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PubMed2026

NS2A V89F mutation in a DENV1 clinical isolate enhances neurotropism and neuroinvasion.

INTRODUCTION: Dengue virus (DENV) neurological complications are increasingly reported, yet the viral genetic determinants of neurotropism remain poorly characterized. METHODS: We screened 25 DENV1 clinical isolates from the 2014 outbreak in Guangdong, China, for neurotropism in suckling mice, and integrated comparative genomics, pre-expression functional assays, population-scale sequence analysis, and OpenFold3 structural modeling to identify mutations associated with enhanced neuroinvasion. RESULTS: We found that only strain P1253 induced neurological symptoms and mortality via subcutaneous inoculation, producing cortical-selective lesions distinct from the diffuse encephalitic damage observed after intracranial inoculation, and P1253 replicated preferentially in human brain microvascular endothelial cells (HBMEC) compared to contemporaneous strains. Comparative genomics identified three unique mutations in P1253 (NS1 175Y→H, NS2A 89V→F, NS4A 2V→I), and pre-expression assays demonstrated that only NS2A 89V→F significantly enhanced viral replication and cytopathic effect in HBMEC. Analysis of 1,990 complete DENV1 genomes revealed five natural mutant types in the NS2A 89 -96 residue region, with P1253 representing the FIPI quadruple-mutant type, and OpenFold3 structural prediction showed that 89V→F introduced on the VIPI background induced the most significant distal domain reorientation (RMSD 1.605 Å), increasing the centroid-to-centroid distance between residues 89 -96 and 185 -218 from 18.221 Å to 27.462 Å. DISCUSSION: These findings identify NS2A 89V→F as a candidate adaptive mutation associated with enhanced neurotropism in DENV1 and provide a framework for monitoring neurovirulent variants.

Dengue Virus↗
PubMed2006

Symbolic-numeric estimation of parameters in biochemical models by quantifier elimination.

The sequencing of complete genomes allows analyses of the interactions between various biological molecules on a genomic scale, which prompted us to simulate the global behaviors of biological phenomena on the molecular level. One of the basic mathematical problems in the simulation is the parameter optimization in the kinetic model for complex dynamics, and many estimation methods have been designed. We introduce a new approach to estimate the parameters in biological kinetic models by quantifier elimination (QE), in combination with numerical simulation methods. The estimation method was applied to a model for the inhibition kinetics of HIV proteinase with ten parameters and nine variables, and attained the goodness of fit to 300 points of observed data with the same magnitude as that obtained by the previous estimation methods, remarkably by using only one or two points of data. Furthermore, the utilization of QE demonstrated the feasibility of the present method for elucidating the behavior of the parameters and the variables in the analyzed model. Therefore, the present symbolic-numeric method is a powerful approach to reveal the fundamental mechanisms of kinetic models, in addition to being a computational engine.

Algorithms↗
PubMedDate not supplied

Analysis of Chlamydomonas reinhardtii genome structure using large-scale sequencing of regions on linkage groups I and III.

Chlamydomonas reinhardtii is a unicellular green alga that has been used as a model organism for the study of flagella and basal bodies as well as photosynthesis. This report analyzes finished genomic DNA sequence for 0.5% of the nuclear genome. We have used three gene prediction programs as well as EST and protein homology data to estimate the total number of genes in Chlamydomonas to be between 12,000 and 16,400. Chlamydomonas appears to have many more genes than any other unicellular organism sequenced to date. Twenty-seven percent of the predicted genes have significant identity to both ESTs and to known proteins in other organisms, 32% of the predicted genes have significant identity to ESTs alone, and 14% have significant similarity to known proteins in other organisms. For gene prediction in Chlamydomonas, GreenGenie appeared to have the highest sensitivity and specificity at the exon level, scoring 71% and 82%. respectively. Two new alternative splicing events were predicted by aligning Chlamydomonas ESTs to the genomic sequence. Finally recombination differs between the two sequenced contigs. The 350-Kb of the Linkage group III contig is devoid of recombination, while the Linkage group I contig is 30 map units long over 33-kb.

