Exclusive enteral nutrition initiates individual protective microbiome changes to induce remission in pediatric Crohn's disease.

Exclusive enteral nutrition (EEN) is a first-line therapy for pediatric Crohn's disease (CD), but protective mechanisms remain unknown. We established a prospective pediatric cohort to characterize the function of fecal microbiota and metabolite changes of treatment-naive CD patients in response to EEN (German Clinical Trials DRKS00013306). Integrated multi-omics analysis identified network clusters from individually variable microbiome profiles, with Lachnospiraceae and medium-chain fatty acids as protective features. Bioorthogonal non-canonical amino acid tagging selectively identified bacterial species in response to medium-chain fatty acids. Metagenomic analysis identified high strain-level dynamics in response to EEN. Functional changes in diet-exposed fecal microbiota were further validated using gut chemostat cultures and microbiota transfer into germ-free Il10-deficient mice. Dietary model conditions induced individual patient-specific strain signatures to prevent or cause inflammatory bowel disease (IBD)-like inflammation in gnotobiotic mice. Hence, we provide evidence that EEN therapy operates through explicit functional changes of temporally and individually variable microbiome profiles.

An overview of food lipids toward food lipidomics.

Increasing evidence regarding lipids' beneficial effects on human health has changed the common perception of consumers and dietary officials about the role(s) of food lipids in a healthy diet. However, lipids are a wide group of molecules with specific nutritional and bioactive properties. To understand their true nutritional and functional value, robust methods are needed for accurate identification and quantification. Specific analytical strategies are crucial to target specific classes, especially the ones present in trace amounts. Finding a unique and comprehensive methodology to cover the full lipidome of each foodstuff is still a challenge. This review presents an overview of the lipids nutritionally relevant in foods and new trends in food lipid analysis for each type/class of lipids. Food lipid classes are described following the LipidMaps classification, fatty acids, endocannabinoids, waxes, C8 compounds, glycerophospholipids, glycerolipids (i.e., glycolipids, betaine lipids, and triglycerides), sphingolipids, sterols, sercosterols (vitamin D), isoprenoids (i.e., carotenoids and retinoids (vitamin A)), quinones (i.e., coenzyme Q, vitamin K, and vitamin E), terpenes, oxidized lipids, and oxylipin are highlighted. The uniqueness of each food group: oil-, protein-, and starch-rich, as well as marine foods, fruits, and vegetables (water-rich) regarding its lipid composition, is included. The effect of cooking, food processing, and storage, in addition to the importance of lipidomics in food quality and authenticity, are also discussed. A critical review of challenges and future trends of the analytical approaches and computational methods in global food lipidomics as the basis to increase consumer awareness of the significant role of lipids in food quality and food security worldwide is presented.

The FeatureCloud Platform for Federated Learning in Biomedicine: Unified Approach.

BACKGROUND: Machine learning and artificial intelligence have shown promising results in many areas and are driven by the increasing amount of available data. However, these data are often distributed across different institutions and cannot be easily shared owing to strict privacy regulations. Federated learning (FL) allows the training of distributed machine learning models without sharing sensitive data. In addition, the implementation is time-consuming and requires advanced programming skills and complex technical infrastructures. OBJECTIVE: Various tools and frameworks have been developed to simplify the development of FL algorithms and provide the necessary technical infrastructure. Although there are many high-quality frameworks, most focus only on a single application case or method. To our knowledge, there are no generic frameworks, meaning that the existing solutions are restricted to a particular type of algorithm or application field. Furthermore, most of these frameworks provide an application programming interface that needs programming knowledge. There is no collection of ready-to-use FL algorithms that are extendable and allow users (eg, researchers) without programming knowledge to apply FL. A central FL platform for both FL algorithm developers and users does not exist. This study aimed to address this gap and make FL available to everyone by developing FeatureCloud, an all-in-one platform for FL in biomedicine and beyond. METHODS: The FeatureCloud platform consists of 3 main components: a global frontend, a global backend, and a local controller. Our platform uses a Docker to separate the local acting components of the platform from the sensitive data systems. We evaluated our platform using 4 different algorithms on 5 data sets for both accuracy and runtime. RESULTS: FeatureCloud removes the complexity of distributed systems for developers and end users by providing a comprehensive platform for executing multi-institutional FL analyses and implementing FL algorithms. Through its integrated artificial intelligence store, federated algorithms can easily be published and reused by the community. To secure sensitive raw data, FeatureCloud supports privacy-enhancing technologies to secure the shared local models and assures high standards in data privacy to comply with the strict General Data Protection Regulation. Our evaluation shows that applications developed in FeatureCloud can produce highly similar results compared with centralized approaches and scale well for an increasing number of participating sites. CONCLUSIONS: FeatureCloud provides a ready-to-use platform that integrates the development and execution of FL algorithms while reducing the complexity to a minimum and removing the hurdles of federated infrastructure. Thus, we believe that it has the potential to greatly increase the accessibility of privacy-preserving and distributed data analyses in biomedicine and beyond.

