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At least 217 records · Page 12Linked to original sources
PubMed2004

The Arabidopsis root transcriptome by serial analysis of gene expression. Gene identification using the genome sequence.

Large-scale identification of genes expressed in roots of the model plant Arabidopsis was performed by serial analysis of gene expression (SAGE), on a total of 144,083 sequenced tags, representing at least 15,964 different mRNAs. For tag to gene assignment, we developed a computational approach based on 26,620 genes annotated from the complete sequence of the genome. The procedure selected warrants the identification of the genes corresponding to the majority of the tags found experimentally, with a high level of reliability, and provides a reference database for SAGE studies in Arabidopsis. This new resource allowed us to characterize the expression of more than 3,000 genes, for which there is no expressed sequence tag (EST) or cDNA in the databases. Moreover, 85% of the tags were specific for one gene. To illustrate this advantage of SAGE for functional genomics, we show that our data allow an unambiguous analysis of most of the individual genes belonging to 12 different ion transporter multigene families. These results indicate that, compared with EST-based tag to gene assignment, the use of the annotated genome sequence greatly improves gene identification in SAGE studies. However, more than 6,000 different tags remained with no gene match, suggesting that a significant proportion of transcripts present in the roots originate from yet unknown or wrongly annotated genes. The root transcriptome characterized in this study markedly differs from those obtained in other organs, and provides a unique resource for investigating the functional specificities of the root system. As an example of the use of SAGE for transcript profiling in Arabidopsis, we report here the identification of 270 genes differentially expressed between roots of plants grown either with NO3- or NH4NO3 as N source.

Arabidopsis↗
PubMed2023

Active learning of enhancer and silencer regulatory grammar in photoreceptors.

Cis-regulatory elements (CREs) direct gene expression in health and disease, and models that can accurately predict their activities from DNA sequences are crucial for biomedicine. Deep learning represents one emerging strategy to model the regulatory grammar that relates CRE sequence to function. However, these models require training data on a scale that exceeds the number of CREs in the genome. We address this problem using active machine learning to iteratively train models on multiple rounds of synthetic DNA sequences assayed in live mammalian retinas. During each round of training the model actively selects sequence perturbations to assay, thereby efficiently generating informative training data. We iteratively trained a model that predicts the activities of sequences containing binding motifs for the photoreceptor transcription factor Cone-rod homeobox (CRX) using an order of magnitude less training data than current approaches. The model's internal confidence estimates of its predictions are reliable guides for designing sequences with high activity. The model correctly identified critical sequence differences between active and inactive sequences with nearly identical transcription factor binding sites, and revealed order and spacing preferences for combinations of motifs. Our results establish active learning as an effective method to train accurate deep learning models of cis-regulatory function after exhausting naturally occurring training examples in the genome.

Journal Article↗
PubMed2006

From information to understanding: the role of model organism databases in comparative and functional genomics.

Data integration is key to functional and comparative genomics because integration allows diverse data types to be evaluated in new contexts. To achieve data integration in a scalable and sensible way, semantic standards are needed, both for naming things (standardized nomenclatures, use of key words) and also for knowledge representation. The Mouse Genome Informatics database and other model organism databases help to close the gap between information and understanding of biological processes because these resources enforce well-defined nomenclature and knowledge representation standards. Model organism databases have a critical role to play in ensuring that diverse kinds of data, especially genome-scale data sets and information, remain useful to the biological community in the long-term. The efforts of model organism database groups ensure not only that organism-specific data are integrated, curated and accessible but also that the information is structured in such a way that comparison of biological knowledge across model organisms is facilitated.

Animals↗
PubMed2005

RNAi as a tool to study cell biology: building the genome-phenome bridge.

In the few short years since its discovery, RNA interference (RNAi) has revolutionized the functional analysis of genomes: both technical and conceptual approaches to the investigation of gene function are being transformed as a result of this new technology. Genome-scale RNAi analyses have already been performed in the model organisms Caenorhabditis elegans (in vivo) and Drosophila melanogaster (in cell lines), ushering in a new era of RNAi-based approaches to probing the inner workings of the cell. The transformation of complex phenotypic data into mineable 'digitized' formats is fostering the emergence of a new area of bioinformatics related to the phenome.

Animals↗
PubMed2002

Toward minimalist models of larger proteins: a ubiquitin-like protein.

