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Background: Comorbidities are expected to impact the pathophysiology of heart failure (HF) with preserved ejection fraction (HFpEF). However, comorbidity profiles are usually reduced to a few comorbid disorders. Systems medicine approaches can model phenome-wide comorbidity profiles to improve our understanding of HFpEF and infer associated genetic profiles., Methods: We retrospectively explored 569 comorbidities in 29,047 HF patients, including 8062 HFpEF and 6585 HF with reduced ejection fraction (HFrEF) patients from a German university hospital. We assessed differences in comorbidity profiles between HF subtypes via multiple correspondence analysis. Then, we used machine learning classifiers to identify distinctive comorbidity profiles of HFpEF and HFrEF patients. Moreover, we built a comorbidity network (HFnet) to identify the main disease clusters that summarized the phenome-wide comorbidity. Lastly, we predicted novel gene candidates for HFpEF by linking the HFnet to a multilayer gene network, integrating multiple databases. To corroborate HFpEF candidate genes, we collected transcriptomic data in a murine HFpEF model. We compared predicted genes with the murine disease signature as well as with the literature., Results: We found a high degree of variance between the comorbidity profiles of HFpEF and HFrEF, while each was more similar to HFmrEF. The comorbidities present in HFpEF patients were more diverse than those in HFrEF and included neoplastic, osteologic and rheumatoid disorders. Disease communities in the HFnet captured important comorbidity concepts of HF patients which could be assigned to HF subtypes, age groups, and sex. Based on the HFpEF comorbidity profile, we predicted and recovered gene candidates, including genes involved in fibrosis (COL3A1, LOX, SMAD9, PTHL), hypertrophy (GATA5, MYH7), oxidative stress (NOS1, GSST1, XDH), and endoplasmic reticulum stress (ATF6). Finally, predicted genes were significantly overrepresented in the murine transcriptomic disease signature providing additional plausibility for their relevance., Conclusions: We applied systems medicine concepts to analyze comorbidity profiles in a HF patient cohort. We were able to identify disease clusters that helped to characterize HF patients. We derived a distinct comorbidity profile for HFpEF, which was leveraged to suggest novel candidate genes via network propagation. The identification of distinctive comorbidity profiles and candidate genes from routine clinical data provides insights that may be leveraged to improve diagnosis and identify treatment targets for HFpEF patients., (© 2023. The Author(s).)

Background: Macrophages play an important role in maintaining liver homeostasis and regeneration. However, it is not clear to what extent the different macrophage populations of the liver differ in terms of their activation state and which other liver cell populations may play a role in regulating the same., Methods: Reverse transcription PCR, flow cytometry, transcriptome, proteome, secretome, single cell analysis, and immunohistochemical methods were used to study changes in gene expression as well as the activation state of macrophages in vitro and in vivo under homeostatic conditions and after partial hepatectomy., Results: We show that F4/80+/CD11bhi/CD14hi macrophages of the liver are recruited in a C-C motif chemokine receptor (CCR2)-dependent manner and exhibit an activation state that differs substantially from that of the other liver macrophage populations, which can be distinguished on the basis of CD11b and CD14 expressions. Thereby, primary hepatocytes are capable of creating an environment in vitro that elicits the same specific activation state in bone marrow-derived macrophages as observed in F4/80+/CD11bhi/CD14hi liver macrophages in vivo. Subsequent analyses, including studies in mice with a myeloid cell-specific deletion of the TGF-β type II receptor, suggest that the availability of activated TGF-β and its downregulation by a hepatocyte-conditioned milieu are critical. Reduction of TGF-βRII-mediated signal transduction in myeloid cells leads to upregulation of IL-6, IL-10, and SIGLEC1 expression, a hallmark of the activation state of F4/80+/CD11bhi/CD14hi macrophages, and enhances liver regeneration., Conclusions: The availability of activated TGF-β determines the activation state of specific macrophage populations in the liver, and the observed rapid transient activation of TGF-β may represent an important regulatory mechanism in the early phase of liver regeneration in this context., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Association for the Study of Liver Diseases.)

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research., Competing Interests: JSR reports funding from GSK, Pfizer and Sanofi and fees from Travere Therapeutics and Astex Pharmaceuticals.

Chronic antigenic stimulation can trigger the differentiation of antigen-experienced CD4 + T cells into T regulatory type 1 (TR1) cells, a subset of interleukin-10-producing Treg cells that do not express FOXP3. The identities of the progenitor(s) and transcriptional regulators of this T-cell subset remain unclear. Here, we show that the peptide-major histocompatibility complex class II (pMHCII) monospecific immunoregulatory T-cell pools that arise in vivo in different genetic backgrounds in response to pMHCII-coated nanoparticles (pMHCII-NPs) are invariably comprised of oligoclonal subpools of T follicular helper (TFH) and TR1 cells with a nearly identical clonotypic composition but different functional properties and transcription factor expression profiles. Pseudotime analyses of scRNAseq data and multidimensional mass cytometry revealed progressive downregulation and upregulation of TFH and TR1 markers, respectively. Furthermore, pMHCII-NPs trigger cognate TR1 cell formation in TFH cell-transfused immunodeficient hosts, and T-cell-specific deletion of Bcl6 or Irf4 blunts both the TFH expansion and TR1 formation induced by pMHCII-NPs. In contrast, deletion of Prdm1 selectively abrogates the TFH-to-TR1 conversion. Bcl6 and Prdm1 are also necessary for anti-CD3 mAb-induced TR1 formation. Thus, TFH cells can differentiate into TR1 cells in vivo, and BLIMP1 is a gatekeeper of this cellular reprogramming event., (© 2023. The Author(s).)

