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PubMed2025

Microbiology Galaxy Lab: The first community-driven gateway for reproducible and FAIR analysis of microbial data.

The explosion of microbial omics data has outpaced the ability of many researchers to analyze it, with complex tools and limited computational resources creating barriers to discovery. To address this gap, we present the Microbiology Galaxy Lab: a free, globally accessible, community-supported platform that combines state-of-the-art analytical power with user-friendly accessibility. Supported by the Galaxy and global microbiology communities, this platform integrates over 315 tool suites and 115 curated workflows, enabling comprehensive metabarcoding, (meta)genomic, (meta)transcriptomic, and (meta)proteomic data analysis within a FAIR-aligned environment. It also supports research in the health and infectious disease sectors, as well as in environmental microbiology. The platform's utility is exemplified through various use cases, including antimicrobial resistance tracking, biomarker prediction, microbiome classification, and functional annotation of key microbes. Built on reproducibility and community engagement, it supports creation, sharing, and updating of best-practice workflows. Over 35 tutorials and learning paths empower scientists, fostering an ecosystem that keeps resources at the forefront of microbial science. The Microbiology Galaxy Lab enables collective analysis, democratising research, thereby accelerating discovery across the global microbiology community (microbiology.usegalaxy.org, .eu, .org.au, .fr).

Journal Article↗
PubMed2005

A rat model of smoke inhalation injury: influence of combustion smoke on gene expression in the brain.

Acute smoke inhalation causes death and injury in victims of home and industrial fires as well as victims of combat situations. The lethal factors in combustion smoke inhalation are toxic gases and oxygen deficiency, with carbon monoxide (CO) as a primary cause of death. In survivors, inhalation of smoke can result in severe immediate and delayed neuropathologies. To gain insight into the progression of molecular events contributing to smoke inhalation sequelae in the brain, we developed a smoke inhalation rat model and conducted a genome-wide analysis of gene expression. Microarray analysis revealed a modified brain transcriptome with changes peaking at 24 h and subsiding within 7 days post-smoke. Overall, smoke inhalation down regulated genes associated with synaptic function, neurotransmission, and neurotrophic support, and upregulated genes associated with stress responses, including nitric oxide synthesis, antioxidant defenses, proteolysis, inflammatory response, and glial activation. Notably, among the affected genes, many have been previously implicated in other types of brain injury, demonstrating the usefulness of microarrays for analysis of changes in gene expression in complex insults. In accord with previously described modulations of nitric oxide homeostasis in CO poisoning, microarray analysis revealed increased brain expression of nitric oxide synthase (NOS) and NOS ligand after inhalation of smoke. Furthermore, immunostaining showed significant elevations in perivascular NOS and in protein nitration, corroborating the involvement of nitric oxide perturbations in post-smoke sequelae in the brain. Thus, the new rat model, in combination with microarray analyses, affords insight into the complex molecular pathophysiology of smoke inhalation in the brain.

Animals↗
PubMed2006

Salmonella transcriptomics: relating regulons, stimulons and regulatory networks to the process of infection.

The advent of Salmonella transcriptomics has heralded a new era for gene expression analysis of this formidable intracellular pathogen. Increasing numbers of Salmonella transcriptomic datasets will contribute to the comprehensive definition of regulons, stimulons and regulatory networks. This task has highlighted the need for sophisticated computational techniques to describe regulatory interactions.

Adaptation, Physiological↗
PubMed2026

A GWAS-derived histone H4 variant linked to ear row number reveals functional insights into the maize ZmHistone gene family.

Ear row number (ERN) is a major yield determinant in maize and a key target for breeding of high-yielding varieties. This study utilized a multi-parent population (MPP) of 780 recombinant inbred lines (RILs) derived from seven inbred lines across three environments. Genotyping-by-sequencing (GBS) of the MPP yielded 638,646 high-quality SNPs. Using genome-wide association study (GWAS), we detected 80 significant SNPs including S2-15316355 and S4-224453431, which were consistently detected in all environments and best linear unbiased prediction (BLUP) analysis. A linkage disequilibrium-defined ±20 kb window around these two lead SNPs contained three positional candidate genes: Zm00001eb072840, Zm00001eb072850 and Zm00001eb202890. Zm00001eb072850 (ZmHistone12), a histone H4 variant, was prioritized for hypothesis-driven follow-up because the lead SNP lies within its coding sequence and the gene is expressed in ear-related tissues. Additionally, we identified 91 ZmHistone genes in the maize genome and described their phylogeny, promoter motif and expression patterns. Public transcriptome and qRT-PCR analysis in seven parental lines provide descriptive evidence of Histone variant genes in maize ear development. These results suggest a potential involvement of chromatin-associated regulation of ERN in maize and provide a foundation for future functional validation.

