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At least 685 records · Page 38Linked to original sources

Genomics and variation of ionotropic glutamate receptors: implications for neuroplasticity.

We used two approaches to identify sequence variants in ionotropic glutamate receptor (IGR) genes: high-throughput screening and resequencing techniques, and "information mining" of public (e.g. dbSNP, ENSEMBL) and private (i.e. Celera Discovery System) sequence databases. Each of the 16 known IGRs is represented in these databases, their positions on a canonical physical map are established. Comparisons of mouse, rat, and human sequences revealed substantial conservation among these genes, which are located on different chromosomes but found within syntenic groups of genes. The IGRs are members of a phylogenetically ancient gene family, sharing similarities with glutamate-like receptors in plants. Parsimony analysis of amino acid sequences groups the IGRs into three distinct clades based on ligand-binding specificity and structural features, such as the channel pore and membrane spanning domains. A collection of 38 variants with amino acid changes was obtained by combining screening, resequencing, and informatics approaches for several of the IGR genes. This represents only a fraction of the sequence variation across these genes, but in fact these may constitute a large fraction of the common polymorphisms at these genes and these polymorphisms are a starting point for understanding the role of these variants in function. Genetically influenced human neurobehavioral phenotypes are likely to be linked to IGR genetic variants. Because ionotropic glutamate receptor activation leads to calcium entry, which is fundamental in brain development and in forms of synaptic plasticity essential for learning and memory and is essential for neuronal survival, it is likely that sequence variants in IGR genes may have profound functional roles in neuronal activation and survival mechanisms.

Amino Acid Substitution↗

Phylogenetic analysis of bacterial isolates from man-made high-pH, high-salt environments and identification of gene-cassette-associated open reading frames.

Environmental samples were collected from high-pH sites in Pakistan, including a uranium heap set up for carbonate leaching, the lime unit of a tannery, and the Khewra salt mine. Another sample was collected from a hot spring on the shore of the soda lake, Magadi, in Kenya. Microbial cultures were enriched from Pakistani samples. Phylogenetic analysis of isolates was carried out by sequencing 16S rRNA genes. Genomic DNA was amplified by polymerase chain reaction using integron gene-cassette-specific primers. Different gene-cassette-linked genes were recovered from the cultured strains related to Halomonas magadiensis, Virgibacillus halodenitrificans, and Yania flava and from the uncultured environmental DNA sample. The usefulness of this technique as a tool for gene mining is indicated.

Alkalies↗

Piezophysiology of genome wide gene expression levels in the yeast Saccharomyces cerevisiae.

Hydrostatic pressure is one of the physical factors affecting cellular physiology. Hydrostatic pressure of a few hundred MPa decreases the viability of yeast cells, and pressure of a few tens MPa decreases the growth rate. To understand the effect of hydrostatic pressure, we employed yeast DNA microarrays and analyzed genome-wide gene-expression levels after the pressure treatment with 180 MPa (immediate) at 4 degrees C and recovery incubation for 1 h and 40 MPa (16 h) at 4 degrees C and recovery incubation for 1 h. The transcription of genes involved in energy metabolism, cell defense, and protein metabolism was significantly induced by the pressure treatment. Genome-wide expression profiles suggested that high pressure caused damage to cellular organelles, since the induced gene products were localized in the membrane structure and/or cellular organelles. Hierarchical clustering analysis suggested that the damage caused by the pressure was similar to that caused by detergents, oils, and freezing/thawing. We also estimated the contribution of induced genes to barotolerance using some strains that have the deletion in the corresponding genes.

Animals↗

Simulating the growth of viruses.

To explore how the genome of an organism defines its growth, we have developed a computer simulation for the intracellular growth of phage T7 on its E. coli host. Our simulation, which incorporates 30 years of genetic, biochemical, physiological, and biophysical data, is used here to study how the intracellular resources of the host, determined by the specific growth rate of the host, contribute toward phage development. It is also used to probe how changes in the linear organization of genetic elements on the T7 genome can affect T7 development. Further, we show how time-series trajectories of T7 mRNA and protein levels generated by the simulation may be used as raw data to test data-mining strategies, specifically, to identify partners in protein-protein interactions. Finally, we suggest how generalization of this work can lead to a knowledge-driven simulation for the growth of any virus.