Amino Acid Sequence↗
PubMed1999

Linear modeling of mRNA expression levels during CNS development and injury.

Large-scale gene expression data sets are revolutionizing the field of functional genomics. However, few data analysis techniques fully exploit this entirely new class of data. We present a linear modeling approach that allows one to infer interactions between all the genes included in the data set. The resulting model can be used to generate interesting hypotheses to direct further experiments.

Animals↗
PubMed2004

Mouse embryonic stem cells efficiently lipofected with nuclear localization peptide result in a high yield of chimeric mice and retain germline transmission potency.

Embryonic stem (ES) cells are an important tool in developmental biology, genomics, and transgenic methods, as well as in potential clinical applications such as gene therapy or tissue engineering. Electroporation is the standard transfection method for mouse ES (mES) cells because lipofection is quite inefficient. It is also unclear if mES cells treated with cationic lipids maintain pluripotency. We have developed a simple lipofection method for high efficiency transfection and stable transgene expression by employing the nonclassical nuclear localization signal M9 derived from the heterogeneous nuclear ribonucleoprotein A1. In contrast to using 20 microg DNA for 10 x 10(6) cells via electroporation which resulted in 10-20 positive cells/mm2, M9-assisted lipofection of 2 x 10(5) cells with 2 microg DNA resulted in > 150 positive cells/mm2. Electroporation produced only 0.16% EGFP positive cells with fluorescence intensity (FI) > 1000 by FACS assay, while M9-lipofection produced 36-fold more highly EGFP positive cells (5.75%) with FI > 1000. Using 2.5 x 10(6) ES cells and 6 microg linearized DNA followed by selection with G418, electroporation yielded 17 EGFP expressing colonies, while M9-assisted lipofection yielded 72 EGFP expressing colonies. The mES cells that stably expressed EGFP following M9-assisted lipofection yielded > 66% chimeric mice (8 of 12) and contributed efficiently to the germline. In an example of gene targeting, a knock-in mouse was produced from an ES clone screened from 200 G418-resistant colonies generated via M9-assisted lipofection. To our knowledge, this is the first report of generation of transgenic or knock-in mice obtained from lipofected mES cells and this method may facilitate large scale genomic studies of ES developmental biology or large scale generation of mouse models of human disease.

Animals↗
PubMed2004

Genomic instability and cancer: scanning the Caenorhabditis elegans genome for tumor suppressors.

Maintaining the stability of the genome is critical to normal cell growth and development. The early notion that cancer is the result of mutations in genes controlling cellular growth implied that gene or genome integrity is vital to the prevention of oncogenesis, and many genes and pathways that prevent genomic deterioration have been identified over the past decades. Recent progress in reverse genetic approaches, principally RNA interference, now allows the systematic analysis of gene function on a genomic scale in an animal system. Here, we discuss genomic approaches in the model organism Caenorhabditis elegans, aimed to identify genes and genetic networks that contribute to genome stability and are thus potentially involved in human carcinogenesis.

Animals↗
PubMed2006

Genomes as geography: using GIS technology to build interactive genome feature maps.

