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At least 883 records · Page 49Linked to original sources
PubMed2003

The influence of organ donor factors on early allograft function.

PURPOSE OF REVIEW: Postischaemic acute renal allograft failure is among the main risk factors for reduced transplant survival. Although new immunosuppressive protocols have reduced the number of acute rejections, the incidence of acute renal failure remained unchanged. On the basis of histomorphology it is not possible to predict donor kidneys at risk of subsequent failure. Some factors are associated with failure, but even combinations of these risk factors can not precisely predict the development of acute renal failure. Studies have therefore evaluated the influence of demographic donor and recipient factors on acute renal failure. New biotechnology and data mining tools are currently being used to study and identify the molecular predictors of acute renal failure. RECENT FINDINGS: Recent studies showed that donor factors contributed to approximately 40% of the variability in early allograft function. Deductive approaches identified some isolated molecular targets, such as adhesion molecules, as risk factors. Explorative analysis of the entire human genome, however, identified several predictive clusters of genes, which can be functionally grouped into categories such as cell death, stress response, cell adhesion, transcription factors, inflammatory response or cell cycle-related genes. Based on this information, preventative strategies using antisense oligonucleotides or antibodies were adopted. Clinical studies identified the use of catecholamines in the organ donor as beneficial. All these efforts aim to reduce renal tubular damage. SUMMARY: A detailed analysis of the molecular events and pathways of renal gene expression in the donor and after reperfusion, together with sophisticated data analysis tools, will provide new insights into the pathophysiology of acute renal failure.

Acute Kidney Injury↗
PubMed2001

Metabolic profiling allows comprehensive phenotyping of genetically or environmentally modified plant systems.

Metabolic profiling using gas chromatography-mass spectrometry technologies is a technique whose potential in the field of functional genomics is largely untapped. To demonstrate the general usefulness of this technique, we applied to diverse plant genotypes a recently developed profiling protocol that allows detection of a wide range of hydrophilic metabolites within a single chromatographic run. For this purpose, we chose four independent potato genotypes characterized by modifications in sucrose metabolism. Using data-mining tools, including hierarchical cluster analysis and principle component analysis, we were able to assign clusters to the individual plant systems and to determine relative distances between these clusters. Extraction analysis allowed identification of the most important components of these clusters. Furthermore, correlation analysis revealed close linkages between a broad spectrum of metabolites. In a second, complementary approach, we subjected wild-type potato tissue to environmental manipulations. The metabolic profiles from these experiments were compared with the data sets obtained for the transgenic systems, thus illustrating the potential of metabolic profiling in assessing how a genetic modification can be phenocopied by environmental conditions. In summary, these data demonstrate the use of metabolic profiling in conjunction with data-mining tools as a technique for the comprehensive characterization of a plant genotype.

Cluster Analysis↗
PubMed2026

Systematic mining and characterization of metal transporter families regulating zinc homeostasis provide insights into metal homeostasis in Camellia sinensis.

BACKGROUND AND AIMS: Zinc is essential for tea plant growth and quality formation, yet its homeostatic mechanisms remain poorly understood. This study identified metal transporter families regulating zinc homeostasis, analyzed their evolution, structure, and expression, and clarified zinc uptake, transport, detoxification networks, and their links to metabolism. METHODS: This study identified zinc homeostasis-related metal transporter families in the tea plant genome, characterized their structural features and expression profiles across tissues and developmental stages through integrative bioinformatics and transcriptomic analyses, and delineated the molecular mechanisms underlying zinc uptake, translocation, and detoxification by systematically integrating published evidence. RESULTS: This study identified 74 metal transporter genes from six families: 13 CsZIPs, 12 CsNRAMPs, 10 CsHMAs, 10 CsYSLs, 14 CsMTPs, and 15 CsCAXs in the 'Shuchazao2' genome, revealing closer affinity to woody species than to Arabidopsis. These proteins exhibit conserved domains, diverse subcellular localizations (cell membrane, vacuole, chloroplast, and Golgi apparatus), and tissue-specific expression with abundant stress/hormone-responsive cis-elements. At the plant-soil interface, tea plants mobilize rhizospheric zinc via proton and organic acid secretion; CsYSLs, CsNRAMPs, and CsZIPs mediate zinc uptake, aided by arbuscular mycorrhizal fungi (AMF) and plant growth-promoting rhizobacteria (PGPR) that expand root absorption zones. Xylem CsHMAs and phloem CsYSLs coordinate root-to-shoot zinc translocation, and vacuolar transporters (CsMTPs, CsCAXs), cell wall immobilization, and antioxidant systems alleviate high-zinc stress injury. CONCLUSIONS: These findings collectively delineate an integrated zinc "acquisition-distribution-buffering" network in tea plants, offering a repertoire of candidate genes with potential utility in zinc biofortification breeding and improving acid soil adaptation. Further experimental validation, including tea transgenesis, zinc-stress qRT-PCR, and heterologous functional complementation, is essential to substantiate their biological roles.