Lipid network and moiety analysis for revealing enzymatic dysregulation and mechanistic alterations from lipidomics data.

Lipidomics is of growing importance for clinical and biomedical research due to many associations between lipid metabolism and diseases. The discovery of these associations is facilitated by improved lipid identification and quantification. Sophisticated computational methods are advantageous for interpreting such large-scale data for understanding metabolic processes and their underlying (patho)mechanisms. To generate hypothesis about these mechanisms, the combination of metabolic networks and graph algorithms is a powerful option to pinpoint molecular disease drivers and their interactions. Here we present lipid network explorer (LINEX$^2$), a lipid network analysis framework that fuels biological interpretation of alterations in lipid compositions. By integrating lipid-metabolic reactions from public databases, we generate dataset-specific lipid interaction networks. To aid interpretation of these networks, we present an enrichment graph algorithm that infers changes in enzymatic activity in the context of their multispecificity from lipidomics data. Our inference method successfully recovered the MBOAT7 enzyme from knock-out data. Furthermore, we mechanistically interpret lipidomic alterations of adipocytes in obesity by leveraging network enrichment and lipid moieties. We address the general lack of lipidomics data mining options to elucidate potential disease mechanisms and make lipidomics more clinically relevant.

Federated machine learning for a facilitated implementation of Artificial Intelligence in healthcare - a proof of concept study for the prediction of coronary artery calcification scores.

The implementation of Artificial Intelligence (AI) still faces significant hurdles and one key factor is the access to data. One approach that could support that is federated machine learning (FL) since it allows for privacy preserving data access. For this proof of concept, a prediction model for coronary artery calcification scores (CACS) has been applied. The FL was trained based on the data in the different institutions, while the centralized machine learning model was trained on one allocation of data. Both algorithms predict patients with risk scores ≥5 based on age, biological sex, waist circumference, dyslipidemia and HbA1c. The centralized model yields a sensitivity of c. 66% and a specificity of c. 70%. The FL slightly outperforms that with a sensitivity of 67% while slightly underperforming it with a specificity of 69%. It could be demonstrated that CACS prediction is feasible via both, a centralized and an FL approach, and that both show very comparable accuracy. In order to increase accuracy, additional and a higher volume of patient data is required and for that FL is utterly necessary. The developed "CACulator" serves as proof of concept, is available as research tool and shall support future research to facilitate AI implementation.

Target deconvolution of HDAC pharmacopoeia reveals MBLAC2 as common off-target.

Drugs that target histone deacetylase (HDAC) entered the pharmacopoeia in the 2000s. However, some enigmatic phenotypes suggest off-target engagement. Here, we developed a quantitative chemical proteomics assay using immobilized HDAC inhibitors and mass spectrometry that we deployed to establish the target landscape of 53 drugs. The assay covers 9 of the 11 human zinc-dependent HDACs, questions the reported selectivity of some widely-used molecules (notably for HDAC6) and delineates how the composition of HDAC complexes influences drug potency. Unexpectedly, metallo-ß-lactamase domain-containing protein 2 (MBLAC2) featured as a frequent off-target of hydroxamate drugs. This poorly characterized palmitoyl-CoA hydrolase is inhibited by 24 HDAC inhibitors at low nanomolar potency. MBLAC2 enzymatic inhibition and knockdown led to the accumulation of extracellular vesicles. Given the importance of extracellular vesicle biology in neurological diseases and cancer, this HDAC-independent drug effect may qualify MBLAC2 as a target for drug discovery.

Kupffer cells are protective in alcoholic steatosis.

