Discovering plant metabolic biomarkers for phenotype prediction using an untargeted approach

Biomarkers are used to predict phenotypical properties before these features become apparent and, therefore, are valuable tools for both fundamental and applied research. Diagnostic biomarkers have been discovered in medicine many decades ago and are now commonly applied. While this is routine in the field of medicine, it is of surprise that in agriculture this approach has never been investigated. Up to now, the prediction of phenotypes in plants was based on growing plants and assaying the organs of interest in a time intensive process. For the first time, we demonstrate in this study the application of metabolomics to predict agronomic important phenotypes of a crop plant that was grown in different environments. Our procedure consists of established techniques to screen untargeted for a large amount of metabolites in parallel, in combination with machine learning methods. By using this combination of metabolomics and biomathematical tools metabolites were identified that can be used as biomarkers to improve the prediction of traits. The predictive metabolites can be selected and used subsequently to develop fast, targeted and low‐cost diagnostic biomarker assays that can be implemented in breeding programs or quality assessment analysis. The identified metabolic biomarkers allow for the prediction of crop product quality. Furthermore, marker‐assisted selection can benefit from the discovery of metabolic biomarkers when other molecular markers come to its limitation. The described marker selection method was developed for potato tubers, but is generally applicable to any crop and trait as it functions independently of genomic information.     

Biomarker Discovery in Human Prostate Cancer: an Update in Metabolomics Studies

Prostate cancer (PCa) is the most frequently diagnosed cancer and the second leading cause of cancer death among men in Western countries. Current screening techniques are based on the measurement of serum prostate specific antigen (PSA) levels and digital rectal examination. A decisive diagnosis of PCa is based on prostate biopsies; however, this approach can lead to false-positive and false-negative results. Therefore, it is important to discover new biomarkers for the diagnosis of PCa, preferably noninvasive ones. Metabolomics is an approach that allows the analysis of the entire metabolic profile of a biological system. As neoplastic cells have a unique metabolic phenotype related to cancer development and progression, the identification of dysfunctional metabolic pathways using metabolomics can be used to discover cancer biomarkers and therapeutic targets. In this study, we review several metabolomics studies performed in prostatic fluid, blood plasma/serum, urine, tissues and immortalized cultured cell lines with the objective of discovering alterations in the metabolic phenotype of PCa and thus discovering new biomarkers for the diagnosis of PCa. Encouraging results using metabolomics have been reported for PCa, with sarcosine being one of the most promising biomarkers identified to date. However, the use of sarcosine as a PCa biomarker in the clinic remains a controversial issue within the scientific community. Beyond sarcosine, other metabolites are considered to be biomarkers for PCa, but they still need clinical validation. Despite the lack of metabolomics biomarkers reaching clinical practice, metabolomics proved to be a powerful tool in the discovery of new biomarkers for PCa detection.     

Identifying responsive modules by mathematical programming: an application to budding yeast cell cycle

High-throughput biological data offer an unprecedented opportunity to fully characterize biological processes. However, how to extract meaningful biological information from these datasets is a significant challenge. Recently, pathway-based analysis has gained much progress in identifying biomarkers for some phenotypes. Nevertheless, these so-called pathway-based methods are mainly individual-gene-based or molecule-complex-based analyses. In this paper, we developed a novel module-based method to reveal causal or dependent relations between network modules and biological phenotypes by integrating both gene expression data and protein-protein interaction network. Specifically, we first formulated the identification problem of the responsive modules underlying biological phenotypes as a mathematical programming model by exploiting phenotype difference, which can also be viewed as a multi-classification problem. Then, we applied it to study cell-cycle process of budding yeast from microarray data based on our biological experiments, and identified important phenotype- and transition-based responsive modules for different stages of cell-cycle process. The resulting responsive modules provide new insight into the regulation mechanisms of cell-cycle process from a network viewpoint. Moreover, the identification of transition modules provides a new way to study dynamical processes at a functional module level. In particular, we found that the dysfunction of a well-known module and two new modules may directly result in cell cycle arresting at S phase. In addition to our biological experiments, the identified responsive modules were also validated by two independent datasets on budding yeast cell cycle.     