BACKGROUND: Many commonly used genome browsers display sequence annotations and related attributes as horizontal data tracks that can be toggled on and off according to user preferences. Most genome browsers use only simple keyword searches and limit the display of detailed annotations to one chromosomal region of the genome at a time. We have employed concepts, methodologies, and tools that were developed for the display of geographic data to develop a Genome Spatial Information System (GenoSIS) for displaying genomes spatially, and interacting with genome annotations and related attribute data. In contrast to the paradigm of horizontally stacked data tracks used by most genome browsers, GenoSIS uses the concept of registered spatial layers composed of spatial objects for integrated display of diverse data. In addition to basic keyword searches, GenoSIS supports complex queries, including spatial queries, and dynamically generates genome maps. Our adaptation of the geographic information system (GIS) model in a genome context supports spatial representation of genome features at multiple scales with a versatile and expressive query capability beyond that supported by existing genome browsers. RESULTS: We implemented an interactive genome sequence feature map for the mouse genome in GenoSIS, an application that uses ArcGIS, a commercially available GIS software system. The genome features and their attributes are represented as spatial objects and data layers that can be toggled on and off according to user preferences or displayed selectively in response to user queries. GenoSIS supports the generation of custom genome maps in response to complex queries about genome features based on both their attributes and locations. Our example application of GenoSIS to the mouse genome demonstrates the powerful visualization and query capability of mature GIS technology applied in a novel domain. CONCLUSION: Mapping tools developed specifically for geographic data can be exploited to display, explore and interact with genome data. The approach we describe here is organism independent and is equally useful for linear and circular chromosomes. One of the unique capabilities of GenoSIS compared to existing genome browsers is the capacity to generate genome feature maps dynamically in response to complex attribute and spatial queries.

Animals↗
PubMed2004

Genome-scale phylogeny and the detection of systematic biases.

Phylogenetic inference from sequences can be misled by both sampling (stochastic) error and systematic error (nonhistorical signals where reality differs from our simplified models). A recent study of eight yeast species using 106 concatenated genes from complete genomes showed that even small internal edges of a tree received 100% bootstrap support. This effective negation of stochastic error from large data sets is important, but longer sequences exacerbate the potential for biases (systematic error) to be positively misleading. Indeed, when we analyzed the same data set using minimum evolution optimality criteria, an alternative tree received 100% bootstrap support. We identified a compositional bias as responsible for this inconsistency and showed that it is reduced effectively by coding the nucleotides as purines and pyrimidines (RY-coding), reinforcing the original tree. Thus, a comprehensive exploration of potential systematic biases is still required, even though genome-scale data sets greatly reduce sampling error.

Genome, Fungal↗
PubMed2003

The transcriptome and its translation during recovery from cell cycle arrest in Saccharomyces cerevisiae.

Complete genome sequences together with high throughput technologies have made comprehensive characterizations of gene expression patterns possible. While genome-wide measurement of mRNA levels was one of the first applications of these advances, other important aspects of gene expression are also amenable to a genomic approach, for example, the translation of message into protein. Earlier we reported a high throughput technology for simultaneously studying mRNA level and translation, which we termed translation state array analysis, or TSAA. The current studies test the proposition that TSAA can identify novel instances of translation regulation at the genome-wide level. As a biological model, cultures of Saccharomyces cerevisiae were cell cycle-arrested using either alpha-factor or the temperature-sensitive cdc15-2 allele. Forty-eight mRNAs were found to change significantly in translation state following release from alpha-factor arrest, including genes involved in pheromone response and cell cycle arrest such as BAR1, SST2, and FAR1. After the shift of the cdc15-2 strain from 37 degrees C to 25 degrees C, 54 mRNAs were altered in translation state, including the products of the stress genes HSP82, HSC82, and SSA2. Thus, regulation at the translational level seems to play a significant role in the response of yeast cells to external physical or biological cues. In contrast, surprisingly few genes were found to be translationally controlled as cells progressed through the cell cycle. Additional refinements of TSAA should allow characterization of both transcriptional and translational regulatory networks on a genomic scale, providing an additional layer of information that can be integrated into models of system biology and function.

Cell Cycle↗
PubMed2005

Subfamily hmms in functional genomics.

The limitations of homology-based methods for prediction of protein molecular function are well known; differences in domain structure, gene duplication events and errors in existing database annotations complicate this process. In this paper we present a method to detect and model protein subfamilies, which can be used in high-throughput, genome-scale phylogenomic inference of protein function. We demonstrate the method on a set of nine PFAM families, and show that subfamily HMMs provide greater separation of homologs and non-homologs than is possible with a single HMM for each family. We also show that subfamily HMMs can be used for functional classification with a very low expected error rate. The BETE method for identifying functional subfamilies is illustrated on a set of serotonin receptors.

