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Results for “Genomic profiling”

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At least 955 records · Page 53Linked to original sources
PubMed2006

Genomics of renal cell cancer-- does it provide breakthrough?

It is a strong hope that the more we characterize the pathways in an individual tumor, the better we will be able to evaluate the response to a specific therapy. Different array technologies could be powerful tools to achieve this goal, i.e. selecting patients on the basis of the genomic and/or proteomic profiles who would really benefit from the target-designed therapy. Genomic analysis of RCC accumulated ample of data which now can be exploited in clinical management of a previously almost uncontrollable disease. Beside the previously identified genetic abnormalities (VHL, MET, EGFR), CAIX seems to be a novel molecular marker of RCC. Array studies also outlined a small set of tumor markers, vimentin, galectin-3, CD74 and parvalbumin, which can define the individual histologic subtypes of RCC. We are at the beginning to take advantage of the genomic results. Some new approaches will interfere with the progression of RCC (anti-VEGF, anti-VEGFR or anti-EGFR therapies). Further novel molecular targets are available, such as HIF, HSP90 or the IFN-regulated genes, which can be used to the fine-tuning of RCC therapy.

Biomarkers, Tumor↗
PubMed2002

Yeast genomic expression studies using DNA microarrays.

The exploration and characterization of yeast genomic expression programs is providing a wealth of information about yeast biology, as well as other organisms. The intriguing biology of yeast species invites characterization of genomic expression patterns to illuminate the details of cellular physiology. In addition to its value as an interesting organism, yeast maintains its role as an excellent model in which to characterize genomic expression programs. Microarray studies are quickly spreading to plant, animal, and microbial organisms that remain in the early stages of characterization. The extensive knowledge of yeast biology, as well as the relative ease with which yeast studies can be performed and controlled, facilitates interpretation of the genomic expression data. Importantly, existing information about yeast biology, including functional annotations for each gene, is captured and efficiently presented in databases such as the Saccharomyces Genome Database (SGD), the Munich Information Center Yeast Genome Database (MIPS), the Yeast and Pombe Protein Databases (YPD and PPD, respectively), and others. A number of databases also allow the exploration of published genomic expression studies, including the "Expression Connection" at SGD and the Microarray Global Viewer (yMGV) organized by Marc et al. Consulting these databases to retrieve known details about gene function and regulation vastly facilitates interpretation of the genomic expression data, allowing biological hypotheses to be formulated and tested. These hypotheses can be applied to other organisms that may execute genomic expression programs similar to those seen in yeast. Furthermore, as more genomic expression studies in multiple organisms emerge, large-scale data comparisons can be conducted, within and across organisms. Incorporating the results of yeast studies into such comparisons is certain to increase our understanding about the function, regulation, and evolution of genomic expression programs.

Carbocyanines↗
PubMed2005

Functional genomics databases on the web.

Experiments involving high-throughput methods for measuring transcripts, proteins and metabolites constitute the area of functional genomics. These experiments are highly context dependent and require much more detail about the experimental design, sample and protocols used than in genomics. Functional genomics databases are needed that follow established and emerging standards. Functional genomic databases are not yet very common; however, there are a few focused on microbial genomes and a couple integrative systems are available for setting up functional genomics databases.

Computational Biology↗
PubMed2000

A probabilistic learning approach to whole-genome operon prediction.

We present a computational approach to predicting operons in the genomes of prokaryotic organisms. Our approach uses machine learning methods to induce predictive models for this task from a rich variety of data types including sequence data, gene expression data, and functional annotations associated with genes. We use multiple learned models that individually predict promoters, terminators and operons themselves. A key part of our approach is a dynamic programming method that uses our predictions to map every known and putative gene in a given genome into its most probable operon. We evaluate our approach using data from the E. coli K-12 genome.

Gene Expression Profiling↗
PubMed2006

[Integrating obtained knowledge from transcriptome data by a new framework for data analysis].

