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At least 1,423 records · Page 79Linked to original sources
PubMed2026

Hypoxia-inducible factor-1α promotes the malignant progression of cervical cancer cells by regulating lactate dehydrogenase A-mediated glycolysis.

BACKGROUND: Enhanced glycolysis is a hallmark of metabolic reprogramming in cervical cancer and plays a key role in tumor progression. Hypoxia-inducible factor-1α (HIF-1α), a core regulator of glycolytic metabolism, remains incompletely characterized in cervical cancer. This study aimed to investigate the expression pattern and clinical significance of HIF-1α in cervical cancer, and to explore its association with malignant biological behavior and lactate dehydrogenase A (LDHA)-related glycolytic metabolism in cervical cancer cells. METHODS: The expression level, clinicopathological features, immune infiltration correlation, and prognostic value of HIF-1α in cervical cancer were analyzed based on the The Cancer Genome Atlas (TCGA) database. HIF-1α overexpression and knockdown models were established in HeLa and Caski cells. Cell viability and invasive ability were assessed by Cell Counting Kit-8 (CCK-8) and Transwell assays, respectively. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) and Western blot were used to detect changes in LDHA expression. Lactate production was measured using a lactate assay kit, and intracellular reactive oxygen species (ROS) levels were determined by flow cytometry. RESULTS: Bioinformatics analysis showed that HIF-1α was highly expressed in cervical cancer and was closely associated with patient age, menopausal status, immune cell infiltration, and poor prognosis. Kaplan-Meier survival analysis demonstrated that patients with high HIF-1α expression had significantly worse overall survival (OS) than those with low expression. In vitro functional experiments further confirmed that HIF-1α overexpression significantly enhanced the viability and invasive ability of HeLa and Caski cells, whereas HIF-1α knockdown produced the opposite effects. HIF-1α overexpression was associated with increased messenger RNA (mRNA) and protein expression levels of LDHA, a key glycolytic molecule, along with increased lactate production and elevated intracellular ROS levels. CONCLUSIONS: HIF-1α is aberrantly highly expressed in cervical cancer and may enhance glycolytic activity by upregulating LDHA expression, thereby promoting the proliferation and invasion of cervical cancer cells. These findings suggest that HIF-1α could serve as a potential diagnostic, prognostic, and therapeutic target biomarker for cervical cancer.

Hypoxia-inducible factor-1α (HIF-1α)↗
PubMed2005

A functional annotation of subproteomes in human plasma.

The data collected by Human Proteome Organization's Plasma Proteome Pilot project phase was analyzed by members of our working group. Accordingly, a functional annotation of the human plasma proteome was carried out. Here, we report the findings of our analyses. First, bioinformatic analyses were undertaken to determine the likely sources of plasma proteins and to develop a protein interaction network of proteins identified in this project. Second, annotation of these proteins was performed in the context of functional subproteomes involved in the coagulation pathway, the mononuclear phagocytic system, the inflammation pathway, the cardiovascular system, and the liver; as well as the subset of proteins associated with DNA binding activities. Our analyses contributed to the Plasma Proteome Database (http://www.plasmaproteomedatabase.org), an annotated database of plasma proteins identified by HPPP as well as from other published studies. In addition, we address several methodological considerations including the selective enrichment of post-translationally modified proteins by the use of multi-lectin chromatography as well as the use of peptidomic techniques to characterize the low molecular weight proteins in plasma. Furthermore, we have performed additional analyses of peptide identification data to annotate cleavage of signal peptides, sites of intra-membrane proteolysis and post-translational modifications. The HPPP-organized, multi-laboratory effort, as described herein, resulted in much synergy and was essential to the success of this project.

Blood Coagulation↗
PubMed1999

Promoter analysis of co-regulated genes in the yeast genome.