Camellia sinensis↗
PubMed2020

Metabolic Dysregulation of the Lysophospholipid/Autotaxin Axis in the Chromosome 9p21 Gene SNP rs10757274.

BACKGROUND: Common chromosome 9p21 single nucleotide polymorphisms (SNPs) increase coronary heart disease risk, independent of traditional lipid risk factors. However, lipids comprise large numbers of structurally related molecules not measured in traditional risk measurements, and many have inflammatory bioactivities. Here, we applied lipidomic and genomic approaches to 3 model systems to characterize lipid metabolic changes in common Chr9p21 SNPs, which confer ≈30% elevated coronary heart disease risk associated with altered expression of ANRIL, a long ncRNA. METHODS: Untargeted and targeted lipidomics was applied to plasma from NPHSII (Northwick Park Heart Study II) homozygotes for AA or GG in rs10757274, followed by correlation and network analysis. To identify candidate genes, transcriptomic data from shRNA downregulation of ANRIL in HEK-293 cells was mined. Transcriptional data from vascular smooth muscle cells differentiated from induced pluripotent stem cells of individuals with/without Chr9p21 risk, nonrisk alleles, and corresponding knockout isogenic lines were next examined. Last, an in-silico analysis of miRNAs was conducted to identify how ANRIL might control lysoPL (lysophosphospholipid)/lysoPA (lysophosphatidic acid) genes. RESULTS: Elevated risk GG correlated with reduced lysoPLs, lysoPA, and ATX (autotaxin). Five other risk SNPs did not show this phenotype. LysoPL-lysoPA interconversion was uncoupled from ATX in GG plasma, suggesting metabolic dysregulation. Significantly altered expression of several lysoPL/lysoPA metabolizing enzymes was found in HEK cells lacking ANRIL. In the vascular smooth muscle cells data set, the presence of risk alleles associated with altered expression of several lysoPL/lysoPA enzymes. Deletion of the risk locus reversed the expression of several lysoPL/lysoPA genes to nonrisk haplotype levels. Genes that were altered across both cell data sets were DGKA, MBOAT2, PLPP1, and LPL. The in-silico analysis identified 4 ANRIL-regulated miRNAs that control lysoPL genes as miR-186-3p, miR-34a-3p, miR-122-5p, and miR-34a-5p. CONCLUSIONS: A Chr9p21 risk SNP associates with complex alterations in immune-bioactive phospholipids and their metabolism. Lipid metabolites and genomic pathways associated with coronary heart disease pathogenesis in Chr9p21 and ANRIL-associated disease are demonstrated.

Chromosomes, Human, Pair 9↗
PubMed2006

Automatic discovery of cross-family sequence features associated with protein function.

BACKGROUND: Methods for predicting protein function directly from amino acid sequences are useful tools in the study of uncharacterized protein families and in comparative genomics. Until now, this problem has been approached using machine learning techniques that attempt to predict membership, or otherwise, to predefined functional categories or subcellular locations. A potential drawback of this approach is that the human-designated functional classes may not accurately reflect the underlying biology, and consequently important sequence-to-function relationships may be missed. RESULTS: We show that a self-supervised data mining approach is able to find relationships between sequence features and functional annotations. No preconceived ideas about functional categories are required, and the training data is simply a set of protein sequences and their UniProt/Swiss-Prot annotations. The main technical aspect of the approach is the co-evolution of amino acid-based regular expressions and keyword-based logical expressions with genetic programming. Our experiments on a strictly non-redundant set of eukaryotic proteins reveal that the strongest and most easily detected sequence-to-function relationships are concerned with targeting to various cellular compartments, which is an area already well studied both experimentally and computationally. Of more interest are a number of broad functional roles which can also be correlated with sequence features. These include inhibition, biosynthesis, transcription and defence against bacteria. Despite substantial overlaps between these functions and their corresponding cellular compartments, we find clear differences in the sequence motifs used to predict some of these functions. For example, the presence of polyglutamine repeats appears to be linked more strongly to the "transcription" function than to the general "nuclear" function/location. CONCLUSION: We have developed a novel and useful approach for knowledge discovery in annotated sequence data. The technique is able to identify functionally important sequence features and does not require expert knowledge. By viewing protein function from a sequence perspective, the approach is also suitable for discovering unexpected links between biological processes, such as the recently discovered role of ubiquitination in transcription.

