Biology subjects

Adamski, J.

Publications and source records attributed to Adamski, J..

14 recordsLinked to original sources

bioRxiv2026

Metabolomic signatures support the diagnostics of peritoneal endometriosis using generalised linear models

Endometriosis, a common inflammatory gynecological disorder affecting up to 10% of women worldwide, is characterized by the presence of endometrium-like tissue outside the uterus. Current diagnostic methods, such as ultrasound and MRI, effectively detect ovarian and deep endometriosis but fail to detect more common peritoneal type. Diagnosing peritoneal endometriosis currently necessitates invasive laparoscopy and histological confirmation. Despite numerous efforts, no new reliable biomarkers have successfully transitioned into routine clinical use. This study aimed to investigate the use of targeted metabolomics to discover metabolite ratios capable of identifying endometriosis in plasma samples. We analyzed a discovery population of 235 patients and a validation population of 278 patients. All cases and controls in both populations were diagnosed by laparoscopy. Control subjects included individuals presenting with symptoms such as pain, dysmenorrhea, infertility, or other benign conditions, but who had no laparoscopic evidence of endometriosis. Using generalized linear models (GLMs) and machine learning, the study identified specific metabolite ratios as potential biomarkers that can distinguish different types of endometriosis and enable mass spectrometry-based diagnostics for peritoneal endometriosis. The best-validated GLM, derived from the concentration ratios of amino acids, acylcarnitines, sphingomyelins, and phosphatidylcholines, consisted of Thr/SM(OH) C22:2 + PC aa C40:5/SFA_PC + lysoPC a C16:0/SM(OH) C16:1. This model yielded an AUC of 0.82 (95% CI 0.619-0.891, with 76% sensitivity and 81% specificity) for peritoneal endometriosis. This innovative approach offers a robust diagnostic model, addressing an unmet medical need by facilitating earlier detection of peritoneal endometriosis and improving overall clinical management.

systems biology↗

bioRxiv2024

Interlaboratory comparison of standardised metabolomics and lipidomics analyses in human and rodent blood using the MxP(R) Quant 500 kit

Metabolomics and lipidomics are pivotal in understanding phenotypic variations beyond genomics. However, quantification and comparability of mass spectrometry (MS)-derived data are challenging. Standardised assays can enhance data comparability, enabling applications in multi-center epidemiological and clinical studies. Here we evaluated the performance and reproducibility of the MxP(R) Quant 500 kit across 14 laboratories. The kit allows quantification of 634 different metabolites from 26 compound classes using triple quadrupole MS. Each laboratory analysed twelve samples, including human plasma and serum, lipaemic plasma, NIST SRM 1950, and mouse and rat plasma, in triplicates. 505 out of the 634 metabolites were measurable above the limit of detection in all laboratories, while eight metabolites were undetectable in our study. Out of the 505 metabolites, 412 were observed in both human and rodent samples. Overall, the kit exhibited high reproducibility with a median coefficient of variation (CV) of 14.3 %. CVs in NIST SRM 1950 reference plasma were below 25 % and 10 % for 494 and 138 metabolites, respectively. To facilitate further inspection of reproducibility for any compound, we provide detailed results from the in-depth evaluation of reproducibility across concentration ranges using Deming regression. Interlaboratory reproducibility was similar across sample types, with some species-, matrix-, and phenotype-specific differences due to variations in concentration ranges. Comparisons with previous studies on the performance of MS-based kits (including the AbsoluteIDQ p180 and the Lipidyzer) revealed good concordance of reproducibility results and measured absolute concentrations in NIST SRM 1950 for most metabolites, making the MxP(R) Quant 500 kit a relevant tool to apply metabolomics and lipidomics in multi-center studies.

biochemistry↗

bioRxiv2024

The Interpretable Multimodal Machine Learning (IMML) framework reveals pathological signatures of distal sensorimotor polyneuropathy

