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HutC/FarR-like bacterial transcription factors of the GntR family contain a small molecule-binding domain of the chorismate lyase fold.

Numerous bacterial transcription factors contain a DNA-binding helix-turn-helix domain and a signaling domain, linked together in a single polypeptide. Typically, this signaling domain is a small-molecule-binding domain that undergoes a conformational change upon recognizing a specific ligand. The HutC/FarR-like transcription factors of the GntR family are one of the largest groups of transcription factors in the proteomes of most free-living bacteria. Using sensitive sequence profile analysis we show that the HutC/FarR-like transcription factors contain a conserved ligand-binding domain, which possesses the same fold as chorismate lyase (Escherichia coli UbiC gene product). This relationship suggests that the C-terminal domain of the HutC/FarR-like transcription factors binds small molecules in a cleft similar to the substrate-binding site of the chorismate lyases. The sequence diversity within the predicted binding cleft of the HutC/FarR ligand-binding domains is consistent with the ability of these transcription factors to respond to diverse small molecules, such as histidine (HutC), fatty acids (FarR), sugars (TreR) and alkylphosphonate (PhnF). UbiC-like chorismate lyases function in the ubiquinone biosynthesis pathway, and have characteristic charged, catalytic residues. Genome comparisons reveal that chorismate lyase orthologs are found in several bacteria, chloroplasts of eukaryotic algae and euryarchaea. In contrast, the GntR transcription regulators lack the conserved catalytic residues of the chorismate lyases, and have so far been detected only in bacteria. An ancestral, generic small-molecule-binding domain appears to have given rise to the enzymatic and non-catalytic ligand-binding versions of the same fold under the influence of different selective pressures.

Archaea↗

Cellular effect of high doses of silica-coated quantum dot profiled with high throughput gene expression analysis and high content cellomics measurements.

Quantum dots (Qdots) are now used extensively for labeling in biomedical research, and this use is predicted to grow because of their many advantages over alternative labeling methods. Uncoated Qdots made of core/shell CdSe/ZnS are toxic to cells because of the release of Cd2+ ions into the cellular environment. This problem has been partially overcome by coating Qdots with polymers, poly(ethylene glycol) (PEG), or other inert molecules. The most promising coating to date, for reducing toxicity, appears to be PEG. When PEG-coated silanized Qdots (PEG-silane-Qdots) are used to treat cells, toxicity is not observed, even at dosages above 10-20 nM, a concentration inducing death when cells are treated with polymer or mercaptoacid coated Qdots. Because of the importance of Qdots in current and future biomedical and clinical applications, we believe it is essential to more completely understand and verify this negative global response from cells treated with PEG-silane-Qdots. Consequently, we examined the molecular and cellular response of cells treated with two different dosages of PEG-silane-Qdots. Human fibroblasts were exposed to 8 and 80 nM of these Qdots, and both phenotypic as well as whole genome expression measurements were made. PEG-silane-Qdots did not induce any statistically significant cell cycle changes and minimal apoptosis/necrosis in lung fibroblasts (IMR-90) as measured by high content image analysis, regardless of the treatment dosage. A slight increase in apoptosis/necrosis was observed in treated human skin fibroblasts (HSF-42) at both the low and the high dosages. We performed genome-wide expression array analysis of HSF-42 exposed to doses 8 and 80 nM to link the global cell response to a molecular and genetic phenotype. We used a gene array containing approximately 22,000 total probe sets, containing 18,400 probe sets from known genes. Only approximately 50 genes (approximately 0.2% of all the genes tested) exhibited a statistically significant change in expression level of greater than 2-fold. Genes activated in treated cells included those involved in carbohydrate binding, intracellular vesicle formation, and cellular response to stress. Conversely, PEG-silane-Qdots induce a down-regulation of genes involved in controlling the M-phase progression of mitosis, spindle formation, and cytokinesis. Promoter analysis of these results reveals that expression changes may be attributed to the down-regulation of FOXM and BHLB2 transcription factors. Remarkably, PEG-silane-Qdots, unlike carbon nanotubes, do not activate genes indicative of a strong immune and inflammatory response or heavy-metal-related toxicity. The experimental evidence shows that CdSe/ZnS Qdots, if appropriately protected, induce negligible toxicity to the model cell system studied here, even when exposed to high dosages. This study indicates that PEG-coated silanized Qdots pose minimal impact to cells and are a very promising alternative to uncoated Qdots.

Cell Line↗

Long noncoding RNA LIRIL2R modulates FOXP3 levels and suppressive function of human CD4+ regulatory T cells by regulating IL2RA.

