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.

Lipids are involved in many vital processes within the cell, and alterations in lipid homeostasis have been associated with various diseases such as cancer or type 2 diabetes. Confidently identifying lipids in samples is a prerequisite for understanding the multiple functions lipids fulfill in health and disease. However, the accurate identification of molecular lipid species based on tandem mass spectrometry data is still a key challenge in lipidomics. Most current approaches rely on using a custom pipeline to process and match the measured spectra against an in-house spectra reference library, which hinders the comparability of results. To address this challenge, a transformer model called LipiDetective was developed and trained on a dataset composed of reference spectra measured from lipid standards, spectra from databases, and published experiments, utilizing both shotgun as well as liquid-chromatography mass spectrometry. LipiDetective demonstrates, for the first time, that artificial neural networks can learn the characteristic lipid fragmentation patterns to automatically and accurately annotate molecular lipids species in tandem mass spectra independently of the experimental setup. The model can even correctly predict lipid species for which it has never seen a spectrum before as it is able to generalize the learned lipid fragmentation patterns. Analysis of the integrated gradients reveals that LipiDetective focuses on relevant peaks that can be matched to known fragments and are thus humanly interpretable. Therefore, LipiDetective has the potential to be a valuable tool to aid in the lipid identification process and support the comparability of results from different sources. Aside from Lipidetective as a "ready-to-use" application, this work primarily offers a deeper understanding of how the model functions and how future deep learning models for lipid identification in mass spectra could be improved. ### Competing Interest Statement The authors have declared no competing interest.

Abstract Background Endoscopic healing (EH) is the major long-term treatment target for inflammatory bowel diseases (IBD) in adults and paediatric patients. However, EH may not represent disease clearance. Hence, risk of relapse remains high and treatment discontinuation after reaching EH often results in disease exacerbation. We aimed to identify persistent cellular, molecular and microbial drivers of IBD under EH and longitudinally collected endoscopic biopsies from paediatric patients for analysis of single T-cell and bulk transcriptomics as well as mucosa-associated microbiota. Methods We followed disease trajectories of paediatric IBD patients treated according to international guidelines and collected mucosal biopsies from the terminal ileum (TI) and sigmoid colon (SC) at baseline (T1) and after approximately 3-12 months (T2) to assess EH. Non-IBD controls showed no macroscopic or histologic signs of inflammation. Host RNA and bacterial DNA were simultaneously extracted from single biopsies for bulk RNA-seq, and 16S rRNA seq. A second biopsy was used for isolation of lamina propria mononuclear cells (LPMCs), followed by CD45+, CD3+ cell sorting and single T-cell analysis (10X Genomics). Data were quality controlled and integrated. Results 217 biopsies (135 simultaneous extraction of host RNA and bacterial DNA; 82 single T-cell analysis) from 32 paediatric IBD patients (mean age 13 ± 3.5 years, 21 Crohn’s disease [CD], 11 ulcerative colitis [UC]; T1 = 32, T2 = 32, T > 2 = 6) and 5 non-IBD controls were analysed. Time between T1 and T2 averages 40 ± 22 weeks. At baseline, 15 patients (14 CD, 1 UC) were newly diagnosed and 31/32 patients had active mucosal inflammation. Twenty-two patients (71%) achieved EH at T2 (SES-CED ≤2 and absence of ulcerations), mostly on anti-TNF therapy. One patient (EH at T1) experienced relapsing disease within 10 weeks after discontinuing medication. In the bulk RNA-seq, we identified 299 differential expressed genes (DEGs; corrected p-value <0.05, FC >2.0), such as DUOX2, SAA2-SAA4, FCGR3B and NOS2, that are associated with active IBD and remained upregulated in EH in contrast to non-IBD controls. In addition single cell analysis revealed an IBD-specific pathogenic Th17 cluster that continued to exist in EH and showed high correlation with top DEGs after data integration. 16S rRNA gene seq highlighted highly individual mucosa-associated bacterial profiles and a reduced α-diversity in active and EH compared to non-IBD controls. Conclusion A persisting IBD signature in EH reflects ongoing cellular, molecular and microbial activity in comparison to non-IBD controls despite EH and mucosal regeneration. These markers may provide targets for future or sequential therapies.