Distal sensorimotor polyneuropathy (DSPN) is a common neurological disorder in elderly adults and people with obesity, prediabetes and diabetes and is associated with high morbidity and premature mortality. DSPN is a multifactorial disease and not fully understood yet. Here, we developed the Interpretable Multimodal Machine Learning (IMML) framework for predicting DSPN prevalence and incidence based on sparse multimodal data. Exploiting IMMLs interpretability further empowered biomarker identification. We leveraged the population-based KORA F4/FF4 cohort including 1,091 participants and their deep multimodal characterisation, i.e. clinical data, genomics, methylomics, transcriptomics, proteomics, inflammatory proteins and metabolomics. Clinical data alone is sufficient to stratify individuals with and without DSPN (AUROC = 0.752), whilst predicting DSPN incidence 6.5{+/-}0.2 years later strongly benefits from clinical data complemented with two or more molecular modalities (improved {Delta}AUROC >0.1, achieved AUROC of 0.714). Important and interpretable features of incident DSPN prediction include up-regulation of proinflammatory cytokines, down-regulation of SUMOylation pathway and essential fatty acids, thus yielding novel insights in the disease pathophysiology. These may become biomarkers for incident DSPN, guide prevention strategies and serve as proof of concept for the utility of IMML in studying complex diseases.

bioinformatics↗

bioRxiv2023

Missing view completion for multi-timepoints omics data via representation disentanglement and temporal knowledge transfer

Longitudinal multi-view omics data offer unique insights into the temporal dynamics of individual-level physiology, which provides opportunities to advance personalized healthcare. However, the common occurrence of incomplete views makes extrapolation tasks difficult, and there is a lack of tailored methods for this critical issue. Here, we introduce LEOPARD, an innovative approach specifically designed to complete missing views in multi-timepoint omics data. By disentangling longitudinal omics data into content and temporal representations, LEOPARD transfers the temporal knowledge to the omics-specific content, thereby completing missing views. The effectiveness of LEOPARD is validated on three benchmark datasets constructed with data from the MGH COVID study and the KORA cohort, spanning periods from 3 days to 14 years. Compared to conventional imputation methods, such as missForest, PMM, GLMM, and cGAN, LEOPARD yields the most robust results across the benchmark datasets. LEOPARD-imputed data also achieve the highest agreement with observed data in our analyses for age-associated metabolites detection, estimated glomerular filtration rate-associated proteins identification, and chronic kidney disease prediction. Our work takes the first step toward a generalized treatment of missing views in longitudinal omics data, enabling comprehensive exploration of temporal dynamics and providing valuable insights into personalized healthcare.

bioinformatics↗

bioRxiv2023

Pantothenate Kinase 4 controls efficient skeletal muscle energy substrate metabolism via acetyl-CoA

Metabolic inflexibility in skeletal muscle (SkM) is closely linked to metabolic diseases. Exercise improves metabolic flexibility, rendering it a valuable discovery tool of mechanisms promoting efficient metabolism of glucose and lipids. We herein discover pantothenate kinase 4 (PanK4) as a conserved exercise target with high abundance in SkM. We go on to show that murine muscle Pank4 is dysregulated with high-fat diet feeding, and identify human PANK4 variants that associate with glycemic control and body mass index traits, indicating important roles of PanK4 in glucose metabolism and growth. Consistent with the latter, germline deletion of PanK4 reduces circulating IGF-1 and stunts growth in mice. Deletion specifically in mouse SkM reveals that PanK4 facilitates fatty acid oxidation by acting as a regulator of SkM acetyl-CoA, a key node in metabolism of both glucose and lipids. Consequently, without PanK4, elevated SkM acetyl-CoA levels allosterically gridlock key enzymes required for efficient lipid and glucose utilization, and these SkM metabolic perturbations manifest in whole-body insulin resistance. As proof of principle, we show that an increase in muscle PanK4 lowers SkM acetyl-CoA and increases SkM glucose utilization. Our findings identify PanK4 as a novel regulator of SkM energy substrate metabolism, warranting inclusion in comprehensive strategies against metabolic disease.

physiology↗

bioRxiv2020

Finding New Molecular Targets of Familiar Natural Products Using In Silico Target Prediction