Our recently developed off-lattice bead model capable of simulating protein structures with mixed alpha/beta content has been extended to model the folding of a ubiquitin-like protein and provides a means for examining the more complex kinetics involved in the folding of larger proteins. Using trajectories generated from constant-temperature Langevin dynamics simulations and sampling with the multiple multi-histogram method over five-order parameters, we are able to characterize the free energy landscape for folding and find evidence for folding through compact intermediates. Our model reproduces the observation that the C-terminus loop structure in ubiquitin is the last to fold in the folding process and most likely plays a spectator role in the folding kinetics. The possibility of a productive metastable intermediate along the folding pathway consisting of collapsed states with no secondary structure, and of intermediates or transition structures involving secondary structural elements occurring early in the sequence, is also supported by our model. The kinetics of folding remain multi-exponential below the folding temperature, with glass-like kinetics appearing at T/T(f) approximately 0.86. This new physicochemical model, designed to be predictive, helps validate the value of modeling protein folding at this level of detail for genomic-scale studies, and motivates further studies of other protein topologies and the impact of more complex energy functions, such as the addition of solvation forces.

Amino Acid Sequence↗
PubMed2026

RP3Net: a deep learning model for predicting recombinant protein production in Escherichia coli.

MOTIVATION: Recombinant protein expression can be a limiting step in the production of protein reagents for drug discovery and other biotechnology applications. We introduce RP3Net (Recombinant Protein Production Prediction Network), an AI model of small-scale heterologous soluble protein expression in Escherichia coli. RP3Net utilizes the most recent protein and genomic foundational models. A curated dataset of internal experimental results from AstraZeneca and publicly available data from the Structural Genomics Consortium was used for training, validation and testing of RP3Net. RESULTS: RP3Net achieves an increase in area under the receiver operator curve (AUROC) of 0.15, compared to a baseline model. When experimentally validated on an independent, prospective, manually selected set of 97 constructs, RP3Net outperformed currently available models, with an AUROC of 0.83, delivering accurate predictions in 77% of the cases, and correctly identifying successfully expressing constructs in 92% of cases. AVAILABILITY AND IMPLEMENTATION: The model, along with installation and running instructions, is available under an MIT licence at https://github.com/RP3Net/RP3Net, DOI 10.5281/zenodo.17243498.

Escherichia coli↗
PubMed2002

A statistical model for the genetic origin of allometric scaling laws in biology.

Many biological processes, from cellular metabolism to population dynamics, are characterized by particular allometric scaling (power-law) relationships between size and rate. Although such allometric relationships may be under genetic determination, their precise genetic mechanisms have not been clearly understood due to a lack of a statistical analytical method. In this paper, we present a basic statistical framework for mapping quantitative genes (or quantitative trait loci, QTL) responsible for universal quarter-power scaling laws of organic structure and function with the entire body size. Our model framework allows the testing of whether a single QTL affects the allometric relationship of two traits or whether more than one linked QTL is segregating. Like traditional multi-trait mapping, this new model can increase the power to detect the underlying QTL and the precision of its localization on the genome. Beyond the traditional method, this model is integrated with pervasive scaling laws to take advantage of the mechanistic relationships of biological structures and processes. Simulation studies indicate that the estimation precision of the QTL position and effect can be improved when the scaling relationship of the two traits is considered. The application of our model in a real example from forest trees leads to successful detection of a QTL governing the allometric relationship of third-year stem height with third-year stem biomass. The model proposed here has implications for genetic, evolutionary, biomedicinal and breeding research.

Animals↗
PubMed2005

Dissecting systems-wide data using mixture models: application to identify affected cellular processes.

BACKGROUND: Functional analysis of data from genome-scale experiments, such as microarrays, requires an extensive selection of differentially expressed genes. Under many conditions, the proportion of differentially expressed genes is considerable, making the selection criteria a balance between the inclusion of false positives and the exclusion of false negatives. RESULTS: We developed an analytical method to determine a p-value threshold from a microarray experiment that is dependent on the quality and design of the data set. To this aim, populations of p-values are modeled as mathematical functions in which the parameters to describe these functions are estimated in an unsupervised manner. The strength of the method is exemplified by its application to a published gene expression data set of sporadic and familial breast tumors with BRCA1 or BRCA2 mutations. CONCLUSION: We present an objective and unsupervised way to set thresholds adapted to the quality and design of the experiment. The resulting mathematical description of the data sets of genome-scale experiments enables a probabilistic approach in systems biology.