The use of high density DNA arrays to monitor gene expression at a genome-wide scale constitutes a fundamental advance in biology. In particular, the expression pattern of all genes in Saccharomyces cerevisiae can be interrogated using microarray analysis where cDNAs are hybridized to an array of more than 6000 genes in the yeast genome. In an effort to build a comprehensive Yeast Promoter Database and to develop new computational methods for mapping upstream regulatory elements, we started recently in an on going collaboration with experimental biologists on analysis of large-scale expression data. It is well known that complex gene expression patterns result from dynamic interacting networks of genes in the genetic regulatory circuitry. Hierarchical and modular organization of regulatory DNA sequence elements are important information for our understanding of combinatorial control of gene expression. As a bioinformatics attempt in this new direction, we have done some computational exploration of various initial experimental data. We will use cell-cycle regulated gene expression as a specific example to demonstrate how one may extract promoter information computationally from such genome-wide screening. Full report of the experiments and of the complete analysis will be published elsewhere when all the experiments are to be finished later in this year (Spellman, P.T., et al. 1998. Mol. Biol. Cell 9, 3273-3297).

Base Sequence↗
PubMed2004

A brief review of computational gene prediction methods.

With the development of genome sequencing for many organisms, more and more raw sequences need to be annotated. Gene prediction by computational methods for finding the location of protein coding regions is one of the essential issues in bioinformatics. Two classes of methods are generally adopted: similarity based searches and ab initio prediction. Here, we review the development of gene prediction methods, summarize the measures for evaluating predictor quality, highlight open problems in this area, and discuss future research directions.

Computational Biology↗
PubMed2004

Optimizing substitution matrices by separating score distributions.

MOTIVATION: Homology search is one of the most fundamental tools in Bioinformatics. Typical alignment algorithms use substitution matrices and gap costs. Thus, the improvement of substitution matrices increases accuracy of homology searches. Generally, substitution matrices are derived from aligned sequences whose relationships are known, and gap costs are determined by trial and error. To discriminate relationships more clearly, we are encouraged to optimize the substitution matrices from statistical viewpoints using both positive and negative examples utilizing Bayesian decision theory. RESULTS: Using Cluster of Orthologous Group (COG) database, we optimized substitution matrices. The classification accuracy of the obtained matrix is better than that of conventional substitution matrices to COG database. It also achieves good performance in classifying with other databases.

Algorithms↗
PubMed2006

bioNMF: a versatile tool for non-negative matrix factorization in biology.

BACKGROUND: In the Bioinformatics field, a great deal of interest has been given to Non-negative matrix factorization technique (NMF), due to its capability of providing new insights and relevant information about the complex latent relationships in experimental data sets. This method, and some of its variants, has been successfully applied to gene expression, sequence analysis, functional characterization of genes and text mining. Even if the interest on this technique by the bioinformatics community has been increased during the last few years, there are not many available simple standalone tools to specifically perform these types of data analysis in an integrated environment. RESULTS: In this work we propose a versatile and user-friendly tool that implements the NMF methodology in different analysis contexts to support some of the most important reported applications of this new methodology. This includes clustering and biclustering gene expression data, protein sequence analysis, text mining of biomedical literature and sample classification using gene expression. The tool, which is named bioNMF, also contains a user-friendly graphical interface to explore results in an interactive manner and facilitate in this way the exploratory data analysis process. CONCLUSION: bioNMF is a standalone versatile application which does not require any special installation or libraries. It can be used for most of the multiple applications proposed in the bioinformatics field or to support new research using this method. This tool is publicly available at http://www.dacya.ucm.es/apascual/bioNMF.

Algorithms↗
PubMed2024

Elucidating the roles of SOD3 correlated genes and reactive oxygen species in rare human diseases using a bioinformatic-ontology approach.