Regulatory T cells (Tregs) are central in controlling immune responses, and dysregulation of their function can lead to autoimmune disorders or cancer. Despite extensive studies on Tregs, the basis of epigenetic regulation of human Treg development and function is incompletely understood. Long intergenic noncoding RNAs (lincRNA)s are important for shaping and maintaining the epigenetic landscape in different cell types. In this study, we identified a gene on the chromosome 6p25.3 locus, encoding a lincRNA, that was up-regulated during early differentiation of human Tregs. The lincRNA regulated the expression of interleukin-2 receptor alpha (IL2RA), and we named it the lincRNA regulator of IL2RA (LIRIL2R). Through transcriptomics, epigenomics, and proteomics analysis of LIRIL2R-deficient Tregs, coupled with global profiling of LIRIL2R binding sites using chromatin isolation by RNA purification, followed by sequencing, we identified IL2RA as a target of LIRIL2R. This nuclear lincRNA binds upstream of the IL2RA locus and regulates its epigenetic landscape and transcription. CRISPR-mediated deletion of the LIRIL2R-bound region at the IL2RA locus resulted in reduced IL2RA expression. Notably, LIRIL2R deficiency led to reduced expression of Treg-signature genes (e.g., FOXP3, CTLA4, and PDCD1), upregulation of genes associated with effector T cells (e.g., SATB1 and GATA3), and loss of Treg-mediated suppression.

Humans↗

Bacterial stress responses lower mRNA-protein level correlations.

Diverse bacterial pathogens have evolved complex regulatory mechanisms to adapt to various environmental stresses during infection. The uncertainty in mRNA-protein levels in response to environmental stressors complicates our understanding of bacterial physiology and their adaptation to stressful environments. To examine this issue, we have integrated transcriptomics and proteomics data on three human bacterial pathogens: Salmonella enterica Typhimurium, Yersinia pseudotuberculosis, and Staphylococcus aureus under 10 infection-relevant stress conditions. We observed positive correlations between mRNA and protein levels, which were decreased under different stress conditions. Essential genes exhibited higher expression levels with lower variation across the conditions and stronger mRNA-protein correlations compared to nonessential genes, highlighting their critical role in bacterial adaptability and survival. Moreover, we identified a substantial number of genes with stress-induced noncorrelating mRNA-protein levels, particularly under conditions triggering strong stress responses. Particularly this level was dramatically lowered for osmotic stress-specific genes affected by impaired translational activity under osmotic stress. Our findings highlight the prevalence of noncorrelating mRNA-protein levels and the potential role of posttranslational modifications in modulating protein levels in response to environmental stressors during infection. This study provides a comprehensive framework for integrating transcriptomics and proteomics data and identifies potential gene products that might significantly impact the ability of diverse bacterial pathogens to adapt to hostile infection environments.

RNA, Messenger↗

Dynamic profiling of the post-translational modifications and interaction partners of epidermal growth factor receptor signaling after stimulation by epidermal growth factor using Extended Range Proteomic Analysis (ERPA).

In a recent report, we introduced Extended Range Proteomic Analysis (ERPA), an intermediate approach between top-down and bottom-up proteomics, for the comprehensive characterization at the trace level (fmol level) of large and complex proteins. In this study, we extended ERPA to determine quantitatively the temporal changes that occur in the tyrosine kinase receptor, epidermal growth factor receptor (EGFR), upon stimulation. Specifically A 431 cells were stimulated with epidermal growth factor after which EGFR was immunoprecipitated at stimulation times of 0, 0.5, 2, and 10 min as well as 4 h. High sequence coverage was obtained (96%), and methods were developed for label-free quantitation of phosphorylation and glycosylation. A total of 13 phosphorylation sites were identified, and the estimated stoichiometry was determined over the stimulation time points, including Thr(P) and Ser(P) sites in addition to Tyr(P) sites. A total of 10 extracellular domain N-glycan sites were also identified, and major glycoforms at each site were quantitated. No change in the extent of glycosylation with stimulation was observed as expected. Finally potential binding partners to EGFR were identified based on changes in the amount of protein pulled down with EGFR as a function of time of stimulation. Many of the 19 proteins identified are known binding partners of EGFR. This work demonstrates that comprehensive characterization provides a powerful tool to aid in the study of important therapeutic targets. The detailed molecular information will prove useful in future studies in tissue.

Amino Acid Sequence↗

Flux balance analysis in the era of metabolomics.

