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Structural genomics analysis of alternative splicing and application to isoform structure modeling.

Alternative splicing is a sophisticated nuclear process that regulates gene expression. It represents an important mechanism for enhancing the functional diversity of proteins. Our current knowledge of alternatively spliced variants is derived mainly from mRNA transcripts, and very little is known about their protein tertiary structures. We carried out a large-scale analysis of known alternatively spliced variants at both protein sequence and structure levels and have shown that threading is, in general, a viable approach for modeling structures of alternatively spliced variants. An examination of alternative splicing at the protein sequence level revealed that the size of splicing events follows the power law distribution and the majority of splicing isoforms harbor only one or two alternations. We examined alternative splicing in the context of protein 3D structures and found that the boundaries of alternative splicing events generally happen in coil regions of secondary structures and exposed residues and the majority of the sequences involved in splicing are located on the surface of proteins. In light of these findings, we then proceeded to demonstrate that threading represents a useful tool for structure prediction of alternative splicing isoforms and addressed the fold stability issue of threading-based structure prediction by molecular dynamics simulation. Our analysis and the insights gained have helped to establish a viable method for structure prediction of alternatively spliced isoforms at the genome scale.

Alternative Splicing↗

DNA microarrays for functional plant genomics.

DNA microarray technology is a key element in today's functional genomics toolbox. The power of the method lies in miniaturization, automation and parallelism permitting large-scale and genome-wide acquisition of quantitative biological information from multiple samples. DNA microarrays are currently fabricated and assayed by two main approaches involving either in situ synthesis of oligonucleotides ('oligonucleotide microarrays') or deposition of pre-synthesized DNA fragments ('cDNA microarrays') on solid surfaces. To date, the main applications of microarrays are in comprehensive, simultaneous gene expression monitoring and in DNA variation analyses for the identification and genotyping of mutations and polymorphisms. Already at a relatively early stage of its application in plant science, microarrays are being utilized to examine a range of biological issues including the circadian clock, plant defence, environmental stress responses, fruit ripening, phytochrome A signalling, seed development and nitrate assimilation. Novel insights are obtained into the molecular mechanisms co-ordinating metabolic pathways, regulatory and signalling networks. Exciting new information will be gained in the years to come not only from genome-wide expression analyses on a few model plant species, but also from extensive studies of less thoroughly studied species on a more limited scale. The value of microarray technology to our understanding of living processes will depend both on the amount of data to be generated and on its clever exploration and integration with other biological knowledge arising from complementary functional genomics tools for 'profiling' the genome, proteome, metabolome and phenome.

Gene Expression Profiling↗

The nuclear genome is involved in heteroplasmy control in a mitochondrial mutant strain of Drosophila subobscura.

Most (78%) mitochondrial genomes in the studied mutant strain of Drosophila subobscura have undergone a large-scale deletion (5 kb) in the coding region. This mutation is stable, and is transmitted intact to the offspring. This animal model of major rearrangements of mitochondrial genomes can be used to analyse the involvement of the nuclear genome in the production and maintenance of these rearrangements. Successive backcrosses between mutant strain females and wild-type males yield a biphasic change in heteroplasmy level: (a) a 5% decrease in mutated genomes per generation (from 78 to 55%), until the nuclear genome is virtually replaced by the wild-type genome (seven to eight crosses); and (b) a continuous decrease of 0.5% per generation when the nuclear context is completely wild-type. In parallel with these changes, NADH dehydrogenase activity, which is halved in the mutant strain (five subunits of this complex are affected by the mutation), gradually increases and stabilizes near the wild-type activity. A return to a nuclear context is accompanied by the opposite phenomena: progressive increase in heteroplasmy level and stabilization at the value seen in the wild-type strain and a decrease in the activity of complex I. These results indicate that the nuclear genome plays an important role in the control of heteroplasmy level and probably in the production of rearranged genomes.

Animals↗

Prediction of protein-protein interaction based on structure.

A great challenge in the proteomics and structural genomics era is to predict protein structure and function from sequence, including the identification of biological partners. The development of a procedure to construct position-specific scoring matrices for the prediction and identification of sequences with putative significant affinity faces this challenge. The local and web applications used for sequence and structure search, sequence alignment, protein modeling, molecule edition and modification, and scoring matrices construction are described in detail. The methodology is based on the information contained in structural databases and takes into account the subtle conformational and sequence details that characterize different structures within a family. Using the matrices, the protein sequence databases can be easily scanned to locate putative partners of biological significance. The success of this methodology opens the way for the prediction of protein-protein interaction at genome scale.