Microarray analyses facilitate the investigation of quantitative information coded in the genome by measuring transcriptome, which records the decoded information from the genome. The state of a cell and differences from other states can be studied through genome information, by comparing one set of transcriptome data to other sets. Clearly, those data should be shared and compared with researchers, and the knowledge should be integrated. Unfortunately, at present data comparisons in microarray analyses are quite difficult; the accuracy as well as the reproducibility is low. The difficulties are originated from data analyses methods. Data comparison requires an intelligent framework, such as that discussed by philosopher Sir Karl R Popper. Frameworks for microarray analyses have been developed by many efforts of bioinformatitians. The frameworks currently used are being inspected and critically discussed. By checking the mathematical models that form the practical frameworks, arbitrariness such as the lack of falsifiability has been pointed out. The paradigm in this field of analyses is also criticized by disagreement with the scientific standard, and it is shown as the origin of errors in analyses. The excessive numbers of frameworks produced in an ad hoc manner has also been criticized, since the existence of so many allows researchers to select different frameworks, discussions beyond frameworks are always difficult. A new framework that uses a parametric model is introduced with an explanation of the bases of the framework and the process of testing. Additionally, differences of obtained results by these frameworks are presented using GeneChip data, in stability of log-ratio measurements and reproducibility of analyses. The possibility of artificial decoding of genome information by an extended framework is also discussed.

Gene Expression Profiling↗
PubMed2026

Metabolic reprogramming and taxonomic drivers in bacterial vaginosis: A large-scale metagenomic meta-analysis.

OBJECTIVE: Bacterial vaginosis (BV) represents a profound ecological shift from a Lactobacillus-dominated microbiota to a diverse polymicrobial biofilm associated with adverse outcomes. While taxonomic signatures are well-documented, the functional mechanisms driving this transition remain obscured. This study elucidates the genomic potential for metabolic reprogramming and the putative "functional handover" underpinning the stability of the dysbiotic state. METHODS: A computational meta-analysis of 3557 vaginal microbiomes from diverse global cohorts was performed using the standardized MGnify pipeline. A high-resolution subset of 187 whole-genome shotgun (WGS) metagenomes was stratified to compare functional potential across demographic groups. Taxon-function interaction networks were constructed, utilizing a dual-filter statistical approach (p&#x202f;<&#x202f;0.05 and effect size ranking), to map the shift from homeostatic maintenance to dysbiotic metabolic potential. RESULTS: BV was characterized by a fundamental shift from "maintenance" pathways to high-turnover "growth-oriented" genomic repertoires. While ABC transporter-like domains were present in healthy communities, dysbiosis was marked by a quantitative expansion and diversification of these systems alongside P-loop NTPases. Network analysis revealed a putative "functional handover": while Gardnerella serves as the adherent structural scaffold, the metabolic burden appears to be associated with secondary anaerobes, specifically BVAB1 and Sneathia, which exhibit strong genomic correlations with nutrient transport and stress response pathways. Crucially, microbiomes from women of African ancestry (Black cohort) exhibited a distinct functional profile with genomic signatures consistent with functions previously associated with resistome expansion (e.g., tetracycline/macrolide resistance), contrasting with Asian cohorts. CONCLUSION: BV is a state of metabolic reprogramming where genomic functional dominance is transferred from Lactobacillus to a cooperative network of anaerobic opportunists. Identifying BVAB1 and Sneathia as candidate metabolic engines, supported by a Gardnerella scaffold, challenges current therapeutic paradigms and highlights the potential for precision medicine targeting specific functional drivers and resistome profiles across diverse populations.

Humans↗
PubMed2005

New insights into Drosophila larval haemocyte functions through genome-wide analysis.

Drosophila blood cells or haemocytes comprise three cell lineages, plasmatocytes, crystal cells and lamellocytes, involved in immune functions such as phagocytosis, melanisation and encapsulation. Transcriptional profiling of activities of distinct haemocyte populations and from naive or infected larvae, was performed to find genes contributing to haemocyte functions. Of the 13 000 genes represented on the microarray, over 2500 exhibited significantly enriched transcription in haemocytes. Among these were genes encoding integrins, peptidoglycan recognition proteins (PGRPs), scavenger receptors, lectins, cell adhesion molecules and serine proteases. One relevant outcome of this analysis was the gain of new insights into the lamellocyte encapsulation process. We showed that lamellocytes require betaPS integrin for encapsulation and that they transcribe one prophenoloxidase gene enabling them to produce the enzyme necessary for melanisation of the capsule. A second compelling observation was that following infection, the gene encoding the cytokine Spatzle was uniquely upregulated in haemocytes and not the fat body. This shows that Drosophila haemocytes produce a signal molecule ready to be activated through cleavage after pathogen recognition, informing distant tissues of infection.

Animals↗
PubMed1990

Polymorphism in polydnavirus genomes.

Polymorphisms were readily detected in polydnavirus DNA extracted from several different species belonging to two different families of parasitic hymenoptera. Heterogeneity was observed as differences in electrophoretic profiles of genome segments, differences in the number of cross-hybridizing genome segments, and restriction fragment length polymorphisms; polymorphism was also detected at the level of an individual genome segment. Some implications drawn from these observations are discussed.

Animals↗
PubMed2001

Chemical inhibition of the Pho85 cyclin-dependent kinase reveals a role in the environmental stress response.

In addition to its well-established role in responding to phosphate starvation, the cyclin-dependent kinase Pho85 has been implicated in a number of other physiological responses of the budding yeast Saccharomyces cerevisiae, including synthesis of glycogen. To comprehensively characterize the range of Pho85-dependent gene expression, we used a chemical genetic approach that enabled us to control Pho85 kinase activity with a cell-permeable inhibitor and whole genome transcript profiling. We found significant phenotypic differences between the rapid loss of activity caused by inhibition and the deletion of the genomic copy of PHO85. We demonstrate that Pho85 controls the expression of not only previously identified glycogen synthetic genes, but also a significant regulon of genes involved in the cellular response to environmental stress. In addition, we show that the effects of this inhibitor are both rapid and reversible, making it well suited to the study of the behavior of dynamic signaling pathways.

Alleles↗
PubMed2002

Recombinase-directed plant transformation for the post-genomic era.

Plant genomics promises to accelerate genetic discoveries for plant improvements. Machine-driven technologies are ushering in gene structural and expressional data at an unprecedented rate. Potential bottlenecks in this crop improvement process are steps involving plant transformation. With few exceptions, genetic transformation is an obligatory final step by which useful traits are engineered into plants. In addition, transgenesis is most often needed to confirm gene function, after deductions made through comparative genomics, expression profiles, and mutation analysis. This article reviews the use of recombinase systems to deliver DNA more efficiently into the plant genome.

Base Sequence↗
PubMed2025

TSC angiofibroma and ungual fibroma have different mutation signatures, with recurrent mutations in KMT2C.

PURPOSE: Tuberous sclerosis complex (TSC) is an autosomal dominant tumor suppressor syndrome characterized by tumors affecting multiple tissues, including skin, due to inactivating TSC1/TSC2 variants. Genome-wide profiling of somatic mutations in a unique collection of angiofibroma (FAF) and ungual fibroma (UF) TSC skin tumors was performed. METHODS: Genome sequencing was performed on 9 samples, comprising 4 FAF and 5 UF, along with 6 matched normal samples from 6 individuals with TSC. RESULTS: TSC-FAF and TSC-UF skin tumors have different mutation signatures, with a predominance of UV-related single-nucleotide variant (SNV; SBS7a and SBS7b) and dinucleotide variant (DNV; DBS1) signatures in FAF, and aging-related SNV (SBS1 and SBS5) signatures in UF. We also identified a novel DNV signature for TSC-UF, with frequent TG>CA and TT>GG substitutions. Furthermore, 3 inactivating somatic mutations in KMT2C were observed in 2 of 4 TSC-FAF and 5 mutations in other cancer genes. CONCLUSION: The distinct SNV mutation signatures seen in TSC-FAF and UF indicate that they develop through distinct pathogenic mechanisms, UV-induced mutagenesis in FAF, and aging-related mutagenesis in UF. The mechanism of the novel DNV signature in UFs merits further investigation. Our observation on the occurrence of KMT2C mutations suggests that KMT2C inactivation contributes to the pathogenesis of TSC-FAF.

Humans↗
PubMedDate not supplied

The role of proteomics in toxicology: identification of biomarkers of toxicity by protein expression analysis.

Proteomics, i.e. the high throughput separation, display and identification of proteins, has the potential to be a powerful tool in drug development. It could increase the predictability of early drug development and identify non-invasive biomarkers of toxicity or efficacy. This review provides an introduction to modern proteomics, with particular reference to applications in toxicology. A literature search was carried out to identify studies in two broad classes: screening/predictive toxicology, and mechanistic toxicology. The strengths and limitations of current methods and the likely impact of techniques in drug development are also considered. Proteomics can increase the speed and sensitivity of toxicological screening by identifying protein markers of toxicity. Proteomics studies have already provided insights into the mechanisms of action of a wide range of substances, from metals to peroxisome proliferators. Current limitations involving speed of throughput are being overcome by increasing automation and the development of new techniques. The isotope-coded affinity tag (ICAT) method appears particularly promising. The application of proteomics to drug development has given rise to the new field of pharmacoproteomics. New associations between proteins and toxicopathological effects are constantly being identified, and major progress is on the horizon as we move into the post-genomic era.

Animals↗