidative stress response upon TNT exposure. Although some increases were anticipated in light of current models for xenobiotic metabolism in plants, evidence for unsuspected conjugation pathways was also noted. Identifying transcriptome-level responses to TNT exposure will better define the metabolic pathways plants use to detoxify this xenobiotic compound, which should help improve phytoremediation strategies directed at TNT and other nitroaromatic compounds.

ATP-Binding Cassette Transporters↗

PubMed2026

Identification of Critical Genes for Recurrent Aphthous Ulcer by Transcriptome Data Analysis and Mendelian Randomization.

PURPOSE: Recurrent aphthous ulcer (RAU) is a common oral mucosal disorder with a poorly understood etiology, significantly affecting patients' quality of life. This study aims to investigate critical genes linked to RAU and explore their biological mechanisms using transcriptomic data and Mendelian randomization (MR) analysis. MATERIALS AND METHODS: RAU-related gene expression data from the GEO database (GSE37265) were analyzed to identify differentially expressed genes (DEGs). A two-sample MR approach was used to assess the causal impact of expression quantitative trait loci (eQTL) on RAU. Critical genes were identified by intersecting DEGs with significant MR findings. GO and KEGG pathway enrichment analyses were performed, along with GSEA and immune cell infiltration analysis, to investigate the functions and mechanisms of these genes in RAU. RESULTS: A total of 184 differentially expressed genes (DEGs) were identified, while 339 RAU-associated genes were screened through MR analysis. Cross-validation further identified 7 critical genes. Among these, CCR1, ERP27, HCK, MICB, and SLC2A3 showed protective associations with RAU risk, whereas CD177 and IFITM1 were positively associated with increased risk. Enrichment analysis revealed that these genes are involved in specific biological processes, including cell migration, immune response, and metabolic regulation, which are closely linked to RAU pathogenesis. CONCLUSION: This systematic study comprehensively investigates the critical causative genes underlying RAU, emphasizing the intricate relationships between immune regulation and metabolic disturbances in its pathology. These findings lay a solid foundation for the development of novel biomarkers and may inform future research on targeted therapeutic strategies for RAU.

Stomatitis, Aphthous↗

PubMed2006

OMICS-driven biomarker discovery in nutrition and health.

While traditional nutrition research has dealt with providing nutrients to nourish populations, it nowadays focuses on improving health of individuals through diet. Modern nutritional research is aiming at health promotion and disease prevention and on performance improvement. As a consequence of these ambitious objectives, the disciplines "nutrigenetics" and "nutrigenomics" have evolved. Nutrigenetics asks the question how individual genetic disposition, manifesting as single nucleotide polymorphisms, copy-number polymorphisms and epigenetic phenomena, affects susceptibility to diet. Nutrigenomics addresses the inverse relationship, that is how diet influences gene transcription, protein expression and metabolism. A major methodological challenge and first pre-requisite of nutrigenomics is integrating genomics (gene analysis), transcriptomics (gene expression analysis), proteomics (protein expression analysis) and metabonomics (metabolite profiling) to define a "healthy" phenotype. The long-term deliverable of nutrigenomics is personalised nutrition for maintenance of individual health and prevention of disease. Transcriptomics serves to put proteomic and metabolomic markers into a larger biological perspective and is suitable for a first "round of discovery" in regulatory networks. Metabonomics is a diagnostic tool for metabolic classification of individuals. The great asset of this platform is the quantitative, non-invasive analysis of easily accessible human body fluids like urine, blood and saliva. This feature also holds true to some extent for proteomics, with the constraint that proteomics is more complex in terms of absolute number, chemical properties and dynamic range of compounds present. Apart from addressing the most complex "-ome", proteomics represents the only platform that delivers not only markers for disposition and efficacy but also targets of intervention. The Omics disciplines applied in the context of nutrition and health have the potential to deliver biomarkers for health and comfort, reveal early indicators for disease disposition, assist in differentiating dietary responders from non-responders, and, last but not least, discover bioactive, beneficial food components. This paper reviews the state-of-the-art of the three Omics platforms, discusses their implication in nutrigenomics and elaborates on applications in nutrition and health such as digestive health, allergy, diabetes and obesity, nutritional intervention and nutrient bioavailability. Proteomic developments, applications and potential in the field of nutrition have been specifically addressed in another review issued by our group.

Animals↗

PubMed2005

Screening for new metabolites from marine microorganisms.

This article gives an overview of current analysis techniques for the screening and the activity analysis of metabolites from marine (micro)organisms. The sequencing of marine genomes and the techniques of functional genomics (including transcriptome, proteome, and metabolome analyses) open up new possibilities for the screening of new metabolites of biotechnological interest. Although the sequencing of microbial marine genomes has been somewhat limited to date, selected genome sequences of marine bacteria and algae have already been published. This report summarizes the application of the techniques of functional genomics, such as transcriptome analysis in combination with high-resolution two-dimensional polyacrylamide gelelectrophoresis and mass spectrometry, for the screening for bioactive compounds of marine microorganisms. Furthermore, the target analysis of antimicrobial compounds by proteome or transcriptome analysis of bacterial model systems is described. Recent high-throughput screening techniques are explained. Finally, new approaches for the screening of metabolites from marine microorganisms are discussed.

Bacteria↗

PubMed2005

Proteomics in nutrition and health.