Prediction of hybrid biomass in Arabidopsis thaliana by selected parental SNP and metabolic markers

A recombinant inbred line (RIL) population, derived from two Arabidopsis thaliana accessions, and the corresponding testcrosses with these two original accessions were used for the development and validation of machine learning models to predict the biomass of hybrids. Genetic and metabolic information of the RILs served as predictors. Feature selection reduced the number of variables (genetic and metabolic markers) in the models by more than 80% without impairing the predictive power. Thus, potential biomarkers have been revealed. Metabolites were shown to bear information on inherited macroscopic phenotypes. This proof of concept could be interesting for breeders. The example population exhibits substantial mid-parent biomass heterosis. The results of feature selection could therefore be used to shed light on the origin of heterosis. In this respect, mainly dominance effects were detected.     

Apoptosis and other immune biomarkers predict influenza vaccine responsiveness

Despite the importance of the immune system in many diseases, there are currently no objective benchmarks of immunological health. In an effort to identifying such markers, we used influenza vaccination in 30 young (20–30 years) and 59 older subjects (60 to >89 years) as models for strong and weak immune responses, respectively, and assayed their serological responses to influenza strains as well as a wide variety of other parameters, including gene expression, antibodies to hemagglutinin peptides, serum cytokines, cell subset phenotypes and in vitro cytokine stimulation. Using machine learning, we identified nine variables that predict the antibody response with 84% accuracy. Two of these variables are involved in apoptosis, which positively associated with the response to vaccination and was confirmed to be a contributor to vaccine responsiveness in mice. The identification of these biomarkers provides new insights into what immune features may be most important for immune health.     

Metabolomic-based clinical studies and murine models for acute pancreatitis disease: A review

Acute pancreatitis (AP) is one of the most common gastroenterological disorders requiring hospitalization and is associated with substantial morbidity and mortality. Metabolomics nowadays not only help us to understand cellular metabolism to a degree that was not previously obtainable, but also to reveal the importance of the metabolites in physiological control, disease onset and development. An in-depth understanding of metabolic phenotyping would be therefore crucial for accurate diagnosis, prognosis and precise treatment of AP. In this review, we summarized and addressed the metabolomics design and workflow in AP studies, as well as the results and analysis of the in-depth of research. Based on the metabolic profiling work in both clinical populations and experimental AP models, we described the metabolites with potential utility as biomarkers and the correlation between the altered metabolites and AP status. Moreover, the disturbed metabolic pathways correlated with biological function were discussed in the end. A practical understanding of current and emerging metabolomic approaches applicable to AP and use of the metabolite information presented will aid in designing robust metabolomics and biological experiments that result in identification of unique biomarkers and mechanisms, and ultimately enhanced clinical decision-making.     

Identifying dynamical persistent biomarker structures for rare events using modern integrative machine learning approach

The evolution of omics and computational competency has accelerated discoveries of the underlying biological processes in an unprecedented way. High throughput methodologies, such as flow cytometry, can reveal deeper insights into cell processes, thereby allowing opportunities for scientific discoveries related to health and diseases. However, working with cytometry data often imposes complex computational challenges due to high-dimensionality, large size, and nonlinearity of the data structure. In addition, cytometry data frequently exhibit diverse patterns across biomarkers and suffer from substantial class imbalances which can further complicate the problem. The existing methods of cytometry data analysis either predict cell population or perform feature selection. Through this study, we propose a “wisdom of the crowd” approach to simultaneously predict rare cell populations and perform feature selection by integrating a pool of modern machine learning (ML) algorithms. Given that our approach integrates superior performing ML models across different normalization techniques based on entropy and rank, our method can detect diverse patterns existing across the model features. Furthermore, the method identifies a dynamic biomarker structure that divides the features into persistently selected, unselected, and fluctuating assemblies indicating the role of each biomarker in rare cell prediction, which can subsequently aid in studies of disease progression.     

