Cíle: Poslední významné pokroky v lidské genomice a postgenomických „omics“ nyní přinášejí nové možnosti zdravotní péče, kterou nazýváme „medicína založená na omics vědách“. V tomto článku jsme zkoumali vývoj a budoucí možnosti medicíny založené na omics vědách. Metody: Rozdělili jsme vývoj medicíny založené na omics vědách do tří generací za účelem ujasnění hlavních klinických cílů a charakterizace informačních metod každé generace spolu s jejich budoucími možnostmi. Výsledky: První generace medicíny založené na omics vědách, začala s „genomickou medicínou“, založenou na vrozených individuálních rozdílech v genomu. Toto otevřelo studování genetických polymorfismů nemocí a napomohlo vývoji personalizovaných léků založených na farmakogenetických/ farmakogenomických rozdílech v reakci na lék. Ve druhé generaci omicsové medicíny, díky pokroku v technologii vysokorychlostního sekvencování, byl otevřen přístup k obrovskému množství různých omicsových dat postgenomických nemocí, obsahujících vyčerpávající molekulární informace o nemocech somatických buněk. Zobrazení průběžného stavu nemoci je tak mnohem věrnější a umožňuje tak prediktivní medicíně předpovězení prognózy nemoci uplatněním datové analýzy. A také, díky rychle se rozvíjejícím znalostem o buňkové molekulární síti, je nyní umožněno porozumět nemoci na systémové úrovni pomocí nového oboru, zvaného systémová patologie. Může plně využít významné znalosti omicsu nemoci a povede ke komplexnímu porozumění postupu nemoci použitím datové analýzy.

Objectives: Recent important advances in the human genomics and post-genomic “omics” are now bringing about a new medical care which we call “omics-based medicine”. In this article, we investigated the development and future possibilities of omics-based medicine. Methods: We divided the development of omics-based medicine into three generations in order to clarify the main clinical goals and characteristics of informatics method of each generation, together with its future possibilities. Results: The first generation of omics-based medicine started with “genomic medicine” based on the inborn individual differences of genome. It has opened the study of genetic polymorphism of the diseases and promoted the personalized medication based on the pharmacogenetic/pharmacogenomic difference of the drug response. In the second generation of omics-based medicine, owing to the advances in the high-throughput technology, vast amount of the various post-genomic disease omics data containing comprehensive molecular information of diseased somatic cells has become available. It reflects the ongoing state of diseases more closely and enables the predictive medicine such as prognosis prediction of disease by applying the data-driven analysis. Finally, due to the rapidly growing knowledge about the cellular molecular network, system-level understanding of the disease, called systems pathology, becomes possible. It can fully exploit the substantial contents of disease omics and will lead to a comprehensive understanding of disease process by using model-driven analysis. Conclusion: Omics-based medicine and systems pathology will realize a new personalized and predictive medicine.

• Klíčová slova
• omics vědy, farmakogenomika,
• MeSH
• farmakogenetika trendy   MeSH
• financování organizované MeSH
• genomika trendy   MeSH
• lidé MeSH
• nemoc genetika   MeSH
• terapie metody   trendy   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• práce podpořená grantem MeSH

Contemporary molecular biology deals with wide and heterogeneous sets of measurements to model and understand underlying biological processes including complex diseases. Machine learning provides a frequent approach to build such models. However, the models built solely from measured data often suffer from overfitting, as the sample size is typically much smaller than the number of measured features. In this paper, we propose a random forest-based classifier that reduces this overfitting with the aid of prior knowledge in the form of a feature interaction network. We illustrate the proposed method in the task of disease classification based on measured mRNA and miRNA profiles complemented by the interaction network composed of the miRNA-mRNA target relations and mRNA-mRNA interactions corresponding to the interactions between their encoded proteins. We demonstrate that the proposed network-constrained forest employs prior knowledge to increase learning bias and consequently to improve classification accuracy, stability and comprehensibility of the resulting model. The experiments are carried out in the domain of myelodysplastic syndrome that we are concerned about in the long term. We validate our approach in the public domain of ovarian carcinoma, with the same data form. We believe that the idea of a network-constrained forest can straightforwardly be generalized towards arbitrary omics data with an available and non-trivial feature interaction network. The proposed method is publicly available in terms of miXGENE system (http://mixgene.felk.cvut.cz), the workflow that implements the myelodysplastic syndrome experiments is presented as a dedicated case study.