Ear development↗
PubMed2026

Genome-wide epigenomic atlas and multi-omics responses of Eriocheir sinensis to natural extreme heat.

BACKGROUND: Global climate warming has led to increasingly frequent and prolonged extreme summer heat events, posing severe environmental challenges to aquaculture systems. Extreme summer heat can disrupt the performance of pond-cultured ectotherms. The Chinese mitten crab (Eriocheir sinensis) is an economically important freshwater crustacean, but coordinated molecular differences following contrasting natural summers remain incompletely characterized. RESULTS: We performed a comprehensive multi-omics analysis integrating meteorological monitoring, mRNA/lncRNA transcriptomics, small-RNA profiling of miRNAs, DNA methylomics, and LC-MS metabolomics in E. sinensis populations collected from Yancheng, China, between 2020 and 2024. Across the ten farms, survival was significantly lower in 2024, whereas yield and the proportion of large individuals showed nonsignificant downward trends. Gene-set analyses showed negative enrichment of cellular heat-response, protein-folding, oxidative-phosphorylation, and mitochondrial ATP-production terms in the 2024 cohort at the time of sampling. The integrated transcript annotation contained 72,240 lncRNAs and 63,833 mRNAs, and CpG was the predominant methylation context. Differential methylation analysis identified 73 regions and 185 cytosines, with hypomethylated events predominating within the significant subset. Metabolomic profiles differed between annual cohorts and mapped to carbohydrate, lipid, and amino-acid pathways. Cross-omics integration prioritized eight candidate genes-ADCY9, UNC79, UBN1, IFT52, ACO2, LOC126986070, LOC127001126, and LOC126997895-and qPCR reproduced the reported directions of expression for selected RNAs. CONCLUSION: This study provides the first integrative multi-omics framework for understanding chronic heat adaptation in E. sinensis. By linking transcriptomic, epigenomic, and metabolic remodeling, we elucidate the molecular mechanisms underlying energy imbalance, epigenetic reprogramming, and immune dysregulation during prolonged thermal stress. These findings offer valuable insights and genomic resources for breeding heat-tolerant crab strains and improving aquaculture resilience under ongoing climate change.

DNA methylation↗
PubMed2004

Different patterns of TCR beta chain regulation following allo- and xeno-transplantation.

BACKGROUND: In the concordant hamster-to-rat cardiac xenograft model, recipients treated with cobra venom factor for the first 10 days following transplantation and daily with Cyclosporine A (CsA) do not reject their grafts. However, when CsA is withdrawn on day 40, an acute cellular rejection occurs within 4 +/- 1 days. Allografts performed in the same conditions are rejected within 18 +/- 4 days. METHODS: In this model, we have compared graft infiltrating T cells through both a quantitative (number of Vbeta transcripts) and qualitative (CDR3 length distribution) assessment of the T cell receptor (TCR) beta chain transcriptome in allo- and xeno-transplantations. RESULTS: We report striking differences in TCR usage at day 15 following allo- and xeno-transplantation as well as during rejection following CsA withdrawal. The number of Vbeta transcripts was high in both rejected allo- and xenografts. However, whereas in xenografts acute rejection occurred without skewing of Vbeta CDR3 length distribution, T cells infiltrating allografts during rejection after CsA interruption had a highly altered CDR3 length distribution pattern. In addition, using a correspondence factor analysis of the beta chain transcriptome, we show that some families can clusterize and can discriminate allo- or xeno-patterns at the level of both the number of Vbeta transcripts and the CDR3 length distribution. CONCLUSIONS: Our data show that, in vivo, even in the hamster-to-rat concordant combination, the anti-xenograft T cell response is strong and will likely represent another challenge for xenotransplantation.

Animals↗
PubMed2026

Genus-Wide Pan-Genome Analysis of Populus bZIP Transcription Factors with Reanalysis of Public Salt-Stress Transcriptomes.