Algorithms↗
PubMed2004

Confirmation of data mining based predictions of protein function.

MOTIVATION: A central problem in bioinformatics is the assignment of function to sequenced open reading frames (ORFs). The most common approach is based on inferred homology using a statistically based sequence similarity (SIM) method, e.g. PSI-BLAST. Alternative non-SIM based bioinformatic methods are becoming popular. One such method is Data Mining Prediction (DMP). This is based on combining evidence from amino-acid attributes, predicted structure and phylogenic patterns; and uses a combination of Inductive Logic Programming data mining, and decision trees to produce prediction rules for functional class. DMP predictions are more general than is possible using homology. In 2000/1, DMP was used to make public predictions of the function of 1309 Escherichia coli ORFs. Since then biological knowledge has advanced allowing us to test our predictions. RESULTS: We examined the updated (20.02.02) Riley group genome annotation, and examined the scientific literature for direct experimental derivations of ORF function. Both tests confirmed the DMP predictions. Accuracy varied between rules, and with the detail of prediction, but they were generally significantly better than random. For voting rules, accuracies of 75-100% were obtained. Twenty-one of these DMP predictions have been confirmed by direct experimentation. The DMP rules also have interesting biological explanations. DMP is, to the best of our knowledge, the first non-SIM based prediction method to have been tested directly on new data. AVAILABILITY: We have designed the "Genepredictions" database for protein functional predictions. This is intended to act as an open repository for predictions for any organism and can be accessed at http://www.genepredictions.org

Abstracting and Indexing↗
PubMed2005

Identification of a gene cluster for the formation of extracellular polysaccharide precursors in the chemolithoautotroph Acidithiobacillus ferrooxidans.

A cluster of five genes, proposed to be involved in the formation of extracellular polysaccharide (EPS) precursors via the Leloir pathway, have been identified in the acidophilic autotroph Acidithiobacillus ferrooxidans. The order of the genes is luxA-galE-galK-pgm-galM, encoding a LuxA-like protein, UDP-glucose 4-epimerase, galactokinase, phosphoglucomutase, and galactose mutarotase, respectively. The gal cluster forms a single transcriptional unit and is therefore an operon. Two other putative genes of the Leloir pathway, galU, potentially encoding UDP-glucose pyrophosphorylase, and a gene designated galT-like, which may encode a galactose-1-phosphate uridylyltransferase-like activity, were found unlinked in the genome. Using semiquantitative reverse transcription-PCR, the genes of the gal operon were shown to be expressed more during growth in iron medium than in growth in sulfur medium. The functions of galE, pgm, galU, and the galT-like gene were validated by complementation of Escherichia coli mutants and by in vitro enzyme assays. The data suggest that A. ferrooxidans is capable of synthesizing the EPS precursors UDP-glucose and UDP-galactose. In addition, genes rfbA, -B, -C, and -D were identified in the genome of A. ferrooxidans, suggesting that it can also synthesize the EPS precursor dTDP-rhamnose. Since EPSs constitute the major bulk of biofilms, this study may provide an initial model for the metabolic pathways involved in biofilm formation in A. ferrooxidans and aid in understanding the role of biofilms in mineral leaching and the formation of acid mine drainage.

Acidithiobacillus↗
PubMed2004

DNA microarray technology in nutraceutical and food safety.

