Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.11Ysciescopu

Metabolome-Informed Microbiome Analysis Refines Metadata Classifications and Reveals Unexpected Medication Transfer in Captive Cheetahs.

Even high-quality collection and reporting of study metadata in microbiome studies can lead to various forms of inadvertently missing or mischaracterized information that can alter the interpretation or outcome of the studies, especially with nonmodel organisms. Metabolomic profiling of fecal microbiome samples can provide empirical insight into unanticipated confounding factors that are not possible to obtain even from detailed care records. We illustrate this point using data from cheetahs from the San Diego Zoo Safari Park. The metabolomic characterization indicated that one cheetah had to be moved from the non-antibiotic-exposed group to the antibiotic-exposed group. The detection of the antibiotic in this second cheetah was likely due to grooming interactions with the cheetah that was administered antibiotics. Similarly, because transit time for stool is variable, fecal samples within the first few days of antibiotic prescription do not all contain detected antibiotics, and the microbiome is not yet affected. These insights significantly altered the way the samples were grouped for analysis (antibiotic versus no antibiotic) and the subsequent understanding of the effect of the antibiotics on the cheetah microbiome. Metabolomics also revealed information about numerous other medications and provided unexpected dietary insights that in turn improved our understanding of the molecular patterns on the impact on the community microbial structure. These results suggest that untargeted metabolomic data provide empirical evidence to correct records and aid in the monitoring of the health of nonmodel organisms in captivity, although we also expect that these methods may be appropriate for other social animals, such as cats.IMPORTANCE Metabolome-informed analyses can enhance omics studies by enabling the correct partitioning of samples by identifying hidden confounders inadvertently misrepresented or omitted from carefully curated metadata. We demonstrate here the utility of metabolomics in a study characterizing the microbiome associated with liver disease in cheetahs. Metabolome-informed reinterpretation of metagenome and metabolome profiles factored in an unexpected transfer of antibiotics, preventing misinterpretation of the data. Our work suggests that untargeted metabolomics can be used to verify, augment, and correct sample metadata to support improved grouping of sample data for microbiome analyses, here for nonmodel organisms in captivity. However, the techniques also suggest a path forward for correcting clinical information in microbiome studies more broadly to enable higher-precision analyses

metaGEM: reconstruction of genome scale metabolic models directly from metagenomes

Metagenomic analyses of microbial communities have revealed a large degree of interspecies and intraspecies genetic diversity through the reconstruction of metagenome assembled genomes (MAGs). Yet, metabolic modeling efforts mainly rely on reference genomes as the starting point for reconstruction and simulation of genome scale metabolic models (GEMs), neglecting the immense intra- and inter-species diversity present in microbial communities. Here, we present metaGEM (https://github.com/franciscozo rrilla/metaGEM), an end-to-end pipeline enabling metabolic modeling of multi-species communities directly from metagenomes. The pipeline automates all steps from the extraction of context-specific prokaryotic GEMs from MAGs to community level flux balance analysis (FBA) simulations. To demonstrate the capabilities of metaGEM, we analyzed 483 samples spanning lab culture, human gut, plant-associated, soil, and ocean metagenomes, reconstructing over 14,000 GEMs. We show that GEMs reconstructed from metagenomes have fully represented metabolism comparable to isolated genomes. We demonstrate that metagenomic GEMs capture intraspecies metabolic diversity and identify potential differences in the progression of type 2 diabetes at the level of gut bacterial metabolic exchanges. Overall, metaGEM enables FBA-ready metabolic model reconstruction directly from metagenomes, provides a resource of metabolic models, and showcases community-level modeling of microbiomes associated with disease conditions allowing generation of mechanistic hypotheses

