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Molecular Research Using Omics Technologies for Human Health

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A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Biology".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 32144

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Special Issue Editors

Dr. Alexey Kononikhin

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1. Laboratory of Mass Spectrometry, CDISE, Skolkovo Institute of Science and Technology, Moscow, Russia
2. Emanuel Institute for Biochemical Physics, Russian Academy of Sciences, Moscow, Russia
3. V.L. Talrose Institute for Energy Problems of Chemical Physics, N.N. Semenov Federal Center of Chemical Physics, Russian Academy of Sciences, Moscow, Russia
Interests: liquid chromatography; mass spectrometry; climate change; spectroscopy; chromatography; sample preparation; proteins; molecular biology
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Dr. Vladimir Frankevich

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National Medical Research Center for Obstetrics, Gynecology and Perinatology Named after Academician V.I.Kulakov, Ministry of Healthcare of Russian Federation, Moscow 117997, Russia
Interests: metabolomics; proteomics; lipidomics; mass-spectrometry; clinical mass spectrometry; high resolution mass-spectrometry; translational medicine
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Prof. Dr. Eugene Nikolaev

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Guest Editor

1. Laboratory of Mass Spectrometry, CDISE, Skolkovo Institute of Science and Technology, Moscow, Russia
2. Emanuel Institute for Biochemical Physics, Russian Academy of Sciences, Moscow, Russia
3. V.L. Talrose Institute for Energy Problems of Chemical Physics, N.N. Semenov Federal Center of Chemical Physics, Russian Academy of Sciences, Moscow, Russia
Interests: mass spectrometry; chromatography; liquid chromatography; proteomics; systems biology; biochemistry; proteins
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Omics technologies are becoming increasingly popular for molecular research and human health assessment. Mass spectrometry (MS) coupled with separation techniques such as electrophoresis/chromatography/ion mobility is the main tool in omics studies (proteomics, metabolomics) and is the most powerful approach for clinical research which ensures reliable identification and discovery of new molecules and potential biomarkers. Overcoming many of its initial challenges related to sampling and MS analysis the new advanced methods for molecular profiling are constantly developed which has a great potential for translation to medical diagnostics. Thousands of new potential and candidate biomarkers were discovered in human body fluids (blood, urine, CSF, etc.), many of which are relevant to the specific clinician syndromes and pathologies

Over the past few years, omics technologies have made many advances, especially in combination with Artificial intelligence (AI) and Machine learning (ML) approaches for assessing health states and combating new challenges and diseases such as COVID-19.

The current issue is focused on advanced mass spectrometry-based omics technologies in combination with artificial intelligence (AI) and machine learning (ML) approaches for assessing health state and new potential biomarkers discovery for diseases such as Alzheimer’s, cardiovascular diseases (CVD), breast cancer, chronic kidney diseases (CKD), COVID-19 and others. Other frontend non-MS omics technologies applicable for online/offline analysis of body fluids and their molecular profiling for clinical applications are also of interest.

Dr. Alexey Kononikhin
Dr. Vladimir Frankevich
Prof. Dr. Eugene Nikolaev
Guest Editors

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Keywords

omics technologies, proteomics, metabolomics
mass spectrometry
artificial intelligence (AI)
machine learning (ML)
human health
biomarkers

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Published Papers (10 papers)

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Research

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29 pages, 4275 KiB

Open AccessArticle

Prediction of Early- and Late-Onset Pre-Eclampsia in the Preclinical Stage via Placenta-Specific Extracellular miRNA Profiling

by Angelika V. Timofeeva, Ivan S. Fedorov, Yuliya V. Sukhova, Tatyana Y. Ivanets and Gennady T. Sukhikh

Int. J. Mol. Sci. 2023, 24(9), 8006; https://doi.org/10.3390/ijms24098006 - 28 Apr 2023