Proteomics, the comprehensive analysis of a protein complement in a cell, tissue or biological fluid at a given time, has been enabled by quantum leaps in mass spectrometric technology, which allowed identification of large, involatile biomolecules. Over the last two decades, this discipline evolved from the sole delivery of protein identities to a platform, which reveals clues to function through e.g. characterisation of protein modifications and interactions as well as through quantitative proteomics, i.e. the global comparison of protein amounts between two defined biological states. Proteomics is an integral part and key player in the family of -omic disciplines as there are genomics (gene analysis), transcriptomics (gene expression analysis) and metabolomics (metabolite profiling). Considering the complexity, dynamics and protein concentration range of any given proteome, proteomics is the most challenging -omic discipline and requires the most sophisticated analysis pipeline. Proteomics represents an established technology in the pharmaceutical industry mainly for biomarker and drug target discovery. The potential of proteomics for research in the food industry is increasingly being recognised and the employment of proteomic approaches to nutrition and health issues is now emerging. This review summarizes (i) major technological achievements in mass spectrometry and proteomics, (ii) deliverables of proteomics in the context of nutrition and health, and (iii) applications of proteomics, and -- if appropriate -- transcriptomics to the research fields of digestive health, obesity and diabetes, immunity and allergy, probiotics, milk, and food preference.

Complex Mixtures↗

PubMed2006

Proteomic methods in nutrition.

PURPOSE OF REVIEW: Proteomics, the comprehensive analysis of a protein complement in a cell, tissue or biological fluid at a given time, is a key player in the family of -omic disciplines, which encompass genomics (gene analysis), transcriptomics (gene expression analysis) and metabolomics (metabolite profiling). This review summarizes the state of the art of proteomics technology and puts it into perspective for food-related research. Learning from proteomic experiences in the pharmaceutical context, this article may help to translate proteomics into nutrition and health. RECENT FINDINGS: Mass spectrometric technology has progressed enormously with regard to mass accuracy, resolution and peptide sequencing power. Likewise, upstream separation, depletion and enrichment techniques now allow us to deal with the large complexity and wide dynamic range of proteomic samples more efficiently. Consequently, proteomic studies now provide a broader, but still far from complete, coverage of a given proteome. SUMMARY: Proteomics adapted and applied to the context of nutrition and health has the potential to deliver biomarkers for health and comfort, reveal early indicators of disease disposition, assist in differentiating dietary responders from non-responders, and, last but not least, discover bioactive, beneficial food components.

Biomarkers↗

PubMed2005

Genomic analysis of anaerobic respiration in the archaeon Halobacterium sp. strain NRC-1: dimethyl sulfoxide and trimethylamine N-oxide as terminal electron acceptors.

We have investigated anaerobic respiration of the archaeal model organism Halobacterium sp. strain NRC-1 by using phenotypic and genetic analysis, bioinformatics, and transcriptome analysis. NRC-1 was found to grow on either dimethyl sulfoxide (DMSO) or trimethylamine N-oxide (TMAO) as the sole terminal electron acceptor, with a doubling time of 1 day. An operon, dmsREABCD, encoding a putative regulatory protein, DmsR, a molybdopterin oxidoreductase of the DMSO reductase family (DmsEABC), and a molecular chaperone (DmsD) was identified by bioinformatics and confirmed as a transcriptional unit by reverse transcriptase PCR analysis. dmsR, dmsA, and dmsD in-frame deletion mutants were individually constructed. Phenotypic analysis demonstrated that dmsR, dmsA, and dmsD are required for anaerobic respiration on DMSO and TMAO. The requirement for dmsR, whose predicted product contains a DNA-binding domain similar to that of the Bat family of activators (COG3413), indicated that it functions as an activator. A cysteine-rich domain was found in the dmsR gene, which may be involved in oxygen sensing. Microarray analysis using a whole-genome 60-mer oligonucleotide array showed that the dms operon is induced during anaerobic respiration. Comparison of dmsR+ and DeltadmsR strains by use of microarrays showed that the induction of the dmsEABCD operon is dependent on a functional dmsR gene, consistent with its action as a transcriptional activator. Our results clearly establish the genes required for anaerobic respiration using DMSO and TMAO in an archaeon for the first time.

Anaerobiosis↗

PubMed2005

Gene expression analysis of ischemic and nonischemic cardiomyopathy: shared and distinct genes in the development of heart failure.

Cardiomyopathy can be initiated by many factors, but the pathways from unique inciting mechanisms to the common end point of ventricular dilation and reduced cardiac output are unclear. We previously described a microarray-based prediction algorithm differentiating nonischemic (NICM) from ischemic cardiomyopathy (ICM) using nearest shrunken centroids. Accordingly, we tested the hypothesis that NICM and ICM would have both shared and distinct differentially expressed genes relative to normal hearts and compared gene expression of 21 NICM and 10 ICM samples with that of 6 nonfailing (NF) hearts using Affymetrix U133A GeneChips and significance analysis of microarrays. Compared with NF, 257 genes were differentially expressed in NICM and 72 genes in ICM. Only 41 genes were shared between the two comparisons, mainly involved in cell growth and signal transduction. Those uniquely expressed in NICM were frequently involved in metabolism, and those in ICM more often had catalytic activity. Novel genes included angiotensin-converting enzyme-2 (ACE2), which was upregulated in NICM but not ICM, suggesting that ACE2 may offer differential therapeutic efficacy in NICM and ICM. In addition, a tumor necrosis factor receptor was downregulated in both NICM and ICM, demonstrating the different signaling pathways involved in heart failure pathophysiology. These results offer novel insight into unique disease-specific gene expression that exists between end-stage cardiomyopathy of different etiologies. This analysis demonstrates that transcriptome analysis offers insight into pathogenesis-based therapies in heart failure management and complements studies using expression-based profiling to diagnose heart failure of different etiologies.

Angiotensin-Converting Enzyme 2↗