Natural products comprise a rich reservoir for innovative drug leads and are a constant source of bioactive compounds. To find pharmacological targets for new or already known natural products using modern computer-aided methods is a current endeavor in drug discovery. Natures treasures, however, could be used more effectively. Yet, reliable pipelines for large scale target prediction of natural products are still rare. We have developed an in silico workflow consisting of four independent, stand-alone target prediction tools and evaluated its performance on dihydrochalcones (DHCs) - a well-known class of natural products. Thereby, we revealed four previously unreported protein targets for DHCs, namely 5-lipoxygenase, cyclooxygenase-1, 17{beta}-hydroxysteroid dehydrogenase 3, and aldo-keto reductase 1C3. Moreover, we provide a thorough strategy on how to perform computational target prediction and guidance on using the respective tools.

bioinformatics↗

bioRxiv2020

Adaptive mitochondrial regulation of the proteasome

The proteasome is the main proteolytic system for targeted protein degradation in the cell. Its function is fine-tuned according to cellular needs. Regulation of proteasome function by mitochondrial metabolism, however, is unknown. Here, we demonstrate that mitochondrial dysfunction reduces the assembly and activity of the 26S proteasome in the absence of oxidative stress. Impaired respiratory complex I function leads to metabolic reprogramming of the Krebs cycle and deficiency in aspartate. Aspartate supplementation activates assembly and activity of 26S proteasomes via transcriptional activation of the proteasome assembly factors p28 and Rpn6. This metabolic adaptation of 26S proteasome function involves sensing of aspartate via the mTORC1 pathway. Metformin treatment of primary human cells similarly reduced assembly and activity of 26S proteasome complexes, which was fully reversible and rescued by supplementation of aspartate or pyruvate. Of note, respiratory dysfunction conferred resistance towards the proteasome inhibitor Bortezomib. Our study uncovers a fundamental novel mechanism of how mitochondrial metabolism adaptively adjusts protein degradation by the proteasome. It thus unravels unexpected consequences of defective mitochondrial metabolism in disease or drug-targeted mitochondrial reprogramming for proteasomal protein degradation in the cell. As metabolic inhibition of proteasome function can be alleviated by treatment with aspartate or pyruvate, our results also have therapeutic implications.

cell biology↗

bioRxiv2020

Correlation guided Network Integration (CoNI) reveals novel genetic regulators of hepatic metabolism

ABSTRACTThe steadily increasing amount of newly generated omics data of various types from genomics to metabolomics is a chance and a challenge to systems biology. To fully use its potential, one key is the meaningful integration of different types of omics. We here present a fully unsupervised and versatile correlation-based method, termed Correlation guided Network Integration (CoNI), to integrate multi-omics data into a hypergraph structure that allows for identification of effective regulators. Our approach further unravels single transcripts mapped to specific densely connected metabolic sub-graphs or pathways. By applying our method on transcriptomics and metabolomics data from murine livers under standard chow or high-fat-diet, we isolated eleven genes with a regulatory effect on hepatic metabolism. Subsequent in vitro and ex vivo experiments in human liver cells and human obtained liver biopsies validated seven candidates including INHBE and COBLL1, to alter lipid metabolism and to correlate with diabetes related traits such as overweight, hepatic fat content and insulin resistance (HOMA-IR). Last, we successfully applied our methods to an independent data-set to confirm its versatile and transferable character.Competing Interest StatementMatthias H. Tschoep is a scientific advisor to Novo Nordisk, and ERX. Jerzy Adamski is a scientific advisor to Biocrates Life Sciences AG. View Full Text

bioinformatics↗

bioRxiv2019

Characterization of bulk phosphatidylcholine compositions in human plasma using side-chain resolving lipidomics