Multiomics metabolic and epigenetics regulatory network in cancer: A systems biology perspective

Genetic, epigenetic, and metabolic alterations are all hallmarks of cancer. However, the epigenome and metabolome are both highly complex and dynamic biological networks in vivo. The interplay between the epigenome and metabolome contributes to a biological system that is responsive to the tumor microenvironment and possesses a wealth of unknown biomarkers and targets of cancer therapy. From this perspective, we first review the state of high-throughput biological data acquisition(i.e. multiomics data)and analysis(i.e. computational tools) and then propose a conceptual in silico metabolic and epigenetic regulatory network(MER-Net) that is based on these current high-throughput methods. The conceptual MER-Net is aimed at linking metabolomic and epigenomic networks through observation of biological processes, omics data acquisition, analysis of network information, and integration with validated database knowledge. Thus, MER-Net could be used to reveal new potential biomarkers and therapeutic targets using deep learning models to integrate and analyze large multiomics networks. We propose that MER-Net can serve as a tool to guide integrated metabolomics and epigenomics research or can be modified to answer other complex biological and clinical questions using multiomics data.     

Ultra-deep next generation mitochondrial genome sequencing reveals widespread heteroplasmy in Chinese hamster ovary cells

Recent sequencing of the Chinese hamster ovary (CHO) cell and Chinese hamster genomes has dramatically advanced our ability to understand the biology of these mammalian cell factories. In this study, we focus on the powerhouse of the CHO cell, the mitochondrion. Utilizing a high-resolution next generation sequencing approach we sequenced the Chinese hamster mitochondrial genome for the first time and surveyed the mutational landscape of CHO cell mitochondrial DNA (mtDNA). Depths of coverage ranging from ~3,319X to 8,056X enabled accurate identification of low frequency mutations (>1%), revealing that mtDNA heteroplasmy is widespread in CHO cells. A total of 197 variants at 130 individual nucleotide positions were identified across a panel of 22 cell lines with 81% of variants occurring at an allele frequency of between 1% and 99%. 89% of the heteroplasmic mutations identified were cell line specific with the majority of shared heteroplasmic SNPs and INDELs detected in clones from 2 cell line development projects originating from the same host cell line. The frequency of common predicted loss of function mutations varied significantly amongst the clones indicating that heteroplasmic mtDNA variation could lead to a continuous range of phenotypes and play a role in cell to cell, production run to production run and indeed clone to clone variation in CHO cell metabolism. Experiments that integrate mtDNA sequencing with metabolic flux analysis and metabolomics have the potential to improve cell line selection and enhance CHO cell metabolic phenotypes for biopharmaceutical manufacturing through rational mitochondrial genome engineering.     

Functional metabolomics: from biomarker discovery to metabolome reprogramming

Metabolomics is emerging as a powerful tool for studying metabolic processes, identifying crucial biomarkers responsible for metabolic characteristics and revealing metabolic mechanisms, which construct the content of discovery metabolomics. The crucial biomarkers can be used to reprogram a metabolome, leading to an aimed metabolic strategy to cope with alteration of internal and external environments, naming reprogramming metabolomics here. The striking feature on the similarity of the basic metabolic pathways and components among vastly differentspeciesmakesthe reprogrammingmetabolomics possible when the engineered metabolites play biological roles in cellular activity as a substrate of enzymes and a regulator to other molecules including proteins. The reprogramming metabolomics approach can be used to clarify metabolic mechanisms of responding to changed internal and external environmental factors and to establish a framework to develop targeted tools for dealing with the changes such as controlling and/or preventing infection with pathogens and enhancing host immunity against pathogens. This review introduces the current state and trends of discovery metabolomics and reprogramming metabolomics and highlights the importance of reprogramming metabolomics.     