Plasma proteome profiling to assess human health and disease

The majority of diagnostic decisions are made on results from blood-based tests, and protein measurements are prominent among them. However, current assays are restricted to individual proteins, whereas it would be much more desirable to measure all of them in an unbiased, hypothesis-free manner. Therefore, characterization of the plasma proteome by mass spectrometry holds great promise for clinical application. Due to great technological challenges and study design issues, plasma proteomics has not yet lived up to its promises: no new biomarkers have been discovered, plasma proteomics has not entered clinical diagnostics and few biologically meaningful insights have been gained. As a consequence, relatively few groups still continue to pursue plasma proteomics, despite the undiminished clinical need. The overall aim of my PhD thesis was to pave the way for biomarker discovery and clinical applications of proteomics by precision characterization of the human blood plasma proteome. First, we streamlined the standard, time consuming and laborintensive proteomic workflow, and replaced it by a rapid, robust and highly reproducible robotic platform. After optimization of digestion conditions, peptide clean-up procedures and LC-MS/MS procedures, we can now prepare 96 samples in a fully-automated way within 3h and we routinely measure hundreds of plasma proteomes. Our workflow decreases hands-on time and opens the field for a new concept in biomarker discovery, which we termed ‘Plasma Proteome Profiling’. It enables the highly reproducibility (CV<20% for most proteins), and quantitative analysis of several hundred proteins from 1 μl of plasma, reflecting an individual’s physiology. The quantified proteins include inflammatory markers, proteins belonging to the lipid homeostasis system, gender-related proteins, sample quality markers and more than 50 FDA-approved biomarkers. One of my major goals was to demonstrate that MS-based proteomics can be applied to large cohorts and that it is possible to gain biologically and medically relevant information from this. We achieved this aim with our first large scale plasma proteomic study in which we analyzed by far the largest plasma proteomics study with almost 1,300 proteomes, which allowed us to define inflammatory and insulin resistance panels in a weight loss cohort. In summary, this PhD thesis has developed the concept and practice of Plasma Proteome Profiling as a fundamentally new approach in biomarker research and medical diagnostics – the system-wide phenotyping of humans in health and disease

The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health.

Food and human health are inextricably linked. As such, revolutionary impacts on health have been derived from advances in the production and distribution of food relating to food safety and fortification with micronutrients. During the past two decades, it has become apparent that the human microbiome has the potential to modulate health, including in ways that may be related to diet and the composition of specific foods. Despite the excitement and potential surrounding this area, the complexity of the gut microbiome, the chemical composition of food, and their interplay in situ remains a daunting task to fully understand. However, recent advances in high-throughput sequencing, metabolomics profiling, compositional analysis of food, and the emergence of electronic health records provide new sources of data that can contribute to addressing this challenge. Computational science will play an essential role in this effort as it will provide the foundation to integrate these data layers and derive insights capable of revealing and understanding the complex interactions between diet, gut microbiome, and health. Here, we review the current knowledge on diet-health-gut microbiota, relevant data sources, bioinformatics tools, machine learning capabilities, as well as the intellectual property and legislative regulatory landscape. We provide guidance on employing machine learning and data analytics, identify gaps in current methods, and describe new scenarios to be unlocked in the next few years in the context of current knowledge

Advancing microbiome research with machine learning : key findings from the ML4Microbiome COST action

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices

Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices

iVikodak—A Platform and Standard Workflow for Inferring, Analyzing, Comparing, and Visualizing the Functional Potential of Microbial Communities