Cited by 6 | Viewed by 2139

Abstract

Pre-eclampsia (PE) is one of the severe complications of pregnancy in 3–8% of all cases and is one of the leading causes of maternal and perinatal mortality. The fundamental role in the pathogenesis of PE is assigned to maternal and/or placental factors, whereby the combination and manifestation of which determines the time of onset of the clinical symptoms of PE (before or after 34 weeks of gestation) and their severity. It is known that the expression level of miRNAs, the regulators of signaling cascades in the cell, depends on gestational age. In the present study, we focused on the identification of the placenta-specific miRNAs that differentiate between early- and late-onset pre-eclampsia (ePE and lPE) throughout pregnancy, from the first to the third trimester. A total of 67 patients were analyzed using small RNA deep sequencing and real-time quantitative PCR, which resulted in a core list of miRNAs (let-7b-5p, let-7d-3p, let-7f-5p, let-7i-5p, miR-22-5p, miR-451a, miR-1246, miR-30e-5p, miR-20a-5p, miR-1307-3p, and miR-320e), which in certain combinations can predict ePE or lPE with 100% sensitivity and 84–100% specificity in the 1st trimester of pregnancy. According to the literature data, these miRNA predictors of PE control trophoblast proliferation, invasion, migration, syncytialization, the endoplasmic reticulum unfolded protein response, immune tolerance, angiogenesis, and vascular integrity. The simultaneous detection of let-7d-3p, miR-451a, and miR-1307-3p, resistant to the repeated freezing/thawing of blood serum samples, in combination with biochemical (b-hCG and PAPP-A) and ultrasound (UAPI) parameters, allowed us to develop a universal model for the prediction of ePE and lPE onset (FPR = 15.7% and FNR = 9.5%), which was validated using a test cohort of 48 patients and demonstrated false-positive results in 26.7% of cases and false negatives in 5.6% of cases. For comparison, the use of the generally accepted Astraia program in the analysis of the test cohort of patients led to worse results: FPR = 62.1% and FNR = 33.3%. Full article

(This article belongs to the Special Issue Molecular Research Using Omics Technologies for Human Health)

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12 pages, 1867 KiB

Open AccessArticle

Identification of Alternative Splicing in Proteomes of Human Melanoma Cell Lines without RNA Sequencing Data

by Anna A. Lobas, Elizaveta M. Solovyeva, Lev I. Levitsky, Anton O. Goncharov, Elena Y. Lyssuk, Sergey S. Larin, Sergei A. Moshkovskii and Mikhail V. Gorshkov

Int. J. Mol. Sci. 2023, 24(3), 2466; https://doi.org/10.3390/ijms24032466 - 27 Jan 2023

Cited by 2 | Viewed by 2335

Abstract

Alternative splicing is one of the main regulation pathways in living cells beyond simple changes in the level of protein expression. Most of the approaches proposed in proteomics for the identification of specific splicing isoforms require a preliminary deep transcriptomic analysis of the sample under study, which is not always available, especially in the case of the re-analysis of previously acquired data. Herein, we developed new algorithms for the identification and validation of protein splice isoforms in proteomic data in the absence of RNA sequencing of the samples under study. The bioinformatic approaches were tested on the results of proteome analysis of human melanoma cell lines, obtained earlier by high-resolution liquid chromatography and mass spectrometry (LC-MS). A search for alternative splicing events for each of the cell lines studied was performed against the database generated from all known transcripts (RefSeq) and the one composed of peptide sequences, which included all biologically possible combinations of exons. The identifications were filtered using the prediction of both retention times and relative intensities of fragment ions in the corresponding mass spectra. The fragmentation mass spectra corresponding to the discovered alternative splicing events were additionally examined for artifacts. Selected splicing events were further validated at the mRNA level by quantitative PCR. Full article

(This article belongs to the Special Issue Molecular Research Using Omics Technologies for Human Health)

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19 pages, 2164 KiB

Open AccessArticle

Mass Spectrometric Blood Metabogram: Acquisition, Characterization, and Prospects for Application

by Petr G. Lokhov, Elena E. Balashova, Oxana P. Trifonova, Dmitry L. Maslov, Anatoly I. Grigoriev, Elena A. Ponomarenko and Alexander I. Archakov

Int. J. Mol. Sci. 2023, 24(2), 1736; https://doi.org/10.3390/ijms24021736 - 15 Jan 2023

Cited by 6 | Viewed by 2443

Abstract

In metabolomics, many metabolites are measured simultaneously in a single run. Such analytical performance opens up prospects for clinical laboratory diagnostics. In this work, a mass spectrometric metabogram was developed as a simplified and clinically applicable way of measuring the blood plasma metabolome. To develop the metabogram, blood plasma samples from healthy male volunteers (n = 48) of approximately the same age, direct infusion mass spectrometry (DIMS) of the low molecular fraction of samples, and principal component analysis (PCA) of the mass spectra were used. The seven components of the metabogram defined by PCA, which cover ~70% of blood plasma metabolome variability, were characterized using a metabolite set enrichment analysis (MSEA) and clinical test results of participating volunteers. It has been established that the components of the metabogram are functionally related groups of the blood metabolome associated with regulation, lipid–carbohydrate, and lipid–amine blood components, eicosanoids, lipid intake into the organism, and liver function thereby providing a lot of clinically relevant information. Therefore, metabogram provides the possibility to apply the metabolomics performance in the clinic. The features of the metabogram are also discussed in comparison with the thin-layer chromatography and with the analysis of blood metabolome by liquid chromatography combined with mass spectrometry. Full article

(This article belongs to the Special Issue Molecular Research Using Omics Technologies for Human Health)

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16 pages, 2943 KiB

Open AccessArticle

Towards Accurate Identification of Antibiotic-Resistant Pathogens through the Ensemble of Multiple Preprocessing Methods Based on MALDI-TOF Spectra

by Chia-Ru Chung, Hsin-Yao Wang, Po-Han Chou, Li-Ching Wu, Jang-Jih Lu, Jorng-Tzong Horng and Tzong-Yi Lee

Int. J. Mol. Sci. 2023, 24(2), 998; https://doi.org/10.3390/ijms24020998 - 5 Jan 2023

Cited by 3 | Viewed by 3436

Abstract

Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has been used to identify microorganisms and predict antibiotic resistance. The preprocessing method for the MS spectrum is key to extracting critical information from complicated MS spectral data. Different preprocessing methods yield different data, and the optimal approach is unclear. In this study, we adopted an ensemble of multiple preprocessing methods––FlexAnalysis, MALDIquant, and continuous wavelet transform-based methods––to detect peaks and build machine learning classifiers, including logistic regressions, naïve Bayes classifiers, random forests, and a support vector machine. The aim was to identify antibiotic resistance in Acinetobacter baumannii, Acinetobacter nosocomialis, Enterococcus faecium, and Group B Streptococci (GBS) based on MALDI-TOF MS spectra collected from two branches of a referral tertiary medical center. The ensemble method was compared with the individual methods. Random forest models built with the data preprocessed by the ensemble method outperformed individual preprocessing methods and achieved the highest accuracy, with values of 84.37% (A. baumannii), 90.96% (A. nosocomialis), 78.54% (E. faecium), and 70.12% (GBS) on independent testing datasets. Through feature selection, important peaks related to antibiotic resistance could be detected from integrated information. The prediction model can provide an opinion for clinicians. The discriminative peaks enabling better prediction performance can provide a reference for further investigation of the resistance mechanism. Full article

(This article belongs to the Special Issue Molecular Research Using Omics Technologies for Human Health)

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13 pages, 3768 KiB

Open AccessArticle

Blood Plasma Proteome: A Meta-Analysis of the Results of Protein Quantification in Human Blood by Targeted Mass Spectrometry

by Anna A. Kliuchnikova, Svetlana E. Novikova, Ekaterina V. Ilgisonis, Olga I. Kiseleva, Ekaterina V. Poverennaya, Victor G. Zgoda, Sergei A. Moshkovskii, Vladimir V. Poroikov, Andrey V. Lisitsa, Alexander I. Archakov and Elena A. Ponomarenko

Int. J. Mol. Sci. 2023, 24(1), 769; https://doi.org/10.3390/ijms24010769 - 1 Jan 2023

Cited by 5 | Viewed by 4163

Abstract

A meta-analysis of the results of targeted quantitative screening of human blood plasma was performed to generate a reference standard kit that can be used for health analytics. The panel included 53 of the 296 proteins that form a “stable” part of the proteome of a healthy individual; these proteins were found in at least 70% of samples and were characterized by an interindividual coefficient of variation <40%. The concentration range of the selected proteins was 10⁻¹⁰–10⁻³ M and enrichment analysis revealed their association with rare familial diseases. The concentration of ceruloplasmin was reduced by approximately three orders of magnitude in patients with neurological disorders compared to healthy volunteers, and those of gelsolin isoform 1 and complement factor H were abruptly reduced in patients with lung adenocarcinoma. Absolute quantitative data of the individual proteome of a healthy and diseased individual can be used as the basis for personalized medicine and health monitoring. Storage over time allows us to identify individual biomarkers in the molecular landscape and prevent pathological conditions. Full article

(This article belongs to the Special Issue Molecular Research Using Omics Technologies for Human Health)