In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. While multiple cell-cell communication tools exist, results are specific to the tool of choice, due to the diverse assumptions made across computational frameworks. Moreover, tools are often limited to analyzing single samples or to performing pairwise comparisons. As experimental design complexity and sample numbers continue to increase in single-cell datasets, so does the need for generalizable methods to decipher cell-cell communication in such scenarios. Here, we integrate two tools, LIANA and Tensor-cell2cell, which combined can deploy multiple existing methods and resources, to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this protocol, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step-by-step in both Python and R, and we provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This protocol typically takes ~1.5h to complete from installation to downstream visualizations on a GPU-enabled computer, for a dataset of ~63k cells, 10 cell types, and 12 samples., Competing Interests: JSR reports funding from GSK, Pfizer and Sanofi and fees from Travere Therapeutics, and Astex. During the course of this work, NEL reports funding from Sanofi, Amgen, Sartorius, and Ionis, and is a co-founder of NeuImmune Inc. and Augment Biologics.

Genetic alterations in cancer cells trigger oncogenic transformation, a process largely mediated by the dysregulation of kinase and transcription factor (TF) activities. While the mutational profiles of thousands of tumours have been extensively characterised, the measurements of protein activities have been technically limited until recently. We compiled public data of matched genomics and (phospho)proteomics measurements for 1,110 tumours and 77 cell lines that we used to estimate activity changes in 218 kinases and 292 TFs. Co-regulation of kinase and TF activities reflects previously known regulatory relationships and allows us to dissect genetic drivers of signalling changes in cancer. We find that loss-of-function mutations are not often associated with the dysregulation of downstream targets, suggesting frequent compensatory mechanisms. Finally, we identified the activities most differentially regulated in cancer subtypes and showed how these can be linked to differences in patient survival. Our results provide broad insights into the dysregulation of protein activities in cancer and their contribution to disease severity., (© 2023 The Authors. Published under the terms of the CC BY 4.0 license.)

Background: The biguanide drug metformin is a safe and widely prescribed drug for type 2 diabetes. Interestingly, hundreds of clinical trials have been set to evaluate the potential role of metformin in the prevention and treatment of cancer including colorectal cancer (CRC). However, the "metformin signaling" remains controversial., Aims and Methods: To interrogate cell signaling induced by metformin in CRC and explore the druggability of the metformin-rewired phosphorylation network, we performed integrative analysis of phosphoproteomics, bioinformatics, and cell proliferation assays on a panel of 12 molecularly heterogeneous CRC cell lines. Using the high-resolute data-independent analysis mass spectrometry (DIA-MS), we monitored a total of 10,142 proteins and 56,080 phosphosites (P-sites) in CRC cells upon a short- and a long-term metformin treatment., Results and Conclusions: We found that metformin tended to primarily remodel cell signaling in the long-term and only minimally regulated the total proteome expression levels. Strikingly, the phosphorylation signaling response to metformin was highly heterogeneous in the CRC panel, based on a network analysis inferring kinase/phosphatase activities and cell signaling reconstruction. A "MetScore" was determined to assign the metformin relevance of each P-site, revealing new and robust phosphorylation nodes and pathways in metformin signaling. Finally, we leveraged the metformin P-site signature to identify pharmacodynamic interactions and confirmed a number of candidate metformin-interacting drugs, including navitoclax, a BCL-2/BCL-xL inhibitor. Together, we provide a comprehensive phosphoproteomic resource to explore the metformin-induced cell signaling for potential cancer therapeutics. This resource can be accessed at https://yslproteomics.shinyapps.io/Metformin/., (© 2023 The Authors. Clinical and Translational Medicine published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.)