Background: The objectives of any metagenomic study typically include identification of resident microbes and their relative proportions (taxonomic analysis), profiling functional diversity (functional analysis), and comparing the identified microbes and functions with available metadata (comparative metagenomics). Given the advantage of cost-effectiveness and convenient data-size, amplicon-based sequencing has remained the technology of choice for exploring phylogenetic diversity of an environment. A recent school of thought, employing the existing genome annotation information for inferring functional capacity of an identified microbiome community, has given a promising alternative to Whole Genome Shotgun sequencing for functional analysis. Although a handful of tools are currently available for function inference, their scope, functionality and utility has essentially remained limited. Need for a comprehensive framework that expands upon the existing scope and enables a standardized workflow for function inference, analysis, and visualization, is therefore felt.Methods: We present iVikodak, a multi-modular web-platform that hosts a logically inter-connected repertoire of functional inference and analysis tools, coupled with a comprehensive visualization interface. iVikodak is equipped with microbial co-inhabitance pattern driven published algorithms along with multiple updated databases of various curated microbe-function maps. It also features an advanced task management and result sharing system through introduction of personalized and portable dashboards.Results: In addition to inferring functions from 16S rRNA gene data, iVikodak enables (a) an in-depth analysis of specific functions of interest (b) identification of microbes contributing to various functions (c) microbial interaction patterns through function-driven correlation networks, and (d) simultaneous functional comparison between multiple microbial communities. We have bench-marked iVikodak through multiple case studies and comparisons with existing state of art. We also introduce the concept of a public repository which provides a first of its kind community-driven framework for scientific data analytics, collaboration and sharing in this area of microbiome research.Conclusion: Developed using modern design and task management practices, iVikodak provides a multi-modular, yet inter-operable, one-stop framework, that intends to simplify the entire approach toward inferred function analysis. It is anticipated to serve as a significant value addition to the existing space of functional metagenomics.iVikodak web-server may be freely accessed at https://web.rniapps.net/iVikodak/

Data-independent acquisition mass spectrometry for human gut microbiota metaproteome analysis

Human digestive tract microbiota is a diverse community of microorganisms having complex interactions between microbes and the human host. Observing the functions carried out by microbes is essential for gaining understanding on the role of gut microbiota in human health and associations to diseases. New methods and tools are needed for acquirement of functional information from complex microbial samples. Metagenomic approaches focus on taxonomy or gene based function potential but lack power in the discovery of the actual functions carried out by the microbes. Metaproteomic methods are required to uncover the functions. The current highthroughput metaproteomics methods are based on mass spectrometry which is capable of identifying and quantifying ionized protein fragments, called peptides. Proteins can be inferred from the peptides and the functions associated with protein expression can be determined by using protein databases. Currently the most widely used data-dependent acquisition (DDA) method records only the most intensive ions in a semi-stochastic manner, which reduces reproducibility and produces incomplete records impairing quantification. Alternative data-independent acquisition (DIA) systematically records all ions and has been proposed as a replacement for DDA. However, recording all ions produces highly convoluted spectra from multiple peptides and, for this reason, it has not been known if and how DIA can be applied to metaproteomics where the number of different peptides is high. This thesis work introduced the DIA method for metaproteomic data analysis. The method was shown to achieve high reproducibility enabling the usage of only a single analysis per sample while DDA requires multiple. An easy to use open source software package, DIAtools, was developed for the analysis. Finally, the DIA analysis method was applied to study human gut microbiota and carbohydrate-active enzymes expressed in members of gut microbiota.Ihmisen suolistomikrobiston analyysi DIAmassaspektrometriamenetelmällä Ihmisen suoliston mikrobisto on monien mikro-organismien yhteisö, joka on vuorovaikutuksessa ihmisen kehon kanssa. Suoliston mikrobien toiminnan ymmärtäminen on keskeistä niiden roolista ihmisen terveyteen ja sairauksiin. Uusia tutkimusmenetelmiä tarvitaan mikrobien toiminnallisuuden määrittämiseen monimutkaisista, useita mikrobeja sisältävistä, näytteistä. Yleisesti käytetyt metagenomiikan menetelmät keskittyvät taksonomiaan tai geenien perusteella ennustettuihin funktioihin, mutta metaproteomiikkaa tarvitaan mikrobien toiminnan selvittämiseen. Metaproteomiikka-analyysiin voidaan käyttää massaspektrometriaa, jolla pystytään tunnistamaan ja määrittämään ionisoitujen proteiinien osasten, peptidien, määrä. Proteiinit voidaan päätellä peptideistä ja näin pystytään määrittämään proteiineihin liittyviä toimintoja hyödyntäen proteiinitietokantoja. Nykyisin käytetty DDA-menetelmä tunnistaa vain runsaimmin esiintyvät ionit, mikä rajoittaa sen hyödyntämistä. Siinä mitattavien ionien valinta on jossain määrin satunnainen, mikä vähentää tulosten toistettavuutta. Vaihtoehtoinen DIA-menetelmä analysoi järjestelmällisesti kaikki ionit ja kyseistä menetelmää on ehdotettu DDA:n tilalle. DIA-menetelmä tuottaa päällekkäisiä peptidispektrejä ja siksi aiemmin ei ole ollut tiedossa, onko se soveltuva menetelmä tai miten sitä olisi mahdollista soveltaa metaproteomiikkaan, jossa on suuri määrä erilaisia peptidejä. Tämä tutkimus esittelee soveltuvia tapoja DIA-menetelmän käyttöön metaproteomiikkadatan analysoinnissa. Työssä osoitetaan, että DIA-metaproteomiikka tuottaa luotettavasti toistettavia tuloksia. DIA-menetelmää käyttäessä riittää, että näyte analysoidaan vain yhden kerran, kun vastaavasti DDA-menetelmän käyttö vaatii useamman analysointikerran. Tutkimuksessa kehitettiin avoimen lähdekoodin ohjelmisto DIAtools, joka toteuttaa kehitetyt DIA-datojen analysointimenetelmät. Lopuksi DIA-analyysiä sovellettiin ruoansulatuskanavan mikrobien ja niiden tuottamien CAZy-entsyymien tutkimiseksi