Metabotypes of breast cancer cell lines revealed by non-targeted metabolomics

We present an analysis of intracellular metabolism by non-targeted, high-throughput metabolomics profiling of 18 breast cell lines. We profiled >900 putatively annotated metabolite ions for >100 samples collected under both normoxic and hypoxic conditions and revealed extensive heterogeneity across all metabolic pathways and cell lines. Cell line–specific metabolome profiles dominated over patterns associated with malignancy or with the clinical nomenclature of breast cancer cells. Such characteristic metabolome profiles were reproducible across different laboratories and experiments and exhibited mild to robust changes with change in experimental conditions. To extract a functional overview of cell line heterogeneity, we devised an unsupervised metabotyping procedure that for each pathway automatically recognized metabolic types from metabolome data and assigned cell lines. Our procedure provided a condensed yet global representation of cell line metabolism, revealing the fine structure of metabolic heterogeneity across all tested pathways and cell lines. In follow-up experiments on selected pathways, we confirmed that different metabolic types correlated to differences in the underlying fluxes and difference sensitivity to gene knockdown or pharmacological inhibition. Thus, the identified metabotypes recapitulated functional differences at the pathway level. Metabotyping provides a powerful compression of multi-dimensional data that preserves functional information and serves as a resource for reconciling or understanding heterogeneous metabolic phenotypes or response to inhibition of metabolic pathways.     

Metabolomics: Creating new potentials for unraveling the mechanisms in response to salt and drought stress and for the biotechnological improvement of xero-halophytes

Breeders have long been interested in understanding the biological function and mechanism of xero-halophytes and their ability for growth in drought-stricken and salinized environments. However, the mechanisms in response to stress have been difficult to unravel because their defenses require regulatory changes to the activation of multiple genes and pathways. Metabolomics is becoming a key tool in comprehensively understanding the cellular response to abiotic stress and represents an important addition to the tools currently employed in genomics-assisted selection for plant improvement. In this review, we highlight the applications of plant metabolomics in characterizing metabolic responses to salt and drought stress, and identifying metabolic quantitative trait loci (QTLs). We also discuss the potential of metabolomics as a tool to unravel stress response mechanisms, and as a viable option for the biotechnological improvement of xero-halophytes when no other genetic information such as linkage maps and QTLs are available, by combining with germplasm-regression-combined marker-trait association identification.     

Metabolomics: creating new potentials for unraveling the mechanisms in response to salt and drought stress and for the biotechnological improvement of xero-halophytes

Breeders have long been interested in understanding the biological function and mechanism of xero-halophytes and their ability for growth in drought-stricken and salinized environments. However, the mechanisms in response to stress have been difficult to unravel because their defenses require regulatory changes to the activation of multiple genes and pathways. Metabolomics is becoming a key tool in comprehensively understanding the cellular response to abiotic stress and represents an important addition to the tools currently employed in genomics-assisted selection for plant improvement. In this review, we highlight the applications of plant metabolomics in characterizing metabolic responses to salt and drought stress, and identifying metabolic quantitative trait loci (QTLs). We also discuss the potential of metabolomics as a tool to unravel stress response mechanisms, and as a viable option for the biotechnological improvement of xero-halophytes when no other genetic information such as linkage maps and QTLs are available, by combining with germplasm-regression-combined marker-trait association identification.     

Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism

With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.     