Superoxide Dismutase 3 (SOD3) scavenges extracellular superoxide giving a hydrogen peroxide metabolite. Both Reactive Oxygen Species diffuse through aquaporins causing oxidative stress and biomolecular damage. SOD3 is differentially expressed in cancer and this research utilises Gene Expression Omnibus data series GSE2109 with 2,158 cancer samples. Genome-wide expression correlation analysis was conducted with SOD3 as the seed gene. Categorical SOD3 Pearson Correlation gene lists incrementing in correlation strength by 0.01 from ρ≥|0.34| to ρ≥|0.41| were extracted from the data. Positively and negatively SOD3 correlated genes were separated for each list and checked for significance against disease overlapping genes in the ClinVar and Orphanet databases via Enrichr. Disease causal genes were added to the relevant gene list and checked against Gene Ontology, Phenotype Ontology, and Elsevier Pathways via Enrichr before the significant ontologies containing causal and non-overlapping genes were reviewed with a literature search for possible disease and oxidative stress associations. 12 significant individually discriminated disorders were identified: Autosomal Dominant Cutis Laxa (p = 6.05x10-7), Renal Tubular Dysgenesis of Genetic Origin (p = 6.05x10-7), Lethal Arteriopathy Syndrome due to Fibulin-4 Deficiency (p = 6.54x10-9), EMILIN-1-related Connective Tissue Disease (p = 6.54x10-9), Holt-Oram Syndrome (p = 7.72x10-10), Multisystemic Smooth Muscle Dysfunction Syndrome (p = 9.95x10-15), Distal Hereditary Motor Neuropathy type 2 (p = 4.48x10-7), Congenital Glaucoma (p = 5.24x210-9), Megacystis-Microcolon-Intestinal Hypoperistalsis Syndrome (p = 3.77x10-16), Classical-like Ehlers-Danlos Syndrome type 1 (p = 3.77x10-16), Retinoblastoma (p = 1.9x10-8), and Lynch Syndrome (p = 5.04x10-9). 35 novel (21 unique) genes across 12 disorders were identified: ADNP, AOC3, CDC42EP2, CHTOP, CNN1, DES, FOXF1, FXR1, HLTF, KCNMB1, MTF2, MYH11, PLN, PNPLA2, REST, SGCA, SORBS1, SYNPO2, TAGLN, WAPL, and ZMYM4. These genes are proffered as potential biomarkers or therapeutic targets for the corresponding rare diseases discussed.

Humans↗
PubMed2006

Residues in substrate proteins that interact with GroEL in the capture process are buried in the native state.

We have used a bioinformatic approach to predict the natural substrate proteins for the Escherichia coli chaperonin GroEL based on two simple criteria. Natural substrate proteins should contain binding motifs similar in sequence to the mobile loop peptide of GroES that displaces the binding motif during the chaperonin cycle. Secondly, each substrate protein should contain multiple copies of the binding motif so that the chaperonin can perform "work" on the substrate protein. To validate these criteria, we have used a database of 252 proteins that have been experimentally shown to interact with the chaperonin machinery in vivo. More than 80% are identified by these criteria. The binding motifs of all 79 proteins in the database with a known three-dimensional structure are buried (<50% solvent-accessible surface area) in the native state. Our results show that the binding motifs are inaccessible in the native state but become solvent-exposed in unfolded state, thus enabling GroEL to distinguish between unfolded and native states. The structures of the binding motif in the native states of the substrate proteins include alpha-helices, beta-strands, and random coils. The diversity of secondary structures implies that there are large and varied conformational transitions in the recognition motifs after their displacement by the mobile loops of GroES.

Binding Sites↗
PubMed2006

The predicted secretome of Lactobacillus plantarum WCFS1 sheds light on interactions with its environment.

The predicted extracellular proteins of the bacterium Lactobacillus plantarum were analysed to gain insight into the mechanisms underlying interactions of this bacterium with its environment. Extracellular proteins play important roles in processes ranging from probiotic effects in the gastrointestinal tract to degradation of complex extracellular carbon sources such as those found in plant materials, and they have a primary role in the adaptation of a bacterium to changing environmental conditions. The functional annotation of extracellular proteins was improved using a wide variety of bioinformatics methods, including domain analysis and phylogenetic profiling. At least 12 proteins are predicted to be directly involved in adherence to host components such as collagen and mucin, and about 30 extracellular enzymes, mainly hydrolases and transglycosylases, might play a role in the degradation of substrates by L. plantarum to sustain its growth in different environmental niches. A comprehensive overview of all predicted extracellular proteins, their domains composition and their predicted function is provided through a database at http://www.cmbi.ru.nl/secretome which could serve as a basis for targeted experimental studies into the function of extracellular proteins.