Kit-based assays, such as AbsoluteIDQ p150, are widely used in large cohort studies and provide a standardized method to quantify blood concentrations of phosphatidylcholines (PCs). Many disease-relevant associations of PCs were reported using this method. However, their interpretation is hampered by lack of functionally relevant information on the detailed fatty acid side chain compositions as only the total number of carbon atoms and double bonds is identified by the kit. To enable more substantiated interpretations, we characterized these PC sums using the side chain resolving Lipidyzer platform, analyzing 223 samples in parallel to the AbsoluteIDQ. Combining these datasets, we estimated the quantitative composition of PC sums and subsequently tested their replication in an independent cohort. We identified major constituents of 28 PC sums, revealing also various unexpected compositions. As an example, PC 16:0_22:5 accounted for more than 50% of the PC sum with in total 38 carbon atoms and 5 double bonds (PC aa 38:5). For 13 PC sums, we found relatively high abundances of odd-chain fatty acids. In conclusion, our study provides insights in PC compositions in human plasma, facilitating interpretation of existing epidemiological datasets and potentially enabling imputation of PC compositions for future meta-analyses of lipidomics data.

epidemiology↗

bioRxiv2019

Induction of the nicotinamide riboside kinase NAD+ salvage pathway in skeletal muscle of H6PDH KO mice

BackgroundHexose-6-Phosphate Dehydrogenase (H6PDH) is a generator of NADPH in the Endoplasmic/Sarcoplasmic Reticulum (ER/SR). Interaction of H6PDH with 11{beta}-hydroxysteroid dehydrogenase type 1 provides NADPH to support oxo-reduction of inactive to active glucocorticoids, but the wider understanding of H6PDH in ER/SR NAD(P)(H) homeostasis is incomplete. Muscle specific lack of H6PDH results in a deteriorating skeletal myopathy, altered glucose homeostasis, ER stress and activation of the unfolded protein response. Here we further assess muscle responses to H6PDH deficiency to delineate pathways that may underpin myopathy and link SR redox status to muscle wide metabolic adaptation.\n\nMethodsWe analysed skeletal muscle from H6PDH knockout (H6PDKO), H6PDH and NRK2 double knockout (DKO) and wild-type (WT) mice. H6PDKO mice were supplemented with the NAD+ precursor nicotinamide riboside. Skeletal muscle samples were subject to biochemical analysis including NAD(H) measurement, LC/MS based metabolomics, Western immunoblotting, and high resolution mitochondrial respirometry. Genetic and supplement models were assessed for degree of myopathy compared to H6PDKO.\n\nResultsH6PDKO skeletal muscle showed adaptations in the routes regulating nicotinamide and NAD+ biosynthesis, with significant activation of the Nicotinamide Riboside Kinase 2 (NRK2) pathway. Associated with changes in NAD+ biosynthesis H6PDKO muscle had impaired mitochondrial respiratory capacity with altered mitochondrial acylcarnitine and acetyl CoA metabolism. Boosting NAD+ levels through the NRK2 pathway using the precursor nicotinamide riboside had no effect to mitigate ER stress and dysfunctional mitochondrial respiratory capacity or Acetyl-CoA metabolism. Similarly, H6PDKO/NRK2 double KO mice did not display an exaggerated timing or severity of myopathy or overt change in mitochondrial metabolism despite depression of NAD+ availability.\n\nConclusionsThese findings suggest a complex metabolic response to changes to muscle SR NADP(H) redox status that result in impaired mitochondrial energy metabolism and activation of cellular NAD+ salvage pathways. It is possible that SR can sense and signal perturbation in NAD(P)(H) that cannot be rectified in the absence of H6PDH. Whether NRK2 pathway activation is a direct response to changes in SR NAD(P)(H) availability or adaptation to deficits in metabolic energy availability remains to be resolved.

physiology↗

bioRxiv2018

Discovery of biomarkers for glycaemic deterioration before and after the onset of type 2 diabetes: an overview of the data from the epidemiological studies within the IMI DIRECT Consortium