Computational Methods towards Personalized Cancer Vaccines and their Application through a Web-based Platform

Cancer immunotherapy is a treatment option that involves or uses components of a patient’s immune system. Today, it is heading towards becoming an integral part of treatment plans together with chemotherapy, surgery, and radiotherapy. Personalized epitope-based vaccines (EVs) serve as one strategy that is truly personalized. Each patient possesses a distinct immune system, and each tumor is unique, rendering the design of a potent vaccine challenging and dependent on the patient and the tumor. The potency of a vaccine is reliant on the ability of its constituent epitopes – short, immunogenic antigen fragments – to trigger an immune response. To assess this ability, one has to take into account the individuality of the immune system, among others conditioned by the variability of the human leukocyte antigen (HLA) gene cluster. Determining the HLA genotype with traditional experimental techniques can be time- and cost-intensive. We proposed a novel HLA genotyping algorithm based on integer linear programming that is independent of dedicated data generation for the sole purpose of HLA typing. On publicly available next-generation sequencing (NGS) data, our method outperformed previously published approaches. HLA binding is a prerequisite for T-cell recognition, and precise prediction algorithms exist. However, this information is not sufficient to assess the immunogenic potential of a peptide. To induce an immune response, reactive T-cell clones with receptors specific for a peptide-HLA complex have to be present. We suggested a method for the prediction of immunogenicity that includes peripheral tolerance models, based on gut microbiome data, in addition to central tolerance, previously shown to increase performance. The comparison to a previously published method suggests that the incorporation of gut microbiome data and HLA-binding stability estimates do not enhance prediction performance. High-throughput sequencing provides the basis for the design of personalized EVs. Through genome and transcriptome sequencing of tumor and matched non-malignant tissue samples, cancer-specific mutations can be identified, which can be further validated using other technologies such as mass spectrometry (MS). Multi-omics approaches can result in the acquisition of several hundreds of gigabytes of data. Handling and analysis of such data usually require data management solutions and high-performance computing (HPC) infrastructures. We developed the web-based platform qPortal for data-driven biomedical research that allows users to manage and analyze quantitative biological data intuitively. To emphasize the advantages of our data-driven approach with an integrated workflow system, we conducted a comparison to Galaxy. Building on qPortal, we implemented the web-based platform iVacPortal for the design of personalized EVs to facilitate data management and data analysis in such projects. Further, we applied the implemented methods through iVacPortal in two studies of two distinct cancer entities, indicating the added value of our platform for the assessment of personalized EV candidates and alternative targets for cancer immunotherapy