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PubMed2006

Cytochrome P450 mono-oxygenases in conifer genomes: discovery of members of the terpenoid oxygenase superfamily in spruce and pine.

Diterpene resin acids, together with monoterpenes and sesquiterpenes, are the most prominent defence chemicals in conifers. These compounds belong to the large group of structurally diverse terpenoids formed by enzymes known as terpenoid synthases. CYPs (cytochrome P450-dependent mono-oxygenases) can further increase the structural diversity of these terpenoids. While most terpenoids are characterized as specialized or secondary metabolites, some terpenoids, such as the phytohormones GA (gibberellic acid), BRs (brassinosteroids) and ABA (abscisic acid), have essential functions in plant growth and development. To date, very few CYP genes involved in conifer terpenoid metabolism have been functionally characterized and were limited to two systems, yew (Taxus) and loblolly pine (Pinus taeda). The characterized yew CYP genes are involved in taxol diterpene biosynthesis, while the only characterized pine terpenoid CYP gene is part of DRA (diterpene resin acid) biosynthesis. These CYPs from yew and pine are members of two apparently conifer-specific CYP families within the larger CYP85 clan, one of four plant CYP multifamily clans. Other CYP families within the CYP85 clan were characterized from a variety of angiosperms with functions in terpenoid phytohormone metabolism of GA, BR, and ABA. The recent development of EST (expressed sequence tag) and FLcDNA (where FL is full-length) sequence databases and cDNA collections for species of two conifers, spruce (Picea) and pine, allows for the discovery of new terpenoid CYPs in gymnosperms by means of large-scale sequence mining, phylogenetic analysis and functional characterization. Here, we present a snapshot of conifer CYP data mining, discovery of new conifer CYPs in all but one family within the CYP85 clan, and suggestions for their functional characterization. This paper will focus on the discovery of conifer CYPs associated with diterpene metabolism and CYP with possible functions in the formation of GA, BR, and ABA in conifers.

Algorithms↗
PubMed2005

HomoMINT: an inferred human network based on orthology mapping of protein interactions discovered in model organisms.

BACKGROUND: The application of high throughput approaches to the identification of protein interactions has offered for the first time a glimpse of the global interactome of some model organisms. Until now, however, such genome-wide approaches have not been applied to the human proteome. RESULTS: In order to fill this gap we have assembled an inferred human protein interaction network where interactions discovered in model organisms are mapped onto the corresponding human orthologs. In addition to a stringent assignment to orthology classes based on the InParanoid algorithm, we have implemented a string matching algorithm to filter out orthology assignments of proteins whose global domain organization is not conserved. Finally, we have assessed the accuracy of our own, and related, inferred networks by benchmarking them against i) an assembled experimental interactome, ii) a network derived by mining of the scientific literature and iii) by measuring the enrichment of interacting protein pairs sharing common Gene Ontology annotation. CONCLUSION: The resulting networks are named HomoMINT and HomoMINT_filtered, the latter being based on the orthology table filtered by the domain architecture matching algorithm. They contains 9749 and 5203 interactions respectively and can be analyzed and viewed in the context of the experimentally verified interactions between human proteins stored in the MINT database. HomoMINT is constantly updated to take into account the growing information in the MINT database.

Algorithms↗
PubMed2004

B.E.A.R. GeneInfo: a tool for identifying gene-related biomedical publications through user modifiable queries.