Bacterial Adhesion↗
PubMed2003

Building a BRIDGE for the integration of heterogeneous data from functional genomics into a platform for systems biology.

The flood of data acquired from the increasing number of publicly available genomes has led to new demands for bioinformatics software. With the growing amount of information resulting from high throughput experiments new questions arise that often focus on the comparison of genes, genomes, and their expression profiles. Inferring new knowledge by combining different kinds of "post-genomics" data obviously necessitates the development of new approaches that allow the integration of variable data sources into a flexible framework. In this paper, we describe our concept for the integration of heterogeneous data into a platform for systems biology. We have implemented a Bioinformatics Resource for the Integration of heterogeneous Data from Genomic Explorations (BRIDGE) and illustrate the usability of our approach as a platform for systems biology for two sample applications.

Algorithms↗
PubMed2002

Protein sequence analysis in silico: application of structure-based bioinformatics to genomic initiatives.

The current pace of high-throughput genome sequencing programs coupled with high-throughput functional genomic screens has provided researchers with a bewildering array of sequence and biological data to contend with. Identification of proteins of interest from a particular biological study requires the application of bioinformatic tools to process and prioritise the data. From a protein function standpoint, transfer of annotation from known proteins to a novel target is currently the only practical way to convert vast quantities of raw sequence data into meaningful information. New bioinformatics tools now provide more sophisticated methods to transfer functional annotation, integrating sequence, family profile and structural search methodology. The importance of these approaches to medical research is increasing as we move to annotate the proteome through functional and structural genomic efforts.

Animals↗
PubMed2003

Making decisions for structural genomics.

A large number of structural genomics programmes have been established worldwide with the common aim of large-scale, high-throughput protein structure determination. Due to the considerable challenges posed by the experimental methods of structural determination (primarily X-ray crystallography and nuclear magnetic resonance spectroscopy) it is important to select and prioritise candidate molecules that will maximise the information gained from each new structure. This paper describes the scientific principles that underlie target selection and the various bioinformatics tools that may be employed in such selection procedures. Then follows a discussion of the availability of resources incorporating these methods and a description of the design and application of a purpose-built target selection resource for structural genomics.

Animals↗
PubMed2002

Computing the chromaffin cell: a research-community curator/user approach to biocomputation for chromaffin cell biology.

Exocytosis, stimulus-secretion coupling, real-time measurements of neurosecretion, and stimulus-secretion-synthesis coupling (stimulus-transcription coupling) were all initially proposed and verified in the chromaffin cell. Detailed analysis of the molecules and pathways responsible for secretion and transsynaptic regulation of gene expression patterns in neuroendoccrine cells have been very fruitfully explored in chromaffin cells and the related PC12 pheochromocytoma cell line, using modern molecular biologcal, cellular imaging, and expression profiling techniques. The time is clearly at hand for a concerted bioinformatics approach to acquiring and synthesizing electrophysiological, biochemical, and proteomic/genomic data on the chromaffin cell. Accelerating this process will fully realize the unique attributes of the chromaffin cell as a homogeneous, accessible, fully functional model of the posttmitotic neuroendocrine cell.

Animals↗
PubMed1998

Towards the Asia-Pacific Bioinformatics Network.

A vigorous effort has been undertaken to develop a robust and high speed network in the Asia-Pacific region which satisfies the needs of the scientific community. Consortium for Asia-Pacific Advanced Network (APAN) are established in some countries in this region. Our group, along with collaborators in Singapore and Australia, is moving towards the establishment of Asia-Pacific Bioinformatics Network (APBioNet) based on APAN. In this paper, we describe the concept of APBioNet and introduce the activities of DNA Data Bank of Japan (DDBJ) which will play an important role in APBioNet. Some salient features of APBioNet are also discussed.

