The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.

• Klíčová slova
• biomarker identification, disease prediction, feature selection, machine learning, microbiome,
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

Background The lack of effective biomarkers for the screening and early detection of ovarian cancer (OC) is one of the most pressing problems in oncogynecology. Because epigenetic alterations occur early in the cancer development, they provide great potential to serve as such biomarkers. In our study, we investigated a potential of a four-gene methylation panel (including CDH13, HNF1B, PCDH17 and GATA4 genes) for the early detection of high-grade serous ovarian carcinoma (HGSOC). Methods For methylation detection we used methylation sensitive high-resolution melting analysis and real-time methylation specific analysis. We also investigated the relation between gene hypermethylation and gene relative expression using the 2-ΔΔCt method. Results The sensitivity of the examined panel reached 88.5%. We were able to detect methylation in 85.7% (12/14) of early stage tumors and in 89.4% (42/47) of late stage tumors. The total efficiency of the panel was 94.4% and negative predictive value reached 90.0%. The specificity and positive predictive value achieved 100% rates. Our results showed lower gene expression in the tumor samples in comparison to control samples. The more pronounced downregulation was measured in the group of samples with detected methylation. Conclusions In our study we designed the four-gene panel for HGSOC detection in ovarian tissue with 100% specificity and sensitivity of 88.5%. The next challenge is translation of the findings to the less invasive source for biomarker examination, such as plasma. Our results indicate that combination of examined genes deserve consideration for further testing in clinical molecular diagnosis of HGSOC.

• Klíčová slova
• DNA methylation, biomarker, high-grade serous ovarian cancer,
• MeSH
• časná detekce nádoru MeSH
• lidé MeSH
• metylace DNA * MeSH
• nádorové biomarkery krev   MeSH
• nádory vaječníků krev   diagnóza   genetika   MeSH
• senzitivita a specificita MeSH
• Check Tag
• lidé MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• nádorové biomarkery MeSH

Data outliers can carry very valuable information and might be most informative for the interpretation. Nevertheless, they are often neglected. An algorithm called cellwise outlier diagnostics using robust pairwise log ratios (cell-rPLR) for the identification of outliers in single cell of a data matrix is proposed. The algorithm is designed for metabolomic data, where due to the size effect, the measured values are not directly comparable. Pairwise log ratios between the variable values form the elemental information for the algorithm, and the aggregation of appropriate outlyingness values results in outlyingness information. A further feature of cell-rPLR is that it is useful for biomarker identification, particularly in the presence of cellwise outliers. Real data examples and simulation studies underline the good performance of this algorithm in comparison with alternative methods.

• Klíčová slova
• biomarker, cellwise outliers, cell‐rPLR, log ratio, metabolomics, robust method,
• Publikační typ
• časopisecké články MeSH

Spectrometric-based surface-enhanced laser desorption/ionization ProteinChip (SELDI-TOF) facilitates rapid and easy analysis of protein mixtures and is often exploited to define potential diagnostic markers from sera. However, SELDI- TOF is a relatively insensitive technique and unable to detect circulating proteins at low levels even if they are differentially expressed in cancer patients. Therefore, we applied this technology to study tissues from renal cell carcinomas (RCC) in comparison to healthy controls. We found that different biomarkers are identified from tissues than those previously identified in serum, and that serum markers are often not produced by the tumors themselves at detectable levels, reflecting the nonspecific nature of many circulating biomarkers. We detected and characterized áB-crystallin as an overexpressed protein in RCC tissues and showed differential expression by immunohistochemistry. We conclude that SELDI-TOF is more useful for the identification of biomarkers that are synthesized by diseased tissues than for the identification of serum biomarkers and identifies a separate set of markers. We suggest that SELDI-TOF should be used to screen human cancer tissues to identify potential tissue-specific proteins and simpler and more sensitive techniques can then be applied to determine their validity as biomarkers in biological fluids.

• MeSH
• alfa-krystaliny - řetězec B analýza   krev   MeSH
• dospělí MeSH
• imunohistochemie MeSH
• karcinom z renálních buněk krev   chemie   diagnóza   MeSH
• ledviny chemie   MeSH
• lidé MeSH
• nádorové biomarkery analýza   krev   MeSH
• nádory ledvin krev   chemie   diagnóza   MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• senzitivita a specificita MeSH
• spektrometrie hmotnostní - ionizace laserem za účasti matrice metody   statistika a číselné údaje   MeSH
• studie případů a kontrol MeSH
• tkáňová distribuce MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• hodnotící studie MeSH
• práce podpořená grantem MeSH
• Názvy látek
• alfa-krystaliny - řetězec B MeSH
• nádorové biomarkery MeSH

Proteomics profiling of intact proteins based on MALDI-TOF MS and derived platforms has been used in cancer biomarker discovery studies. This approach suffers from a number of limitations such as low resolution, low sensitivity, and that no knowledge is available on the identity of the respective proteins in the discovery mode. Nevertheless, it remains the most high-throughput, untargeted mode of clinical proteomics studies to date. Here we compare key protein separation and MS techniques available for protein biomarker identification in this type of studies and define reasons of uncertainty in protein peak identity. As a result of critical data analysis, we consider 3D protein separation and identification workflows as optimal procedures. Subsequently, we present a new protocol based on 3D LC-MS/MS with top-down at high resolution that enabled the identification of HNRNP A2/B1 intact peptide as correlating with the estrogen receptor expression in breast cancer tissues. Additional development of this general concept toward next generation, top-down based protein profiling at high resolution is discussed.