Algorithms↗

Inherited disorder phenotypes: controlled annotation and statistical analysis for knowledge mining from gene lists.

BACKGROUND: Analysis of inherited diseases and their associated phenotypes is of great importance to gain knowledge of underlying genetic interactions and could ultimately give clinically useful insights into disease processes, including complex diseases influenced by multiple genetic loci. Nevertheless, to date few computational contributions have been proposed for this purpose, mainly due to lack of controlled clinical information easily accessible and structured for computational genome-wise analyses. To allow performing phenotype analyses of inherited disorder related genes we implemented new original modules within GFINDer http://www.bioinformatics.polimi.it/GFINDer/, a Web system we previously developed that dynamically aggregates functional annotations of user uploaded gene lists and allows performing their statistical analysis and mining. RESULTS: New GFINDer modules allow annotating large numbers of user classified biomolecular sequence identifiers with morbidity and clinical information, classifying them according to genetic disease phenotypes and their locations of occurrence, and statistically analyzing the obtained classifications. To achieve this we exploited, normalized and structured the information present in textual form in the Clinical Synopsis sections of the Online Mendelian Inheritance in Man (OMIM) databank. Such valuable information delineates numerous signs and symptoms accompanying many genetic diseases and it is divided into phenotype location categories, either by organ system or type of finding. CONCLUSION: Supporting phenotype analyses of inherited diseases and biomolecular functional evaluations, GFINDer facilitates a genomic approach to the understanding of fundamental biological processes and complex cellular mechanisms underlying patho-physiological phenotypes.

Computational Biology↗

Analysis of the plant proteome.

For many years the analysis of plant proteomes has been restricted to the construction of descriptive catalogues or the search for markers. The analysis of plant proteomes is now gaining a functional dimension, however, because the focus has shifted onto well-defined plant-specific tissues and organelles, the simultaneous mining of proteomic and physiological data and specific methodological efforts.

Biotechnology↗

Spatiotemporal genomic analysis and risk assessment of the plasmids carrying blaOXA-48-like genes based on a large-scale international dataset.

BACKGROUND: The spread of OXA-48-like carbapenemases represents a major public health challenge. Although previous studies have investigated OXA-48-like carbapenemases risk factors, nosocomial dissemination, and plasmid dynamics, an integrated plasmid-centered framework combining complete plasmid mining, transmission-unit analysis, phylogenetic reconstruction, and machine learning-based risk assessment remains limited. METHODS: We systematically collected 747 complete plasmid sequences carrying blaOXA-48-like genes from the NCBI database, establishing the largest collections of complete plasmid sequences to date. Using an integrative framework of population genomics, phylogenetic dating, and machine learning, this study aimed to characterize the dissemination patterns, plasmid replicon diversity, transmission units, mobile genetic elements, co-resistance profiles, and risk classification of these plasmid. RESULTS: Plasmids carrying blaOXA-48-like genes were detected across 50 countries on six continents, with blaOXA-48 predominating in Europe, blaOXA-181 in South Asia, and blaOXA-232 largely in Asia. IncL and ColKP3/IncX3 replicons, together with Tn1999.2 and other MGEs, were central drivers of plasmid maintenance and spread. Sixteen transmission units were defined, with AA068_Cluster3 estimated to have originated in the Netherlands around 2005 before expanding to Europe, the Middle East, Asia, and North America. Co-resistance analyses revealed frequent modules involving aminoglycoside and quinolone resistance, with qnrS1 and aph(3'')-Ib most prevalent. Notably, high-risk transposon structures were often identified in non-clinical environments, underscoring their cross-ecological transmission potential. Machine learning-based classification models showed good internal performance for predefined composite-risk categories, with plasmid mobility, clinical/non-clinical source composition, and host background contributing to the classification results. CONCLUSIONS: This study provides a large-scale plasmid-centered genomic analysis of publicly available complete plasmid sequences carrying blaOXA-48-like genes, integrating transmission-unit inference, phylogeographic reconstruction, mobile genetic element and co-resistance profiling, and composite genomic risk stratification. This gene-centered framework may support future One Health-oriented antimicrobial resistance surveillance and prioritization of plasmids with higher dissemination and resistance potential.

