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PubMed2002

Genetics of schizophrenia: from animal models to clinical studies.

Genetic epidemiological studies strongly suggest that additive and interactive genes, each with small effects, mediate the genetic vulnerability for schizophrenia. With the human genome working draft at hand, candidate gene (and ultimately large-scale genome-wide) association studies are gaining renewed interest in the effort to unravel the complex genetics of schizophrenia. In the absence of an unequivocally established biological theory for schizophrenia, identifying candidate genes to be tested in an association paradigm remains a challenging task. We maintain that it is possible to use animal models to map genes or loci involved in behavioural traits that are relevant to schizophrenia. The human genes (or syntenic loci) homologous to those identified in mice can subsequently be tested in patients with schizophrenia who have been carefully phenotyped for traits "isomorphic" to the ones modelled in mice. If confirmed in humans, these genes may be further analyzed in the animal model to identify their role and the biological network they are involved in. To tackle the complex and intimidating problem of the genetics of schizophrenia, it may be necessary to go from animal models to human studies and vice versa; this strategy has been proven to be efficient in less complicated, though complex, human diseases.

Animals↗

PubMed2004

A computational method for assessing peptide- identification reliability in tandem mass spectrometry analysis with SEQUEST.

High-throughput protein identification in mass spectrometry is predominantly achieved by first identifying tryptic peptides by a database search and then by combining the peptide hits for protein identification. One of the popular tools used for the database search is SEQUEST. Peptide identification is carried out by selecting SEQUEST hits above a specified threshold, the value of which is typically chosen empirically in an attempt to separate true identifications from false ones. These SEQUEST scores are not normalized with respect to the composition, length and other parameters of the peptides. Furthermore, there is no rigorous reliability estimate assigned to the protein identifications derived from these scores. Hence, the interpretation of SEQUEST hits generally requires human involvement, making it difficult to scale up the identification process for genome-scale applications. To overcome these limitations, we have developed a method, which combines a neural network and a statistical model, for normalizing SEQUEST scores, and also for providing a reliability estimate for each SEQUEST hit. This method improves the sensitivity and specificity of peptide identification compared to the standard filtering procedure used in the SEQUEST package, and provides a basis for estimating the reliability of protein identifications.

Algorithms↗

PubMed2026

Unraveling causal links between chronic rhinosinusitis and peripheral artery diseases: insights from genetic correlations through genome-wide association studies.

OBJECTIVES: Chronic Rhinosinusitis (CRS) shares epidemiological links with Cardiovascular Diseases (CVDs), however, their shared genetic basis remains unclear. We hypothesized that pleiotropic genetic variants underlie CRS-CVDs links via distinct biological pathways. METHODS: Using large-scale GWAS data from European-ancestry individuals, we assessed global and local genetic correlations. We applied Genomic Structural Equation Modeling (Genomic SEM) to dissect shared genetic architecture, performed bidirectional Mendelian Randomization (MR) to infer causality, and conducted cis-eQTL colocalization to identify shared genetic signals. Finally, in vitro endothelial models (HUVECs) validated the functional dynamics of candidate genes under CRS-mimicking inflammatory stress. RESULTS: CRS showed significant genetic correlations with multiple CVDs. Genomic SEM revealed a latent factor structuring shared genetic risk through three pathways: artery diseases, myocardial diseases, and heart failure. Local genetic correlations identified significant local genetic correlations specifically between CRS and Peripheral Atherosclerosis (PAS)/Peripheral Artery Disease (PAD) specifically within the chr6: 31.57‒33.24 Mb locus. MR demonstrated causal effects of CRS on PAD (OR = 1.23, p = 0.022) and PAS (OR = 1.21, p = 0.011), but not vice versa. Genetically predicted HLA-DRB1, APOM, and COL11A2 expression conferred protection, while HLA-DQA2 increased risk. Crucially, in vitro validation corroborated these pathogenic trajectories, inflammatory stress significantly downregulated the protective APOM and upregulated the risk-associated HLA-DQA2 alongside pro-atherogenic VCAM-1, while HLA-DRB1 exhibited a compensatory upregulation (p < 0.05). CONCLUSION: CRS shares global genetic liability with CVDs, structured through three primary etiological pathways. Causal effects of CRS on peripheral artery diseases are mediated by immune and lipid-related genes within the chr6 locus, revealing divergent pleiotropic mechanisms. Our integrated genetic and in vitro evidence provides a mechanistic framework wherein chronic mucosal inflammation contributes to systemic endothelial vulnerability, thereby highlighting candidate targets for mechanism-directed therapy.

Humans↗

PubMed2006

A new perspective on isochore evolution.