Metabolomic and proteomic analyses of human plasma and serum samples harbor the power to advance our understanding of disease biology. Pre-analytical factors may contribute to variability and bias in the detection of analytes, especially when multiple labs are involved, caused by sample handling, processing time, and differing operating procedures. To better understand the impact of pre-analytical factors that are relevant to implementing a unified proteomic and metabolomic approach in a clinical setting, we assessed the influence of temperature, sitting times, and centrifugation speed on the plasma and serum metabolomes and proteomes from six healthy volunteers. We used targeted metabolic profiling (497 metabolites) and data-independent acquisition (DIA) proteomics (572 proteins) on the same samples generated with well-defined pre-analytical conditions to evaluate criteria for pre-analytical SOPs for plasma and serum samples. Time and temperature showed the strongest influence on the integrity of plasma and serum proteome and metabolome. While rapid handling and low temperatures (4°C) are imperative for metabolic profiling, the analyzed proteomics data set showed variability when exposed to temperatures of 4°C for more than 2 h, highlighting the need for compromises in a combined analysis. We formalized a quality control scoring system to objectively rate sample stability and tested this score using external data sets from other pre-analytical studies. Stringent and harmonized standard operating procedures (SOPs) are required for pre-analytical sample handling when combining proteomics and metabolomics of clinical samples to yield robust and interpretable data on a longitudinal scale and across different clinics. To ensure an adequate level of practicability in a clinical routine for metabolomics and proteomics studies, we suggest keeping blood samples up to 2 h on ice (4°C) prior to snap-freezing as a compromise between stability and operability. Finally, we provide the methodology as an open-source R package allowing the systematic scoring of proteomics and metabolomics data sets to assess the stability of plasma and serum samples., Competing Interests: JS-R reports funding from GSK and Sanofi and fees from Travere Therapeutics and Astex Therapeutics. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Gegner, Naake, Dugourd, Müller, Czernilofsky, Kliewer, Jäger, Helm, Kunze-Rohrbach, Klingmüller, Hopf, Müller-Tidow, Dietrich, Saez-Rodriguez, Huber, Hell, Poschet and Krijgsveld.)

Metabolic reprogramming is critical for tumor initiation and progression. However, the exact impact of specific metabolic changes on cancer progression is poorly understood. Here, we integrate multimodal analyses of primary and metastatic clonally-related clear cell renal cancer cells (ccRCC) grown in physiological media to identify key stage-specific metabolic vulnerabilities. We show that a VHL loss-dependent reprogramming of branched-chain amino acid catabolism sustains the de novo biosynthesis of aspartate and arginine enabling tumor cells with the flexibility of partitioning the nitrogen of the amino acids depending on their needs. Importantly, we identify the epigenetic reactivation of argininosuccinate synthase (ASS1), a urea cycle enzyme suppressed in primary ccRCC, as a crucial event for metastatic renal cancer cells to acquire the capability to generate arginine, invade in vitro and metastasize in vivo. Overall, our study uncovers a mechanism of metabolic flexibility occurring during ccRCC progression, paving the way for the development of novel stage-specific therapies., (© 2022. The Author(s).)

Introduction: SARS-CoV-2 infection results in varying disease severity, ranging from asymptomatic infection to severe illness. A detailed understanding of the immune response to SARS-CoV-2 is critical to unravel the causative factors underlying differences in disease severity and to develop optimal vaccines against new SARS-CoV-2 variants., Methods: We combined single-cell RNA and T cell receptor sequencing with CITE-seq antibodies to characterize the CD8 + T cell response to SARS-CoV-2 infection at high resolution and compared responses between mild and severe COVID-19., Results: We observed increased CD8 + T cell exhaustion in severe SARS-CoV-2 infection and identified a population of NK-like, terminally differentiated CD8 + effector T cells characterized by expression of FCGR3A (encoding CD16). Further characterization of NK-like CD8 + T cells revealed heterogeneity among CD16 + NK-like CD8 + T cells and profound differences in cytotoxicity, exhaustion, and NK-like differentiation between mild and severe disease conditions., Discussion: We propose a model in which differences in the surrounding inflammatory milieu lead to crucial differences in NK-like differentiation of CD8 + effector T cells, ultimately resulting in the appearance of NK-like CD8 + T cell populations of different functionality and pathogenicity. Our in-depth characterization of the CD8 + T cell-mediated response to SARS-CoV-2 infection provides a basis for further investigation of the importance of NK-like CD8 + T cells in COVID-19 severity., Competing Interests: Author JS-R declares funding from GSK and Sanofi and consultant fees from Travere Therapeutics, Owkin and Astex Pharmaceuticals unrelated to this study. Author SH has funding from Novo Nordisk. Author RK has grants from Travere Therapeutics, Galapagos, Chugai, and Novo Nordisk and is a consultant or received honoraria from Bayer, Pfizer, Novo Nordisk, Lilly-Pharma and Gruenenthal. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Schreibing, Hannani, Kim, Nagai, Ticconi, Fewings, Bleckwehl, Begemann, Torow, Kuppe, Kurth, Kranz, Frank, Anslinger, Ziegler, Kraus, Enczmann, Balz, Windhofer, Balfanz, Kurts, Marx, Marx, Dreher, Schneider, Saez-Rodriguez, Costa, Hayat and Kramann.)