The induction of endoplasmic reticulum unfolded protein responses (UPR ER ) contributes to cancer development and progression. We recently linked microbiota-related triggers to the tumor-promoting role of signal transducer activating transcription factor 6 (ATF6) in the colon. Here we substantiate the clinical relevance of ATF6 and related bacterial genera in colorectal cancer patient cohorts. Spatial and longitudinal bacterial profiling in ATF6 transgenic mice (nATF6 IEC ) identified tumor-initiating and tumor-progressing shifts in the mucosa-associated microbiota. Transcriptional analysis in intestinal epithelial cells (IEC) of germ-free and specific pathogen-free nATF6 IEC mice defined bacteria-specific changes in cellular metabolism enriched for fatty acid biosynthesis. Untargeted metabolomics, isotope-labeling in intestinal organoids and FASN inhibition confirmed ATF6-mediated involvement of long-chain fatty acids in tumorigenesis. Multi-omics data integration identified a bacteria-lipid network characterized by fatty acid efflux, catabolism and detoxification. We postulate chronic ATF6 signaling to drive a clinically relevant pathologic response altering lipid metabolism to select for a tumor-promoting microbiota. Graphical Abstract Chronic ATF6 signaling in the colonic epithelium alters lipid metabolism to select a tumor-promoting microbiota nATF6 expression is induced in intestinal epithelial cells in transgenic mice (nATF6 IEC ) Biallelic nATF6 IEC germ-free mice (grey circle) and floxed or monoallelic specific pathogen-free mice (black circle) remain in a state of homeostasis (yellow circle), while biallelic specific pathogen-free mice (black circle) develop spontaneous colon tumorigenesis (red circle) Mechanistically, biallelic nATF6 IEC mice in the presence of an intestinal microbiota alter colonic lipid metabolism, including the upregulation of Fasn The microbial lipid-response to the altered lipid milieu results in dysbiosis and the subsequent formation of colon adenomas Inhibition of FASN in biallelic nATF6 IEC mice prevents colon tumor formation (yellow arrow) Created with BioRender.com nATF6: activated activating transcription factor 6; LCFA: long-chain fatty acids; SAFA: saturated fatty acids; Fasn: fatty acid synthase; C75 i.p.: intraperitoneal injection of the Fasn inhibitor C75.

Metabolomics has become increasingly popular in biological and biomedical research, especially for multi-omics studies, due to the many associations of metabolism with diseases. This development is driven by improvements in metabolite identification and generating large amounts of data, increasing the need for computational solutions for data interpretation. In particular, only few computational approaches directly generating mechanistic hypotheses exist, making the biochemical interpretation of metabolomics data difficult. We present mantra , an approach to estimate how metabolic reactions change their activity between biological conditions without requiring absolute quantification of metabolites. Starting with a data-specific metabolic network we utilize linear models between substrates and products of a metabolic reaction to approximate deviations in activity. The obtained estimates can subsequently be used for network enrichment and integration with other omics data. By applying mantra to untargeted metabolomics measurements of Triple-Negative Breast Cancer biopsies, we show that it can accurately pinpoint biomarkers. On a dataset of stool metabolomics from Inflammatory Bowel Disease patients, we demonstrate that predictions on our proposed reaction metric generalize to an independent validation cohort and that it can be used for multi-omics network integration. By allowing mechanistic interpretation we facilitate knowledge extraction from metabolomics experiments.

■ AUTHOR INFORMATION Corresponding Authors Gerhard G. Thallinger − Institute of Biomedical Informatics, Graz University of Technology, 8010 Graz, Austria; orcid.org/0000-0002-2864-5404; Email: gerhard.thallinger@tugraz.at Zhixu Ni − Center of Membrane Biochemistry and Lipid Research, Faculty of Medicine Carl Gustav Carus of, TU Dresden, 01307 Dresden, Germany; orcid.org/00000003-3662-2621; Email: zhixu.ni@tu-dresden.de Laura Goracci − Department of Chemistry, Biology and Biotechnology, University of Perugia, 06123 Perugia, Italy; orcid.org/0000-0002-9282-9013; Email: laura.goracci@ unipg.it

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.