Animals↗
PubMed2005

E-RNAi: a web application to design optimized RNAi constructs.

RNA interference (RNAi) has become a powerful genetic approach to systematically dissect gene function on a genome-wide scale. Owing to the penetrance and efficiency of RNAi in invertebrates, model organisms such as Drosophila melanogaster and Caenorhabditis elegans have contributed significantly to the identification of novel components of diverse biological pathways, ranging from early development to fat storage and aging. For the correct assessment of phenotypes, a key issue remains the stringent quality control of long double-stranded RNAs (dsRNA) to calculate potential off-target effects that may obscure the phenotypic data. We here describe a web-based tool to evaluate and design optimized dsRNA constructs. Moreover, the application also gives access to published predesigned dsRNAs. The E-RNAi web application is available at http://e-rnai.dkfz.de/.

Animals↗
PubMed2006

Interrogating the druggable genome with structural informatics.

Structural genomics projects are producing protein structure data at an unprecedented rate. In this paper, we present the Target Informatics Platform (TIP), a novel structural informatics approach for amplifying the rapidly expanding body of experimental protein structure information to enhance the discovery and optimization of small molecule protein modulators on a genomic scale. In TIP, existing experimental structure information is augmented using a homology modeling approach, and binding sites across multiple target families are compared using a clique detection algorithm. We report here a detailed analysis of the structural coverage for the set of druggable human targets, highlighting drug target families where the level of structural knowledge is currently quite high, as well as those areas where structural knowledge is sparse. Furthermore, we demonstrate the utility of TIP's intra- and inter-family binding site similarity analysis using a series of retrospective case studies. Our analysis underscores the utility of a structural informatics infrastructure for extracting drug discovery-relevant information from structural data, aiding researchers in the identification of lead discovery and optimization opportunities as well as potential "off-target" liabilities.

Databases, Factual↗
PubMed1999

Exploring the new world of the genome with DNA microarrays.

Thousands of genes are being discovered for the first time by sequencing the genomes of model organisms, an exhilarating reminder that much of the natural world remains to be explored at the molecular level. DNA microarrays provide a natural vehicle for this exploration. The model organisms are the first for which comprehensive genome-wide surveys of gene expression patterns or function are possible. The results can be viewed as maps that reflect the order and logic of the genetic program, rather than the physical order of genes on chromosomes. Exploration of the genome using DNA microarrays and other genome-scale technologies should narrow the gap in our knowledge of gene function and molecular biology between the currently-favoured model organisms and other species.

Animals↗
PubMed2025

Integrating Imaging-Derived Clinical Endotypes with Plasma Proteomics and External Polygenic Risk Scores Enhances Coronary Microvascular Disease Risk Prediction.

Coronary microvascular disease (CMVD) is an underdiagnosed but significant contributor to the burden of ischemic heart disease, characterized by angina and myocardial infarction. The development of risk prediction models such as polygenic risk scores (PRS) for CMVD has been limited by a lack of large-scale genome-wide association studies (GWAS). However, there is significant overlap between CMVD and enrollment criteria for coronary artery disease (CAD) GWAS. In this study, we developed CMVD PRS models by selecting variants identified in a CMVD GWAS and applying weights from an external CAD GWAS, using CMVD-associated loci as proxies for the genetic risk. We integrated plasma proteomics, clinical measures from perfusion PET imaging, and PRS to evaluate their contributions to CMVD risk prediction in comprehensive machine and deep learning models. We then developed a novel unsupervised endotyping framework for CMVD from perfusion PET-derived myocardial blood flow data, revealing distinct patient subgroups beyond traditional case-control definitions. This imaging-based stratification substantially improved classification performance alongside plasma proteomics and PRS, achieving AUROCs between 0.65 and 0.73 per class, significantly outperforming binary classifiers and existing clinical models, highlighting the potential of this stratification approach to enable more precise and personalized diagnosis by capturing the underlying heterogeneity of CMVD. This work represents the first application of imaging-based endotyping and the integration of genetic and proteomic data for CMVD risk prediction, establishing a framework for multimodal modeling in complex diseases.

