MetalinksDB: a flexible and contextualizable resource of metabolite-protein interactions.

From the catalytic breakdown of nutrients to signaling, interactions between metabolites and proteins play an essential role in cellular function. An important case is cell-cell communication, where metabolites, secreted into the microenvironment, initiate signaling cascades by binding to intra- or extracellular receptors of neighboring cells. Protein-protein cell-cell communication interactions are routinely predicted from transcriptomic data. However, inferring metabolite-mediated intercellular signaling remains challenging, partially due to the limited size of intercellular prior knowledge resources focused on metabolites. Here, we leverage knowledge-graph infrastructure to integrate generalistic metabolite-protein with curated metabolite-receptor resources to create MetalinksDB. MetalinksDB is an order of magnitude larger than existing metabolite-receptor resources and can be tailored to specific biological contexts, such as diseases, pathways, or tissue/cellular locations. We demonstrate MetalinksDB's utility in identifying deregulated processes in renal cancer using multi-omics bulk data. Furthermore, we infer metabolite-driven intercellular signaling in acute kidney injury using spatial transcriptomics data. MetalinksDB is a comprehensive and customizable database of intercellular metabolite-protein interactions, accessible via a web interface (https://metalinks.omnipathdb.org/) and programmatically as a knowledge graph (https://github.com/biocypher/metalinks). We anticipate that by enabling diverse analyses tailored to specific biological contexts, MetalinksDB will facilitate the discovery of disease-relevant metabolite-mediated intercellular signaling processes.

Metabolic Communication by SGLT2 Inhibition.

BACKGROUND: SGLT2 (sodium-glucose cotransporter 2) inhibitors (SGLT2i) can protect the kidneys and heart, but the underlying mechanism remains poorly understood. METHODS: To gain insights on primary effects of SGLT2i that are not confounded by pathophysiologic processes or are secondary to improvement by SGLT2i, we performed an in-depth proteomics, phosphoproteomics, and metabolomics analysis by integrating signatures from multiple metabolic organs and body fluids after 1 week of SGLT2i treatment of nondiabetic as well as diabetic mice with early and uncomplicated hyperglycemia. RESULTS: Kidneys of nondiabetic mice reacted most strongly to SGLT2i in terms of proteomic reconfiguration, including evidence for less early proximal tubule glucotoxicity and a broad downregulation of the apical uptake transport machinery (including sodium, glucose, urate, purine bases, and amino acids), supported by mouse and human SGLT2 interactome studies. SGLT2i affected heart and liver signaling, but more reactive organs included the white adipose tissue, showing more lipolysis, and, particularly, the gut microbiome, with a lower relative abundance of bacteria taxa capable of fermenting phenylalanine and tryptophan to cardiovascular uremic toxins, resulting in lower plasma levels of these compounds (including p-cresol sulfate). SGLT2i was detectable in murine stool samples and its addition to human stool microbiota fermentation recapitulated some murine microbiome findings, suggesting direct inhibition of fermentation of aromatic amino acids and tryptophan. In mice lacking SGLT2 and in patients with decompensated heart failure or diabetes, the SGLT2i likewise reduced circulating p-cresol sulfate, and p-cresol impaired contractility and rhythm in human induced pluripotent stem cell-derived engineered heart tissue. CONCLUSIONS: SGLT2i reduced microbiome formation of uremic toxins such as p-cresol sulfate and thereby their body exposure and need for renal detoxification, which, combined with direct kidney effects of SGLT2i, including less proximal tubule glucotoxicity and a broad downregulation of apical transporters (including sodium, amino acid, and urate uptake), provides a metabolic foundation for kidney and cardiovascular protection.

A single-sample workflow for joint metabolomic and proteomic analysis of clinical specimens.

Understanding the interplay of the proteome and the metabolome helps to understand cellular regulation and response. To enable robust inferences from such multi-omics analyses, we introduced and evaluated a workflow for combined proteome and metabolome analysis starting from a single sample. Specifically, we integrated established and individually optimized protocols for metabolomic and proteomic profiling (EtOH/MTBE and autoSP3, respectively) into a unified workflow (termed MTBE-SP3), and took advantage of the fact that the protein residue of the metabolomic sample can be used as a direct input for proteome analysis. We particularly evaluated the performance of proteome analysis in MTBE-SP3, and demonstrated equivalence of proteome profiles irrespective of prior metabolite extraction. In addition, MTBE-SP3 combines the advantages of EtOH/MTBE and autoSP3 for semi-automated metabolite extraction and fully automated proteome sample preparation, respectively, thus advancing standardization and scalability for large-scale studies. We showed that MTBE-SP3 can be applied to various biological matrices (FFPE tissue, fresh-frozen tissue, plasma, serum and cells) to enable implementation in a variety of clinical settings. To demonstrate applicability, we applied MTBE-SP3 and autoSP3 to a lung adenocarcinoma cohort showing consistent proteomic alterations between tumour and non-tumour adjacent tissue independent of the method used. Integration with metabolomic data obtained from the same samples revealed mitochondrial dysfunction in tumour tissue through deregulation of OGDH, SDH family enzymes and PKM. In summary, MTBE-SP3 enables the facile and reliable parallel measurement of proteins and metabolites obtained from the same sample, benefiting from reduced sample variation and input amount. This workflow is particularly applicable for studies with limited sample availability and offers the potential to enhance the integration of metabolomic and proteomic datasets.