Breast Neoplasms↗
PubMed2006

Legume genome evolution viewed through the Medicago truncatula and Lotus japonicus genomes.

Genome sequencing of the model legumes, Medicago truncatula and Lotus japonicus, provides an opportunity for large-scale sequence-based comparison of two genomes in the same plant family. Here we report synteny comparisons between these species, including details about chromosome relationships, large-scale synteny blocks, microsynteny within blocks, and genome regions lacking clear correspondence. The Lotus and Medicago genomes share a minimum of 10 large-scale synteny blocks, each with substantial collinearity and frequently extending the length of whole chromosome arms. The proportion of genes syntenic and collinear within each synteny block is relatively homogeneous. Medicago-Lotus comparisons also indicate similar and largely homogeneous gene densities, although gene-containing regions in Mt occupy 20-30% more space than Lj counterparts, primarily because of larger numbers of Mt retrotransposons. Because the interpretation of genome comparisons is complicated by large-scale genome duplications, we describe synteny, synonymous substitutions and phylogenetic analyses to identify and date a probable whole-genome duplication event. There is no direct evidence for any recent large-scale genome duplication in either Medicago or Lotus but instead a duplication predating speciation. Phylogenetic comparisons place this duplication within the Rosid I clade, clearly after the split between legumes and Salicaceae (poplar).

Chromosomes, Plant↗
PubMed2026

Evidence supporting the role of GIGYF2 in synapse development and autism.

Autism spectrum disorder (ASD) is a heterogeneous condition in which genetically defined subtypes offered insights into underlying biological mechanisms and potential targeted treatments. Here, we investigate the clinical and pathogenic significance of GIGYF2 variants in ASD through an integrated approach combining clinical genetics, conditional knockout (cKO) mouse models, neurobiology, and molecular studies. Through targeted sequencing, large-scale genomic data analysis of neurodevelopmental disorder cohorts, and international collaborations, we identified ten affected individuals from eight families harboring de novo or dominantly inherited likely gene-disruptive (LGD) variants and 13 affected individuals from 13 families with de novo missense variants in GIGYF2. Clinical characterization of 16 probands with GIGYF2 variants revealed common features, including ASD, language problems, intellectual disability, and anxiety. In a Gigyf2 cKO mouse model, we observed pronounced autistic-like behaviors, cognitive deficits, and anxiety-like behaviors, mirroring phenotypes observed in affected individuals. Mechanistically, Gigyf2 deficiency disrupted synaptic homeostasis, as evidenced by altered spine density and miniature excitatory postsynaptic currents, and impaired IGF-1R/mTOR signaling, along with dysregulation of synapse-related genes such as Nrp2. Pharmacological inhibition of mTOR with rapamycin or Torin1, as well as Nrp2 knockdown rescued synaptic defects in Gigyf2 KO neurons. These findings define a novel ASD subtype associated with GIGYF2 variants and establish GIGYF2 as a key regulator of synaptic development and function, implicating GIGYF2 dysfunction in ASD pathogenesis and highlighting the IGF-1R/mTOR pathway as a potential therapeutic target for GIGYF2-related ASD subtype.

Journal Article↗
PubMed2001

Functional analysis of pathogenicity genes in a genomics world.

Genome-wide mutational and expression analyses have been performed in yeast and provide a model for large-scale analysis of gene function in filamentous fungi. The recent completion of the Neurospora crassa genome offers a resource for comparative analysis with plant pathogenic filamentous fungi. These advances have important implications for molecular genetic studies of pathogenicity genes.

Fungal Proteins↗
PubMed2000

MHC class II pseudogene and genomic signature of a 32-kb cosmid in the house finch (Carpodacus mexicanus).