• MeSH
• genové regulační sítě MeSH
• lidé MeSH
• messenger RNA genetika   MeSH
• mikro RNA genetika   MeSH
• umělá inteligence MeSH
• výpočetní biologie metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

BACKGROUND: Integration of multi-omics data can provide a more complex view of the biological system consisting of different interconnected molecular components, the crucial aspect for developing novel personalised therapeutic strategies for complex diseases. Various tools have been developed to integrate multi-omics data. However, an efficient multi-omics framework for regulatory network inference at the genome level that incorporates prior knowledge is still to emerge. RESULTS: We present IntOMICS, an efficient integrative framework based on Bayesian networks. IntOMICS systematically analyses gene expression, DNA methylation, copy number variation and biological prior knowledge to infer regulatory networks. IntOMICS complements the missing biological prior knowledge by so-called empirical biological knowledge, estimated from the available experimental data. Regulatory networks derived from IntOMICS provide deeper insights into the complex flow of genetic information on top of the increasing accuracy trend compared to a published algorithm designed exclusively for gene expression data. The ability to capture relevant crosstalks between multi-omics modalities is verified using known associations in microsatellite stable/instable colon cancer samples. Additionally, IntOMICS performance is compared with two algorithms for multi-omics regulatory network inference that can also incorporate prior knowledge in the inference framework. IntOMICS is also applied to detect potential predictive biomarkers in microsatellite stable stage III colon cancer samples. CONCLUSIONS: We provide IntOMICS, a framework for multi-omics data integration using a novel approach to biological knowledge discovery. IntOMICS is a powerful resource for exploratory systems biology and can provide valuable insights into the complex mechanisms of biological processes that have a vital role in personalised medicine.

• MeSH
• algoritmy MeSH
• Bayesova věta MeSH
• genové regulační sítě MeSH
• lidé MeSH
• nádory tračníku * MeSH
• systémová biologie metody   MeSH
• variabilita počtu kopií segmentů DNA * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Careful and thoughtful experimental design is crucial to the success of any SIP experiment. This chapter discusses the essential aspects of designing a SIP experiment, focusing primarily on DNA- and RNA-SIP. The design aspects discussed here begin with considerations for carrying out the incubation, such as, the effect of choosing different stable isotopes and target biomolecules, to what degree should a labeled substrate be enriched, what concentration to use, and how long should the incubation take. Then tips and pitfalls in the technical execution of SIP are listed, including how much nucleic acids should be loaded, how many fractions to collect, and what centrifuge rotor to use. Lastly, a brief overview of the current methods for analyzing SIP data is presented, focusing on high-throughput amplicon sequencing, together with a discussion on how the choice of analysis method might affect the experimental design.

• MeSH
• analýza dat MeSH
• bakteriální RNA chemie   MeSH
• deuterium analýza   MeSH
• DNA bakterií chemie   MeSH
• DNA metabolismus   MeSH
• izotopové značení metody   MeSH
• izotopy dusíku analýza   MeSH
• izotopy kyslíku analýza   MeSH
• izotopy uhlíku analýza   MeSH
• RNA metabolismus   MeSH
• vysoce účinné nukleotidové sekvenování MeSH
• výzkumný projekt MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

• Publikační typ
• souhrny MeSH

Lithium is the gold standard treatment for bipolar disorder (BD). However, its mechanism of action is incompletely understood, and prediction of treatment outcomes is limited. In our previous multi-omics study of the Pharmacogenomics of Bipolar Disorder (PGBD) sample combining transcriptomic and genomic data, we found that focal adhesion, the extracellular matrix (ECM), and PI3K-Akt signaling networks were associated with response to lithium. In this study, we replicated the results of our previous study using network propagation methods in a genome-wide association study of an independent sample of 2039 patients from the International Consortium on Lithium Genetics (ConLiGen) study. We identified functional enrichment in focal adhesion and PI3K-Akt pathways, but we did not find an association with the ECM pathway. Our results suggest that deficits in the neuronal growth cone and PI3K-Akt signaling, but not in ECM proteins, may influence response to lithium in BD.

• MeSH
• bipolární porucha * farmakoterapie   genetika   MeSH
• celogenomová asociační studie MeSH
• fokální adheze MeSH
• fosfatidylinositol-3-kinasy genetika   MeSH
• lidé MeSH
• lithium * farmakologie   terapeutické užití   MeSH
• multiomika MeSH
• protoonkogenní proteiny c-akt genetika   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Lithium (Li) is one of the most effective drugs for treating bipolar disorder (BD), however, there is presently no way to predict response to guide treatment. The aim of this study is to identify functional genes and pathways that distinguish BD Li responders (LR) from BD Li non-responders (NR). An initial Pharmacogenomics of Bipolar Disorder study (PGBD) GWAS of lithium response did not provide any significant results. As a result, we then employed network-based integrative analysis of transcriptomic and genomic data. In transcriptomic study of iPSC-derived neurons, 41 significantly differentially expressed (DE) genes were identified in LR vs NR regardless of lithium exposure. In the PGBD, post-GWAS gene prioritization using the GWA-boosting (GWAB) approach identified 1119 candidate genes. Following DE-derived network propagation, there was a highly significant overlap of genes between the top 500- and top 2000-proximal gene networks and the GWAB gene list (Phypergeometric = 1.28E-09 and 4.10E-18, respectively). Functional enrichment analyses of the top 500 proximal network genes identified focal adhesion and the extracellular matrix (ECM) as the most significant functions. Our findings suggest that the difference between LR and NR was a much greater effect than that of lithium. The direct impact of dysregulation of focal adhesion on axon guidance and neuronal circuits could underpin mechanisms of response to lithium, as well as underlying BD. It also highlights the power of integrative multi-omics analysis of transcriptomic and genomic profiling to gain molecular insights into lithium response in BD.