Basic leucine zipper (bZIP) transcription factors regulate plant development and stress responses, but their genus-wide diversity in Populus remains unclear. We analyzed 19 Populus genomes and retained 1764 bZIP proteins, including 21 independent new loci and four annotation corrections. Of these, 1762 were assigned to 79 orthologous gene groups (OGGs), comprising 43 core, 20 soft-core, 15 shell and one cloud OGG, of which 59 showed copy-number variation. Phylogenetic analysis assigned 74 representative pangenes to 13 subfamilies, with five remaining unclassified and motif patterns differing among subfamilies. Whole-genome duplication (WGD)/segmental duplication accounted for 81.0% of OGG-assigned proteins and contributed predominantly to the conserved component. Although 72.2% of bZIP proteins overlapped a transposable element within the gene body or 2-kb flanks, this proportion was modestly lower than in matched non-bZIP genes, and copy-number-variable OGGs showed no greater TE coverage than invariant OGGs. Among retained homologous comparisons, 97.6% had Ka/Ks ≤ 1, supporting predominant purifying selection. Across the heterogeneous public salt-stress RNA-seq datasets analyzed, no OGG showed a significant, directionally concordant response in at least two Populus taxa. These results reveal a conserved bZIP framework shaped mainly by ancient duplication alongside variable genomic contexts and transcriptional responses.

Populus↗
PubMed2025

LungGENIE: the lung gene-expression and network imputation engine.

BACKGROUND: Few cohorts have study populations large enough to conduct molecular analysis of ex vivo lung tissue for genomic analyses. Transcriptome imputation is a non-invasive alternative with many potential applications. We present a novel transcriptome-imputation method called the Lung Gene Expression and Network Imputation Engine (LungGENIE) that uses principal components from blood gene-expression levels in a linear regression model to predict lung tissue-specific gene-expression. METHODS: We use paired blood and lung RNA sequencing data from the Genotype-Tissue Expression (GTEx) project to train LungGENIE models. We replicate model performance in a unique dataset, where we generated RNA sequencing data from paired lung and blood samples available through the SUNY Upstate Biorepository (SUBR). We further demonstrate proof-of-concept application of LungGENIE models in an independent blood RNA sequencing data from the Genetic Epidemiology of COPD (COPDGene) study. RESULTS: We show that LungGENIE prediction accuracies have higher correlation to measured lung tissue expression compared to existing cis-expression quantitative trait loci-based methods (median Pearson's r = 0.25, IQR 0.19-0.32), with close to half of the reliably predicted transcripts being replicated in the testing dataset. Finally, we demonstrate significant correlation of differential expression results in chronic obstructive pulmonary disease (COPD) from imputed lung tissue gene-expression and differential expression results experimentally determined from lung tissue. CONCLUSION: Our results demonstrate that LungGENIE provides complementary results to existing expression quantitative trait loci-based methods and outperforms direct blood to lung results across internal cross-validation, external replication, and proof-of-concept in an independent dataset. Taken together, we establish LungGENIE as a tool with many potential applications in the study of lung diseases.

Humans↗
PubMed2007

NMR and cDNA array analysis prior to heart failure reveals an increase of unsaturated lipids, a glutamine/glutamate ratio decrease and a specific transcriptome adaptation in obese rat heart.

Obesity is a risk factor for heart failure through a set of hemodynamic and hormonal adaptations, but its contribution at the molecular level is not clearly known. Therefore, we investigated the kinetic cardiac transcriptome and metabolome in the Spontaneous Hypertensive Heart Failure (SHHF) rat. The SHHF rat is devoid of leptin signaling when homozygous for a mutation of the leptin receptor (ObR) gene. The ObR-/- SHHF rat is obese at 4 months of age and prone to heart failure after 14 months whereas its lean counterpart ObR-/+ is prone to heart failure after 16 months. We used a set of rat pangenomic high-density macroarrays to monitor left ventricle cardiac transcriptome regulation in 4- and 10-month-old, lean and obese animals. Comparative analysis of left ventricle of 4- and 10-month-old lean rat revealed 222 differentially expressed genes while 4- and 10-month-old obese rats showed 293 differentially expressed genes. (1)H NMR analysis of the metabolome of left ventricular extracts displayed a global decrease of metabolites, except for taurine, and lipid concentration. This may be attributed to gene expression regulation and likely increased extracellular mass. The glutamine to glutamate ratio was significantly lower in the obese group. The relative unsaturation of lipids increased in the obese heart; in particular, omega-3 lipid concentration was higher in the 10-month-old obese heart. Overall, several specific kinetic molecular patterns act as a prelude to heart failure in the leptin signaling deficient SHHF obese rat.