Plasmids↗

Identification and analysis of single-nucleotide polymorphisms in the gemcitabine pharmacologic pathway.

Significant variability in the antitumor efficacy and systemic toxicity of gemcitabine has been observed in cancer patients. However, there are currently no tools for prospective identification of patients at risk for untoward events. This study has identified and validated single-nucleotide polymorphisms (SNP) in genes involved in gemcitabine metabolism and transport. Database mining was conducted to identify SNPs in 14 genes involved in gemcitabine metabolism. Pyrosequencing was utilized to determine the SNP allele frequencies in genomic DNA from European and African populations (n=190). A total of 14 genetic variants (including 12 SNPs) were identified in eight of the gemcitabine metabolic pathway genes. The majority of the database variants were observed in population samples. Nine of the 14 (64%) polymorphisms analyzed have allele frequencies that were found to be significantly different between the European and African populations (P<0.05). This study provides the first step to identify markers for predicting variability in gemcitabine response and toxicity.

Antimetabolites, Antineoplastic↗

Integrating explainable artificial intelligence with multiomics systems biology and electronic health record data mining for personalized drug repurposing in Alzheimer's disease.

Alzheimer's disease (AD) is characterized by region- and patient-specific molecular heterogeneity, which hinders therapeutic design. In this study, we introduce PRISM-ML (PRecision-medicine using Interpretable Systems and Multiomics with Machine Learning), an open-source integrated analysis pipeline that combines interpretable machine learning with systems biology and electronic health records data mining to elucidate the molecular diversity of AD and predict promising drug repurposing opportunities. First, we integrated and harmonized transcriptomic (bulk RNA-seq) and genomic (genome-wide association study) data from 2105 brain samples, each with matched data from the same individual (1363 AD patients, 742 controls; 9 tissues), sourced from three independent studies. Random forest classifiers with SHapley Additive exPlanations identified patient-specific biomarkers; unsupervised clustering resolved 36 molecularly distinct subtissues (defined as clusters of samples within a brain tissue that share a specific expression pattern); and gene-gene coexpression networks prioritized 262 high-centrality bottleneck genes as putative regulators of dysregulated pathways. Next, knowledge graph-based drug repurposing predicted six Food and Drug Administration (FDA)-approved drugs that simultaneously target multiple bottleneck genes and multiple AD-relevant pathways. Notably, in a large US de-identified insurance-claims database (n&#x2009;=&#x2009;364&#xa0;733), exposure to promethazine, one of the candidate drugs, was associated with a 57%-62% lower incidence of AD versus an active antihistamine comparator (adjusted hazard ratio 0.38; inverse-probability weighted 0.43; both P&#x2009;<&#x2009;.001), providing real-world support for its repurposing potential. In summary, PRISM-ML, as an explainable multiomics analysis pipeline, is readily transferable to other complex diseases, advancing precision medicine.

Alzheimer Disease↗

Phenotypic data in FlyBase.

Phenotypic analysis combined with molecular genetics is a powerful tool for mapping gene function onto the genome. Phenotypic data are, by their nature, descriptive, and as varied as the range of mutant phenotypes that can be presented by the organism under study. This paper discusses the mechanisms FlyBase has implemented to systematise published phenotypic data about Drosophila, and provides an introduction to the query tools available for the mining of the data. Though FlyBase is specific to Drosophila, the issues faced in devising protocols for capturing, storing and reporting data are the same issues faced by any database with an interest in using phenotypic data to maximise the potential of genomic analysis.

Alleles↗

Lineages of acidophilic archaea revealed by community genomic analysis.