Flux balance analysis (FBA) has emerged as an effective means to analyse biological networks in a quantitative manner. Much progress has been made on the extension of FBA to incorporate a priori biological knowledge, provide more practical descriptions of observed cell behaviours, and predict the outcome of network perturbations. Metabolomics is independently advancing as a set of high-throughput data acquisition tools providing dynamic profiles of metabolites in an unbiased manner. These data sets are neither yet sufficiently comprehensive nor accurate enough for generating large-scale kinetic models. Thus, there is a pressing need to develop quantitative techniques that can make use of the emerging data and embrace the associated uncertainties. This article reviews recent advances in FBA to meet this need and discusses the utility of FBA as a complement to metabolomics and the expected synergy as a result of combining these two techniques.

Animals↗

Phylogenomic inference of protein molecular function: advances and challenges.

MOTIVATION: Protein families evolve a multiplicity of functions through gene duplication, speciation and other processes. As a number of studies have shown, standard methods of protein function prediction produce systematic errors on these data. Phylogenomic analysis--combining phylogenetic tree construction, integration of experimental data and differentiation of orthologs and paralogs--has been proposed to address these errors and improve the accuracy of functional classification. The explicit integration of structure prediction and analysis in this framework, which we call structural phylogenomics, provides additional insights into protein superfamily evolution. RESULTS: Results of protein functional classification using phylogenomic analysis show fewer expected false positives overall than when pairwise methods of functional classification are employed. We present an overview of the motivations and fundamental principles of phylogenomic analysis, new methods developed for the key tasks, benchmark datasets for these tasks (when available) and suggest procedures to increase accuracy. We also discuss some of the methods used in the Celera Genomics high-throughput phylogenomic classification of the human genome. AVAILABILITY: Software tools from the Berkeley Phylogenomics Group are available at http://phylogenomics.berkeley.edu

Algorithms↗

A rapid method for computationally inferring transcriptome coverage and microarray sensitivity.

MOTIVATION: There are many different gene expression technologies, including cDNA and oligo-based microarrays, SAGE and MPSS. For each organism of interest, coverage of the transcriptome and the genome will be different. We address the question of what level of coverage is required to exploit the sensitivity of the different technologies, and what is the sensitivity of the different approaches in the experimental study. RESULTS: We estimate the transcriptome coverage by randomly sampling transcripts from a pre-defined tag-to-gene mapping function. For a given microarray experiment, we locate the thresholds in intensities that define the distribution of transcript abundance. These values are compared against the distribution obtained by applying the same thresholds to the intensities from differentially expressed genes. The ratio of these two distributions meets at the equilibrium defining sensitivity. We conclude that a collection of approximately 340,000 sequences is adequate for microarrays, but not large enough for maximum utilization of tag-based technologies. In the absence of large-scale sequencing, the majority of the tags detected by the latter approaches will remain unidentified until the genome sequence is available.

Algorithms↗

Filling gaps in a metabolic network using expression information.

MOTIVATION: The metabolic models of both newly sequenced and well-studied organisms contain reactions for which the enzymes have not been identified yet. We present a computational approach for identifying genes encoding such missing metabolic enzymes in a partially reconstructed metabolic network. RESULTS: The metabolic expression placement (MEP) method relies on the coexpression properties of the metabolic network and is complementary to the sequence homology and genome context methods that are currently being used to identify missing metabolic genes. The MEP algorithm predicts over 20% of all known Saccharomyces cerevisiae metabolic enzyme-encoding genes within the top 50 out of 5594 candidates for their enzymatic function, and 70% of metabolic genes whose expression level has been significantly perturbed across the conditions of the expression dataset used. AVAILABILITY: Freely available (in Supplementary information).

Algorithms↗

Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles.

MOTIVATION: Gene expression profiling experiments in cell lines and animal models characterized by specific genetic or molecular perturbations have yielded sets of genes annotated by the perturbation. These gene sets can serve as a reference base for interrogating other expression datasets. For example, a new dataset in which a specific pathway gene set appears to be enriched, in terms of multiple genes in that set evidencing expression changes, can then be annotated by that reference pathway. We introduce in this paper a formal statistical method to measure the enrichment of each sample in an expression dataset. This allows us to assay the natural variation of pathway activity in observed gene expression data sets from clinical cancer and other studies. RESULTS: Validation of the method and illustrations of biological insights gleaned are demonstrated on cell line data, mouse models, and cancer-related datasets. Using oncogenic pathway signatures, we show that gene sets built from a model system are indeed enriched in the model system. We employ ASSESS for the use of molecular classification by pathways. This provides an accurate classifier that can be interpreted at the level of pathways instead of individual genes. Finally, ASSESS can be used for cross-platform expression models where data on the same type of cancer are integrated over different platforms into a space of enrichment scores. AVAILABILITY: Versions are available in Octave and Java (with a graphical user interface). Software can be downloaded at http://people.genome.duke.edu/assess.