Adaptation, Biological↗
PubMed2003

Can transcriptome size be estimated from SAGE catalogs?

MOTIVATION: SAGE (Serial Analysis of Gene Expression) can be used to estimate the number of unique transcripts in a transcriptome. A simple estimator that corrects for sequencing and sampling errors was applied to a SAGE library (137 832 tags) obtained from mouse embryonic stem cells, and also to Monte Carlo simulated libraries generated using assumed distributions of 'true' expression levels consistent with the data. RESULTS: When the corrected data themselves were taken as the underlying model of 'ground truth', the estimator converged to the 'true' value (53 535) only after counting 300 000 simulated tags, more than twice the number in the experiment. The SAGE data could also be well fit by a Monte Carlo model based on a truncated inverse-square distribution of expression levels, with 130 000 'true' transcripts and 10(6) samples needed for convergence. We conclude that the size of a transcriptome is ill-determined from SAGE libraries of even moderately large size. In order to obtain a valid estimate, one must sample a number of tags inversely proportional to the lowest abundance level, which is not known a priori. This constrains the design of SAGE experiments intended to determine biological complexity. AVAILABILITY: The 'homemade' software used for this analysis was not designed for general or 'production' use, but the authors will be happy to share Fortran sourcecode with interested parties. CONTACT: sternm@grc.nia.nih.gov

Algorithms↗
PubMed2004

Kinome profiling for studying lipopolysaccharide signal transduction in human peripheral blood mononuclear cells.

The DNA array technique allows comprehensive analysis of the genome and transcriptome, but the high throughput array-based assessment of intracellular signal transduction remains troublesome. The goal of this study was to test a new peptide array technology for studying the activity of all kinases of whole cell lysates, the kinome. Cell lysates from human peripheral blood mononuclear cells before and after stimulation with lipopolysaccharide were used for in vitro phosphorylation with [gamma-33P]ATP arrays consisting of 192 peptides (substrates for kinases) spotted on glass. The usefulness of peptide arrays for studying signal transduction was demonstrated by the generation of the first comprehensive description of the temporal kinetics of phosphorylation events induced by lipopolysaccharide stimulation. Furthermore analysis of the signals obtained suggested activation of p21Ras by lipopolysaccharide, and this was confirmed by direct measurement of p21Ras GTP levels in lipopolysaccharide-stimulated human peripheral blood mononuclear cells, which represents the first direct demonstration of p21Ras activation by stimulation of a Toll receptor family member. Further confidence in the usefulness of peptide array technology for studying signal transduction came from Western blot analysis of lipopolysaccharide-stimulated cells, which corroborated the signals obtained using peptide arrays as well as from the demonstration that kinase inhibitors effected peptide array phosphorylation patterns consistent with the expected action of these inhibitors. We conclude that this first metabolic array is a useful method to determine the enzymatic activities of a large group of kinases, offering high throughput analysis of cellular metabolism and signal transduction.

Amino Acid Sequence↗
PubMed2002

Temperature-regulated transcription in the pathogenic fungus Cryptococcus neoformans.

The basidiomycete fungus Cryptococcus neoformans is an opportunistic pathogen of worldwide importance that causes meningitis, leading to death in immunocompromised individuals. Unlike many basidiomycete fungi, C. neoformans is thermotolerant, and its ability to grow at 37 degrees C is considered to be a virulence factor. We used serial analysis of gene expression (SAGE) to characterize the transcriptomes of C. neoformans strains that represent two varieties with different polysaccharide capsule serotypes. These include a serotype D strain of the C. neoformans variety neoformans and a serotype A strain of variety grubii. In this report, we describe the construction and characterization of SAGE libraries from each strain grown at 25 degrees C and 37 degrees C. The SAGE data reveal transcriptome differences between the two strains, even at this early stage of analysis, and identify sets of genes with higher transcript levels at 25 degrees C or 37 degrees C. Notably, growth at the lower temperature increased transcript levels for histone genes, indicating a general influence of temperature on chromatin structure. At 37 degrees C, we noted elevated transcript levels for several genes encoding heat shock proteins and translation machinery. Some of these genes may play a role in temperature-regulated phenotypes in C. neoformans, such as the adaptation of the fungus to growth in the host and the dimorphic transition between budding and filamentous growth. Overall, this work provides the most comprehensive gene expression data available for C. neoformans; this information will be a critical resource both for gene discovery and genome annotation in this pathogen.