• MeSH
• lidé MeSH
• molekulární sekvence - údaje MeSH
• nádorové biomarkery metabolismus   MeSH
• nádorové proteiny chemie   izolace a purifikace   metabolismus   MeSH
• nádory prsu metabolismus   MeSH
• peptidy chemie   metabolismus   MeSH
• proteomika metody   MeSH
• sekvence aminokyselin MeSH
• spektrometrie hmotnostní - ionizace laserem za účasti matrice metody   MeSH
• Check Tag
• lidé MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• nádorové biomarkery MeSH
• nádorové proteiny MeSH
• peptidy MeSH

In-depth proteome discovery analysis represents new strategy in an effort to identify novel reliable specific protein markers for hypertrophic cardiomyopathy and other life threatening cardiovascular diseases. To systematically identify novel protein biomarkers of cardiovascular diseases with high mortality we employed an isobaric tag for relative and absolute quantitation (iTRAQ) proteome technology to make comparative analysis of plasma samples obtained from patients suffering from non-obstructive hypertrophic cardiomyopathy, stable dilated cardiomyopathy, aortic valve stenosis, chronic stable coronary artery disease and stable arterial hypertension. We found 128 plasma proteins whose abundances were uniquely regulated among the analyzed cardiovascular pathologies. 49 of them have not been described yet. Additionally, application of statistical exploratory analyses of the measured protein profiles indicated the relationship in pathophysiology of the examined cardiovascular pathologies.

• MeSH
• biologické markery krev   MeSH
• dospělí MeSH
• hypertrofická kardiomyopatie krev   komplikace   MeSH
• lidé středního věku MeSH
• lidé MeSH
• proteom MeSH
• proteomika metody   MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• srdeční selhání krev   etiologie   MeSH
• studie případů a kontrol MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mužské pohlaví MeSH
• senioři nad 80 let MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• biologické markery MeSH
• proteom MeSH

Mandatory disclosure of the species identity, production method, and geographical origin are embedded in the regulations and traceability systems, governing international seafood trade. A high-resolution mass spectrometry-based metabolomics approach could simultaneously authenticate the species identity and geographical origin of commercially important shrimps. The highly innovative approach spared the need for multiple testing methods which are in routine use currently. A robust chemometric model, developed using the metabolite fingerprint dataset, could accurately predict the species identity of the shrimp samples. Subsequently, species-specific biomarkers were discovered and a tandem mass spectrometry method for authentication of the species was developed. Two other chemometric models from the metabolomics experiment accurately predicted the geographical origin of king prawns and tiger prawns. The study has shown for the first time that food-metabolomics along with chemometrics can simultaneously check for multiple seafood fraud issues in the global seafood supply-chain.

• Klíčová slova
• Biomarker identification, Chemometrics, Shrimp fraud, Species authentication, Untargeted metabolomics,
• MeSH
• analýza potravin metody   MeSH
• biologické markery analýza   chemie   MeSH
• Decapoda (Crustacea) chemie   klasifikace   MeSH
• druhová specificita MeSH
• metabolomika * MeSH
• potrava z moře (živočišná) analýza   MeSH
• tandemová hmotnostní spektrometrie MeSH
• zeměpis MeSH
• zvířata MeSH
• Check Tag
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• biologické markery MeSH

Surface active maghemite nanoparticles (SAMNs) are able to recognize and bind selected proteins in complex biological systems, forming a hard protein corona. Upon a 5-min incubation in bovine whey from mastitis-affected cows, a significant enrichment of a single peptide characterized by a molecular weight at 4338 Da originated from the proteolysis of aS1-casein was observed. Notably, among the large number of macromolecules in bovine milk, the detection of this specific peptide can hardly be accomplished by conventional analytical techniques. The selective formation of a stable binding between the peptide and SAMNs is due to the stability gained by adsorption-induced surface restructuration of the nanomaterial. We attributed the surface recognition properties of SAMNs to the chelation of iron(III) sites on their surface by sterically compatible carboxylic groups of the peptide. The specific peptide recognition by SAMNs allows its easy determination by MALDI-TOF mass spectrometry, and a threshold value of its normalized peak intensity was identified by a logistic regression approach and suggested for the rapid diagnosis of the pathology. Thus, the present report proposes the analysis of hard protein corona on nanomaterials as a perspective for developing fast analytical procedures for the diagnosis of mastitis in cows. Moreover, the huge simplification of proteome complexity by exploiting the selectivity derived by the peculiar SAMN surface topography, due to the iron(III) distribution pattern, could be of general interest, leading to competitive applications in food science and in biomedicine, allowing the rapid determination of hidden biomarkers by a cutting edge diagnostic strategy. Graphical abstract The topography of iron(III) sites on surface active maghemite nanoparticles (SAMNs) allows the recognition of sterically compatible carboxylic groups on proteins and peptides in complex biological matrixes. The analysis of hard protein corona on SAMNs led to the determination of a biomarker for cow mastitis in milk by MALDI-TOF mass spectrometry.