Abstract/SummaryO_ST_ABSBackground and aimsC_ST_ABSUnderstanding the aetiology, clinical presentation and prognosis of type 2 diabetes (T2D) and optimizing its treatment might be facilitated by biomarkers that help predict a persons susceptibility to the risk factors that cause diabetes or its complications, or response to treatment. The IMI DIRECT (Diabetes Research on Patient Stratification) Study is a European Union (EU) Innovative Medicines Initiative (IMI) project that seeks to test these hypotheses in two recently established epidemiological cohorts. Here, we describe the characteristics of these cohorts at baseline and at the first main follow-up examination (18-months).\n\nMaterials and methodsFrom a sampling-frame of 24,682 European-ancestry adults in whom detailed health information was available, participants at varying risk of glycaemic deterioration were identified using a risk prediction algorithm and enrolled into a prospective cohort study (n=2127) undertaken at four study centres across Europe (Cohort 1: prediabetes). We also recruited people from clinical registries with recently diagnosed T2D (n=789) into a second cohort study (Cohort 2: diabetes). The two cohorts were studied in parallel with matched protocols. Endogenous insulin secretion and insulin sensitivity were modelled from frequently sampled 75g oral glucose tolerance (OGTT) in Cohort 1 and with mixed-meal tolerance tests (MMTT) in Cohort 2. Additional metabolic biochemistry was determined using blood samples taken when fasted and during the tolerance tests. Body composition was assessed using MRI and lifestyle measures through self-report and objective methods.\n\nResultsUsing ADA-2011 glycaemic categories, 33% (n=693) of Cohort 1 (prediabetes) had normal glucose regulation (NGR), and 67% (n=1419) had impaired glucose regulation (IGR). 76% of the cohort was male, age=62(6.2) years; BMI=27.9(4.0) kg/m2; fasting glucose=5.7(0.6) mmol/l; 2-hr glucose=5.9(1.6) mmol/l [mean(SD)]. At follow-up, 18.6(1.4) months after baseline, fasting glucose=5.8(0.6) mmol/l; 2-hr OGTT glucose=6.1(1.7) mmol/l [mean(SD)]. In Cohort 2 (diabetes): 65% (n=508) were lifestyle treated (LS) and 35% (n=271) were lifestyle + metformin treated (LS+MET). 58% of the cohort was male, age=62(8.1) years; BMI=30.5(5.0) kg/m2; fasting glucose=7.2(1.4)mmol/l; 2-hr glucose=8.6(2.8) mmol/l [mean(SD)]. At follow-up, 18.2(0.6) months after baseline, fasting glucose=7.8(1.8) mmol/l; 2-hr MMTT glucose=9.5(3.3) mmol/l [mean(SD)].\n\nConclusionThe epidemiological IMI DIRECT cohorts are the most intensely characterised prospective studies of glycaemic deterioration to date. Data from these cohorts help illustrate the heterogeneous characteristics of people at risk of or with T2D, highlighting the rationale for biomarker stratification of the disease - the primary objective of the IMI DIRECT consortium.\n\nAbbreviations

epidemiology↗

bioRxiv2018

Unraveling rice tolerance mechanisms against Schizotetranychus oryzae mite infestation

Infestation of Schizotetranychus oryzae (Acari: Tetranychidae) causes great losses in rice productivity. Infestation in Puita INTA-CL cultivar reduced the number of seeds/plant, percentage of full seeds, 1,000 seeds weight, and seed length, whereas infestation in IRGA423 increased 1,000 seeds weight and seed length. Reduction in seed weight/plant caused by infestation was higher in Puita INTA-CL than IRGA423. Thus, Puita INTA-CL was established as susceptible, and IRGA423 as tolerant to S. oryzae infestation. Photosynthetic parameters were less affected by infestation in IRGA423 than in Puita INTA-CL. Infestation also caused accumulation of H2O2, decreased cell membrane integrity and accelerated senescence in leaves of Puita INTA-CL, while leaves of IRGA423 presented higher levels of phenolics compounds. Using proteomic analysis, we identified proteins related to plant defense, such as jasmonate synthesis, and related to other mechanisms of tolerance such as oxidative stress, photosynthesis, and DNA structure maintenance, more abundant in IRGA423 after seven days of infestation. We detected higher levels of silicon (as amorphous silica cells) in leaves of infested IRGA423 plants compared to Puita INTA-CL, an element previously linked to plant defense. Our data shows that IRGA423 presents tolerance to S. oryzae infestation, and that multiple mechanisms might be employed by this cultivar.\n\nHighlightThis is the first report evaluating the defense responses (tolerance and susceptiblity) of two contrasting rice cultivars to Schizotetranychus oryzae mite infestation.