• MeSH
• antimanika farmakologie   terapeutické užití   MeSH
• bipolární porucha * farmakoterapie   genetika   MeSH
• celogenomová asociační studie * metody   MeSH
• farmakogenetika metody   MeSH
• fokální adheze * účinky léků   genetika   MeSH
• genomika metody   MeSH
• genové regulační sítě * účinky léků   genetika   MeSH
• indukované pluripotentní kmenové buňky účinky léků   metabolismus   MeSH
• lidé MeSH
• lithium * farmakologie   terapeutické užití   MeSH
• multiomika MeSH
• neurony metabolismus   účinky léků   MeSH
• sloučeniny lithia farmakologie   terapeutické užití   MeSH
• stanovení celkové genové exprese metody   MeSH
• transkriptom * genetika   účinky léků   MeSH
• Check Tag
• lidé MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Research Support, N.I.H., Extramural MeSH

Among medical specialties, laboratory medicine is the largest producer of structured data and must play a crucial role for the efficient and safe implementation of big data and artificial intelligence in healthcare. The area of personalized therapies and precision medicine has now arrived, with huge data sets not only used for experimental and research approaches, but also in the "real world". Analysis of real world data requires development of legal, procedural and technical infrastructure. The integration of all clinical data sets for any given patient is important and necessary in order to develop a patient-centered treatment approach. Data-driven research comes with its own challenges and solutions. The Findability, Accessibility, Interoperability, and Reusability (FAIR) Guiding Principles provide guidelines to make data findable, accessible, interoperable and reusable to the research community. Federated learning, standards and ontologies are useful to improve robustness of artificial intelligence algorithms working on big data and to increase trust in these algorithms. When dealing with big data, the univariate statistical approach changes to multivariate statistical methods significantly shifting the potential of big data. Combining multiple omics gives previously unsuspected information and provides understanding of scientific questions, an approach which is also called the systems biology approach. Big data and artificial intelligence also offer opportunities for laboratories and the In Vitro Diagnostic industry to optimize the productivity of the laboratory, the quality of laboratory results and ultimately patient outcomes, through tools such as predictive maintenance and "moving average" based on the aggregate of patient results.

• MeSH
• algoritmy MeSH
• big data * MeSH
• individualizovaná medicína metody   MeSH
• lidé MeSH
• poskytování zdravotní péče MeSH
• umělá inteligence * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

Sepsis is a multifactorial clinical syndrome with an extremely dynamic clinical course and with high diverse clinical phenotype. Early diagnosis is crucial for the final clinical outcome. Previous studies have not identified a biomarker for the diagnosis of sepsis which would have sufficient sensitivity and specificity. Identification of the infectious agents or the use of molecular biology, next gene sequencing, has not brought significant benefit for the patient in terms of early diagnosis. Therefore, we are currently searching for biomarkers, through "omics" technologies with sufficient diagnostic specificity and sensitivity, able to predict the clinical course of the disease and the patient response to therapy. Current progress in the use of systems biology technologies brings us hope that by using big data from clinical trials such biomarkers will be found.

• MeSH
• biologické markery analýza   MeSH
• genomika MeSH
• infekční nemoci diagnóza   terapie   MeSH
• kritický stav terapie   MeSH
• lidé MeSH
• proteomika MeSH
• sepse krev   diagnóza   terapie   MeSH
• systémová biologie MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

Omics-based methods are increasingly used in current ecotoxicology. Therefore, a large number of observations for various toxic substances and organisms are available and may be used for identifying modes of action, adverse outcome pathways, or novel biomarkers. For these purposes, good statistical analysis of toxicogenomic data is vital. In contrast to established ecotoxicological techniques, concentration-response modeling is rarely used for large datasets. Instead, statistical hypothesis testing is prevalent, which provides only a limited scope for inference. The present study therefore applied automated concentration-response modeling for 3 different ecotoxicotranscriptomic and ecotoxicometabolomic datasets. The modeling process was performed by simultaneously applying 9 different regression models, representing distinct mechanistic, toxicological, and statistical ideas that result in different curve shapes. The best-fitting models were selected by using Akaike's information criterion. The linear and exponential models represented the best data description for more than 50% of responses. Models generating U-shaped curves were frequently selected for transcriptomic signals (30%), and sigmoid models were identified as best fit for many metabolomic signals (21%). Thus, selecting the models from an array of different types seems appropriate, because concentration-response functions may vary because of the observed response type, and they also depend on the compound, the organism, and the investigated concentration and exposure duration range. The application of concentration-response models can help to further tap the potential of omics data and is a necessary step for quantitative mixture effect assessment at the molecular response level.

• MeSH
• dánio pruhované růst a vývoj   metabolismus   MeSH
• ekosystém * MeSH
• embryo nesavčí účinky léků   metabolismus   MeSH
• genomika * MeSH
• látky znečišťující životní prostředí toxicita   MeSH
• lineární modely MeSH
• metabolomika * MeSH
• rychlé screeningové testy MeSH
• sekvenční analýza hybridizací s uspořádaným souborem oligonukleotidů MeSH
• tetrachlorethylen toxicita   MeSH
• transkriptom účinky léků   MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH