Navigating the Human Metabolome for Biomarker Identification and Design of Pharmaceutical Molecules

Metabolomics is a rapidly evolving discipline that involves the systematic study of endogenous small molecules that characterize the metabolic pathways of biological systems. The study of metabolism at a global level has the potential to contribute significantly to biomedical research, clinical medical practice, as well as drug discovery. In this paper, we present the most up-to-date metabolite and metabolic pathway resources, and we summarize the statistical, and machine-learning tools used for the analysis of data from clinical metabolomics. Through specific applications on cancer, diabetes, neurological and other diseases, we demonstrate how these tools can facilitate diagnosis and identification of potential biomarkers for use within disease diagnosis. Additionally, we discuss the increasing importance of the integration of metabolomics data in drug discovery. On a case-study based on the Human Metabolome Database (HMDB) and the Chinese Natural Product Database (CNPD), we demonstrate the close relatedness of the two data sets of compounds, and we further illustrate how structural similarity with human metabolites could assist in the design of novel pharmaceuticals and the elucidation of the molecular mechanisms of medicinal plants

Novel diagnostic and therapeutic techniques reveal changed metabolic profiles in recurrent focal segmental glomerulosclerosis

Idiopathic forms of Focal Segmental Glomerulosclerosis (FSGS) are caused by circulating permeability factors, which can lead to early recurrence of FSGS and kidney failure after kidney transplantation. In the past three decades, many research endeavors were undertaken to identify these unknown factors. Even though some potential candidates have been recently discussed in the literature, “the” actual factor remains elusive. Therefore, there is an increased demand in FSGS research for the use of novel technologies that allow us to study FSGS from a yet unexplored angle. Here, we report the successful treatment of recurrent FSGS in a patient after living-related kidney transplantation by removal of circulating factors with CytoSorb apheresis. Interestingly, the classical published circulating factors were all in normal range in this patient but early disease recurrence in the transplant kidney and immediate response to CytoSorb apheresis were still suggestive for pathogenic circulating factors. To proof the functional effects of the patient’s serum on podocytes and the glomerular filtration barrier we used a podocyte cell culture model and a proteinuria model in zebrafish to detect pathogenic effects on the podocytes actin cytoskeleton inducing a functional phenotype and podocyte effacement. We then performed Raman spectroscopy in the < 50 kDa serum fraction, on cultured podocytes treated with the FSGS serum and in kidney biopsies of the same patient at the time of transplantation and at the time of disease recurrence. The analysis revealed changes in podocyte metabolome induced by the FSGS serum as well as in focal glomerular and parietal epithelial cell regions in the FSGS biopsy. Several altered Raman spectra were identified in the fractionated serum and metabolome analysis by mass spectrometry detected lipid profiles in the FSGS serum, which were supported by disturbances in the Raman spectra. Our novel innovative analysis reveals changed lipid metabolome profiles associated with idiopathic FSGS that might reflect a new subtype of the disease

Defining NASH from a multi-omics systems biology perspective

Non-alcoholic steatohepatitis (NASH) is a chronic liver disease affecting up to 6.5% of the general population. There is no simple definition of NASH, and the molecular mechanism underlying disease pathogenesis remains elusive. Studies applying single omics technologies have enabled a better understanding of the molecular profiles associated with steatosis and hepatic inflammation—the commonly accepted histologic features for diagnosing NASH, as well as the discovery of novel candidate biomarkers. Multi-omics analysis holds great potential to uncover new insights into disease mechanism through integrating multiple layers of molecular information. Despite the technical and computational challenges associated with such efforts, a few pioneering studies have successfully applied multi-omics technologies to investigate NASH. Here, we review the most recent technological developments in mass spectrometry (MS)-based proteomics, metabolomics, and lipidomics. We summarize multi-omics studies and emerging omics biomarkers in NASH and highlight the biological insights gained through these integrated analyses

Big science and big data in nephrology

There have been tremendous advances during the last decade in methods for large-scale, high-throughput data generation and in novel computational approaches to analyze these datasets. These advances have had a profound impact on biomedical research and clinical medicine. The field of genomics is rapidly developing toward single-cell analysis, and major advances in proteomics and metabolomics have been made in recent years. The developments on wearables and electronic health records are poised to change clinical trial design. This rise of ‘big data’ holds the promise to transform not only research progress, but also clinical decision making towards precision medicine. To have a true impact, it requires integrative and multi-disciplinary approaches that blend experimental, clinical and computational expertise across multiple institutions. Cancer research has been at the forefront of the progress in such large-scale initiatives, so-called ‘big science,’ with an emphasis on precision medicine, and various other areas are quickly catching up. Nephrology is arguably lagging behind, and hence these are exciting times to start (or redirect) a research career to leverage these developments in nephrology. In this review, we summarize advances in big data generation, computational analysis, and big science initiatives, with a special focus on applications to nephrology

Psoriaasi, atoopilise dermatiidi ja ateroskleroosi metaboloomne profileerimine

Väitekirja elektrooniline versioon ei sisalda publikatsiooneMetaboloomika on teadusharu, mis tegeleb madalmolekulaarsete ühendite mõõtmise ja analüüsimisega. Nendeks on aminohapped, biogeensed amiinid, süsivesikud, rasvhapped, nukleiinhapped või peptiidid, mis võivad olla nii eksogeenset kui ka endogeenset päritolu. Nende ainete samaaegne mõõtmine võimaldab näha ainevahetusradade otsest peegeldust, nö. metaboloomset sõrmejälge. Psoriaas on laialt levinud krooniline põletikuline nahahaigus, mis esineb kuni 1%-l lastest ja 2%-3% üldpopulatsioonist. Haiguse teke on seotud mitme põhjusega, sealhulgas geneetiline eelsoodumus ja vastuvõtlikkus, keskkonna mõjutegurid koos immuunsüsteemi düsfunktsiooni ja nahabarjääri häirega. Atoopiline dermatiit on laialt levinud ja kompleksne nahahaigus, mis mõjutab kuni 15% lapsi ja täiskasvanuid üldpopulatsioonis. Kuigi enamik lapsi kasvab haigusest välja, hõlmab see teatud juhtudel ka täiskasvanuid, mõjutades patsientide heaolu ja põhjustades rida kaasuvaid haigusi, sealhulgas allergiad, astma, tähelepanuhäired ning aneemiat. Ateroskleroos on põletikuline haigus, hõlmates arterite seinu, kuhu kogunevad põletikulised rakud ja lipiidid. See viib arterite ahenemiseni, mis võib päädida trombi tekkega, põhjustades infarkti. Ateroskleroosi kõige levinumad vormid on perifeerne arterite haigus ja koronaar-arteri haigus, millest mõlemast on saanud suured rahvatervise probleemid. Käesoleva doktoritöö peamiseks eesmärgiks oli analüüsida psoriaasi, atoopilise dermatiidi ja ateroskleroosi patsientide metaboloomseid profiile ning hinnata sarnasusi ja erinevusi leitud metaboliitides.Metabolomics concerns with the measurement and analysis of small molecule compounds (< 1 kDa, e.g. amino acids, biogenic amines, carbohydrates, fatty acids, nucleic acids, peptides) of both exogenous and endogenous origins. These are the substrates and products of various chemical reactions within metabolic pathways. Psoriasis (PS) is a widespread chronic inflammatory skin disease affecting 2%-3% of the population in the world. The disease is considered to be multifactorial with a number of key contributing factors including genetic predisposition and susceptibility, environmental influences along with immune dysfunction and the disruption of the skin barrier. Atopic dermatitis (AD) is a widespread and complex condition that affects up to 15% adults and children worldwide. Although children have an increased prevalence of atopic dermatitis, many adults remain affected throughout their life. Atherosclerosis is classified as an inflammatory disease that involves the arterial wall and is characterized by the continuous accumulation of inflammatory cells and lipids within the intima of large arteries. The metabolomic profiles of patients with psoriasis and atopic dermatitis were explored to find possible disease-specific metabolites that could be used to characterise and better understand the underlying mechanisms of the disease pathogenesis. The application of the established methods was expanded to peripheral arterial disease and coronary arterial disease to further search for similarities and differences in the metabolomic profiles of the diseaseshttps://www.ester.ee/record=b522842

Comparison of Analytical Methods Of Serum Untargeted Metabolomics

Funding Information: IV. ACKNOWLEDGEMENTS This research was funded by Fundação para a Ciência e a Tecnologia (FCT), grant DSAIPA/DS/0117/2020 and RNEM-LISBOA-01-0145-FEDER-022125 (Portuguese Mass Spectrometry Network). Centro de Química Estrutural is a Research Unit funded by FCT through projects UIDB/00100/2020 and UIDP/00100/2020. Institute of Molecular Sciences is an Associate Laboratory funded by FCT through project LA/P/0056/2020. Publisher Copyright: © 2023 IEEE.Metabolomics has emerged as a powerful tool in the discovery of new biomarkers for medical diagnosis and prognosis. However, there are numerous challenges, such as the methods used to characterize the system metabolome. In the present work, the comparison of two analytical platforms to acquire the serum metabolome of critically ill patients was conducted. The untargeted serum metabolome analysis by ultraperformance liquid chromatography coupled to tandem mass spectrometry (UPLC-MS/MS) enabled to identify a set of metabolites statistically different between deceased and discharged patients. This set of metabolites also enabled to develop a very good predictive model, based on linear discriminant analysis (LDA) with a sensitivity and specificity of 80% and 100%, respectively. Fourier Transform Infrared (FTIR) spectroscopy was also applied in a high-throughput, simple and rapid mode to analyze the serum metabolome. Despite this technique not enabling the identification of metabolites, it allowed to identify molecular fingerprints associated to each patient group, while leading to a good predictive model, based on principal component analysis-LDA, with a sensitivity and specificity of 100% and 90%, respectively. Therefore, both analytical techniques presented complementary characteristics, that should be further explored for metabolome characterization and application as for biomarkers discovery for medical diagnosis and prognosis.publishersversionpublishe

An integrative systems biology study to understand immune aging in people living with HIV

Antiretroviral therapy (ART) reduces viral replication, restores T helper cells and improves the survival of people living with HIV (PWH), transforming a life-threatening disease into a manageable chronic infection. Nevertheless, PWH under ART shows aging-related diseases such as bone abnormalities, non-HIV-associated cancers, and cardiovascular and neurocognitive diseases. The complex immune metabolic dysregulation leading to these comorbidities is called immune aging. The main question raised by my thesis was, what are the complex mechanisms responsible for immune aging in HIV? Using advanced system biology and machine learning tools, I used multi-omics-based patient stratification to identify biologic perturbations associated with immune aging in PWH. First, we investigated PWH with Metabolic Syndrome (MetS), a relatively common agingrelated disease in HIV-1. In paper I, we identified the dysregulation of glutamate metabolism in PWH with MetS using plasma metabolomics and measure of cell transporters by flow cytrometry. Then, we investigated the mechanisms of differing PWH on long-term successful ART from HIV-negative controls (HC). In paper II, we identified the dysregulation of amino acids and, more specifically, glutaminolysis (i.e., lysis of glutamine to glutamate) in PWH compared to HC using metabolomics in two independent cohorts to avoid the potential cohort biases. We identified five neurosteroids to be lower in PWH and potentially create neurological impairments in PWH. The glutaminolysis inhibition in chronically infected HIV-1 promonocytic (U1) cells induced apoptosis and latency reversal which could clear HIV reservoirs. The first two papers universally clarified our knowledge about dysregulated metabolic traits following a prolonged ART in PWH. However, we observed heterogeneity among the clinically defined PWH. Therefore, we focused more on the multi-omics data-driven approaches to stratify the at-risk group who were either dysregulated metabolically atrisk PWH (paper III) or immunometabolic at-risk group (paper IV) and clarified the biological aging process by measuring transcriptomics age (paper V). In paper III, we found three groups of PWH based on multi-omics integration of lipidomics, metabolomics, and microbiome. The severe at-risk metabolic complications showed increased weight-related comorbidities and di- and triglycerides compared to the other clusters. At-risk and HC-like groups displayed similar metabolic profiles but were different from HC. An increase in Prevotella was linked to the overrepresentation of men having sex with men (MSM) in the at-risk group. The microbiome-associated metabolites (MAM) appeared dysregulated in all HIV groups compared to controls. We improved this clustering by adding transcriptomics and proteomics data for a refined immunometabolic at-risk-related clustering in PWH. In paper IV, immune-driven HC-like and at-risk groups were clustered based on metabolomics, transcriptomics, and proteomics. Several biomarkers from central carbon metabolism (CCM) and senescence-associated proteins were linked to the at-risk phenotype based on random forest, structural causal modeling, and co-expression networks. Senescent protein changes were associated with a deficiency in macrophage function based on single-cell data, cell profiling, flow cytometry, and proteomics from macrophage data and in vitro validation. We also developed personalized and group-level genome-scale metabolic models (GSMM) and confirmed the implication of metabolites from CCM and polyamides in at-risk phenotypes. Finally, we investigated the accelerated aging process (AAP) in PWH. In paper V, we calculated the biological age of PWH using transcriptomics data and grouped patients into aging groups; The decelerated aging process (DAP) group was linked with higher age, European origin, and a higher proportion of tenofovir disoproxil fumarate /alafenamide (TDF/TAF). AAP had a downregulation of metabolic pathways and an upregulation of inflammatory pathways. In conclusion, my thesis identifies underlying mechanisms of immune aging using system biology tools in three independent cohorts of PWH for mechanistic studies and to improve their care and achieve healthy aging