The quality and quantity of diet is a key determinant of health and disease. Molecular diagnostics may play a key role in food safety related to genetically modified foods, food-borne pathogens and novel nutraceuticals. Functional outcomes in biology are determined, for the most part, by net balance between sets of genes related to the specific outcome in question. The DNA microarray technology offers a new dimension of strength in molecular diagnostics by permitting the simultaneous analysis of large sets of genes. Automation of assay and novel bioinformatics tools make DNA microarrays a robust technology for diagnostics. Since its development a few years ago, this technology has been used for the applications of toxicogenomics, pharmacogenomics, cell biology, and clinical investigations addressing the prevention and intervention of diseases. Optimization of this technology to specifically address food safety is a vast resource that remains to be mined. Efforts to develop diagnostic custom arrays and simplified bioinformatics tools for field use are warranted.

Animals↗
PubMed2006

[Acquired tumor "robustness" in the case of prostate cancer].

Tumors are highly robust and maintain their proliferative potential against both a wide range of host-defense mechanisms and anticancer therapies. In this study, we investigated the levels of human leukocyte antigen (HLA) class I, multi-drug resistance 1 (MDR1), and androgen receptor (AR) expressions in untreated prostate cancers harvested by radical prostectomy. The mean percentages of cancer cells expressing HLA class I, MDR1, and AR among the 10 cancer samples were 41, 35, and 74%, respectively. In addition, double-staining of HLA class I and MDR1 revealed the four definite populations (HLA class I(+)/MDR(+), HLA class I(+)/MDR(-), HLA class I (-)/MDR(+), and HLA class I(-)/MDR(-)) in cancer tissues from the majority of cancer patients tested, and the mean percentages of cells expressing these combinations were 13, 29, 22, and 38%, respectively. Similar results were obtained by double staining of HLA class I and AR, except for 2 cases in which HLA class I(-)/AR (+) cancer cells predominated. These results indicated that untreated prostate cancer cells acquired a wide range of genomic mutation, which may have been caused by internal host pressure to eliminate malignant cells, and would provide evidence of the robustness of untreated prostate cancer.

Aged↗
PubMed2006

Subcellular localization of mammalian type II membrane proteins.

Application of a computational membrane organization prediction pipeline, MemO, identified putative type II membrane proteins as proteins predicted to encode a single alpha-helical transmembrane domain (TMD) and no signal peptides. MemO was applied to RIKEN's mouse isoform protein set to identify 1436 non-overlapping genomic regions or transcriptional units (TUs), which encode exclusively type II membrane proteins. Proteins with overlapping predicted InterPro and TMDs were reviewed to discard false positive predictions resulting in a dataset comprised of 1831 transcripts in 1408 TUs. This dataset was used to develop a systematic protocol to document subcellular localization of type II membrane proteins. This approach combines mining of published literature to identify subcellular localization data and a high-throughput, polymerase chain reaction (PCR)-based approach to experimentally characterize subcellular localization. These approaches have provided localization data for 244 and 169 proteins. Type II membrane proteins are localized to all major organelle compartments; however, some biases were observed towards the early secretory pathway and punctate structures. Collectively, this study reports the subcellular localization of 26% of the defined dataset. All reported localization data are presented in the LOCATE database (http://www.locate.imb.uq.edu.au).

Animals↗
PubMed2004

Automatic annotation of protein motif function with Gene Ontology terms.

BACKGROUND: Conserved protein sequence motifs are short stretches of amino acid sequence patterns that potentially encode the function of proteins. Several sequence pattern searching algorithms and programs exist foridentifying candidate protein motifs at the whole genome level. However, a much needed and important task is to determine the functions of the newly identified protein motifs. The Gene Ontology (GO) project is an endeavor to annotate the function of genes or protein sequences with terms from a dynamic, controlled vocabulary and these annotations serve well as a knowledge base. RESULTS: This paper presents methods to mine the GO knowledge base and use the association between the GO terms assigned to a sequence and the motifs matched by the same sequence as evidence for predicting the functions of novel protein motifs automatically. The task of assigning GO terms to protein motifs is viewed as both a binary classification and information retrieval problem, where PROSITE motifs are used as samples for mode training and functional prediction. The mutual information of a motif and aGO term association is found to be a very useful feature. We take advantage of the known motifs to train a logistic regression classifier, which allows us to combine mutual information with other frequency-based features and obtain a probability of correct association. The trained logistic regression model has intuitively meaningful and logically plausible parameter values, and performs very well empirically according to our evaluation criteria. CONCLUSIONS: In this research, different methods for automatic annotation of protein motifs have been investigated. Empirical result demonstrated that the methods have a great potential for detecting and augmenting information about the functions of newly discovered candidate protein motifs.