Animals↗
PubMed2006

Automated reprocessing pipeline for searching heterogeneous mass spectrometric data of the HUPO Brain Proteome Project pilot phase.

The newly available techniques for sensitive proteome analysis and the resulting amount of data require a new bioinformatics focus on automatic methods for spectrum reprocessing and peptide/protein validation. Manual validation of results in such studies is not feasible and objective enough for quality relevant interpretation. The necessity for tools enabling an automatic quality control is, therefore, important to produce reliable and comparable data in such big consortia as the Human Proteome Organization Brain Proteome Project. Standards and well-defined processing pipelines are important for these consortia. We show a way for choosing the right database model, through collecting data, processing these with a decoy database and end up with a quality controlled protein list merged from several search engines, including a known false-positive rate.

Algorithms↗
PubMed2006

The LCB Data Warehouse.

UNLABELLED: The Linnaeus Centre for Bioinformatics Data Warehouse (LCB-DWH) is a web-based infrastructure for reliable and secure microarray gene expression data management and analysis that provides an online service for the scientific community. The LCB-DWH is an effort towards a complete system for storage (using the BASE system), analysis and publication of microarray data. Important features of the system include: access to established methods within R/Bioconductor for data analysis, built-in connection to the Gene Ontology database and a scripting facility for automatic recording and re-play of all the steps of the analysis. The service is up and running on a high performance server. At present there are more than 150 registered users. AVAILABILITY: An open functional version is available at https://dw.lcb.uu.se/index.phtml?i_login=test. User accounts are created upon request. Additional facilities including plug-ins, user documentation and a password protected data storage system are available from http://www.lcb.uu.se/lcbdw.php

Computer Graphics↗
PubMed2025

Establishing the ELIXIR Microbiome Community.

Microbiome research has grown substantially over the past decade in terms of the range of biomes sampled, identified taxa, and the volume of data derived from the samples. In particular, experimental approaches such as metagenomics, metabarcoding, metatranscriptomics and metaproteomics have provided profound insights into the vast, hitherto unknown, microbial biodiversity. The ELIXIR Marine Metagenomics Community, initiated amongst researchers focusing on marine microbiomes, has concentrated on promoting standards around microbiome-derived sequence analysis, as well as understanding the gaps in methods and reference databases, and identifying solutions to the computational overheads of performing such analyses. Nevertheless, the methods used and the challenges faced are not confined to marine microbiome studies, but are broadly applicable to other biomes. Thus, expanding this Marine Metagenomics Community to a more inclusive ELIXIR Microbiome Community will enable it to encompass a broader range of biomes and link expertise across 'omics technologies. Furthermore, engaging with a large number of researchers will improve the efficiency and sustainability of bioinformatics infrastructure and resources for microbiome research (standards, data, tools, workflows, training), which will enable a deeper understanding of the function and taxonomic composition of the different microbial communities.

Computational Biology↗
PubMed2005

Using bioinformatics for drug target identification from the genome.

Genomics and proteomics technologies have created a paradigm shift in the drug discovery process, with bioinformatics having a key role in the exploitation of genomic, transcriptomic, and proteomic data to gain insights into the molecular mechanisms that underlie disease and to identify potential drug targets. We discuss the current state of the art for some of the bioinformatic approaches to identifying drug targets, including identifying new members of successful target classes and their functions, predicting disease relevant genes, and constructing gene networks and protein interaction networks. In addition, we introduce drug target discovery using the strategy of systems biology, and discuss some of the data resources for the identification of drug targets. Although bioinformatics tools and resources can be used to identify putative drug targets, validating targets is still a process that requires an understanding of the role of the gene or protein in the disease process and is heavily dependent on laboratory-based work.

Alzheimer Disease↗