Novel, low-abundance microbial species can be easily overlooked in standard polymerase chain reaction (PCR)-based surveys. We used community genomic data obtained without PCR or cultivation to reconstruct DNA fragments bearing unusual 16S ribosomal RNA (rRNA) and protein-coding genes from organisms belonging to novel archaeal lineages. The organisms are minor components of all biofilms growing in pH 0.5 to 1.5 solutions within the Richmond Mine, California. Probes specific for 16S rRNA showed that the fraction less than 0.45 micrometers in diameter is dominated by these organisms. Transmission electron microscope images revealed that the cells are pleomorphic with unusual folded membrane protrusions and have apparent volumes of <0.006 cubic micrometer.

Amino Acid Sequence↗

The human proteomics initiative (HPI).

The availability of the human genome sequence has enabled the exploration and exploitation of the human genome and proteome to begin. Research has now focussed on the annotation of the genome and in particular of the proteome. With expert annotation extracted from the literature by biologists as the foundation, it has been possible to expand into the areas of data mining and automatic annotation. With further development and integration of pattern recognition methods and the application of alignments clustering, proteome analysis can now be provided in a meaningful way. These various approaches have been integrated to attach, extract and combine as much relevant information as possible to the proteome. This resource should be valuable to users from both research and industry.

Algorithms↗

Genome function--a virus-world view.

By studying viruses one may begin to understand how static genomes can define dynamic processes of development. This talk will describe some of the approaches we are taking, using computer simulations and laboratory experiments, to account for the many molecular-level processes and interactions that occur when a common bacterium, E. coli, is infected by one of its viruses, phage T7. We accounted for processes of phage genome entry, transcription, translation, and DNA replication, including protein-DNA and protein-protein regulatory interactions, and we predicted the dynamics of phage progeny formation. The simulations have enabled us to identify limiting host-cell resources in phage growth, discover novel anti-viral strategies, and suggest frameworks for mining data from global mRNA and protein studies.

Bacteriophage T7↗

PlasmoDB: exploring genomics and post-genomics data of the malaria parasite, Plasmodium falciparum.

The recent completion of the genome sequence of Plasmodium falciparum 3D7 provides the foundation for genome-wide analysis of the parasite. In addition to DNA and gene sequence data, postgenomic methods including microarray-based transcript profiling and high-throughput proteomics are now accessible to Plasmodium researchers. The Plasmodium Genome database ( ) was developed to provide rapid and convenient access to the terabytes of genomic-scale data now being generated around the world. All data are available in a relational framework, permitting convenient downloading, browsing, and analysis. Combinatorial use of data analysis tools enables powerful data mining queries, such as combining gene and protein expression data to monitor changes through various life-cycle stages. Functional predictions can be used to explore potential targets for antimalarial drug development. This report outlines the use of PlasmoDB to examine redox-active functions in Plasmodium.

Animals↗

The evolutionarily conserved single-copy gene for murine Tpr encodes one prevalent isoform in somatic cells and lacks paralogs in higher eukaryotes.

Vertebrate Tpr and its probable homologs in insects and yeast are heptad repeat-dominated nuclear proteins of M(r) 195,000 to M(r) 267,000 the functions of which are still largely unknown. Whereas two homologs exist in Saccharomyces cerevisiae, it has remained uncertain whether metazoans possess different paralogs or isoforms of Tpr that might explain controversial reports on the subcellular localization of this protein. To address these possibilities, we first determined the sequence and structure of the murine tpr gene, revealing a TATA box-less gene of approximately 57 kb and 52 exons. Southern hybridization of genomic DNA and radiation hybrid mapping showed that murine tpr exists as a single-copy gene on chromosome 1; RNA blotting analyses and EST (expressed sequence tag) database mining revealed that its expression results in only one major mRNA in embryonic and most adult tissues. Accordingly, novel antibodies against the N- and C-terminus of Tpr identified the full-length protein as the major translation product in different somatic cell types; reinvestigation of Tpr localization by confocal microscopy corroborated a predominant localization at the nuclear pore complexes in these cells. Antibody specificity and reliability of Tpr localization was demonstrated by post-transcriptional tpr gene silencing using siRNAs that eliminated the Tpr signal at the nuclear periphery but did not affect intranuclear staining of Tpr-unrelated proteins. Finally, we defined several sequence and structural features that characterize Tpr polypeptides in different species and used these as a guideline to search whole-genome sequence databases for putative paralogs of Tpr in higher eukaryotes. This approach resulted in identification of the Tpr orthologs in Arabidopsis thaliana and Caenorhabditis elegans, but also in the realization that no further paralogs of Tpr exist in several metazoan model organisms or in humans. In summary, these results reveal Tpr to be a unique protein localized at the nuclear periphery of the somatic cell in mammals.