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19 pages, 2417 KiB

Open AccessArticle

Past COVID-19: The Impact on IVF Outcomes Based on Follicular Fluid Lipid Profile

by Natalia Lomova, Natalia Dolgushina, Alisa Tokareva, Vitaly Chagovets, Natalia Starodubtseva, Ilya Kulikov, Gennady Sukhikh and Vladimir Frankevich

Int. J. Mol. Sci. 2023, 24(1), 10; https://doi.org/10.3390/ijms24010010 - 20 Dec 2022

Cited by 6 | Viewed by 1940

Abstract

Follicular fluid is an important component of follicle growth and development. Negative effects of COVID-19 on follicular function are still open. The aim of this work was to study the features of the lipid profile of follicular fluid and evaluate the results of the in vitro fertilization (IVF) program in women after COVID-19 to identify biomarkers with prognostic potential. The study involved samples of follicular fluid collected from 237 women. Changes in the lipid composition of the follicular fluid of patients who underwent COVID-19 in mild and severe forms before entering the IVF program and women who did not have COVID-19 were studied by mass spectrometry. Several lipids were identified that significantly changed their level. On the basis of these findings, models were developed for predicting the threat of miscarriage in patients who had a severe course of COVID-19 and models for predicting the success of the IVF procedure, depending on the severity of COVID-19. Of practical interest is the possibility of using the developed predictive models in working with patients who have undergone COVID-19 before entering the IVF program. The results of the study suggest that the onset of pregnancy and its outcome after severe COVID-19 may be associated with changes in lipid metabolism in the follicular fluid. Full article

(This article belongs to the Special Issue Molecular Research Using Omics Technologies for Human Health)

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15 pages, 2851 KiB

Open AccessArticle

Hepatoprotective Activity of Lignin-Derived Polyphenols Dereplicated Using High-Resolution Mass Spectrometry, In Vivo Experiments, and Deep Learning

by Alexey Orlov, Savva Semenov, Gleb Rukhovich, Anastasia Sarycheva, Oxana Kovaleva, Alexander Semenov, Elena Ermakova, Ekaterina Gubareva, Anna E. Bugrova, Alexey Kononikhin, Elena I. Fedoros, Evgeny Nikolaev and Alexander Zherebker

Int. J. Mol. Sci. 2022, 23(24), 16025; https://doi.org/10.3390/ijms232416025 - 16 Dec 2022

Cited by 3 | Viewed by 2092

Abstract

Chronic liver diseases affect more than 1 billion people worldwide and represent one of the main public health issues. Nonalcoholic fatty liver disease (NAFLD) accounts for the majority of mortal cases, while there is no currently approved therapeutics for its treatment. One of the prospective approaches to NAFLD therapy is to use a mixture of natural compounds. They showed effectiveness in alleviating NAFLD-related conditions including steatosis, fibrosis, etc. However, understanding the mechanism of action of such mixtures is important for their rational application. In this work, we propose a new dereplication workflow for deciphering the mechanism of action of the lignin-derived natural compound mixture. The workflow combines the analysis of molecular components with high-resolution mass spectrometry, selective chemical tagging and deuterium labeling, liver tissue penetration examination, assessment of biological activity in vitro, and computational chemistry tools used to generate putative structural candidates. Molecular docking was used to propose the potential mechanism of action of these structures, which was assessed by a proteomic experiment. Full article

(This article belongs to the Special Issue Molecular Research Using Omics Technologies for Human Health)

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19 pages, 928 KiB

Open AccessArticle

DRPreter: Interpretable Anticancer Drug Response Prediction Using Knowledge-Guided Graph Neural Networks and Transformer

by Jihye Shin, Yinhua Piao, Dongmin Bang, Sun Kim and Kyuri Jo

Int. J. Mol. Sci. 2022, 23(22), 13919; https://doi.org/10.3390/ijms232213919 - 11 Nov 2022

Cited by 20 | Viewed by 6682

Abstract

Some of the recent studies on drug sensitivity prediction have applied graph neural networks to leverage prior knowledge on the drug structure or gene network, and other studies have focused on the interpretability of the model to delineate the mechanism governing the drug response. However, it is crucial to make a prediction model that is both knowledge-guided and interpretable, so that the prediction accuracy is improved and practical use of the model can be enhanced. We propose an interpretable model called DRPreter (drug response predictor and interpreter) that predicts the anticancer drug response. DRPreter learns cell line and drug information with graph neural networks; the cell-line graph is further divided into multiple subgraphs with domain knowledge on biological pathways. A type-aware transformer in DRPreter helps detect relationships between pathways and a drug, highlighting important pathways that are involved in the drug response. Extensive experiments on the GDSC (Genomics of Drug Sensitivity and Cancer) dataset demonstrate that the proposed method outperforms state-of-the-art graph-based models for drug response prediction. In addition, DRPreter detected putative key genes and pathways for specific drug–cell-line pairs with supporting evidence in the literature, implying that our model can help interpret the mechanism of action of the drug. Full article