Therapeutic promotion of intestinal regeneration holds great promise, but defining the cellular mechanisms that influence tissue regeneration remains an unmet challenge. To gain insight into the process of mucosal healing, we longitudinally examined the immune cell composition during intestinal damage and regeneration. B cells were the dominant cell type in the healing colon, and single-cell RNA sequencing (scRNA-seq) revealed expansion of an IFN-induced B cell subset during experimental mucosal healing that predominantly located in damaged areas and associated with colitis severity. B cell depletion accelerated recovery upon injury, decreased epithelial ulceration, and enhanced gene expression programs associated with tissue remodeling. scRNA-seq from the epithelial and stromal compartments combined with spatial transcriptomics and multiplex immunostaining showed that B cells decreased interactions between stromal and epithelial cells during mucosal healing. Activated B cells disrupted the epithelial-stromal cross talk required for organoid survival. Thus, B cell expansion during injury impairs epithelial-stromal cell interactions required for mucosal healing, with implications for the treatment of IBD., Competing Interests: Declaration of interests E.J.V. has received research grants from F. Hoffmann-La Roche. C.E., L.L., and J.L. are scientific consultants for 10X Genomics Inc. J.S.-R. receives funding from GSK and Sanofi and consultant fees from Travere Therapeutics and Astex Pharmaceuticals., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Droplet microfluidics is a powerful tool for a variety of biological applications including single-cell genetics, antibody discovery and directed evolution. All these applications make use of genetic libraries, illustrating the difficulty of generating chemically distinct droplets for screening applications. This protocol describes our Braille Display valving platform for on-demand generation of droplets with different chemical contents (16 different reagents and combinations thereof), as well as sorting droplets with different chemical properties, on the basis of fluorescence signals. The Braille Display platform is compact, versatile and cost efficient (only ~US$1,000 on top of a standard droplet microfluidics setup). The procedure includes manufacturing of microfluidic chips, assembly of custom hardware, co-encapsulation of cells and drugs into droplets, fluorescence detection of readout signals and data analysis using shared, freely available LabVIEW and Python packages. As a first application, we demonstrate the complete workflow for screening cancer cell drug sensitivities toward 74 conditions. Furthermore, we describe here an assay enabling the normalization of the observed drug sensitivity to the number of cancer cells per droplet, which additionally increases the robustness of the system. As a second application, we also demonstrate the sorting of droplets according to enzymatic activity. The drug screening application can be completed within 2 d; droplet sorting takes ~1 d; and all preparatory steps for manufacturing molds, chips and setting up the Braille controller can be accomplished within 1 week., (© 2022. Springer Nature Limited.)

Multiple sclerosis (MS) is a multifocal and progressive inflammatory disease of the central nervous system (CNS). However, the compartmentalized pathology of the disease affecting various anatomical regions including gray and white matter and lack of appropriate disease models impede understanding of the disease. Utilizing single-nucleus RNA-sequencing and multiplex spatial RNA mapping, we generated an integrated transcriptomic map comprising leukocortical, cerebellar and spinal cord areas in normal and MS tissues that captures regional subtype diversity of various cell types with an emphasis on astrocytes and oligodendrocytes. While we found strong cross-regional diversity among glial subtypes in control tissue, regional signatures become more obscure in MS. This suggests that patterns of transcriptomic changes in MS are shared across regions and converge on specific pathways, especially those regulating cellular stress and immune activation. In addition, we found evidence that a subtype of white matter oligodendrocytes appearing across all three CNS regions adopt pro-remyelinating gene signatures in MS. In summary, our data suggest that cross-regional transcriptomic glial signatures overlap in MS, with different reactive glial cell types capable of either exacerbating or ameliorating pathology., (© 2022. The Author(s).)