Large-scale sequencing studies in vertebrates have thus far focused primarily on the genomes of a few model organisms. Birds are of interest to genomics because of their much smaller and highly streamlined genomes compared to mammals. However, large-scale genetic work has been confined almost exclusively to the chicken; we know little about general aspects of genomes in nongame birds. This study examines the organization of a genomic region containing an Mhc class II B gene in a representative of another important lineage of the avian tree, the songbirds (Passeriformes). We used a shotgun sequencing approach to determine the sequence of a 32-kb cosmid insert containing a strongly hybridizing Mhc fragment from house finches (Carpodacus mexicanus). There were a total of three genes found on the cosmid clone, about the gene density expected for the mammalian Mhc: a class II Mhc beta-chain gene (Came-DAB1), a serine-threonine kinase, and a zinc finger motif. Frameshift mutations in both the second and third exons of Came-DAB1 and the unalignability of the gene after the third exon suggest that it is a nonfunctional pseudogene. In addition, the identifiable introns of Came-DAB1 are more than twice as large as those of chickens. Nucleotide diversity in the peptide-binding region of Came-DAB1 (Pi = 0.03) was much lower than polymorphic chicken and other functional Mhc genes but higher than the expected diversity for a neutral locus in birds, perhaps because of hitchhiking on a selected Mhc locus close by. The serine-threonine kinase gene is likely functional, whereas the zinc finger motif is likely nonfunctional. A paucity of long simple-sequence repeats and retroelements is consistent with emerging rules of chicken genomics, and a pictorial analysis of the "genomic signature" of this sequence, the first of its kind for birds, bears strong similarity to mammalian signatures, suggesting common higher-order structures in these homeothermic genomes. The house finch sequence is among a very few of its kind from nonmodel vertebrates and provides insight into the evolution of the avian Mhc and of avian genomes generally.

Animals↗
PubMed2007

Neural network analyses of infrared spectra for classifying cell wall architectures.

About 10% of plant genomes are devoted to cell wall biogenesis. Our goal is to establish methodologies that identify and classify cell wall phenotypes of mutants on a genome-wide scale. Toward this goal, we have used a model system, the elongating maize (Zea mays) coleoptile system, in which cell wall changes are well characterized, to develop a paradigm for classification of a comprehensive range of cell wall architectures altered during development, by environmental perturbation, or by mutation. Dynamic changes in cell walls of etiolated maize coleoptiles, sampled at one-half-d intervals of growth, were analyzed by chemical and enzymatic assays and Fourier transform infrared spectroscopy. The primary walls of grasses are composed of cellulose microfibrils, glucuronoarabinoxylans, and mixed-linkage (1 --> 3),(1 --> 4)-beta-D-glucans, together with smaller amounts of glucomannans, xyloglucans, pectins, and a network of polyphenolic substances. During coleoptile development, changes in cell wall composition included a transient appearance of the (1 --> 3),(1 --> 4)-beta-D-glucans, a gradual loss of arabinose from glucuronoarabinoxylans, and an increase in the relative proportion of cellulose. Infrared spectra reflected these dynamic changes in composition. Although infrared spectra of walls from embryonic, elongating, and senescent coleoptiles were broadly discriminated from each other by exploratory principal components analysis, neural network algorithms (both genetic and Kohonen) could correctly classify infrared spectra from cell walls harvested from individuals differing at one-half-d interval of growth. We tested the predictive capabilities of the model with a maize inbred line, Wisconsin 22, and found it to be accurate in classifying cell walls representing developmental stage. The ability of artificial neural networks to classify infrared spectra from cell walls provides a means to identify many possible classes of cell wall phenotypes. This classification can be broadened to phenotypes resulting from mutations in genes encoding proteins for which a function is yet to be described.

Algorithms↗
PubMed2003

Exposure of human primary colon carcinoma cells to anti-Fas interactions influences the emergence of pre-existing Fas-resistant metastatic subpopulations.

Fas, an important death receptor-mediated signaling pathway, has been shown to be down-regulated during human colon tumorigenesis; however, how alterations in Fas expression influence the metastatic process remains unresolved. In mouse models, loss of Fas function was found to be both necessary and sufficient for tumor progression. In this study, we investigated the link between functional Fas status and malignant phenotype using a matched pair of naturally occurring primary (Fas-sensitive) and metastatic (Fas-resistant) human colon carcinoma cell lines in both in vitro and in vivo (xenograft) settings. Metastatic sublines were produced in vitro from the primary tumor cell line by functional elimination of Fas-responsive cells. Conversely, sublines derived from the primary tumor in vivo at distal metastatic sites were Fas-resistant. In contrast, simply disrupting the Fas pathway by molecular-based strategies in the Fas-sensitive primary tumor failed to achieve the same metastatic outcome. Interestingly, both in vitro- and in vivo-produced sublines resembled the naturally occurring metastatic population, based on functional and morphologic studies and genome-scale gene expression profiling. Overall, using this human colon carcinoma model, we: 1) showed that loss of Fas function was linked to, but alone was insufficient for, acquisition of a detectable metastatic phenotype; 2) demonstrated that metastatic subpopulations pre-existed within the heterogeneous primary tumor, and that anti-Fas interactions served as a selective pressure for their outgrowth; and 3) identified a large set of differentially expressed genes distinguishing the primary from metastatic malignant phenotypes. Thus, Fas-based interactions may represent a novel mechanism for the biologic or immunologic selection of certain types of Fas-resistant neoplastic clones with enhanced metastatic ability.