The genomes of mammals and birds show dramatic variation in base composition over large scales, the so called isochore structure of the genome. The origin of isochores is still controversial: various neutral and selectionist models have been proposed--and criticized--since the discovery of isochores in the 1970s. The availability of complete mammalian genomes has yielded new opportunities for addressing this question. In particular, it was recently proposed that (i) the isochore structure is declining in many mammalian groups, and that (ii) GC-content is influenced by local recombination rate, possibly via the mechanism of GC-biased gene conversion. In this article we review the existing support for these two hypotheses, and discuss how they can be combined to provide a new perspective on isochore evolution.

Animals↗

PubMed2003

Molecular pathogenesis of thyroid cancer.

A number of molecular abnormalities have been described in association with the progression from normal thyroid tissue to benign adenomas to well-differentiated and finally anaplastic epithelial thyroid cancer. These include upregulation of proliferative factors, such as growth hormones and oncogenes, downregulation of apoptotic and cell-cycle inhibitory factors, such as tumor suppressors, disruption of normal cell-to-cell interactions, and cellular immortalization. The progression model for thyroid carcinoma has not been proven, but evidence suggests that an evolutionary molecular process is involved, especially in the development of follicular thyroid cancers for which there are distinct intermediate phenotypes. We present a comprehensive evaluation of factors involved in thyroid tumorigenesis and attempt to describe preliminary attributes of a progression model. The organization of this model should also provide a template for the incorporation of new information as it is derived from large-scale genomic studies.

Apoptosis↗

PubMed2005

BioMOBY successfully integrates distributed heterogeneous bioinformatics Web Services. The PlaNet exemplar case.

The burden of non-interoperability between on-line genomic resources is increasingly the rate-limiting step in large-scale genomic analysis. BioMOBY is a biological Web Service interoperability initiative that began as a retreat of representatives from the model organism database community in September, 2001. Its long-term goal is to provide a simple, extensible platform through which the myriad of on-line biological databases and analytical tools can offer their information and analytical services in a fully automated and interoperable way. Of the two branches of the larger BioMOBY project, the Web Services branch (MOBY-S) has now been deployed over several dozen data sources worldwide, revealing some significant observations about the nature of the integrative biology problem; in particular, that Web Service interoperability in the domain of bioinformatics is, unexpectedly, largely a syntactic rather than a semantic problem. That is to say, interoperability between bioinformatics Web Services can be largely achieved simply by specifying the data structures being passed between the services (syntax) even without rich specification of what those data structures mean (semantics). Thus, one barrier of the integrative problem has been overcome with a surprisingly simple solution. Here, we present a non-technical overview of the critical components that give rise to the interoperable behaviors seen in MOBY-S and discuss an exemplar case, the PlaNet consortium, where MOBY-S has been deployed to integrate the on-line plant genome databases and analytical services provided by a European consortium of databases and data service providers.

Computational Biology↗

PubMed2001

[Murine models for human diseases].

This article is a bibliographic review concerning mouse mutations, spontaneous, induced or genetically engineered, as models of human genetic diseases. Since the beginning of the last century, mouse models have been instrumental in the understanding of the pathogenesis of many diseases and designing of new therapies. A number of recent technological advances in embryo manipulation and many large-scale mutagenesis screens will dramatically increase the availability of new mouse models in the near future. In the "post-genomic" era, mouse mutants will have a significant role as a model system for functional genome analysis of the upcoming whole-genome information of the human and mouse genomes projects.

Animals↗

PubMed2026

The molecular similarity landscape of preclinical cancer models to patient tumors.

Selecting appropriate preclinical models is fundamental for translational oncology, yet a large-scale, multi-omic quantitative comparison of their similarity to primary human tumors is lacking. To address this, we integrated transcriptomic, proteomic, and genomic profiles from over 10,000 primary tumors from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC), alongside 4,000 preclinical models. Using a robust computational framework, we revealed a clear hierarchy of transcriptomic and proteomic similarity to patient tumors: with patient-dervied xenografts (PDXs) having greater transcriptomic and proteomic similarity to patient tumors (>) compared with patient-derived organoids (PDOs), which are equal in hierarchy to that of PDX-dervied organoids (PDXOs) > cell lines. We also quantified high molecular conservation (Pearson correlation coefficient = 0.96) across paired in vitro to in vivo platform (organoids to PDX) transitions. Furthermore, genomic analysis demonstrated that whole-exome sequencing (WES) outperforms RNA-seq in detecting DNA variants, and it identified a clonal complexity hierarchy (cell lines > PDXOs > PDXs > PDOs) reflecting the effect of passaging history on intratumor heterogeneity. Ultimately, this study delivers a comprehensive quantitative benchmark, establishing a population-level hierarchy of molecular similarity between preclinical models and primary tumors and providing a data-driven reference for model selection. These findings offer a data-driven framework for selecting models that balance biological representativeness with experimental practicality.