Cardiovascular Disease↗
PubMed2002

Functional genomics as applied to mapping transcription regulatory networks.

The sequencing of the human genome and the entire genomes of many model organisms has resulted in the identification of many genes. Many large-scale experiments for generating gene disruptions and analyzing the phenotypes are underway to ascertain gene function. A future challenge will be to determine interaction and regulation of all the genes of an organism. Recent advances in functional genomic technology have begun to shine light on such gene network problems at both transcriptomic and proteomic levels. Functional genomics will not only elucidate what the genes do, but will also help determine when, where and how they are expressed as an orchestrated system. In this review, we discuss the functional genomics approaches to extract knowledge about transcription regulatory mechanisms from combinations of sequence data, microarray data and ChIP data. We focus in particular on the budding yeast Saccharomyces cerevisiae.

Gene Expression Regulation, Fungal↗
PubMed2004

MuTrack: a genome analysis system for large-scale mutagenesis in the mouse.

BACKGROUND: Modern biological research makes possible the comprehensive study and development of heritable mutations in the mouse model at high-throughput. Using techniques spanning genetics, molecular biology, histology, and behavioral science, researchers may examine, with varying degrees of granularity, numerous phenotypic aspects of mutant mouse strains directly pertinent to human disease states. Success of these and other genome-wide endeavors relies on a well-structured bioinformatics core that brings together investigators from widely dispersed institutions and enables them to seamlessly integrate data, observations and discussions. DESCRIPTION: MuTrack was developed as the bioinformatics core for a large mouse phenotype screening effort. It is a comprehensive collection of on-line computational tools and tracks thousands of mutagenized mice from birth through senescence and death. It identifies the physical location of mice during an intensive phenotype screening process at several locations throughout the state of Tennessee and collects raw and processed experimental data from each domain. MuTrack's statistical package allows researchers to access a real-time analysis of mouse pedigrees for aberrant behavior, and subsequent recirculation and retesting. The end result is the classification of potential and actual heritable mutant mouse strains that become immediately available to outside researchers who have expressed interest in the mutant phenotype. CONCLUSION: MuTrack demonstrates the effectiveness of using bioinformatics techniques in data collection, integration and analysis to identify unique result sets that are beyond the capacity of a solitary laboratory. By employing the research expertise of investigators at several institutions for a broad-ranging study, the TMGC has amplified the effectiveness of any one consortium member. The bioinformatics strategy presented here lends future collaborative efforts a template for a comprehensive approach to large-scale analysis.

Animals↗
PubMed2004

Emerging genomic and proteomic evidence on relationships among the animal, plant and fungal kingdoms.

Sequence-based molecular phylogenies have provided new models of early eukaryotic evolution. This includes the widely accepted hypothesis that animals are related most closely to fungi, and that the two should be grouped together as the Opisthokonta. Although most published phylogenies have supported an opisthokont relationship, a number of genes contain a tree-building signal that clusters animal and green plant sequences, to the exclusion of fungi. The alternative tree-building signal is especially intriguing in light of emerging data from genomic and proteomic studies that indicate striking and potentially synapomorphic similarities between plants and animals. This paper reviews these new lines of evidence, which have yet to be incorporated into models of broad scale eukaryotic evolution.

Animals↗
PubMed2006

The application of systems biology to drug discovery.

Recent advances in the 'omics' technologies, scientific computing and mathematical modeling of biological processes have started to fundamentally impact the way we approach drug discovery. Recent years have witnessed the development of genome-scale functional screens, large collections of reagents, protein microarrays, databases and algorithms for data and text mining. Taken together, they enable the unprecedented descriptions of complex biological systems, which are testable by mathematical modeling and simulation. While the methods and tools are advancing, it is their iterative and combinatorial application that defines the systems biology approach.

Animals↗