Animals↗
PubMedDate not supplied

Single-molecule detection as an approach to rapid DNA sequencing.

Current sequencing technologies are insufficient to cope with large-scale projects such as sequencing the human genome and genomes of model organisms. In addition, as genetic lesions associated with specific human diseases are identified, DNA sequencing will be used increasingly for clinical applications. Thus, new approaches are needed to combine high-throughput with accuracy for both research and diagnostic purposes. A novel technology based on detection of individual fluorescent nucleotides in a flowing sample stream is under development.

Animals↗
PubMed1999

Automated MAD and MIR structure solution.

Obtaining an electron-density map from X-ray diffraction data can be difficult and time-consuming even after the data have been collected, largely because MIR and MAD structure determinations currently require many subjective evaluations of the qualities of trial heavy-atom partial structures before a correct heavy-atom solution is obtained. A set of criteria for evaluating the quality of heavy-atom partial solutions in macromolecular crystallography have been developed. These have allowed the conversion of the crystal structure-solution process into an optimization problem and have allowed its automation. The SOLVE software has been used to solve MAD data sets with as many as 52 selenium sites in the asymmetric unit. The automated structure-solution process developed is a major step towards the fully automated structure-determination, model-building and refinement procedure which is needed for genomic scale structure determinations.

Algorithms↗
PubMed2004

Fabrication of high quality cDNA microarray using a small amount of cDNA.

DNA microarray technology has become an essential part of biological research. It enables the genome-scale analysis of gene expression in various types of model systems. Manufacturing high quality cDNA microarrays of microdeposition type depends on some key factors including a printing device, spotting pins, glass slides, spotting solution, and humidity during spotting. UsingEthe Microgrid II TAS model printing device, this study defined the optimal conditions for producing high density, high quality cDNA microarrays with the least amount of cDNA product. It was observed that aminosilane-modified slides were superior to other types of surface modified-slides. A humidity of 30+/-3% in a closed environment and the overnight drying of the spotted slides gave the best conditions for arraying. In addition, the cDNA dissolved in 30% DMSO gave the optimal conditions for spotting compared to the 1X ArrayIt, 3X SSC and 50% DMSO. Lastly, cDNA in the concentration range of 100-300 ng/ micro l was determined to be best for arraying and post-processing. Currently, the printing system in this study yields reproducible 9000 spots with a spot size 150 mm diameter, and a 200 nm spot spacing.

DNA, Complementary↗
PubMed2004

Identification of a zebrafish model of muscular dystrophy.

1. Large-scale mutagenic screens of the zebrafish genome have identified a number of different classes of mutations that disrupt skeletal muscle formation. Of particular interest and relevance to human health is a class of recessive lethal mutations in which muscle differentiation occurs normally, but is followed by tissue-specific degeneration reminiscent of human muscular dystrophies. 2. We have shown that one member of this class of mutations, sapje (sap), results from mutations within the zebrafish orthologue of the human Duchenne muscular dystrophy (DMD) gene. Mutations in this locus cause Duchenne or Becker muscular dystrophies in human patients and are thought to result in a dystrophic pathology by disrupting the link between the actin cytoskeleton and the extracellular matrix in skeletal muscle cells. 3. We have found that the progressive muscle degeneration phenotype of sapje-mutant zebrafish embryos is caused by the failure of somitic muscle attachments at the embryonic myotendinous junction (MTJ). 4. Although a role for dystrophin at the MTJ has been postulated previously and MTJ structural abnormalities have been identified in the dystrophin-deficient mdx mouse model, in vivo evidence of pathology based on muscle attachment failure is thus far lacking. Therefore, the sapjre mutation may provide a model for a novel pathological mechanism of Duchenne muscular dystrophy and other muscle diseases. In the present review, we discuss this finding in light of previously postulated models of dystrophin function.

Animals↗