Algorithms↗

Structure alignment via Delaunay tetrahedralization.

A novel protein structure alignment technique has been developed reducing much of the secondary and tertiary structure to a sequential representation greatly accelerating many structural computations, including alignment. Constructed from incidence relations in the Delaunay tetrahedralization, alignments of the sequential representation describe structural similarities that cannot be expressed with rigid-body superposition and complement existing techniques minimizing root-mean-squared distance through superposition. Restricting to the largest substructure superimposable by a single rigid-body transformation determines an alignment suitable for root-mean-squared distance comparisons and visualization. Restricted alignments of a test set of histones and histone-like proteins determined superpositions nearly identical to those produced by the established structure alignment routines of DaliLite and ProSup. Alignment of three, increasingly complex proteins: ferredoxin, cytidine deaminase, and carbamoyl phosphate synthetase, to themselves, demonstrated previously identified regions of self-similarity. All-against-all similarity index comparisons performed on a test set of 45 class I and class II aminoacyl-tRNA synthetases closely reproduced the results of established distance matrix methods while requiring 1/16 the time. Principal component analysis of pairwise tetrahedral decomposition similarity of 2300 molecular dynamics snapshots of tryptophanyl-tRNA synthetase revealed discrete microstates within the trajectory consistent with experimental results. The method produces results with sufficient efficiency for large-scale multiple structure alignment and is well suited to genomic and evolutionary investigations where no geometric model of similarity is known a priori.

Algorithms↗

Whole-mount in situ hybridization to mouse embryos.

The mouse is well-established as the major animal model for the study of mammalian development. Rapid progress in large-scale cDNA and also genomic sequencing projects is identifying new mouse genes at an unprecedented rate. As a first step toward understanding the function of these novel genes, it is important to determine their developmental expression pattern. Here we provide a reliable, sensitive method for whole-mount in situ hybridization using the mouse embryo.

Animals↗

RNA interference.

A conserved biological response to double-stranded RNA, known variously as RNA interference (RNAi) or post-transcriptional gene silencing, mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes. RNAi has been cultivated as a means to manipulate gene expression experimentally and to probe gene function on a whole-genome scale.

Animals↗

Inference of combinatorial regulation in yeast transcriptional networks: a case study of sporulation.

Decomposing transcriptional regulatory networks into functional modules and determining logical relations between them is the first step toward understanding transcriptional regulation at the system level. Modules based on analysis of genome-scale data can serve as the basis for inferring combinatorial regulation and for building mathematical models to quantitatively describe the behavior of the networks. We present here an algorithm called modem to identify target genes of a transcription factor (TF) from a single expression experiment, based on a joint probabilistic model for promoter sequence and gene expression data. We show how this method can facilitate the discovery of specific instances of combinatorial regulation and illustrate this for a specific case of transcriptional networks that regulate sporulation in the yeast Saccharomyces cerevisiae. Applying this method to analyze two crucial TFs in sporulation, Ndt80p and Sum1p, we were able to delineate their overlapping binding sites. We proposed a mechanistic model for the competitive regulation by the two TFs on a defined subset of sporulation genes. We show that this model accounts for the temporal control of the "middle" sporulation genes and suggest a similar regulatory arrangement can be found in developmental programs in higher organisms.

Algorithms↗

[Importance of development of ecotoxicogenomics in understanding molecular mechanisms of chemicals in developing animals].