Blotting, Northern↗
PubMed2026

Advances in the diagnosis and classification of B-ALL: comparative insights from updated guidelines.

Accurate molecular classification is essential for diagnosis, risk stratification, and treatment selection in B-cell lymphoblastic leukemia (B-ALL). In this study, we performed a comprehensive, real-world reclassification of 1015 consecutively diagnosed B-ALL patients using the fifth edition of the World Health Organization Classification of Haematolymphoid Tumours (WHO-HAEM5) and the International Consensus Classification (ICC). An integrative genomic strategy that combined whole transcriptome sequencing, fusion detection, mutational analysis, and cytogenetics enabled reclassification according to both the WHO-HAEM5 and ICC frameworks, thereby substantially reducing the proportion of unclassifiable B-ALL from 41.9% (2016 WHO revision [WHO-HAEM4R]) to 15.9% (WHO-HAEM5) and 11.9% (ICC). Distinct clinical and prognostic features were identified across newly defined subtypes. Multivariable analysis confirmed that this genomic classification is a robust, independent predictor of survival after adjusting for age, minimal residual disease status, and transplant intervention. Specifically, HLF-rearranged and MEF2D-rearranged B-ALL conferred a persistently poor prognosis across all age groups despite allogeneic hematopoietic stem cell transplantation, highlighting an urgent need for novel therapeutic strategies. Gene expression profiling resolved cryptic subtypes, including ETV6::RUNX1-like, ZNF384-rearranged-like, and BCR::ABL1-like B-ALL, and uncovered diagnostic ambiguity in patients with concurrent lesions. In addition, we report emerging high-risk groups, including IDH1/2- and ZEB2 Q1072-mutated B-ALL, that may warrant recognition as distinct molecular entities. Our findings demonstrate the clinical use of integrative transcriptomic profiling in refining B-ALL taxonomy in guiding risk-adapted therapies and informing future revisions of diagnostic standards. This study supports the incorporation of high-throughput molecular diagnostics into routine leukemia classification and precision treatment planning.

Humans↗
PubMed2005

A simple approach for estimating gene expression in Candida albicans directly from a systemic infection site.

Gene expression analysis after the host-pathogen interaction is revolutionizing our understanding of the host response to infection. Numerous studies have utilized microarray analysis to follow host cell transcriptome alterations in response to interactions with infectious pathogens. However, similar analyses of pathogen transcriptional adaptation at the infection site have been limited. Understanding the nature of this interaction from the pathogen perspective at different sites and stages of infection is central to strategies for development of new anti-infective therapies. Toward this end, we developed a protocol to analyze changes in gene expression for a eukaryotic pathogen, Candida albicans, during systemic infection in mice. The experimental approach takes advantage of the resistance of the cell wall of many fungal pathogens to cell lysis, relative to mammalian cells. After lysis of mammalian cells, the tissue mixture containing fungal cells is depleted of mammalian RNA by centrifugation, followed by enzymatic digestion. RNA-digesting enzymes are then inhibited before eukaryotic cell lysis and RNA isolation. The protocol provides a reproducible quantity of RNA based on pathogen cell number. The quality of the RNA allowed reliable downstream transcriptional analysis using reverse-transcription polymerase chain reaction and microarrays. The in vivo gene expression data confirmed involvement of several putative pathogenesis genes. More importantly, the results provided a wealth of biologically interesting hypotheses to direct future investigation.

Animals↗
PubMed2002

Gene array analysis and the liver.

Functional genomics methods promise a previously unparalleled high-throughput examination of intrahepatic gene expression. Profiling transcriptomes as well as examining the coordinate expression of many genes in diverse pathobiologic pathways is now possible with techniques such as gene array analysis. However, the nature of the hepatic transcriptome, limitations of the functional genomics methodologies used, and analysis of the data generated are often poorly understood. Further, the use of replicates during analysis and the downstream confirmation of results using a supplemental methodology are important but frequently neglected aspects of experimental design. This review presents an overview of functional genomics methodologies in the organ-specific context of studying intrahepatic gene expression. The focus of the review is the most widely used functional genomic methodology, gene array analysis. The nature of the hepatic transcriptome is discussed, together with a summary of gene array analysis methodologies. Analysis of data generated by gene array analysis, determination of intrahepatic cellular origin of transcript expression, and studies of intrahepatic gene expression using gene array analysis are also reviewed. Finally, the importance of a coordinate experimental approach examining both liver protein and mRNA expression is presented.