Plants often face simultaneous abiotic and biotic stress conditions; however, physiological and transcriptional responses under such combined stress conditions are still not fully understood. Spring barley (Hordeum vulgare) is susceptible to Fusarium head blight (FHB), which is strongly affected by weather conditions. We therefore studied the potential influence of drought on FHB severity and plant responses in three varieties of different susceptibility. We found strongly reduced FHB severity in susceptible varieties under drought. The number of differentially expressed genes (DEGs) and strength of transcriptomic regulation reflected the concentrations of physiological stress markers such as abscisic acid or fungal DNA contents. Infection-related gene expression was associated with susceptibility rather than resistance. Weighted gene co-expression network analysis revealed 18 modules of co-expressed genes that reflected the pathogen- or drought-response in the three varieties. A generally infection-related module contained co-expressed genes for defence, programmed cell death, and mycotoxin detoxification, indicating that the diverse genotypes used a similar defence strategy towards FHB, albeit with different degrees of success. Further, DEGs showed co-expression in drought- or genotype-associated modules that correlated with measured phytohormones or the osmolyte proline. The combination of drought stress with infection led to the highest numbers of DEGs and resulted in a modular composition of the single-stress responses rather than a specific transcriptional output.

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.

The improving access to increasing amounts of biomedical data provides completely new chances for advanced patient stratification and disease subtyping strategies. This requires computational tools that produce uniformly robust results across highly heterogeneous molecular data. Unsupervised machine learning methodologies are able to discover de novo patterns in such data. Biclustering is especially suited by simultaneously identifying sample groups and corresponding feature sets across heterogeneous omics data. The performance of available biclustering algorithms heavily depends on individual parameterization and varies with their application. Here, we developed MoSBi (molecular signature identification using biclustering), an automated multialgorithm ensemble approach that integrates results utilizing an error model-supported similarity network. We systematically evaluated the performance of 11 available and established biclustering algorithms together with MoSBi. For this, we used transcriptomics, proteomics, and metabolomics data, as well as synthetic datasets covering various data properties. Profiting from multialgorithm integration, MoSBi identified robust group and disease-specific signatures across all scenarios, overcoming single algorithm specificities. Furthermore, we developed a scalable network-based visualization of bicluster communities that supports biological hypothesis generation. MoSBi is available as an R package and web service to make automated biclustering analysis accessible for application in molecular sample stratification.

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.

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.

Lipids play an important role in biological systems and have the potential to serve as biomarkers in medical applications. Advances in lipidomics allow identification of hundreds of lipid species from biological samples. However, a systems biological analysis of the lipidome, by incorporating pathway information remains challenging, leaving lipidomics behind compared to other omics disciplines. An especially uncharted territory is the integration of statistical and network-based approaches for studying global lipidome changes. Here we developed the Lipid Network Explorer (LINEX), a web-tool addressing this gap by providing a way to visualize and analyze functional lipid metabolic networks. It utilizes metabolic rules to match biochemically connected lipids on a species level and combine it with a statistical correlation and testing analysis. Researchers can customize the biochemical rules considered, to their tissue or organism specific analysis and easily share them. We demonstrate the benefits of combining network-based analyses with statistics using publicly available lipidomics data sets. LINEX facilitates a biochemical knowledge-based data analysis for lipidomics. It is availableas a web-application and as a publicly available docker container.

Pyrrolizidine alkaloids (PAs) are heterocyclic secondary metabolites with a typical pyrrolizidine motif predominantly produced by plants as defense chemicals against herbivores. They display a wide structural diversity and occur in a vast number of species with novel structures and occurrences continuously being discovered. These alkaloids exhibit strong hepatotoxic, genotoxic, cytotoxic, tumorigenic, and neurotoxic activities, and thereby pose a serious threat to the health of humans since they are known contaminants of foods including grain, milk, honey, and eggs, as well as plant derived pharmaceuticals and food supplements. Livestock and fodder can be affected due to PA-containing plants on pastures and fields. Despite their importance as toxic contaminants of agricultural products, there is limited knowledge about their biosynthesis. While the intermediates were well defined by feeding experiments, only one enzyme involved in PA biosynthesis has been characterized so far, the homospermidine synthase catalyzing the first committed step in PA biosynthesis. This review gives an overview about structural diversity of PAs, biosynthetic pathways of necine base, and necic acid formation and how PA accumulation is regulated. Furthermore, we discuss their role in plant ecology and their modes of toxicity towards humans and animals. Finally, several examples of PA-producing crop plants are discussed.