Adult kidney organoids have been described as strictly tubular epithelia and termed tubuloids. While the cellular origin of tubuloids has remained elusive, here we report that they originate from a distinct CD24 + epithelial subpopulation. Long-term-cultured CD24 + cell-derived tubuloids represent a functional human kidney tubule. We show that kidney tubuloids can be used to model the most common inherited kidney disease, namely autosomal dominant polycystic kidney disease (ADPKD), reconstituting the phenotypic hallmark of this disease with cyst formation. Single-cell RNA sequencing of CRISPR-Cas9 gene-edited PKD1- and PKD2-knockout tubuloids and human ADPKD and control tissue shows similarities in upregulation of disease-driving genes. Furthermore, in a proof of concept, we demonstrate that tolvaptan, the only approved drug for ADPKD, has a significant effect on cyst size in tubuloids but no effect on a pluripotent stem cell-derived model. Thus, tubuloids are derived from a tubular epithelial subpopulation and represent an advanced system for ADPKD disease modeling., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Acute myeloid leukemia (AML) is a heterogeneous disease with variable patient responses to therapy. Selinexor, an inhibitor of nuclear export, has shown promising clinical activity for AML. To identify the molecular context for monotherapy sensitivity as well as rational drug combinations, we profile selinexor signaling responses using phosphoproteomics in primary AML patient samples and cell lines. Functional phosphosite scoring reveals that p53 function is required for selinexor sensitivity consistent with enhanced efficacy of selinexor in combination with the MDM2 inhibitor nutlin-3a. Moreover, combining selinexor with the AKT inhibitor MK-2206 overcomes dysregulated AKT-FOXO3 signaling in resistant cells, resulting in synergistic anti-proliferative effects. Using high-throughput spatial proteomics to profile subcellular compartments, we measure global proteome and phospho-proteome dynamics, providing direct evidence of nuclear translocation of FOXO3 upon combination treatment. Our data demonstrate the potential of phosphoproteomics and functional phosphorylation site scoring to successfully pinpoint key targetable signaling hubs for rational drug combinations., Competing Interests: Declaration of interests C.W., H.N., K.M., and P.B.-J. are full-time employees and hold equity in Acrivon Therapeutics. J.V.O. and J.S.-R. have funding from Acrivon Therapeutics. J.S.-R. has received funding from GSK and Sanofi and consultant fees from Travere Therapeutics and Astex Pharmaceuticals. J.V.O. is a co-founder of Acrivon Therapeutics and a member of its scientific advisory board., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Myocardial infarction is a leading cause of death worldwide 1 . Although advances have been made in acute treatment, an incomplete understanding of remodelling processes has limited the effectiveness of therapies to reduce late-stage mortality 2 . Here we generate an integrative high-resolution map of human cardiac remodelling after myocardial infarction using single-cell gene expression, chromatin accessibility and spatial transcriptomic profiling of multiple physiological zones at distinct time points in myocardium from patients with myocardial infarction and controls. Multi-modal data integration enabled us to evaluate cardiac cell-type compositions at increased resolution, yielding insights into changes of the cardiac transcriptome and epigenome through the identification of distinct tissue structures of injury, repair and remodelling. We identified and validated disease-specific cardiac cell states of major cell types and analysed them in their spatial context, evaluating their dependency on other cell types. Our data elucidate the molecular principles of human myocardial tissue organization, recapitulating a gradual cardiomyocyte and myeloid continuum following ischaemic injury. In sum, our study provides an integrative molecular map of human myocardial infarction, represents an essential reference for the field and paves the way for advanced mechanistic and therapeutic studies of cardiac disease., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, and therapeutic options for advanced HCC are limited. Here, we observe that intestinal dysbiosis affects antitumor immune surveillance and drives liver disease progression towards cancer. Dysbiotic microbiota, as seen in Nlrp6 -/- mice, induces a Toll-like receptor 4 dependent expansion of hepatic monocytic myeloid-derived suppressor cells (mMDSC) and suppression of T-cell abundance. This phenotype is transmissible via fecal microbiota transfer and reversible upon antibiotic treatment, pointing to the high plasticity of the tumor microenvironment. While loss of Akkermansia muciniphila correlates with mMDSC abundance, its reintroduction restores intestinal barrier function and strongly reduces liver inflammation and fibrosis. Cirrhosis patients display increased bacterial abundance in hepatic tissue, which induces pronounced transcriptional changes, including activation of fibro-inflammatory pathways as well as circuits mediating cancer immunosuppression. This study demonstrates that gut microbiota closely shapes the hepatic inflammatory microenvironment opening approaches for cancer prevention and therapy., (© 2022. The Author(s).)

Signal transduction governs cellular behavior, and its dysregulation often leads to human disease. To understand this process, we can use network models based on prior knowledge, where nodes represent biomolecules, usually proteins, and edges indicate interactions between them. Several computational methods combine untargeted omics data with prior knowledge to estimate the state of signaling networks in specific biological scenarios. Here, we review, compare, and classify recent network approaches according to their characteristics in terms of input omics data, prior knowledge and underlying methodologies. We highlight existing challenges in the field, such as the general lack of ground truth and the limitations of prior knowledge. We also point out new omics developments that may have a profound impact, such as single-cell proteomics or large-scale profiling of protein conformational changes. We provide both an introduction for interested users seeking strategies to study cell signaling on a large scale and an update for seasoned modelers., (© 2022 The Authors. Published under the terms of the CC BY 4.0 license.)

The growing availability of single-cell data, especially transcriptomics, has sparked an increased interest in the inference of cell-cell communication. Many computational tools were developed for this purpose. Each of them consists of a resource of intercellular interactions prior knowledge and a method to predict potential cell-cell communication events. Yet the impact of the choice of resource and method on the resulting predictions is largely unknown. To shed light on this, we systematically compare 16 cell-cell communication inference resources and 7 methods, plus the consensus between the methods' predictions. Among the resources, we find few unique interactions, a varying degree of overlap, and an uneven coverage of specific pathways and tissue-enriched proteins. We then examine all possible combinations of methods and resources and show that both strongly influence the predicted intercellular interactions. Finally, we assess the agreement of cell-cell communication methods with spatial colocalisation, cytokine activities, and receptor protein abundance and find that predictions are generally coherent with those data modalities. To facilitate the use of the methods and resources described in this work, we provide LIANA, a LIgand-receptor ANalysis frAmework as an open-source interface to all the resources and methods., (© 2022. The Author(s).)

Elevated production of collagen-rich extracellular matrix is a hallmark of cancer-associated fibroblasts (CAFs) and a central driver of cancer aggressiveness. Here we find that proline, a highly abundant amino acid in collagen proteins, is newly synthesized from glutamine in CAFs to make tumour collagen in breast cancer xenografts. PYCR1 is a key enzyme for proline synthesis and highly expressed in the stroma of breast cancer patients and in CAFs. Reducing PYCR1 levels in CAFs is sufficient to reduce tumour collagen production, tumour growth and metastatic spread in vivo and cancer cell proliferation in vitro. Both collagen and glutamine-derived proline synthesis in CAFs are epigenetically upregulated by increased pyruvate dehydrogenase-derived acetyl-CoA levels. PYCR1 is a cancer cell vulnerability and potential target for therapy; therefore, our work provides evidence that targeting PYCR1 may have the additional benefit of halting the production of a pro-tumorigenic extracellular matrix. Our work unveils new roles for CAF metabolism to support pro-tumorigenic collagen production., (© 2022. The Author(s).)