BACKGROUND: Once specific genes are identified through high throughput genomics technologies there is a need to sort the final gene list to a manageable size for validation studies. The triaging and sorting of genes often relies on the use of supplemental information related to gene structure, metabolic pathways, and chromosomal location. Yet in disease states where the genes may not have identifiable structural elements, poorly defined metabolic pathways, or limited chromosomal data, flexible systems for obtaining additional data are necessary. In these situations having a tool for searching the biomedical literature using the list of identified genes while simultaneously defining additional search terms would be useful. RESULTS: We have built a tool, BEAR GeneInfo, that allows flexible searches based on the investigators knowledge of the biological process, thus allowing for data mining that is specific to the scientist's strengths and interests. This tool allows a user to upload a series of GenBank accession numbers, Unigene Ids, Locuslink Ids, or gene names. BEAR GeneInfo takes these IDs and identifies the associated gene names, and uses the lists of gene names to query PubMed. The investigator can add additional modifying search terms to the query. The subsequent output provides a list of publications, along with the associated reference hyperlinks, for reviewing the identified articles for relevance and interest. An example of the use of this tool in the study of human prostate cancer cells treated with Selenium is presented. CONCLUSIONS: This tool can be used to further define a list of genes that have been identified through genomic or genetic studies. Through the use of targeted searches with additional search terms the investigator can limit the list to genes that match their specific research interests or needs. The tool is freely available on the web at http://prostategenomics.org1, and the authors will provide scripts and database components if requested mdatta@mcw.edu

Databases, Genetic↗
PubMed2005

Using co-occurrence network structure to extract synonymous gene and protein names from MEDLINE abstracts.

BACKGROUND: Text-mining can assist biomedical researchers in reducing information overload by extracting useful knowledge from large collections of text. We developed a novel text-mining method based on analyzing the network structure created by symbol co-occurrences as a way to extend the capabilities of knowledge extraction. The method was applied to the task of automatic gene and protein name synonym extraction. RESULTS: Performance was measured on a test set consisting of about 50,000 abstracts from one year of MEDLINE. Synonyms retrieved from curated genomics databases were used as a gold standard. The system obtained a maximum F-score of 22.21% (23.18% precision and 21.36% recall), with high efficiency in the use of seed pairs. CONCLUSION: The method performs comparably with other studied methods, does not rely on sophisticated named-entity recognition, and requires little initial seed knowledge.

Algorithms↗
PubMed2026

Livestock Multi-Omics Integration: A Systematic Framework From Statistical Association to Causal Interpretation.

Livestock multi-omics integration is key to unraveling complex trait regulation, yet systematic, livestock-specific strategies remain scarce. This review traces the progression from single-omics accumulation to multi-dimensional integration, highlighting how large-scale genomic, epigenomic, and transcriptomic projects lay the foundation for functional dissection. We identify core impediments: extreme species diversity, marked data heterogeneity, limited sample sizes, and a pervasive reduction of multi-omics data to simplistic differential screens, resulting in low translational efficiency. We critically appraise four common pitfalls-overinterpreting correlation as causation, relegating proteomics to corroborating transcriptomics, incomplete microbiome-host integration lacking environmental context, and systematic neglect of metabolic fluxomics-and show how exposomics and fluxomics add necessary causal and dynamic dimensions. To address these, we propose a livestock-adapted three-tier analytical framework: (1) statistical association of cross-omics covariation patterns; (2) machine learning-driven feature mining and integrative modeling; and (3) causal interpretation encompassing Mendelian randomization, prior-knowledge-guided network inference, and physical causal evidence via fluxomics and metabolic control analysis. We further discuss how multimodal sequencing (single-cell, spatial, temporal) and generative AI can fundamentally mitigate heterogeneity and strengthen causal evidence. Finally, we outline future priorities in database standardization, livestock-specific benchmarking, and translational pipelines, charting a path from correlation-centric reporting to mechanistic causality and precision breeding.

Animals↗
PubMed2000

Mouse homologue of the human SART3 gene encoding tumor-rejection antigen.