Multiomic profiling of breast cancer cells uncovers stress MAPK-associated sensitivity to AKT degradation.

More than 50% of human tumors display hyperactivation of the serine/threonine kinase AKT. Despite evidence of clinical efficacy, the therapeutic window of the current generation of AKT inhibitors could be improved. Here, we report the development of a second-generation AKT degrader, INY-05-040, which outperformed catalytic AKT inhibition with respect to cellular suppression of AKT-dependent phenotypes in breast cancer cell lines. A growth inhibition screen with 288 cancer cell lines confirmed that INY-05-040 had a substantially higher potency than our first-generation AKT degrader (INY-03-041), with both compounds outperforming catalytic AKT inhibition by GDC-0068. Using multiomic profiling and causal network integration in breast cancer cells, we demonstrated that the enhanced efficacy of INY-05-040 was associated with sustained suppression of AKT signaling, which was followed by induction of the stress mitogen-activated protein kinase (MAPK) c-Jun N-terminal kinase (JNK). Further integration of growth inhibition assays with publicly available transcriptomic, proteomic, and reverse phase protein array (RPPA) measurements established low basal JNK signaling as a biomarker for breast cancer sensitivity to AKT degradation. Together, our study presents a framework for mapping the network-wide signaling effects of therapeutically relevant compounds and identifies INY-05-040 as a potent pharmacological suppressor of AKT signaling.

Collaborative effect of Csnk1a1 haploinsufficiency and mutant p53 in Myc induction can promote leukemic transformation.

ABSTRACT: It is still not fully understood how genetic haploinsufficiency in del(5q) myelodysplastic syndrome (MDS) contributes to malignant transformation of hematopoietic stem cells. We asked how compound haploinsufficiency for Csnk1a1 and Egr1 in the common deleted region on chromosome 5 affects hematopoietic stem cells. Additionally, Trp53 was disrupted as the most frequently comutated gene in del(5q) MDS using CRISPR/Cas9 editing in hematopoietic progenitors of wild-type (WT), Csnk1a1-/+, Egr1-/+, Csnk1a1/Egr1-/+ mice. A transplantable acute leukemia only developed in the Csnk1a1-/+Trp53-edited recipient. Isolated blasts were indefinitely cultured ex vivo and gave rise to leukemia after transplantation, providing a tool to study disease mechanisms or perform drug screenings. In a small-scale drug screening, the collaborative effect of Csnk1a1 haploinsufficiency and Trp53 sensitized blasts to the CSNK1 inhibitor A51 relative to WT or Csnk1a1 haploinsufficient cells. In vivo, A51 treatment significantly reduced blast counts in Csnk1a1 haploinsufficient/Trp53 acute leukemias and restored hematopoiesis in the bone marrow. Transcriptomics on blasts and their normal counterparts showed that the derived leukemia was driven by MAPK and Myc upregulation downstream of Csnk1a1 haploinsufficiency cooperating with a downregulated p53 axis. A collaborative effect of Csnk1a1 haploinsufficiency and p53 loss on MAPK and Myc upregulation was confirmed on the protein level. Downregulation of Myc protein expression correlated with efficient elimination of blasts in A51 treatment. The "Myc signature" closely resembled the transcriptional profile of patients with del(5q) MDS with TP53 mutation.

Drug-target identification in COVID-19 disease mechanisms using computational systems biology approaches.

Introduction: The COVID-19 Disease Map project is a large-scale community effort uniting 277 scientists from 130 Institutions around the globe. We use high-quality, mechanistic content describing SARS-CoV-2-host interactions and develop interoperable bioinformatic pipelines for novel target identification and drug repurposing. Methods: Extensive community work allowed an impressive step forward in building interfaces between Systems Biology tools and platforms. Our framework can link biomolecules from omics data analysis and computational modelling to dysregulated pathways in a cell-, tissue- or patient-specific manner. Drug repurposing using text mining and AI-assisted analysis identified potential drugs, chemicals and microRNAs that could target the identified key factors. Results: Results revealed drugs already tested for anti-COVID-19 efficacy, providing a mechanistic context for their mode of action, and drugs already in clinical trials for treating other diseases, never tested against COVID-19. Discussion: The key advance is that the proposed framework is versatile and expandable, offering a significant upgrade in the arsenal for virus-host interactions and other complex pathologies.