In diabetic patients, dyslipidemia frequently contributes to organ damage such as diabetic kidney disease (DKD). Dyslipidemia is associated with both excessive deposition of triacylglycerol (TAG) in lipid droplets (LDs) and lipotoxicity. Yet, it is unclear how these two effects correlate with each other in the kidney and how they are influenced by dietary patterns. By using a diabetes mouse model, we find here that high-fat diet enriched in the monounsaturated oleic acid (OA) caused more lipid storage in LDs in renal proximal tubular cells (PTCs) but less tubular damage than a corresponding butter diet with the saturated palmitic acid (PA). This effect was particularly evident in S2/S3 but not S1 segments of the proximal tubule. Combining transcriptomics, lipidomics, and functional studies, we identify endoplasmic reticulum (ER) stress as the main cause of PA-induced PTC injury. Mechanistically, ER stress is caused by elevated levels of saturated TAG precursors, reduced LD formation, and, consequently, higher membrane order in the ER. Simultaneous addition of OA rescues the cytotoxic effects by normalizing membrane order and increasing both TAG and LD formation. Our study thus emphasizes the importance of monounsaturated fatty acids for the dietary management of DKD by preventing lipid bilayer stress in the ER and promoting TAG and LD formation in PTCs., Competing Interests: AP, SR, JL, AD, MM, LD, KG, AM, MP, SL, MS No competing interests declared, JS has received funding from GSK and Sanofi and consultant fees from Travere Therapeutics, (© 2022, Pérez-Martí et al.)

Increasing evidence points towards the key role of the epithelium in the systemic and over-activated immune response to viral infection, including SARS-CoV-2 infection. Yet, how viral infection alters epithelial-immune cell interactions regulating inflammatory responses, is not well known. Available experimental approaches are insufficient to properly analyse this complex system, and computational predictions and targeted data integration are needed as an alternative approach. In this work, we propose an integrated computational biology framework that models how infection alters intracellular signalling of epithelial cells and how this change impacts the systemic immune response through modified interactions between epithelial cells and local immune cell populations. As a proof-of-concept, we focused on the role of intestinal and upper-airway epithelial infection. To characterise the modified epithelial-immune interactome, we integrated intra- and intercellular networks with single-cell RNA-seq data from SARS-CoV-2 infected human ileal and colonic organoids as well as from infected airway ciliated epithelial cells. This integrated methodology has proven useful to point out specific epithelial-immune interactions driving inflammation during disease response, and propose relevant molecular targets to guide focused experimental analysis., (© 2022. The Author(s).)

The advancement of highly multiplexed spatial technologies requires scalable methods that can leverage spatial information. We present MISTy, a flexible, scalable, and explainable machine learning framework for extracting relationships from any spatial omics data, from dozens to thousands of measured markers. MISTy builds multiple views focusing on different spatial or functional contexts to dissect different effects. We evaluated MISTy on in silico and breast cancer datasets measured by imaging mass cytometry and spatial transcriptomics. We estimated structural and functional interactions coming from different spatial contexts in breast cancer and demonstrated how to relate MISTy's results to clinical features., (© 2022. The Author(s).)

Comparing SARS-CoV-2 infection-induced gene expression signatures to drug treatment-induced gene expression signatures is a promising bioinformatic tool to repurpose existing drugs against SARS-CoV-2. The general hypothesis of signature-based drug repurposing is that drugs with inverse similarity to a disease signature can reverse disease phenotype and thus be effective against it. However, in the case of viral infection diseases, like SARS-CoV-2, infected cells also activate adaptive, antiviral pathways, so that the relationship between effective drug and disease signature can be more ambiguous. To address this question, we analysed gene expression data from in vitro SARS-CoV-2 infected cell lines, and gene expression signatures of drugs showing anti-SARS-CoV-2 activity. Our extensive functional genomic analysis showed that both infection and treatment with in vitro effective drugs leads to activation of antiviral pathways like NFkB and JAK-STAT. Based on the similarity-and not inverse similarity-between drug and infection-induced gene expression signatures, we were able to predict the in vitro antiviral activity of drugs. We also identified SREBF1/2, key regulators of lipid metabolising enzymes, as the most activated transcription factors by several in vitro effective antiviral drugs. Using a fluorescently labeled cholesterol sensor, we showed that these drugs decrease the cholesterol levels of plasma-membrane. Supplementing drug-treated cells with cholesterol reversed the in vitro antiviral effect, suggesting the depleting plasma-membrane cholesterol plays a key role in virus inhibitory mechanism. Our results can help to more effectively repurpose approved drugs against SARS-CoV-2, and also highlights key mechanisms behind their antiviral effect., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: JSR reports funding from GSK and Sanofi and fees from Travere Therapeutics and Astex. BS is a full time employee of Turbine Ltd., Budapest, Hungary.