Algorithms↗

Multiomics approaches to cardiovascular disease: technological innovations and clinical translation.

Cardiovascular diseases (CVDs) remain the leading cause of global morbidity and mortality, reflecting a persistent gap between clinical phenotyping and the molecular mechanisms that govern disease initiation, progression, and interindividual variability. Recent advances in emerging technologies have fundamentally reshaped cardiovascular physiology by enabling high-resolution, cross-layer profiling of the heart and vasculature across genomic, epigenomic, transcriptomic, proteomic, metabolomic, lipidomic, glycomic, and fluxomic layers, increasingly at single-cell and spatial resolution. These approaches reveal CVD as a coordinated, multilayered process driven by dynamic interactions among cell types, regulatory programs, and metabolic states, rather than isolated gene-level defects. In this review, we synthesize how emerging multiomic, computational, and functional genomic technologies are redefining the study of cardiovascular disease across molecular, cellular, and tissue levels. We highlight recent innovations in single-cell and spatial atlases, long-read sequencing, proteomics and metabolomics, integrative data modeling, and functional omics approaches, including genome-scale perturbation screens and single-cell perturbation frameworks. These platforms enable mechanistic dissection of regulatory circuits, distinguish primary disease drivers from secondary adaptations, and directly assess therapeutic reversibility, advancing the field beyond associative biomarker discovery toward mechanism-guided target prioritization. We further discuss key methodological and translational challenges accompanying high-dimensional cardiovascular data, including preanalytical variability, control selection, temporal misalignment across molecular layers, population diversity, and reference bias. By integrating technological innovation with computational rigor and functional validation, this review frames emerging omics-enabled strategies as a unified, physiologically grounded framework for translating molecular insight into clinically meaningful cardiovascular phenotypes and advancing precision cardiovascular medicine.

Humans↗

Protein profiling comes of age.

Ever since DNA microarrays were first applied to the quantitation of RNA levels, there has been considerable interest in generating a protein homolog that can be used to assay cellular protein expression. A recent paper describes the first microarray that can be used for such protein profiling.

Gene Expression Profiling↗

Unveiling microbial risks in Chinese household dust: a comprehensive analysis from absolute abundance to virulence unit.

BACKGROUND: People spend the majority of their lives indoors, yet the risk and virulence potential of household microbiota remain largely unexplored, particularly in developing countries. RESULTS: Here, we conducted a nationwide survey on both dust samples and health information across 118 Chinese households. The microbiota composition and its functional units were analyzed using absolute 16S rRNA/ITS sequencing, metagenomics, and metaproteomics. Cross-domain network analysis of the core microbial communities revealed robust co-occurrence patterns in household dust. The mean absolute abundance of potentially pathogenic bacteria and fungi in households was 2.39 × 105 and 2.83 × 106 DNA copies/g dust. The potentially pathogenic community was primarily influenced by latitude, relative humidity, and average temperature. Although total absolute abundance was substantially lower in urban areas, the relative abundance of potentially pathogenic bacteria was markedly higher compared to rural environments. While urban-rural differences existed, the underlying statistical drivers were the environmental variables. The absolute abundance of potential pathogens was significantly associated with the prevalence of rhinitis, wheeze, and dermatitis in 266 participants. Children were identified as the highest-risk group from inhalation exposure of average daily dose. A total of 170 bacterial, 223 fungal virulence factors (VFs), and 370 antibiotic resistance genes (ARGs) were detected in dust and dust extracellular vesicle (EV)-associated DNA. EV-associated cargoes contributed 47.13% to the bacterial VF profiles, 11.90% to fungal VF profiles, and 44.45% to ARG profiles. Metaproteomic analysis confirmed the presence of VF profiles in dust EVs, which was further verified by curated proteomics data from 35 household pathogens. CONCLUSIONS: This study provides a comprehensive, quantitative framework linking indoor microbial exposure to health risks, highlighting EVs as a non-negligible, novel, extracellular mechanistic pathway for health impact in household environments. Video Abstract.

Child↗

New analytical tools for studying autoimmune diseases.

Protein microarrays with immobilised proteins on their surface are new analytical tools to overcome the current limits with respect to sample volume and throughput. They have a great potential as well with respect to multiplexing of complex samples, as a research tool and in diagnostics. Based on recent advances in this technology, new applications for protein microarrays in studying autoimmune diseases were described. Required tools for bioinformatical analysis of protein microarrays concerning normalisation, clustering and classification methods are discussed. The huge potential of this technology as well as future requirements such as protein microarray based diagnostics are presented.

Animals↗