Animals↗

Computational cluster validation in post-genomic data analysis.

MOTIVATION: The discovery of novel biological knowledge from the ab initio analysis of post-genomic data relies upon the use of unsupervised processing methods, in particular clustering techniques. Much recent research in bioinformatics has therefore been focused on the transfer of clustering methods introduced in other scientific fields and on the development of novel algorithms specifically designed to tackle the challenges posed by post-genomic data. The partitions returned by a clustering algorithm are commonly validated using visual inspection and concordance with prior biological knowledge--whether the clusters actually correspond to the real structure in the data is somewhat less frequently considered. Suitable computational cluster validation techniques are available in the general data-mining literature, but have been given only a fraction of the same attention in bioinformatics. RESULTS: This review paper aims to familiarize the reader with the battery of techniques available for the validation of clustering results, with a particular focus on their application to post-genomic data analysis. Synthetic and real biological datasets are used to demonstrate the benefits, and also some of the perils, of analytical clustervalidation. AVAILABILITY: The software used in the experiments is available at http://dbkweb.ch.umist.ac.uk/handl/clustervalidation/. SUPPLEMENTARY INFORMATION: Enlarged colour plots are provided in the Supplementary Material, which is available at http://dbkweb.ch.umist.ac.uk/handl/clustervalidation/.

Algorithms↗

Knowledge discovery in traditional Chinese medicine: state of the art and perspectives.

OBJECTIVE: As a complementary medical system to Western medicine, traditional Chinese medicine (TCM) provides a unique theoretical and practical approach to the treatment of diseases over thousands of years. Confronted with the increasing popularity of TCM and the huge volume of TCM data, historically accumulated and recently obtained, there is an urgent need to explore these resources effectively by the techniques of knowledge discovery in database (KDD). This paper aims at providing an overview of recent KDD studies in TCM field. METHODS: A literature search was conducted in both English and Chinese publications, and major studies of knowledge discovery in TCM (KDTCM) reported in these materials were identified. Based on an introduction to the state of the art of TCM data resources, a review of four subfields of KDTCM research was presented, including KDD for the research of Chinese medical formula, KDD for the research of Chinese herbal medicine, KDD for TCM syndrome research, and KDD for TCM clinical diagnosis. Furthermore, the current state and main problems in each subfield were summarized based on a discussion of existing studies, and future directions for each subfield were also proposed accordingly. RESULTS: A series of KDD methods are used in existing KDTCM researches, ranging from conventional frequent itemset mining to state of the art latent structure model. Considerable interesting discoveries are obtained by these methods, such as novel TCM paired drugs discovered by frequent itemset analysis, functional community of related genes discovered under syndrome perspective by text mining, the high proportion of toxic plants in the botanical family Ranunculaceae disclosed by statistical analysis, the association between M-cholinoceptor blocking drug and Solanaceae revealed by association rule mining, etc. It is particularly inspiring to see some studies connecting TCM with biomedicine, which provide a novel top-down view for functional genomics research. However, further developments of KDD methods are still expected to better adapt to the features of TCM. CONCLUSIONS: Existing studies demonstrate that KDTCM is effective in obtaining medical discoveries. However, much more work needs to be done in order to discover real diamonds from TCM domain. The usage and development of KDTCM in the future will substantially contribute to the TCM community, as well as modern life science.

Databases, Factual↗