Motivation: Omics data are broadly used to get a snapshot of the molecular status of cells. In particular, changes in omics can be used to estimate the activity of pathways, transcription factors and kinases based on known regulated targets, that we call footprints. Then the molecular paths driving these activities can be estimated using causal reasoning on large signalling networks., Results: We have developed FUNKI, a FUNctional toolKIt for footprint analysis. It provides a user-friendly interface for an easy and fast analysis of transcriptomics, phosphoproteomics and metabolomics data, either from bulk or single-cell experiments. FUNKI also features different options to visualize the results and run post-analyses, and is mirrored as a scripted version in R., Availability and Implementation: FUNKI is a free and open-source application built on R and Shiny, available at https://github.com/saezlab/ShinyFUNKI and https://saezlab.shinyapps.io/funki/., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press.)

Aims: Atherosclerotic plaque hypoxia is detrimental for macrophage function. Prolyl hydroxylases (PHDs) initiate cellular hypoxic responses, possibly influencing macrophage function in plaque hypoxia. Thus, we aimed to elucidate the role of myeloid PHDs in atherosclerosis., Methods and Results: Myeloid-specific PHD knockout (PHDko) mice were obtained via bone marrow transplantation (PHD1ko, PHD3ko) or conditional knockdown through lysozyme M-driven Cre recombinase (PHD2cko). Mice were fed high cholesterol diet for 6-12 weeks to induce atherosclerosis. Aortic root plaque size was significantly augmented 2.6-fold in PHD2cko, and 1.4-fold in PHD3ko compared to controls but was unchanged in PHD1ko mice. Macrophage apoptosis was promoted in PHD2cko and PHD3ko mice in vitro and in vivo, via the hypoxia-inducible factor (HIF) 1α/BNIP3 axis. Bulk and single-cell RNA data of PHD2cko bone marrow-derived macrophages (BMDMs) and plaque macrophages, respectively, showed enhanced HIF1α/BNIP3 signalling, which was validated in vitro by siRNA silencing. Human plaque BNIP3 mRNA was positively associated with plaque necrotic core size, suggesting similar pro-apoptotic effects in human. Furthermore, PHD2cko plaques displayed enhanced fibrosis, while macrophage collagen breakdown by matrix metalloproteinases, collagen production, and proliferation were unaltered. Instead, PHD2cko BMDMs enhanced fibroblast collagen secretion in a paracrine manner. In silico analysis of macrophage-fibroblast communication predicted SPP1 (osteopontin) signalling as regulator, which was corroborated by enhanced plaque SPP1 protein in vivo. Increased SPP1 mRNA expression upon PHD2cko was preferentially observed in foamy plaque macrophages expressing 'triggering receptor expressed on myeloid cells-2' (TREM2hi) evidenced by single-cell RNA, but not in neutrophils. This confirmed enhanced fibrotic signalling by PHD2cko macrophages to fibroblasts, in vitro as well as in vivo., Conclusion: Myeloid PHD2cko and PHD3ko enhanced atherosclerotic plaque growth and macrophage apoptosis, while PHD2cko macrophages further activated collagen secretion by fibroblasts in vitro, likely via paracrine SPP1 signalling through TREM2hi macrophages., (© The Author(s) 2021. Published by Oxford University Press on behalf of the European Society of Cardiology.)

The field of single-cell technologies, in particular single-cell genomics with transcriptomics and epigenomics, and most recently single-cell proteomics, is rapidly growing and holds promise to advance our understanding of organ homoeostasis and disease, and facilitate the identification of novel therapeutic targets and biomarkers. This review offers an introduction to these technologies. In addition, as the size and complexity of the data require sophisticated computational methods for analysis and interpretation, we will also provide an overview of these methods and summarize the single-cell literature specifically pertaining to the kidney., (© The Author(s) 2020. Published by Oxford University Press on behalf of the ERA.)

Summary: Many methods allow us to extract biological activities from omics data using information from prior knowledge resources, reducing the dimensionality for increased statistical power and better interpretability. Here, we present decoupleR, a Bioconductor and Python package containing computational methods to extract these activities within a unified framework. decoupleR allows us to flexibly run any method with a given resource, including methods that leverage mode of regulation and weights of interactions, which are not present in other frameworks. Moreover, it leverages OmniPath, a meta-resource comprising over 100 databases of prior knowledge. Using decoupleR, we evaluated the performance of methods on transcriptomic and phospho-proteomic perturbation experiments. Our findings suggest that simple linear models and the consensus score across top methods perform better than other methods at predicting perturbed regulators., Availability and Implementation: decoupleR's open-source code is available in Bioconductor (https://www.bioconductor.org/packages/release/bioc/html/decoupleR.html) for R and in GitHub (https://github.com/saezlab/decoupler-py) for Python. The code to reproduce the results is in GitHub (https://github.com/saezlab/decoupleR_manuscript) and the data in Zenodo (https://zenodo.org/record/5645208)., Supplementary Information: Supplementary data are available at Bioinformatics Advances online., (© The Author(s) 2022. Published by Oxford University Press.)