Massive accumulation of lipids is a characteristic of alcoholic liver disease. Excess of hepatic fat activates Kupffer cells (KCs), which affect disease progression. Yet, KCs contribute to the resolution and advancement of liver injury. Aim of the present study was to evaluate the effect of KC depletion on markers of liver injury and the hepatic lipidome in liver steatosis (Lieber-DeCarli diet, LDC, female mice, mixed C57BL/6J and DBA/2J background). LDC increased the number of dead hepatocytes without changing the mRNA levels of inflammatory cytokines in the liver. Animals fed LDC accumulated elevated levels of almost all lipid classes. KC ablation normalized phosphatidylcholine and phosphatidylinositol levels in LDC livers, but had no effect in the controls. A modest decline of trigylceride and diglyceride levels upon KC loss was observed in both groups. Serum aminotransferases and hepatic ceramide were elevated in all animals upon KC depletion, and in particular, cytotoxic very long-chain ceramides increased in the LDC livers. Meta-biclustering revealed that eight lipid species occurred in more than 40% of the biclusters, and four of them were very long-chain ceramides. KC loss was further associated with excess free cholesterol levels in LDC livers. Expression of inflammatory cytokines did, however, not increase in parallel. In summary, the current study described a function of KCs in hepatic ceramide and cholesterol metabolism in an animal model of LDC liver steatosis. High abundance of cytotoxic ceramides and free cholesterol predispose the liver to disease progression suggesting a protective role of KCs in alcoholic liver diseases.

Susceptibility to diet-induced obesity at thermoneutral conditions is independent of UCP1.

Activation of uncoupling protein 1 (UCP1) in brown adipose tissue (BAT) upon cold stimulation leads to substantial increase in energy expenditure to defend body temperature. Increases in energy expenditure after a high-caloric food intake, termed diet-induced thermogenesis, are also attributed to BAT. These properties render BAT a potential target to combat diet-induced obesity. However, studies investigating the role of UCP1 to protect against diet-induced obesity are controversial and rely on the phenotyping of a single constitutive UCP1-knockout model. To address this issue, we generated a novel UCP1-knockout model by Cre-mediated deletion of exon 2 in the UCP1 gene. We studied the effect of constitutive UCP1 knockout on metabolism and the development of diet-induced obesity. UCP1 knockout and wild-type mice were housed at 30°C and fed a control diet for 4 wk followed by 8 wk of high-fat diet. Body weight and food intake were monitored continuously over the course of the study, and indirect calorimetry was used to determine energy expenditure during both feeding periods. Based on Western blot analysis, thermal imaging and noradrenaline test, we confirmed the lack of functional UCP1 in knockout mice. However, body weight gain, food intake, and energy expenditure were not affected by loss of UCP1 function during both feeding periods. We introduce a novel UCP1-KO mouse enabling the generation of conditional UCP1-knockout mice to scrutinize the contribution of UCP1 to energy metabolism in different cell types or life stages. Our results demonstrate that UCP1 does not protect against diet-induced obesity at thermoneutrality.NEW & NOTEWORTHY We provide evidence that the abundance of UCP1 does not influence energy metabolism at thermoneutrality studying a novel Cre-mediated UCP1-KO mouse model. This model will be a foundation for a better understanding of the contribution of UCP1 in different cell types or life stages to energy metabolism.

Liver Lipids of Patients with Hepatitis B and C and Associated Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) still remains a difficult to cure malignancy. In recent years, the focus has shifted to lipid metabolism for the treatment of HCC. Very little is known about hepatitis B virus (HBV) and C virus (HCV)-related hepatic lipid disturbances in non-malignant and cancer tissues. The present study showed that triacylglycerol and cholesterol concentrations were similar in tumor adjacent HBV and HCV liver, and were not induced in the HCC tissues. Higher levels of free cholesterol, polyunsaturated phospholipids and diacylglycerol species were noted in non-tumorous HBV compared to HCV liver. Moreover, polyunsaturated phospholipids and diacylglycerols, and ceramides declined in tumors of HBV infected patients. All of these lipids remained unchanged in HCV-related HCC. In HCV tumors, polyunsaturated phosphatidylinositol levels were even induced. There were no associations of these lipid classes in non-tumor tissues with hepatic inflammation and fibrosis scores. Moreover, these lipids did not correlate with tumor grade or T-stage in HCC tissues. Lipid reprogramming of the three analysed HBV/HCV related tumors mostly resembled HBV-HCC. Indeed, lipid composition of non-tumorous HCV tissue, HCV tumors, HBV tumors and HBV/HCV tumors was highly similar. The tumor suppressor protein p53 regulates lipid metabolism. The p53 and p53S392 protein levels were induced in the tumors of HBV, HCV and double infected patients, and this was significant in HBV infection. Negative correlation of tumor p53 protein with free cholesterol indicates a role of p53 in cholesterol metabolism. In summary, the current study suggests that therapeutic strategies to target lipid metabolism in chronic viral hepatitis and associated cancers have to consider disease etiology.