Developing organisms are sensitive to estrogenic chemicals. Exposure to estrogens or estrogenic chemicals during critical periods of development induces persistent changes in reproductive as well as nonreproductive organs, including persistent molecular alterations. Chemicals released into the environment potentially disrupt the endocrine system in wild animals and humans, some of which exhibit estrogenic or anti-androgenic activity by binding to estrogen or androgen receptors. Estrogen-responsive genes and critical developmental windows of various animal species, therefore, should be identified to understand the molecular basis of estrogenic activity during embryonic development. To understand molecular mechanisms of estrogenic chemicals in developing organisms, we identified estrogen-responsive genes using cDNA microarrays and a quantitative reverse transcriptase-polymerase chain reaction, and genes related to estrogen-independent vaginal changes in mice induced by estrogens during a critical window. We also analyzed genes in Xenopus laevis embryos related to abnormalities induced by estrogenic chemicals using cDNA microarrays. Estrogen receptors and other steroid hormone receptors were cloned in various species, including the American alligator, crocodile, Florida red-bellied turtle, Silurana tropicalis, roach, Fundulus and mosquito fish. An environmental androgen, trenbolone, was found to induce persistent changes in the gonopodium and ovotestis in mosquito fish. In Daphnia magna, juvenile hormone agonists induce the production of male offspring. Tributyltin (TBT) induces imposex in rockshell through retinoid X receptor (RXR) activation. Microarray technology is a powerful tool for understanding the molecular mechanism of receptor-mediated toxicology in various animal species. Ecotoxicogenomics is a multidisciplinary field concerned with elucidating how the entire genome is involved in the biological response of wild animals exposed to environmental toxicants and stressors. Ecotoxicogenomics combines information from studies of genomic-scale mRNA profiling by microarray analysis (transcriptomics), protein profiling (proteomics), metabolite profiling (metabolonomics) and computational models to understand the roles of gene-environment interaction in the chemical toxicity. To understand the molecular mechanism of chemicals in developing wildlife, the establishment of ecotoxicogenomics is essential in the near future.

Amphibians↗

The yeast genome project: what did we learn?

The bakers' yeast, Saccharomyces cerevisiae, a microorganism of major importance for bioindustries, and one of the favored model organisms for basic biological research, is the first eukaryote whose genome is entirely sequenced. Beyond the wealth of novel biological information, it is the extent of what remains to be understood in the genome of a simple unicellular organism that is the most striking result: a significant proportion of yeast genes are orphans of unpredictable function. Offering the possibility of large-scale reverse genetics, yeast will be a powerful model for post-sequencing studies. But geneticists are now faced with the difficulty of asking novel questions.

Chromosomes↗

Generating three-dimensional genome structures with a variational quantum algorithm.

Chromosome conformation capture experiments have revealed the underlying spatial interactions that govern three-dimensional (3D) genome organization and topology. Detecting 3D contacts between genomic loci considerably enhances our understanding of fundamental regulatory processes. Modeling 3D structures from experimental contact matrices can further contextualize the relationship between 3D genome organization and regulation. While classical algorithms have been successful in reconstructing genomic conformations, we investigate the prospect of quantum computation to aid in modeling the conformational space. In this context, we propose a novel variational quantum algorithm (VQA) to model the distribution of 3D genomic structures from experimental contact data. Through rigorous evaluations, we demonstrate the capability of our algorithm to sample ensembles of viable 3D conformations that agree well with experimental and simulated contact data. Furthermore, we extend our methodology to model the conformational space of a single cell or a population of cells. In the advent of sufficient quantum utility, the insights gained from this study can serve as a foundation for investigating high-resolution, large-scale ensembles of genomic conformations through generative VQAs.

Algorithms↗

Towards integrated clinico-genomic models for personalized medicine: combining gene expression signatures and clinical factors in breast cancer outcomes prediction.

Genomic data, particularly genome-scale measures of gene expression derived from DNA microarray studies, has the potential for adding enormous information to the analysis of biological phenotypes. Perhaps the most successful application of this data has been in the characterization of human cancers, including the ability to predict clinical outcomes. Nevertheless, most analyses have used gene expression profiles to define broad group distinctions, similar to the use of traditional clinical risk factors. As a result, there remains considerable heterogeneity within the broadly defined groups and thus predictions fall short of providing accurate predictions for individual patients. One strategy to resolve this heterogeneity is to make use of multiple gene expression patterns that are more powerful in defining individual characteristics and predicting outcomes than any single gene expression pattern. Statistical tree-based classification systems provide a framework for assessing multiple patterns, that we term metagenes, selecting those that are most capable of resolving the biological heterogeneity. Moreover, this framework provides a mechanism to combine multiple forms of data, both genomic and clinical, to most effectively characterize individual patients and achieve the goal of personalized predictions of clinical outcomes.

Breast Neoplasms↗

Genomic medicine: genetic variation and its impact on the future of health care.