Amino Acid Motifs↗
PubMed2002

Unusual DNA conformations: implications for telomeres.

DNA is prone to structural polymorphism: its three-dimensional structure can differ markedly from the classical double helix. Nucleic acid structures composed of more than two strands have also been observed. The guanine-rich sequence of both the telomere and centromere can form a quadruplex based on G-quartets while the complementary cytosine-rich strand can fold into an intercalated tetramer called the i-motif. The G-quartet is a gold mine for structural biologists and the telomere has become a target for anti-cancer drug design since it was observed that deregulation of telomerase favors proliferation of certain tumors. Other DNA sequences may adopt unusual conformations. Polypurine-polypyrimidine sequences capable of forming a triple-stranded structure called H-DNA are found abundantly in the eukaryotic genome and may play a significant role in DNA metabolism, transcription and replication. Triplex-forming oligonucleotides are currently being developed as "anti-gene" agents. Unusual DNA structures may therefore be implicated in fundamental processes such as gene expression and represent unique targets for both structural-specific and sequence-specific agents. In this review, we present work characterizing some of these unusual conformations in terms of structure, stability and formation kinetics and discuss their biological implications.

Animals↗
PubMed2006

The future of epidemiology: methodological challenges and multilevel inference.

A decade ago there was considerable debate about the appropriate objectives and paradigms of modern epidemiologic research. One concern put forth in these debates was that "risk factor epidemiology" might be forcing our field to focus more on individuals and less on populations and public health. Today, most epidemiologists acknowledge that public health is influenced by both population-level and individual-level determinants. Ecologic studies are valuable tools for generating hypotheses and addressing group-level determinants of disease risk. Traditional risk factor studies and genomic studies have helped establish the multifactorial concept of disease causation. Individual-level studies also have provided the biomedical community with hypotheses that have stimulated research into disease mechanisms that have led to reductions in morbidity and mortality for diseases such as HIV/AIDS, cardiovascular disease, and cancer. Current debates about the role of genomic data in epidemiology and public health mirror the debates about risk factor epidemiology one decade ago. Genomic variation is measured at the individual level, but how this variation is maintained in human populations is a group-level (population) phenomenon that is worthy of epidemiologic investigation in its own right. Multilevel epidemiology seeks to understand multiple levels of inference, from genes to individuals to populations and could combine hypothesis-driven research with aspects of data mining. Multilevel epidemiology calls for the study of health and disease determinants defined at the population level and individual level for a more comprehensive strategy to understanding human disease etiology. With the continued development of multilevel statistical methods and the advent of data mining, the technical constraints of the past will become less relevant to the next generation of epidemiologists who wish to embrace a more multilevel epidemiology.

Data Interpretation, Statistical↗
PubMed2006

In silico comparison of gene expression levels in ten human tumor types reveals candidate genes associated with carcinogenesis.

Most human cancers are characterized by genomic instability. Changes associated with such may result in altered expression of numerous genes. The sequence information available in the public databases can be used to identify transcripts differentially expressed in cancers. Determining cancer-related genes that are commonly deregulated in different tumor types may facilitate identification of targets for cancer diagnoses and therapeutic treatments. Using a data-mining tool named Digital Differential Display (DDD) from the UniGene database at the NCBI web site, gene expression levels of ten different tumor types and their counterpart normal tissues were analyzed. Unigenes which showed transcriptional regulation in more than five tumor types with > or =2-fold differences from normal tissues were identified. The expression data of selected Unigenes were subjected to clustering analysis. 127 commonly up-regulated genes and 92 commonly down-regulated genes were identified. Clustering analysis using these genes showed that most tumor types can be clustered into a separate branch from most normal tissues. Nineteen genes that have been shown to be involved in carcinogenesis by experimental evidence were also identified. Present computational analyses revealed 219 candidate cancer-related genes that are commonly deregulated in ten human tumor types which may contribute to the progress of carcinogenesis.

Gene Expression↗
PubMedDate not supplied

Not your father's health care.