Humans↗

PubMed2006

GAIA: an easy-to-use web-based application for interaction analysis of case-control data.

BACKGROUND: The advent of cheap, large scale genotyping has led to widespread adoption of genetic association mapping as the tool of choice in the search for loci underlying susceptibility to common complex disease. Whilst simple single locus analysis is relatively trivial to conduct, this is not true of more complex analysis such as those involving interactions between loci. The importance of testing for interactions between loci in association analysis has been highlighted in a number of recent high profile publications. RESULTS: Genetic Association Interaction Analysis (GAIA) is a web-based application for testing for statistical interactions between loci. It is based upon the widely used case-control study design for genetic association analysis and is designed so that non-specialists may routinely apply tests for interaction. GAIA allows simple testing of both additive and additive plus dominance interaction models and includes permutation testing to appropriately correct for multiple testing. The application will find use both in candidate gene based studies and in genome-wide association studies. For large scale studies GAIA includes a screening approach which prioritizes loci (based on the significance of main effects at one or both loci) for further interaction analysis. CONCLUSION: GAIA is available at http://www.bbu.cf.ac.uk/html/research/biostats.htm.

Case-Control Studies↗

PubMed2006

Circadian rhythms lit up in Chlamydomonas.

Recent work on the circadian clock of the unicellular green alga Chlamydomonas reinhardtii strengthens its standing as a convenient model system for circadian study. It was shown to be amenable to molecular engineering using a luciferase-based real-time reporter for circadian rhythms. Together with the completed draft genomic sequence, the new system opens the door for genome-scale forward and reverse genetic analysis.

Animals↗

PubMed2004

Decoding human regulatory circuits.

Clusters of transcription factor binding sites (TFBSs) which direct gene expression constitute cis-regulatory modules (CRMs). We present a novel algorithm, based on Gibbs sampling, which locates, de novo, the cis features of these CRMs, their component TFBSs, and the properties of their spatial distribution. The algorithm finds 69% of experimentally reported TFBSs and 85% of the CRMs in a reference data set of regions upstream of genes differentially expressed in skeletal muscle cells. A discriminant procedure based on the output of the model specifically discriminated regulatory sequences in muscle-specific genes in an independent test set. Application of the method to the analysis of 2710 10-kb fragments upstream of annotated human genes identified 17 novel candidate modules with a false discovery rate </=0.05, demonstrating the applicability of the method to genome-scale data.

Algorithms↗

PubMed2025

Gene regulatory network structure informs the distribution of perturbation effects.

Gene regulatory networks (GRNs) govern many core developmental and biological processes underlying human complex traits. Even with broad-scale efforts to characterize the effects of molecular perturbations and interpret gene coexpression, it remains challenging to infer the architecture of gene regulation in a precise and efficient manner. Key properties of GRNs, like hierarchical structure, modular organization, and sparsity, provide both challenges and opportunities for this objective. Here, we seek to better understand properties of GRNs using a new approach to simulate their structure and model their function. We produce realistic network structures with a novel generating algorithm based on insights from small-world network theory, and we model gene expression regulation using stochastic differential equations formulated to accommodate modeling molecular perturbations. With these tools, we systematically describe the effects of gene knockouts within and across GRNs, finding a subset of networks that recapitulate features of a recent genome-scale perturbation study. With deeper analysis of these exemplar networks, we consider future avenues to map the architecture of gene expression regulation using data from cells in perturbed and unperturbed states, finding that while perturbation data are critical to discover specific regulatory interactions, data from unperturbed cells may be sufficient to reveal regulatory programs.

Gene Regulatory Networks↗

PubMed2000

Genomic approaches to plant stress tolerance.

Past efforts to improve plant tolerance to drought, high salinity and low-temperature through breeding and genetic engineering have had limited success owing to the genetic complexity of stress responses. Progress is now anticipated through comparative genomics studies of an evolutionarily diverse set of model organisms, and through the use of techniques such as high-throughput analysis of expressed sequence tags, large-scale parallel analysis of gene expression, targeted or random mutagenesis, and gain-of-function or mutant complementation. The discovery of novel genes, determination of their expression patterns in response to abiotic stress, and an improved understanding of their roles in stress adaptation (obtained by the use of functional genomics) will provide the basis of effective engineering strategies leading to greater stress tolerance.

Disasters↗

PubMed2005

A model of the statistical power of comparative genome sequence analysis.