Loss of hepatic Mboat7 leads to liver fibrosis.

OBJECTIVE: The rs641738C>T variant located near the membrane-bound O-acyltransferase domain containing 7 (MBOAT7) locus is associated with fibrosis in liver diseases, including non-alcoholic fatty liver disease (NAFLD), alcohol-related liver disease, hepatitis B and C. We aim to understand the mechanism by which the rs641738C>T variant contributes to pathogenesis of NAFLD. DESIGN: Mice with hepatocyte-specific deletion of MBOAT7 (Mboat7Δhep) were generated and livers were characterised by histology, flow cytometry, qPCR, RNA sequencing and lipidomics. We analysed the association of rs641738C>T genotype with liver inflammation and fibrosis in 846 NAFLD patients and obtained genotype-specific liver lipidomes from 280 human biopsies. RESULTS: Allelic imbalance analysis of heterozygous human liver samples pointed to lower expression of the MBOAT7 transcript on the rs641738C>T haplotype. Mboat7Δhep mice showed spontaneous steatosis characterised by increased hepatic cholesterol ester content after 10 weeks. After 6 weeks on a high fat, methionine-low, choline-deficient diet, mice developed increased hepatic fibrosis as measured by picrosirius staining (p<0.05), hydroxyproline content (p<0.05) and transcriptomics, while the inflammatory cell populations and inflammatory mediators were minimally affected. In a human biopsied NAFLD cohort, MBOAT7 rs641738C>T was associated with fibrosis (p=0.004) independent of the presence of histological inflammation. Liver lipidomes of Mboat7Δhep mice and human rs641738TT carriers with fibrosis showed increased total lysophosphatidylinositol levels. The altered lysophosphatidylinositol and phosphatidylinositol subspecies in MBOAT7Δhep livers and human rs641738TT carriers were similar. CONCLUSION: Mboat7 deficiency in mice and human points to an inflammation-independent pathway of liver fibrosis that may be mediated by lipid signalling and a potentially targetable treatment option in NAFLD.

PTPN2 Deficiency Enhances Programmed T Cell Expansion and Survival Capacity of Activated T Cells.

Manipulating molecules that impact T cell receptor (TCR) or cytokine signaling, such as the protein tyrosine phosphatase non-receptor type 2 (PTPN2), has significant potential for advancing T cell-based immunotherapies. Nonetheless, it remains unclear how PTPN2 impacts the activation, survival, and memory formation of T cells. We find that PTPN2 deficiency renders cells in vivo and in vitro less dependent on survival-promoting cytokines, such as interleukin (IL)-2 and IL-15. Remarkably, briefly ex vivo-activated PTPN2-deficient T cells accumulate in 3- to 11-fold higher numbers following transfer into unmanipulated, antigen-free mice. Moreover, the absence of PTPN2 augments the survival of short-lived effector T cells and allows them to robustly re-expand upon secondary challenge. Importantly, we find no evidence for impaired effector function or memory formation. Mechanistically, PTPN2 deficiency causes broad changes in the expression and phosphorylation of T cell expansion and survival-associated proteins. Altogether, our data underline the therapeutic potential of targeting PTPN2 in T cell-based therapies to augment the number and survival capacity of antigen-specific T cells.

Exploring the SARS-CoV-2 virus-host-drug interactome for drug repurposing.

Coronavirus Disease-2019 (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Various studies exist about the molecular mechanisms of viral infection. However, such information is spread across many publications and it is very time-consuming to integrate, and exploit. We develop CoVex, an interactive online platform for SARS-CoV-2 host interactome exploration and drug (target) identification. CoVex integrates virus-human protein interactions, human protein-protein interactions, and drug-target interactions. It allows visual exploration of the virus-host interactome and implements systems medicine algorithms for network-based prediction of drug candidates. Thus, CoVex is a resource to understand molecular mechanisms of pathogenicity and to prioritize candidate therapeutics. We investigate recent hypotheses on a systems biology level to explore mechanistic virus life cycle drivers, and to extract drug repurposing candidates. CoVex renders COVID-19 drug research systems-medicine-ready by giving the scientific community direct access to network medicine algorithms. It is available at https://exbio.wzw.tum.de/covex/.