• Klíčová slova
• Biomarker, MALDI-TOF, Magnetic nanoparticles, Milk, Protein corona,
• MeSH
• biologické markery analýza   MeSH
• mastitida skotu diagnóza   MeSH
• mléčné bílkoviny analýza   MeSH
• mléko chemie   MeSH
• molekulární modely MeSH
• nanočástice chemie   MeSH
• peptidy analýza   MeSH
• proteinová korona analýza   MeSH
• proteomika metody   MeSH
• sekvence aminokyselin MeSH
• skot MeSH
• spektrometrie hmotnostní - ionizace laserem za účasti matrice metody   MeSH
• syrovátka chemie   MeSH
• železité sloučeniny chemie   MeSH
• zvířata MeSH
• Check Tag
• skot MeSH
• ženské pohlaví MeSH
• zvířata MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• biologické markery MeSH
• ferric oxide MeSH Prohlížeč
• mléčné bílkoviny MeSH
• peptidy MeSH
• proteinová korona MeSH
• železité sloučeniny MeSH

Myasthenia gravis is a chronic antibody-mediated autoimmune disease disrupting neuromuscular synaptic transmission. Informative biomarkers remain an unmet need to stratify patients with active disease requiring intensified monitoring and therapy; their identification is the primary objective of this study. We applied mass spectrometry-based proteomic serum profiling for biomarker discovery. We studied an exploration and a prospective validation cohort consisting of 114 and 140 anti-acetylcholine receptor antibody (AChR-Ab)-positive myasthenia gravis patients, respectively. For downstream analysis, we applied a machine learning approach. Protein expression levels were confirmed by ELISA and compared to other myasthenic cohorts, in addition to myositis and neuropathy patients. Anti-AChR-Ab levels were determined by a radio receptor assay. Immunohistochemistry and immunofluorescence of intercostal muscle biopsies were employed for validation in addition to interactome studies of inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3). Machine learning identified ITIH3 as potential serum biomarker reflective of disease activity. Serum levels correlated with disease activity scores in the exploration and validation cohort and were confirmed by ELISA. Lack of correlation between anti-AChR-Ab levels and clinical scores underlined the need for biomarkers. In a subgroup analysis, ITIH3 was indicative of treatment responses. Immunostaining of muscle specimens from these patients demonstrated ITIH3 localization at the neuromuscular endplates in myasthenia gravis but not in controls, thus providing a structural equivalent for our serological findings. Immunoprecipitation of ITIH3 and subsequent proteomics lead to identification of its interaction partners playing crucial roles in neuromuscular transmission. This study provides data on ITIH3 as a potential pathophysiological-relevant biomarker of disease activity in myasthenia gravis. Future studies are required to facilitate translation into clinical practice.

• Klíčová slova
• Acetylcholine receptor, Biomarker, Inter-alpha-trypsin inhibitor heavy chain H3, Muscle proteomics, Myasthenia gravis, Neuromuscular junction,
• MeSH
• autoprotilátky krev   MeSH
• biologické markery * krev   metabolismus   MeSH
• dospělí MeSH
• kohortové studie MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• myasthenia gravis * krev   diagnóza   patologie   metabolismus   MeSH
• proteomika metody   MeSH
• receptory cholinergní imunologie   metabolismus   MeSH
• sekreční inhibitory proteinas krev   MeSH
• senioři MeSH
• strojové učení MeSH
• Check Tag
• dospělí MeSH
• lidé středního věku MeSH
• lidé MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• senioři MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH
• Názvy látek
• autoprotilátky MeSH
• biologické markery * MeSH
• receptory cholinergní MeSH
• sekreční inhibitory proteinas MeSH

Cancer belongs to multifactorial diseases characterized by uncontrolled growth and proliferation of abnormal cells. Breast cancer, non-small cell lung cancer, and colorectal cancer are the most frequently diagnosed malignancies with a high mortality rate. These carcinomas typically contain multiple genetically distinct subpopulations of tumor cells leading to tumor heterogeneity, which promotes the aggressiveness of the disease. Early diagnosis is necessary to increase patient progression-free survival. Particularly, miRNAs present in exosomes derived from tumors represent potential biomarkers suitable for early cancer diagnosis. Identification of miRNAs by liquid biopsy enables a personalized approach with the subsequent better clinical management of patients. This review article highlights the potential of circulating exosomal miRNAs in early breast, non-small cell lung, and colorectal cancer diagnosis.

• MeSH
• cirkulující mikroRNA * genetika   MeSH
• kolorektální nádory * diagnóza   genetika   MeSH
• lidé MeSH
• mikro RNA * MeSH
• nádorové biomarkery genetika   MeSH
• nádory plic * diagnóza   genetika   MeSH
• nemalobuněčný karcinom plic * diagnóza   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH
• Názvy látek
• cirkulující mikroRNA * MeSH
• mikro RNA * MeSH
• nádorové biomarkery MeSH