We recently isolated a human SART3 (hSART3) gene encoding a tumor-rejection antigen recognized by HLA-A2402-restricted cytotoxic T lymphocytes (CTLs). The hSART3 was also found to exist as an RNA-binding nuclear protein of unknown biological function. In this study, we cloned and analyzed the homologous mouse SART3 (mSART3) gene in order to understand better the function of hSART3, and to aid in establishing animal models of specific immunotherapy. The cloned 3586-bp cDNA encoded a 962-amino acid polypeptide with high homology to hSART3 (80% or 86% identity at the nucleotide or protein level, respectively). Nonapeptides recognized by the HLA-A2402-restricted CTLs and all of the RNA-binding motifs were conserved between hSART3 and mSART3. The mSART3 mRNA was ubiquitously expressed in normal tissues, with low level expression in the liver, heart, and skeletal muscle. It was widely expressed in various organs from as early as day 7 of gestation. mSART3 was mapped to chromosome 5, a syntenic region for human chromosome 12q23-24, and its genomic DNA extended over 28-kb and consisted of 19 exons. This information should be important for studies of the biological functions of the SART3 protein and for the establishment of animal models of specific cancer immunotherapy.

Amino Acid Sequence↗
PubMed2006

Penaeus monodon gene discovery project: the generation of an EST collection and establishment of a database.

A large-scale expressed sequence tag (EST) sequencing project was undertaken for the purpose of gene discovery in the black tiger shrimp Penaeus monodon. Initially, 15 cDNA libraries were constructed from different tissues (eyestalk, hepatopancrease, haematopoietic tissue, haemocyte, lymphoid organ, and ovary) of shrimp, reared under normal or stress conditions, to identify tissue-specific genes and genes responding to infection and heat stress. A total of 10,100 clones were analyzed by single-pass sequencing from the 5' end. Clustering and assembling of these ESTs resulted in a total of 4845 unique sequences with 917 overlapping contigs and 3928 singletons. The redundancy of each cDNA library ranged from 13.4% to 61.3% with an overall redundancy of 61.1%. About half of these ESTs (2365 clones, 48.8%) showed significant homology (BLASTX, e-values <10(-4)) to known genes. A high proportion of P. monodon ESTs was most similar to the predicted protein sequences from various organisms, e.g. Homo sapiens (9%), Mus musculus (7%), Drosophila (6%), Gallus sp.(6%), and Anopheles (5%). Only 6% showed the highest similarity to other known genes from shrimp due to the limited sequence entries of the species in the public database. Several tissue-specific transcripts were identified as well as the candidate genes that may be implicated in the immune response. In addition, bioinformatic mining of microsatellites from the P. monodon ESTs identified 997 unique microsatellite containing ESTs in which 74 loci resided within the genes of known functions. Consequently, the P. monodon EST database was established. The EST sequence data and the BLAST results were stored and made available through a web-accessible database (). This EST database provides a useful resource for gene identification and functional genomic studies of shrimp.

Animals↗
PubMed2005

BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis.

biomaRt is a new Bioconductor package that integrates BioMart data resources with data analysis software in Bioconductor. It can annotate a wide range of gene or gene product identifiers (e.g. Entrez-Gene and Affymetrix probe identifiers) with information such as gene symbol, chromosomal coordinates, Gene Ontology and OMIM annotation. Furthermore biomaRt enables retrieval of genomic sequences and single nucleotide polymorphism information, which can be used in data analysis. Fast and up-to-date data retrieval is possible as the package executes direct SQL queries to the BioMart databases (e.g. Ensembl). The biomaRt package provides a tight integration of large, public or locally installed BioMart databases with data analysis in Bioconductor creating a powerful environment for biological data mining.

Algorithms↗
PubMed2002

Metabolomics--the link between genotypes and phenotypes.

Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. In parallel to the terms 'transcriptome' and proteome', the set of metabolites synthesized by a biological system constitute its 'metabolome'. Yet, unlike other functional genomics approaches, the unbiased simultaneous identification and quantification of plant metabolomes has been largely neglected. Until recently, most analyses were restricted to profiling selected classes of compounds, or to fingerprinting metabolic changes without sufficient analytical resolution to determine metabolite levels and identities individually. As a prerequisite for metabolomic analysis, careful consideration of the methods employed for tissue extraction, sample preparation, data acquisition, and data mining must be taken. In this review, the differences among metabolite target analysis, metabolite profiling, and metabolic fingerprinting are clarified, and terms are defined. Current approaches are examined, and potential applications are summarized with a special emphasis on data mining and mathematical modelling of metabolism.

Arabidopsis↗
PubMed2006

A quantitative analysis of secondary RNA structure using domination based parameters on trees.

BACKGROUND: It has become increasingly apparent that a comprehensive database of RNA motifs is essential in order to achieve new goals in genomic and proteomic research. Secondary RNA structures have frequently been represented by various modeling methods as graph-theoretic trees. Using graph theory as a modeling tool allows the vast resources of graphical invariants to be utilized to numerically identify secondary RNA motifs. The domination number of a graph is a graphical invariant that is sensitive to even a slight change in the structure of a tree. The invariants selected in this study are variations of the domination number of a graph. These graphical invariants are partitioned into two classes, and we define two parameters based on each of these classes. These parameters are calculated for all small order trees and a statistical analysis of the resulting data is conducted to determine if the values of these parameters can be utilized to identify which trees of orders seven and eight are RNA-like in structure. RESULTS: The statistical analysis shows that the domination based parameters correctly distinguish between the trees that represent native structures and those that are not likely candidates to represent RNA. Some of the trees previously identified as candidate structures are found to be "very" RNA like, while others are not, thereby refining the space of structures likely to be found as representing secondary RNA structure. CONCLUSION: Search algorithms are available that mine nucleotide sequence databases. However, the number of motifs identified can be quite large, making a further search for similar motif computationally difficult. Much of the work in the bioinformatics arena is toward the development of better algorithms to address the computational problem. This work, on the other hand, uses mathematical descriptors to more clearly characterize the RNA motifs and thereby reduce the corresponding search space. These preliminary findings demonstrate that graph-theoretic quantifiers utilized in fields such as computer network design hold significant promise as an added tool for genomics and proteomics.

Algorithms↗
PubMed2025

Integrated data mining and network pharmacology to explore the prescription patterns from a senior TCM oncologist's clinical practice in treating chemotherapy-induced hand-foot syndrome.

Hand-foot syndrome (HFS) is a common and refractory adverse effect of chemotherapy lacking specific therapeutic strategies currently. Traditional Chinese medicine (TCM) has shown empirical efficacy in clinical HFS management. This study integrated data mining and network pharmacology to systematically elucidate the medication principles and molecular mechanisms underlying Professor Gang Xie's prescriptions for HFS. All medical records from Professor Xie's specialist clinic (January 2020 to March 2025) were retrospectively collected and standardized in Excel. Prescriptions were analyzed through frequency statistics, association and clustering. Active ingredients of core herb pairs and their disease-related targets were identified using TCMSP, HERB, GeneCards, PharmGKB and GEO databases. Protein-protein interaction (PPI) networks, gene ontology (GO), and Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses were performed. Molecular docking validated interactions between key bioactive compounds and targets. This study involved 217 prescriptions containing 150 herbs. Core herb combinations comprised Radix Astragali (Huangqi), Poria (Fuling), and Radix Pseudostellariae (Taizishen), predominantly classified as spleen-tonifying agents with warm properties, targeting lung, spleen, and stomach meridians. Network analysis identified 67 bioactive compounds and 899 disease targets. Quercetin, kaempferol, acacetin and luteolin were identified the key ingredients. The core targets (TP53, STAT3, PIK3CA, HSP90AA1, AKT1, CTNNB1, PI3KR1, MAPK1) were enriched in MAPK and PI3K-Akt signaling pathways. Molecular docking confirmed strong binding affinity between key compounds and targets. Professor Xie's therapeutic strategy for HFS emphasizes "spleen fortification, phlegm elimination, and stasis resolution." The core herb combination likely exerts anti-HFS effects via modulation of MAPK and PI3K-Akt pathways, providing a pharmacological basis for TCM-driven HFS management.

Network Pharmacology↗
PubMed2001

Alkaliphilus transvaalensis gen. nov., sp. nov., an extremely alkaliphilic bacterium isolated from a deep South African gold mine.

A novel extreme alkaliphile was isolated from a mine water containment dam at 3.2 km below land surface in an ultra-deep gold mine near Carletonville, South Africa. The cells of this bacterium were straight to slightly curved rods, motile by flagella and formed endospores. Growth was observed over the temperature range 20-50 degrees C (optimum 40 degrees C; 45 min doubling time) and pH range 8.5-12.5 (optimum pH 10.0). The novel isolate, one of the most alkaliphilic micro-organisms yet described, was a strictly anaerobic chemo-organotroph capable of utilizing proteinaceous substrates such as yeast extract, peptone, tryptone and casein. Elemental sulfur, thiosulfate or fumarate, when included as accessory electron acceptors, improved growth. The G+C content of genomic DNA was 36.4 mol %. Phylogenetic analysis based on the 16S rDNA sequence indicated that the isolate is a member of cluster XI within the low G+C gram-positive bacteria, but only distantly related to previously described members. On the basis of physiological and molecular properties, the isolate represents a novel species, for which the name Alkaliphilus transvaalensis gen. nov., sp. nov. is proposed (type strain SAGM1T = JCM 10712T = ATCC 700919T). The mechanism of generation of the highly alkaline microbial habitat and the possible source of the alkaliphile are discussed.

Bacillaceae↗
PubMed2004

New strategy for the representation and the integration of biomolecular knowledge at a cellular scale.

The combination of sequencing and post-sequencing experimental approaches produces huge collections of data that are highly heterogeneous both in structure and in semantics. We propose a new strategy for the integration of such data. This strategy uses structured sets of sequences as a unified representation of biological information and defines a probabilistic measure of similarity between the sets. Sets can be composed of sequences that are known to have a biological relationship (e.g. proteins involved in a complex or a pathway) or that share similar values for a particular attribute (e.g. expression profile). We have developed a software, BlastSets, which implements this strategy. It exploits a database where the sets derived from diverse biological information can be deposited using a standard XML format. For a given query set, BlastSets returns target sets found in the database whose similarity to the query is statistically significant. The tool allowed us to automatically identify verified relationships between correlated expression profiles and biological pathways using publicly available data for Saccharomyces cerevisiae. It was also used to retrieve the members of a complex (ribosome) based on the mining of expression profiles. These first results validate the relevance of the strategy and demonstrate the promising potential of BlastSets.

Computational Biology↗
PubMed2006

The mouse genome database (MGD): new features facilitating a model system.

The mouse genome database (MGD, http://www.informatics.jax.org/), the international community database for mouse, provides access to extensive integrated data on the genetics, genomics and biology of the laboratory mouse. The mouse is an excellent and unique animal surrogate for studying normal development and disease processes in humans. Thus, MGD's primary goals are to facilitate the use of mouse models for studying human disease and enable the development of translational research hypotheses based on comparative genotype, phenotype and functional analyses. Core MGD data content includes gene characterization and functions, phenotype and disease model descriptions, DNA and protein sequence data, polymorphisms, gene mapping data and genome coordinates, and comparative gene data focused on mammals. Data are integrated from diverse sources, ranging from major resource centers to individual investigator laboratories and the scientific literature, using a combination of automated processes and expert human curation. MGD collaborates with the bioinformatics community on the development of data and semantic standards, and it incorporates key ontologies into the MGD annotation system, including the Gene Ontology (GO), the Mammalian Phenotype Ontology, and the Anatomical Dictionary for Mouse Development and the Adult Anatomy. MGD is the authoritative source for mouse nomenclature for genes, alleles, and mouse strains, and for GO annotations to mouse genes. MGD provides a unique platform for data mining and hypothesis generation where one can express complex queries simultaneously addressing phenotypic effects, biochemical function and process, sub-cellular location, expression, sequence, polymorphism and mapping data. Both web-based querying and computational access to data are provided. Recent improvements in MGD described here include the incorporation of single nucleotide polymorphism data and search tools, the addition of PIR gene superfamily classifications, phenotype data for NIH-acquired knockout mice, images for mouse phenotypic genotypes, new functional graph displays of GO annotations, and new orthology displays including sequence information and graphic displays.

Animals↗
PubMed2005

Development of ESTs from chickpea roots and their use in diversity analysis of the Cicer genus.

BACKGROUND: Chickpea is a major crop in many drier regions of the world where it is an important protein-rich food and an increasingly valuable traded commodity. The wild annual Cicer species are known to possess unique sources of resistance to pests and diseases, and tolerance to environmental stresses. However, there has been limited utilization of these wild species by chickpea breeding programs due to interspecific crossing barriers and deleterious linkage drag. Molecular genetic diversity analysis may help predict which accessions are most likely to produce fertile progeny when crossed with chickpea cultivars. While, trait-markers may provide an effective tool for breaking linkage drag. Although SSR markers are the assay of choice for marker-assisted selection of specific traits in conventional breeding populations, they may not provide reliable estimates of interspecific diversity, and may lose selective power in backcross programs based on interspecific introgressions. Thus, we have pursued the development of gene-based markers to resolve these problems and to provide candidate gene markers for QTL mapping of important agronomic traits. RESULTS: An EST library was constructed after subtractive suppressive hybridization (SSH) of root tissue from two very closely related chickpea genotypes (Cicer arietinum). A total of 106 EST-based markers were designed from 477 sequences with functional annotations and these were tested on C. arietinum. Forty-four EST markers were polymorphic when screened across nine Cicer species (including the cultigen). Parsimony and PCoA analysis of the resultant EST-marker dataset indicated that most accessions cluster in accordance with the previously defined classification of primary (C. arietinum, C. echinospermum and C. reticulatum), secondary (C. pinnatifidum, C. bijugum and C. judaicum), and tertiary (C. yamashitae, C. chrossanicum and C. cuneatum) gene-pools. A large proportion of EST alleles (45%) were only present in one or two of the accessions tested whilst the others were represented in up to twelve of the accessions tested. CONCLUSION: Gene-based markers have proven to be effective tools for diversity analysis in Cicer and EST diversity analysis may be useful in identifying promising candidates for interspecific hybridization programs. The EST markers generated in this study have detected high levels of polymorphism amongst both common and rare alleles. This suggests that they would be useful for allele-mining of germplasm collections for identification of candidate accessions in the search for new sources of resistance to pests / diseases, and tolerance to abiotic stresses.

Biomarkers↗
PubMed2004

Survey in the sugarcane expressed sequence tag database (SUCEST) for simple sequence repeats.

Sugarcane microsatellites or simple sequence repeats (SSR) were developed in an economical and practical way by mining EST databases. A survey in the SUCEST (sugarcane EST) database revealed a total of 2005 clusters out of 43,141 containing SSRs. Of these, 8.2% were dinucleotide, 30.5% were trinucleotide, and 61.3% were tetranucleotide repeats. Except for dinucleotides, the CG-rich motif types were the most common. Differences in abundance of trinucleotide motif types were observed between EST-SSRs and those isolated from sugarcane genomic libraries. Among the different cDNA libraries used for EST sequencing, SSRs were more frequent in the ones derived from leaf roll (LR). Twenty-three out of 30 tested SSRs produced scorable polymorphisms in 18 sugarcane commercial clones. These EST-SSRs showed a moderate level of polymorphism with some SSRs producing unique fingerprints. The number of alleles observed among the 18 clones evaluated varied from 2 to 15, with an average of 6.04 alleles/locus. The polymorphism information content (PIC) values ranged from 0.28 to 0.90 with a mean of 0.66. The EST-SSRs screened over both parents (SP 80-180; SP 80-4966) and 6 F1 individuals produced 52 segregating markers that could potentially be used for sugarcane mapping. The EST-SSRs were found in clusters that had significant homology to proteins involved in important metabolic pathways such as sugar biosynthesis, proving that EST-SSRs are a valuable tool for the construction of a functional sugarcane map.