We in health care are living and working in a world that, for all its technical changes, differs little in its basic assumptions, structures, payment systems, beliefs, expectations, and job titles from the world of health care a generation back. How much change can we expect over the coming years? A lot more than we are prepared for. Look at the array of new technologies headed our way, from genomic sciences to customized vaccinations. Many of the breakthroughs promise incredible abilities to prevent disease, to profile our proclivities, and to manage our genetic predispositions over long periods of time, rather than merely wait until the disease manifests in an acute phase, then treat the symptoms. Digital technologies bring physicians executives enormous opportunities for new ways of gathering, storing, and mining information, for new types of communication between medical professionals, for new communications with customers, and new ways of steering large, complex enterprises. Unprecedented opportunities for change keep piling in through the door. Vast pressures for change keep building from every side. And the rewards for anyone who can lead the change keep compounding.

Computer Systems↗
PubMed2005

A global view of CK2 function and regulation.

The wealth of biochemical, molecular, genetic, genomic, and bioinformatic resources available in S. cerevisiae make it an excellent system to explore the global role of CK2 in a model organism. Traditional biochemical and genetic studies have revealed that CK2 is required for cell viability, cell cycle progression, cell polarity, ion homeostasis, and other functions, and have identified a number of potential physiological substrates of the enzyme. Data mining of available bioinformatic resources indicates that (1) there are likely to be hundreds of CK2 targets in this organism, (2) the majority of predicted CK2 substrates are involved in various aspects of global gene expression, (3) CK2 is present in several nuclear protein complexes predicted to have a role in chromatin structure and remodeling, transcription, or RNA metabolism, and (4) CK2 is localized predominantly in the nucleus. These bioinformatic results suggest that the observed phenotypic consequences of CK2 depletion may lie downstream of primary defects in chromatin organization and/or global gene expression. Further progress in defining the physiological role of CK2 will almost certainly require a better understanding of the mechanism of regulation of the enzyme. Beginning with the crystal structure of the human CK2 holoenzyme, we present a molecular model of filamentous CK2 that is consistent with earlier proposals that filamentous CK2 represents an inactive form of the enzyme. The potential role of filamentous CK2 in regulation in vivo is discussed.

Casein Kinase II↗
PubMed2005

Interethnic variability of ERCC2 polymorphisms.

Excision Repair Cross-Complementing Rodent Repair Group 2 (ERCC2) plays an important role in DNA repair by eliminating bulky DNA adducts produced by platinum agents during the nucleotide excision repair pathway. Several studies have associated polymorphisms in ERCC2 with response to platinum therapy, lung cancer risk, and DNA repair capacity. This study examined ERCC2 polymorphisms and haplotype structure across 18.9 kb in 95 European, 95 African, and 95 Asian individuals. Single-nucleotide polymorphisms (SNPs) (ERCC2 -9164 A>T, -1989 A>G, -516 G>A, 468 C>A [Arg156Arg], 1737 C>T [Val579Val], 2133 C>T [Asp711Asp], and 2251 T>G [Lys751Gln]) were mined and mapped using Golden Path, PolyMAPr, and Promolign. Genotyping was performed using PCR and pyrosequencing. Allele frequencies ranged from 0 to 0.47 (Europeans), 0.05 to 0.72 (Africans), and 0 to 0.47 (Asians). The synonymous cSNP at codon 579 could not be confirmed in our populations. There were significant differences in haplotype structure and frequency between populations. This information on ERCC2 genomic structure will allow the construction of definitive studies to clarify the clinical role of this important gene.

Adult↗
PubMed2005

Pharmacogenomics and its potential impact on drug and formulation development.

Recent advances in genomic research have provided the basis for new insights into the importance of genetic and genomic markers during the different stages of drug development. A new field of research, pharmacogenomics, which studies the relationship between drug effects and the genome, has emerged. Structural pharmacogenomics maps the complete DNA sequences of whole genomes (genotypes) including individual variations, and functional pharmacogenomics assesses the expression levels of thousands of genes in one single experiment. Together, these two areas of pharmacogenomics have generated massive databases, which have become a challenge for the research field of informatics and have fostered a new branch of research, bioinformatics. If skillfully used, the databases generated by pharmacogenomics together with data mining on the Web promise to improve the drug development process in a variety of areas: identification of drug targets, evaluation of toxicity, classification of diseases, evaluation of formulations, assessment of drug response and treatment, post-marketing applications, and development of personalized medicines.

Animals↗