后基因组时代微生物天然产物高效发掘体系设计与展望

微生物天然产物具有丰富的化学结构多样性和诱人的生物活性,持续启迪着创新医药和农药的发现。近年来,随着高通量测序技术的快速发展,巨大的微生物基因组数据揭示了多样生物合成和新颖天然产物的潜能远高于以前的认知。然而,如何高效地激活隐性的生物合成基因簇 (BGCs) 并识别相应的化合物,以及避免已知代谢产物的重复发现等挑战依然严峻。本文描述了面对这些问题时基因组学、生物信息学、机器学习、代谢组学、基因编辑和合成生物学等新技术在发现药用先导化合物过程中提供的机遇;总结并论述了在潜力菌株优选、BGCs的生物信息学预测、沉默 BGCs的高效激活以及目标产物的识别和跟踪方面的新见解;提出了基于潜力菌株选择和多组学挖掘技术从微生物天然产物中高效发现先导结构的系统线路 (SPLSD),并讨论了未来天然产物药用先导发现的机遇和挑战。     

Metabolomics, pathway regulation, and pathway discovery

Metabolomics is a data-based research strategy, the aims of which are to identify biomarker pictures of metabolic systems and metabolic perturbations and to formulate hypotheses to be tested. It involves the assay by mass spectrometry or NMR of many metabolites present in the biological system investigated. In this minireview, we outline studies in which metabolomics led to useful biomarkers of metabolic processes. We also illustrate how the discovery potential of metabolomics is enhanced by associating it with stable isotopic techniques.     

Chemometric approaches to improve PLSDA model outcome for predicting human non-alcoholic fatty liver disease using UPLC-MS as a metabolic profiling tool

An MS-based metabolomics strategy including variable selection and PLSDA analysis has been assessed as a tool to discriminate between non-steatotic and steatotic human liver profiles. Different chemometric approaches for uninformative variable elimination were performed by using two of the most common software packages employed in the field of metabolomics (i.e., MATLAB and SIMCA-P). The first considered approach was performed with MATLAB where the PLS regression vector coefficient values were used to classify variables as informative or not. The second approach was run under SIMCA-P, where variable selection was performed according to both the PLS regression vector coefficients and VIP scores. PLSDA models performance features, such as model validation, variable selection criteria, and potential biomarker output, were assessed for comparison purposes. One interesting finding is that variable selection improved the classification predictiveness of all the models by facilitating metabolite identification and providing enhanced insight into the metabolic information acquired by the UPLC-MS method. The results prove that the proposed strategy is a potentially straightforward approach to improve model performance. Among others, GSH, lysophospholipids and bile acids were found to be the most important altered metabolites in the metabolomic profiles studied. However, further research and more in-depth biochemical interpretations are needed to unambiguously propose them as disease biomarkers.     

Metabolomic profiling reveals bacterial metabolic adaptation strategies and new metabolites

How has metabolomics helped our understanding of infectious diseases? With the threat of antimicrobial resistance to human health around the world, metabolomics has emerged as a powerful tool to comprehensively characterize metabolic pathways to identify new drug targets. However, its output is constrained to known metabolites and their metabolic pathways. Recent advances in instrumentation, methodologies, and computational mass spectrometry have accelerated the use of metabolomics to understand pathogen–host metabolic interactions. This short review discusses a selection of recent publications using metabolomics in infectious/bacterial diseases. These studies unravel the links between metabolic adaptations to environments and host metabolic responses. Moreover, they highlight the importance of enzyme function and metabolite characterization in identifying new drug targets and biomarkers, as well as precision medicine in monitoring therapeutics and diagnosing diseases.     

Metabolic footprinting in microbiology: methods and applications in functional genomics and biotechnology

Metabolomics embraces several strategies that aim to quantify cell metabolites in order to increase our understanding of how metabolite levels and interactions influence phenotypes. Metabolic footprinting represents a niche within metabolomics, because it focuses on the analysis of extracellular metabolites. Although metabolic footprinting represents only a fraction of the entire metabolome, it provides important information for functional genomics and strain characterization, and it can also provide scientists with a key understanding of cell communication mechanisms, metabolic engineering and industrial biotechnological processes. Due to the tight and convoluted relationship between intracellular metabolism and metabolic footprinting, metabolic footprinting can provide precious information about the intracellular metabolic status. Hereby, we state that integrative information from metabolic footprinting can assist in further interpretation of metabolic networks.