Advances in genome technology and other fruits of the Human Genome Project are playing a growing role in the delivery of health care. With the development of new technologies and opportunities for large-scale analysis of the genome, transcriptome, proteome and metabolome, the genome sciences are poised to have a profound impact on clinical medicine. Cancer prognostics will be among the first major test cases for a genomic medicine paradigm, given that all cancer is caused by genomic instability, and microarrays allow assessment of patients' entire expressed genomes. Analysis of breast cancer patients' expression patterns can already be highly correlated with recurrence risks. By integrating clinical data with gene expression profiles, imaging, metabolomic profiles and proteomic data, the prospect for developing truly individualized care becomes ever more real. Notwithstanding these promises, daunting challenges remain for genomic medicine. Success will require planning robust prospective trials, analysing health care economic and outcome data, assuaging insurance and privacy concerns, developing health delivery models that are commercially viable and scaling up to meet the needs of the whole population.

Delivery of Health Care↗

GUANinE v1.1 reveals complementarity of supervised and genomic language models.

There has been much debate about the benefits of supervised versus unsupervised learning on genomes. Determining which is better in what contexts requires developing comprehensive benchmarks spanning functional and evolutionary tasks. Importantly, such benchmarks need large sample sizes to enable well-powered ranking of models. Having developed and applied such a benchmark here (GUANinE v1.1), we conclusively demonstrate each paradigm offers key advantages and outperforms on certain tasks. In accordance with training, supervised sequence-to-function models exhibit strong performance when annotating functional states characterized by chromatin accessibility or histone marks, while self-supervised language models outperform on evolutionary conservation. Our hundreds of new evaluations in this v1.1 expansion provide evidence for a tradeoff between input context size and model parameter count for a fixed compute budget, which we depict with new metrics such as kiloparameters/base pair. We also construct two new large-scale variant interpretation tasks in v1.1: cadd-snv measuring deleteriousness, and clinvar-snv measuring clinical pathogenicity. We find that conservation scores, and by extension, genomic language models, predict deleteriousness well, but successfully translating deleteriousness predictions to pathogenicity remains challenging. GUANinE v1.1 newly evaluates dozens of pretrained genomic models, and we conclude that moderate-context hybrid or post-trained language models may define the next era of machine learning in genomics.

Genomics↗

Chromosomal evolution in Saccharomyces.

The chromosomal speciation model invokes chromosomal rearrangements as the primary cause of reproductive isolation. In a heterozygous carrier, chromosomes bearing reciprocal translocations mis-segregate at meiosis, resulting in reduced fertility or complete sterility. Thus, chromosomal rearrangements act as a post-zygotic isolating mechanism. Reproductive isolation in yeast is due to post-zygotic barriers, as many species mate successfully but the hybrids are sterile. Reciprocal translocations are thought to be the main form of large-scale rearrangement since the hypothesized duplication of the whole yeast genome 10(8) years ago. To test the chromosomal speciation model in yeast, we have characterized chromosomal translocations among the genomes of six closely related species in the Saccharomyces 'sensu stricto' complex. Here we show that rearrangements have occurred between closely related species, whereas more distant ones have colinear genomes. Thus, chromosomal rearrangements are not a prerequisite for speciation in yeast and the rate of formation of translocations is not constant. These rearrangements appear to result from ectopic recombination between Ty elements or other repeated sequences.

Chromosomes, Fungal↗

The Karyote physico-chemical genomic, proteomic, metabolic cell modeling system.

Modeling approaches to the dynamics of a living cell are presented that are strongly based on its underlying physical and chemical processes and its hierarchical spatio-temporal organization. Through the inclusion of a broad spectrum of processes and a rigorous analysis of the multiple scale nature of cellular dynamics, we are attempting to advance cell modeling and its applications. The presentation focuses on our cell modeling system, which integrates data archiving and quantitative physico-chemical modeling and information theory to provide a seamless approach to the modeling/data analysis endeavor. Thereby the rapidly growing mess of genomic, proteomic, metabolic, and cell physiological data can be automatically used to develop and calibrate a predictive cell model. The discussion focuses on the Karyote cell modeling system and an introduction to the CellX and VirusX models. The Karyote software system integrates three elements: (1) a model-building and data archiving module that allows one to define a cell type to be modeled through its reaction network, structure, and transport processes as well as to choose the surrounding medium and other parameters of the phenomenon to be modeled; (2) a genomic, proteomic, metabolic cell simulator that solves the equations of metabolic reaction, transcription/translation polymerization and the exchange of molecules between parts of the cell and with the surrounding medium; and (3) an information theory module (ITM) that automates model calibration and development, and integrates a variety of data types with the cell dynamic computations. In Karyote, reactions may be fast (equilibrated) or slow (finite rate), and the special effects of enzymes and other minority species yielding steady-state cycles of arbitrary complexities are accounted for. These features of the dynamics are handled via rigorous multiple scale analysis. A user interface allows for an automated generation and solution of the equations of multiple timescale, compartmented dynamics. Karyote is based on a fixed intracellular structure. However, cell response to changes in the host medium, damage, development or transformation to abnormality can involve dramatic changes in intracellular structure. As this changes the nature of the cellular dynamics, a new model, CellX, is being developed based on the spatial distribution of concentration and other variables. This allows CellX to capture the self-organizing character of cellular behavior. The self-assembly of organelles, viruses, and other subcellular bodies is being addressed in a second new model, VirusX, that integrates molecular mechanics and continuum theory. VirusX is designed to study the influence of a host medium on viral self-assembly, structural stability, infection of a single cell, and transmission of disease.