Automated, parallel mass spectrometry imaging and structural identification of lipids.

We report a method that enables automated data-dependent acquisition of lipid tandem mass spectrometry data in parallel with a high-resolution mass spectrometry imaging experiment. The method does not increase the total image acquisition time and is combined with automatic structural assignments. This lipidome-per-pixel approach automatically identified and validated 104 unique molecular lipids and their spatial locations from rat cerebellar tissue.

Proposal for a common nomenclature for fragment ions in mass spectra of lipids.

Advances in mass spectrometry-based lipidomics have in recent years prompted efforts to standardize the annotation of the vast number of lipid molecules that can be detected in biological systems. These efforts have focused on cataloguing, naming and drawing chemical structures of intact lipid molecules, but have provided no guidelines for annotation of lipid fragment ions detected using tandem and multi-stage mass spectrometry, albeit these fragment ions are mandatory for structural elucidation and high confidence lipid identification, especially in high throughput lipidomics workflows. Here we propose a nomenclature for the annotation of lipid fragment ions, describe its implementation and present a freely available web application, termed ALEX123 lipid calculator, that can be used to query a comprehensive database featuring curated lipid fragmentation information for more than 430,000 potential lipid molecules from 47 lipid classes covering five lipid categories. We note that the nomenclature is generic, extendable to stable isotope-labeled lipid molecules and applicable to automated annotation of fragment ions detected by most contemporary lipidomics platforms, including LC-MS/MS-based routines.

Quantitative lipidomics reveals age-dependent perturbations of whole-body lipid metabolism in ACBP deficient mice.

The acyl-CoA binding protein (ACBP) plays a key role in chaperoning long-chain acyl-CoAs into lipid metabolic processes and acts as an important regulatory hub in mammalian physiology. This is highlighted by the recent finding that mice devoid of ACBP suffer from a compromised epidermal barrier and delayed weaning, the physiological process where newborns transit from a fat-based milk diet to a carbohydrate-rich diet. To gain insights into how ACBP impinges on weaning and the concomitant remodeling of whole-body lipid metabolism we performed a comparative lipidomics analysis charting the absolute abundance of 613 lipid molecules in liver, muscle and plasma from weaning and adult Acbp knockout and wild type mice. Our results reveal that ACBP deficiency affects primarily lipid metabolism of liver and plasma during weaning. Specifically, we show that ACBP deficient mice have elevated levels of hepatic cholesteryl esters, and that lipids featuring an 18:1 fatty acid moiety are increased in Acbp depleted mice across all tissues investigated. Our results also show that the perturbation of systemic lipid metabolism in Acbp knockout mice is transient and becomes normalized and similar to that of wild type as mice grow older. These findings demonstrate that ACBP serves crucial functions in maintaining lipid metabolic homeostasis in mice during weaning.

Elucidation of epithelial-mesenchymal transition-related pathways in a triple-negative breast cancer cell line model by multi-omics interactome analysis.

In life sciences, and particularly biomedical research, linking aberrant pathways exhibiting phenotype-specific alterations to the underlying physical condition or disease is an ongoing challenge. Computationally, a key approach for pathway identification is data enrichment, combined with generation of biological networks. This allows identification of intrinsic patterns in the data and their linkage to a specific context such as cellular compartments, diseases or functions. Identification of aberrant pathways by traditional approaches is often limited to biological networks based on either gene expression, protein expression or post-translational modifications. To overcome single omics analysis, we developed a set of computational methods that allow a combined analysis of data collections from multiple omics fields utilizing hybrid interactome networks. We apply these methods to data obtained from a triple-negative breast cancer cell line model, combining data sets of gene and protein expression as well as protein phosphorylation. We focus on alterations associated with the phenotypical differences arising from epithelial-mesenchymal transition in two breast cancer cell lines exhibiting epithelial-like and mesenchymal-like morphology, respectively. Here we identified altered protein signaling activity in a complex biologically relevant network, related to focal adhesion and migration of breast cancer cells. We found dysregulated functional network modules revealing altered phosphorylation-dependent activity in concordance with the phenotypic traits and migrating potential of the tested model. In addition, we identified Ser267 on zyxin, a protein coupled to actin filament polymerization, as a potential in vivo phosphorylation target of cyclin-dependent kinase 1.