Comparative genome sequence analysis is powerful, but sequencing genomes is expensive. It is desirable to be able to predict how many genomes are needed for comparative genomics, and at what evolutionary distances. Here I describe a simple mathematical model for the common problem of identifying conserved sequences. The model leads to some useful rules of thumb. For a given evolutionary distance, the number of comparative genomes needed for a constant level of statistical stringency in identifying conserved regions scales inversely with the size of the conserved feature to be detected. At short evolutionary distances, the number of comparative genomes required also scales inversely with distance. These scaling behaviors provide some intuition for future comparative genome sequencing needs, such as the proposed use of "phylogenetic shadowing" methods using closely related comparative genomes, and the feasibility of high-resolution detection of small conserved features.

Animals↗

PubMedDate not supplied

Functional inferences from blind ab initio protein structure predictions.

Ab initio protein structure prediction methods have improved dramatically in the past several years. Because these methods require only the sequence of the protein of interest, they are potentially applicable to the open reading frames in the many organisms whose sequences have been and will be determined. Ab initio methods cannot currently produce models of high enough resolution for use in rational drug design, but there is an exciting potential for using the methods for functional annotation of protein sequences on a genomic scale. Here we illustrate how functional insights can be obtained from low-resolution predicted structures using examples from blind ab initio structure predictions from the third and fourth critical assessment of structure prediction (CASP3, CASP4) experiments.

Computational Biology↗

PubMed2003

TOUCHSTONE II: a new approach to ab initio protein structure prediction.

We have developed a new combined approach for ab initio protein structure prediction. The protein conformation is described as a lattice chain connecting C(alpha) atoms, with attached C(beta) atoms and side-chain centers of mass. The model force field includes various short-range and long-range knowledge-based potentials derived from a statistical analysis of the regularities of protein structures. The combination of these energy terms is optimized through the maximization of correlation for 30 x 60,000 decoys between the root mean square deviation (RMSD) to native and energies, as well as the energy gap between native and the decoy ensemble. To accelerate the conformational search, a newly developed parallel hyperbolic sampling algorithm with a composite movement set is used in the Monte Carlo simulation processes. We exploit this strategy to successfully fold 41/100 small proteins (36 approximately 120 residues) with predicted structures having a RMSD from native below 6.5 A in the top five cluster centroids. To fold larger-size proteins as well as to improve the folding yield of small proteins, we incorporate into the basic force field side-chain contact predictions from our threading program PROSPECTOR where homologous proteins were excluded from the data base. With these threading-based restraints, the program can fold 83/125 test proteins (36 approximately 174 residues) with structures having a RMSD to native below 6.5 A in the top five cluster centroids. This shows the significant improvement of folding by using predicted tertiary restraints, especially when the accuracy of side-chain contact prediction is >20%. For native fold selection, we introduce quantities dependent on the cluster density and the combination of energy and free energy, which show a higher discriminative power to select the native structure than the previously used cluster energy or cluster size, and which can be used in native structure identification in blind simulations. These procedures are readily automated and are being implemented on a genomic scale.

Algorithms↗

PubMed2006

BiologicalNetworks: visualization and analysis tool for systems biology.

Systems level investigation of genomic scale information requires the development of truly integrated databases dealing with heterogeneous data, which can be queried for simple properties of genes or other database objects as well as for complex network level properties, for the analysis and modelling of complex biological processes. Towards that goal, we recently constructed PathSys, a data integration platform for systems biology, which provides dynamic integration over a diverse set of databases [Baitaluk et al. (2006) BMC Bioinformatics 7, 55]. Here we describe a server, BiologicalNetworks, which provides visualization, analysis services and an information management framework over PathSys. The server allows easy retrieval, construction and visualization of complex biological networks, including genome-scale integrated networks of protein-protein, protein-DNA and genetic interactions. Most importantly, BiologicalNetworks addresses the need for systematic presentation and analysis of high-throughput expression data by mapping and analysis of expression profiles of genes or proteins simultaneously on to regulatory, metabolic and cellular networks. BiologicalNetworks Server is available at http://brak.sdsc.edu/pub/BiologicalNetworks.

Computer Graphics↗

PubMed1997

Searching for regulatory elements in human noncoding sequences.

Important progress has been made in the past two years in the identification of Pol II promoters. For most other regulatory elements, however, current biological knowledge is still insufficient to allow the development of prediction tools. The phylogenetic-footprinting strategy, which is based on the comparative analysis of homologous sequences, is a very efficient approach to identify new unknown regulatory elements. The recent organization of large-scale sequencing projects for some model vertebrate organisms will be extremely valuable for the prediction of regulatory elements in the human genome.

Animals↗