(This article belongs to the Special Issue Molecular Research Using Omics Technologies for Human Health)

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17 pages, 2785 KiB

Open AccessArticle

Prognosis of Alzheimer’s Disease Using Quantitative Mass Spectrometry of Human Blood Plasma Proteins and Machine Learning

by Alexey S. Kononikhin, Natalia V. Zakharova, Savva D. Semenov, Anna E. Bugrova, Alexander G. Brzhozovskiy, Maria I. Indeykina, Yana B. Fedorova, Igor V. Kolykhalov, Polina A. Strelnikova, Anna Yu. Ikonnikova, Dmitry A. Gryadunov, Svetlana I. Gavrilova and Evgeny N. Nikolaev

Int. J. Mol. Sci. 2022, 23(14), 7907; https://doi.org/10.3390/ijms23147907 - 18 Jul 2022

Cited by 14 | Viewed by 3936

Abstract

Early recognition of the risk of Alzheimer’s disease (AD) onset is a global challenge that requires the development of reliable and affordable screening methods for wide-scale application. Proteomic studies of blood plasma are of particular relevance; however, the currently proposed differentiating markers are poorly consistent. The targeted quantitative multiple reaction monitoring (MRM) assay of the reported candidate biomarkers (CBs) can contribute to the creation of a consistent marker panel. An MRM-MS analysis of 149 nondepleted EDTA–plasma samples (MHRC, Russia) of patients with AD (n = 47), mild cognitive impairment (MCI, n = 36), vascular dementia (n = 8), frontotemporal dementia (n = 15), and an elderly control group (n = 43) was performed using the BAK 125 kit (MRM Proteomics Inc., Canada). Statistical analysis revealed a significant decrease in the levels of afamin, apolipoprotein E, biotinidase, and serum paraoxonase/arylesterase 1 associated with AD. Different training algorithms for machine learning were performed to identify the protein panels and build corresponding classifiers for the AD prognosis. Machine learning revealed 31 proteins that are important for AD differentiation and mostly include reported earlier CBs. The best-performing classifiers reached 80% accuracy, 79.4% sensitivity and 83.6% specificity and were able to assess the risk of developing AD over the next 3 years for patients with MCI. Overall, this study demonstrates the high potential of the MRM approach combined with machine learning to confirm the significance of previously identified CBs and to propose consistent protein marker panels. Full article

(This article belongs to the Special Issue Molecular Research Using Omics Technologies for Human Health)

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Review

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15 pages, 2868 KiB

Open AccessReview

Protein Interactome Profiling of Stable Molecular Complexes in Biomaterial Lysate

by Yuri Mezentsev, Pavel Ershov, Evgeniy Yablokov, Leonid Kaluzhskiy, Konstantin Kupriyanov, Oksana Gnedenko and Alexis Ivanov

Int. J. Mol. Sci. 2022, 23(24), 15697; https://doi.org/10.3390/ijms232415697 - 10 Dec 2022

Viewed by 1696

Abstract

Most proteins function as part of various complexes, forming via stable and dynamic protein–protein interactions (PPIs). The profiling of PPIs expands the fundamental knowledge about the structures, functions, and regulation patterns of protein complexes and intracellular molecular machineries. Protein interactomics aims at solving three main tasks: (1) identification of protein partners and parts of complex intracellular structures; (2) analysis of PPIs parameters (affinity, molecular-recognition specificity, kinetic rate constants, and thermodynamic-parameters determination); (3) the study of the functional role of novel PPIs. The purpose of this work is to update the current state and prospects of multi-omics approaches to profiling of proteins involved in the formation of stable complexes. Methodological paradigm includes a development of protein-extraction and -separation techniques from tissues or cellular lysates and subsequent identification of proteins using mass-spectrometry analysis. In addition, some aspects of authors’ experimental platforms, based on high-performance size-exclusion chromatography, procedures of molecular fishing, and protein identification, as well as the possibilities of interactomic taxonomy of each protein, are discussed. Full article

(This article belongs to the Special Issue Molecular Research Using Omics Technologies for Human Health)

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