Metabolomic Profile of Young Adults Born Preterm

Prematurity is a risk factor for the development of chronic adult diseases. Metabolomics can correlate the biochemical changes to a determined phenotype, obtaining real information about the state of health of a subject at that precise moment. Significative differences in the metabolomic profile of preterm newborns compared to those born at term have been already identified at birth. An observational case–control study was performed at the University Hospital of Siena. The aim was to evaluate and compare the metabolomic profiles of young adults born preterm to those born at term. Urinary samples were collected from 67 young adults (18–23 years old) born preterm (mean gestational age of 30 weeks, n = 49), and at term of pregnancy (mean gestational age of 38 weeks, n = 18). The urinary spectra of young adults born preterm was different from those born at term and resembled what was previously described at birth. The Random Forest algorithm gave the best classification (accuracy 82%) and indicated the following metabolites as responsible for the classification: citrate, CH2 creatinine, fumarate and hippurate. Urine spectra are promising tools for the early identification of neonates at risk of disease in adulthood and may provide insight into the pathogenesis and effects of fetal programming and infants’ outcomes

Biomarkers and in vitro strategies for nephrotoxicity and renal disease assessment

Acute kidney injury (AKI) is a global public health concern, impacting nearly 13.3 million patients and resulting in three million deaths per year. Chronic kidney disease has increased by 135% since 1990, representing the pathology with the fastest growth rate worldwide. The annual costs of dialysis and kidney transplants range between US$35,000 and US$100,000 per patient. Despite its great impact, kidney disease has remained mostly asymptomatic for many years. AKI continues to be a major, unmet medical condition for which there are no pharmacological treatments available, while animal models are limited to provide direction for therapeutic translation into humans. Currently, serum creatinine is the standard biomarker to identify nephrotoxicity; however, it is a late stage biomarker. Hence, there is a pressing need to study in vitro biomarkers for the assessment of nephrotoxicity in order to develop new and safer drugs. Understanding of the mechanisms by which molecules produce nephrotoxicity is vital in order to both prevent adversity and treat kidney injury. In this review, we address new technologies and models that may be used to identify earlier biomarkers and pathways involved in nephrotoxicity, such as cell culture, omics, bioinformatics platform, CRISPR/Cas9 genome-editing, in silico, organoids and 3D bioprinting, considering AOP

Evidence of a causal and modifiable relationship between kidney function and circulating trimethylamine N-oxide

The host-microbiota co-metabolite trimethylamine N-oxide (TMAO) is linked to increased cardiovascular risk but how its circulating levels are regulated remains unclear. We applied "explainable" machine learning, univariate, multivariate and mediation analyses of fasting plasma TMAO concentration and a multitude of phenotypes in 1,741 adult Europeans of the MetaCardis study. Here we show that next to age, kidney function is the primary variable predicting circulating TMAO, with microbiota composition and diet playing minor, albeit significant, roles. Mediation analysis suggests a causal relationship between TMAO and kidney function that we corroborate in preclinical models where TMAO exposure increases kidney scarring. Consistent with our findings, patients receiving glucose-lowering drugs with reno-protective properties have significantly lower circulating TMAO when compared to propensity-score matched control individuals. Our analyses uncover a bidirectional relationship between kidney function and TMAO that can potentially be modified by reno-protective anti-diabetic drugs and suggest a clinically actionable intervention for decreasing TMAO-associated excess cardiovascular risk