plant biology↗

bioRxiv2017

Low-level mitochondrial heteroplasmy modulates DNA replication, glucose metabolism and lifespan in mice

Mutations in mitochondrial DNA (mtDNA) lead to heteroplasmy, i.e. the intracellular coexistence of wild-type and mutant mtDNA strands, which impact a wide spectrum of diseases but also physiological processes, including endurance exercise performance in athletes. However, the phenotypic consequences of limited levels of naturally-arising heteroplasmy have not been experimentally studied to date. We hence generated a conplastic mouse strain carrying the mitochondrial genome of a AKR/J mouse strain (B6-mtAKR) together with a C57BL/6J nuclear genomic background, leading to >20% heteroplasmy in the origin of light-strand DNA replication (OriL). These conplastic mice demonstrate a shorter lifespan as well as dysregulation of multiple metabolic pathways, culminating in impaired glucose metabolism, compared to wild-type C57BL/6J mice carrying lower levels of heteroplasmy. Our results indicate that physiologically relevant differences in mtDNA heteroplasmy levels at a single, functionally important site impair metabolic health and lifespan in mice.\n\nHighlightsO_LIWe identify heteroplasmy of the adenine-repeat variation (9 to 13A) in nt5172 in the origin of light-strand DNA replication (OriL) in inbred mice.\nC_LIO_LIB6-mtAKR mice carry >20% 12A heteroplasmy in the OriL, while B6 mice carry only [~] 10% heteroplasmy.\nC_LIO_LIThe level of 12A heteroplasmy correlates to mtDNA copy number, glucose metabolism, and lifespan in mice.\nC_LIO_LIGiven the established role of mtDNA heteroplasmy in regards to endurance exercise performance in athletes, these findings may impact our understanding of metabolism and aging in humans.\nC_LI

genetics↗

bioRxiv2016

Network based conditional genome wide association analysis of human metabolomics

BackgroundGenome-wide association studies (GWAS) have identified hundreds of loci influencing complex human traits, however, their biological mechanism of action remains mostly unknown. Recent accumulation of functional genomics ( omics) including metabolomics data opens up opportunities to provide a new insight into the functional role of specific changes in the genome. Functional genomic data are characterized by high dimensionality, presence of (strong) statistical dependencies between traits, and, potentially, complex genetic control. Therefore, analysis of such data asks for development of specific statistical genetic methods.\n\nResultsWe propose a network-based, conditional approach to evaluate the impact of genetic variants on omics phenotypes (conditional GWAS, cGWAS). For each trait of interest, based on biological network, we select a set of other traits to be used as covariates in GWAS. The network could be reconstructed either from biological pathway databases or directly from the data. We evaluated our approach using data from a population-based KORA study (n=1,784, 1.7 M SNPs) with measured metabolomics data (151 metabolites) and demonstrated that our approach allows for identification of up to five additional loci not detected by conventional GWAS. We show that this gain in power is achieved through increased precision of genetic effect estimates, and in presence of specific contra-intuitive pleiotropic scenarios (when genetic and environmental sources of covariance are acting in opposite manner). We justify existence of such scenarios, and discuss possible applications of our method beyond metabolomics.\n\nConclusionsWe demonstrate that in context of metabolomics network-based, conditional genome-wide association analysis is able to dramatically increase power of identification of loci with specific contra-intuitive pleiotropic architecture. Our method has modest computational costs, can utilize summary level GWAS data, and is applicable to other omics data types. We anticipate that application of our method to new and existing data sets will facilitate progress in understanding genetic bases of control of molecular and complex phenotypes.\n\nShort abstractWe propose a network-based, conditional approach for genome-wide analysis of multivariate omics phenotypes. Our methods can incorporate prior biological knowledge about biological pathways from external sources. We evaluated our approach using metabolomics data and demonstrated that our approach has bigger power and allows for identification of additional loci. We show that gain in power is achieved through increased precision of genetic effect estimates, and in presence of specific contra-intuitive pleiotropic scenarios (when genetic and environmental sources of covariance are acting in opposite manner). We justify existence of such scenarios, and discuss possible applications of our method beyond metabolomics.

genomics↗