Animals↗

SeqQC-former: A sequence-quality fusion framework for QC-aware review prioritization of candidate somatic SNVs in cancer genomics.

The accurate prioritization of candidate somatic single-nucleotide variants (SNVs) remains a challenge due to the substantial variability in sequencing quality across genomic loci. SeqQC-Former is a sequence-quality fusion framework that integrates the local nucleotide context with read-level quality-control (QC) covariates derived from matched tumor-normal sequencing data. This integration generates QC-aware prioritization scores for the downstream review of candidate variants. Unlike conventional variant callers, SeqQC-Former is designed not to infer biological truth but to support post-calling review and prioritization under heterogeneous sequencing conditions. The framework was trained and evaluated on a SEQC2-derived dataset comprising 89,447 candidate loci, including 1378 positive and 88,069 negative loci. In chromosome-held-out validation, which aims to reduce potential genomic-position leakage, SeqQC-Former demonstrated strong discrimination (AUROC = 0.9479; AUPRC = 0.9448), indicating good generalization to previously unseen chromosomes. Given that the SEQC2-derived labels contain QC-associated information; these results should be interpreted as an evaluation of QC-aware prioritization capability rather than an independent validation of biological variant correctness. Ablation analyses revealed that structured QC covariates provided the dominant predictive signal under the current SEQC2-derived labeling regime. SeqQC-Former achieved a significantly higher AUROC than classical machine-learning baselines, as determined by DeLong's test (p&#x202f;<&#x202f;0.01). Application to 53,164 glioblastoma variants demonstrated that external predictions were sensitive to QC scaling and threshold selection, underscoring that model outputs should be interpreted as QC-dependent prioritization scores rather than calibrated probabilities or definitive biological classifications. Overall, SeqQC-Former offers a reproducible post-calling QC-aware prioritization framework for large-scale somatic SNV review and underscores the importance of explicitly modeling sequencing-quality information when interpreting structured cancer genomics datasets.

Humans↗

Functional genomics tools for the analysis of zebrafish pigment.

Genetic model organisms are increasingly valuable in the post-genomics era to provide a basis for comparative analysis of the human genome. For higher order processes of vertebrate pigment cell biology and development, the mouse has historically been the model of choice. A complementary organism, the zebrafish (Danio rerio), shares many of the signaling and biological processes of vertebrates, e.g. neural crest development. The zebrafish has a number of characteristics that make it an especially valuable model for the study of pigment cell biology and disease. Large-scale genetic screens have identified a collection of pigmentation mutants that have already made valuable contributions to pigment research. An increasing repertoire of genomic resources such as an expressed sequence tag-based Gene Index (The Institute for Genomic Research) and improving methods of mutagenesis, transgenesis, and gene targeting make zebrafish a particularly attractive model. Morpholino phosphorodiamidate oligonucleotide (MO) 'knockdown' of pigment gene expression provides a non-conventional antisense tool for the analysis of genes involved in pigment cell biology and disease. In addition, an ongoing, reverse-genetic, MO-based screen for the rapid identification of gene function promises to be a valuable complement to other high-throughput microarray and proteomic approaches for understanding pigment cell biology. Novel reagents for zebrafish transgenesis, such as the Sleeping Beauty transposon system, continue to improve the capacity for genetic analysis in this system and ensure that the zebrafish will be a valuable genetic model for understanding a variety of biological processes and human diseases for years to come.

Animals↗