Humans↗
PubMed2005

The Arabidopsis leaf as a model system for investigating the role of cell cycle regulation in organ growth.

The role of cell cycle regulation and cell division in plant growth and organ development is controversial. Some experimental data are most easily interpreted from the 'cellular perspective' that cell division drives growth, whereas other observations are more consistent with the 'organismal perspective' that cell division is merely a consequence of growth, and to a large extent facultative. Here we develop a model of cell cycle regulation in the context of leaf development based on literature, published kinematic analysis, flow-cytometric and transcriptomic data obtained from growing Arabidopsis leaves. We tested this model by comparing the in silico inhibition of the cell cycle progression with the experimental observations of transgenic plants overexpressing the cell cycle inhibitor Arath;KRP2. The model simulates the behaviour of proliferating cells quite well, but is inadequate in describing the effects on expanding cells. This may point to a difference in the nature of the expansion process during the proliferating and non-dividing phase of leaf development.

Arabidopsis↗
PubMed2006

Tale of two metal reducers: comparative proteome analysis of Geobacter sulferreducens PCA and Shewanella oneidensis MR-1.

Geobacter sulfurreducens and S. oneidensis are the subjects of intense research efforts due to their potential applications to bioremediation. The characterization of their proteomes, being done in parallel with the analysis of their genome sequences, transcriptomes, and metabolomes, is providing valuable insights to both their similarities and their differences. A primary target of interest in the proteomes of both of these metal-reducing microbes is the characterization of their c-type cytochromes. The discovery of their full compliment of c-type cytochromes and the description of what growth conditions trigger their expression is central to harnessing their bioremediation potential. Proteome analyses thus far show that both G. sulfurreducens and S. oneidensis share the common location of a majority of their c-type cytochromes in their outer membranes. The c-type cytochromes of G. sulfurreducens, however, appear to be less soluble and therefore more difficult to isolate from the membranes than those expressed by S. oneidensis. The majority of the G. sulfurreducens c-type cytochromes also differ from those of S. oneidensis in that they have higher isoelectric points, most higher than pH 8.0. These characteristics of solubility and isoelectric point could be related and could indicate an underlying functional difference in the strategy for metal reduction between these two microbes. The global proteome results available for G. sulfurreducens and S. oneidensis at the time of this writing are primarily the result of 2DE analysis coupled to protein identification by LC-MS/MS of tryptic peptides from in-gel digests and represent the most abundant proteins detected by Coomassie blue or silver nitrate staining. Currently, several complimentary efforts utilising the 2D-LC-MS/MS approaches are in progress, promising a more complete protein inventory for these microbes in the near future. As these data are added to those already available, the intricate network of metabolic processes, regulation of protein synthesis and protein function, transport of nutrients, and signal transduction will be elucidated. The existing tools of proteomics will be complimented with newer methods such as protein chips and phage display to further characterize these microbial systems. The end result, in the not too distant future, will be predictive models of G. sulfurreducens and S. oneidensis behavior in their natural habitats under a variety of environmental conditions.

Bacterial Proteins↗
PubMed2003

Non-random distribution of GATC sequences in regions of promoters stimulated by the SeqA protein of Escherichia coli.

The SeqA protein of Escherichia coli is not only the main negative regulator of DNA replication initiation but also a specific transcription factor. It binds to hemimethylated GATC sequences and, with somewhat different specificity, to fully methylated GATC regions. Recently, a microarray analysis was reported, in which transcriptomes of wild-type and DeltaseqA strains were compared. Although in the seqA mutant the levels of some transcripts were significantly decreased while certain transcripts were evidently more abundant relative to wild-type bacteria, no correlation between the presence of GATC motifs in promoter sequences and transcription activity was found. However, here we show that when larger DNA fragments, encompassing positions from -250 to +250 relative to the transcription start site, are analyzed, some common features of GATC distribution near the promoters activated by SeqA can be demonstrated. Nevertheless, it seems that the GATC pattern is not the only determinant of SeqA-dependence of promoter activity.

Bacterial Outer Membrane Proteins↗