Alleles↗
PubMed2002

Molecular relationship between two groups of the genus Leptospirillum and the finding that Leptospirillum ferriphilum sp. nov. dominates South African commercial biooxidation tanks that operate at 40 degrees C.

Iron-oxidizing bacteria belonging to the genus Leptospirillum are of great importance in continuous-flow commercial biooxidation reactors, used for extracting metals from minerals, that operate at 40 degrees C or less. They also form part of the microbial community responsible for the generation of acid mine drainage. More than 16 isolates of leptospirilla were included in this study, and they were clearly divisible into two major groups. Group I leptospirilla had G+C moles percent ratios within the range 49 to 52% and had three copies of rrn genes, and based on 16S rRNA sequence data, these isolates clustered together with the Leptospirillum ferrooxidans type strain (DSM2705 or L15). Group II leptospirilla had G+C moles percent ratios of 55 to 58% and had two copies of rrn genes, and based on 16S rRNA sequence data, they form a separate cluster. Genome DNA-DNA hybridization experiments indicated that three similarity subgroups were present among the leptospirilla tested, with two DNA-DNA hybridization similarity subgroups found within group I. The two groups could also be distinguished based on the sizes of their 16S-23S rRNA gene spacer regions. We propose that the group II leptospirilla should be recognized as a separate species with the name Leptospirillum ferriphilum sp. nov. Members of the two species can be rapidly distinguished from each other by amplification of their 16S rRNA genes and by carrying out restriction enzyme digests of the products. Several, but not all, isolates of the group II leptospirilla, but none from group I (L. ferrooxidans), were capable of growth at 45 degrees C. All the leptospirilla isolated from commercial biooxidation tanks in South Africa were from group II.

Bioreactors↗
PubMed2004

Association of a four-amino acid residue insertion polymorphism of the HS1 gene with systemic lupus erythematosus: molecular and functional analysis.

OBJECTIVE: To investigate whether polymorphism(s) or mutation(s) in the hematopoietic cell-specific Lyn substrate 1 (HS1) gene are involved in the pathogenesis of systemic lupus erythematosus (SLE). METHODS: The entire coding region of the HS1 gene was analyzed by reverse transcriptase-polymerase chain reaction/single-strand conformational polymorphism analysis. HS1-transfected WEHI-231 cells or B lymphocytes from patients with SLE were studied for apoptosis, activation, and proliferation by flow cytometric analysis and MTT assay. RESULTS: We identified a glutamic acid-proline-glutamic acid-proline insertion between codons 366 and 367 (EPEP366-367ins) and 2 amino acid substitutions (A235T and E361K). The genotype frequency among individuals homozygous for the EPEP+ allele was 0.184 in 201 patients with SLE but only 0.098 in 184 healthy individuals (P = 0.016). The allele frequency of EPEP366-367ins was 0.408 in patients with SLE; this frequency was significantly higher than that in healthy controls (0.312) (P = 0.006). WEHI-231 cells transfected with EPEP+ HS1 were 100-fold more sensitive to B cell receptor (BCR)-mediated apoptosis than were those transfected with HS1 without EPEP. B lymphocytes from SLE patients with the EPEP+ allele were significantly more apoptotic without BCR stimulation and less activated after BCR stimulation than were those from SLE patients without the EPEP allele. CONCLUSION: These results suggest that HS1 with the EPEP insertion polymorphism transmits accelerated signals from BCR and is involved in the pathogenesis of SLE.

Adaptor Proteins, Signal Transducing↗