Extrapulmonary manifestations of COVID-19 have gained attention due to their links to clinical outcomes and their potential long-term sequelae 1 . Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) displays tropism towards several organs, including the heart and kidney. Whether it also directly affects the liver has been debated 2,3 . Here we provide clinical, histopathological, molecular and bioinformatic evidence for the hepatic tropism of SARS-CoV-2. We find that liver injury, indicated by a high frequency of abnormal liver function tests, is a common clinical feature of COVID-19 in two independent cohorts of patients with COVID-19 requiring hospitalization. Using autopsy samples obtained from a third patient cohort, we provide multiple levels of evidence for SARS-CoV-2 liver tropism, including viral RNA detection in 69% of autopsy liver specimens, and successful isolation of infectious SARS-CoV-2 from liver tissue postmortem. Furthermore, we identify transcription-, proteomic- and transcription factor-based activity profiles in hepatic autopsy samples, revealing similarities to the signatures associated with multiple other viral infections of the human liver. Together, we provide a comprehensive multimodal analysis of SARS-CoV-2 liver tropism, which increases our understanding of the molecular consequences of severe COVID-19 and could be useful for the identification of organ-specific pharmacological targets., (© 2022. The Author(s).)

Hypoxia-inducible transcription factor 1 (HIF-1) has been shown to enhance microbial killing and ameliorate the course of bacterial infections. While the impact of HIF-1 on inflammatory diseases of the gut has been studied intensively, its function in bacterial infections of the gastrointestinal tract remains largely elusive. With the help of a publicly available gene expression data set, we inferred significant activation of HIF-1 after oral infection of mice with Salmonella enterica serovar Typhimurium. Immunohistochemistry and Western blot analyses confirmed marked HIF-1α protein stabilization, especially in the intestinal epithelium. This prompted us to analyze conditional Hif1a -deficient mice to examine cell type-specific functions of HIF-1 in this model. Our results demonstrate enhanced noncanonical induction of HIF-1 activity upon Salmonella infection in the intestinal epithelium as well as in macrophages. Surprisingly, Hif1a deletion in intestinal epithelial cells did not impact inflammatory gene expression, bacterial spread, or disease outcomes. In contrast, Hif1a deletion in myeloid cells enhanced intestinal Cxcl2 expression and reduced the cecal Salmonella load. In vitro , HIF-1α-deficient macrophages showed overall impaired transcription of mRNA encoding proinflammatory factors; however, the intracellular survival of Salmonella was not impacted by HIF-1α deficiency.

Prostate cancer is the second most occurring cancer in men worldwide. To better understand the mechanisms of tumorigenesis and possible treatment responses, we developed a mathematical model of prostate cancer which considers the major signalling pathways known to be deregulated. We personalised this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients. A total of 488 prostate samples were used to build patient-specific models and compared to available clinical data. Additionally, eight prostate cell line-specific models were built to validate our approach with dose-response data of several drugs. The effects of single and combined drugs were tested in these models under different growth conditions. We identified 15 actionable points of interventions in one cell line-specific model whose inactivation hinders tumorigenesis. To validate these results, we tested nine small molecule inhibitors of five of those putative targets and found a dose-dependent effect on four of them, notably those targeting HSP90 and PI3K. These results highlight the predictive power of our personalised Boolean models and illustrate how they can be used for precision oncology., Competing Interests: AM, JB, PT, BS, EB, LC No competing interests declared, LT is a full-time employee and shareholder of AstraZeneca, VS is a full-time employee of AstraZeneca, RA is CEO of Astridbio Technologies Ltd, LP is a scientific advisor of Astridbio Technologies Ltd, AV Reviewing editor, eLife, JS reports funding from GSK and Sanofi and fees from Travere Therapeutics and Astex, (© 2022, Montagud et al.)

The intestinal barrier is composed of a complex cell network defining highly compartmentalized and specialized structures. Here, we use spatial transcriptomics to define how the transcriptomic landscape is spatially organized in the steady state and healing murine colon. At steady state conditions, we demonstrate a previously unappreciated molecular regionalization of the colon, which dramatically changes during mucosal healing. Here, we identified spatially-organized transcriptional programs defining compartmentalized mucosal healing, and regions with dominant wired pathways. Furthermore, we showed that decreased p53 activation defined areas with increased presence of proliferating epithelial stem cells. Finally, we mapped transcriptomics modules associated with human diseases demonstrating the translational potential of our dataset. Overall, we provide a publicly available resource defining principles of transcriptomic regionalization of the colon during mucosal healing and a framework to develop and progress further hypotheses., (© 2022. The Author(s).)

Mutations in the Melanoma-Associated Antigen D2 (MAGED2) cause antenatal Bartter syndrome type 5 (BARTS5). This rare disease is characterized by perinatal loss of urinary concentration capability and large urine volumes. The underlying molecular mechanisms of this disease are largely unclear. Here, we study the effect of MAGED2 knockdown on kidney cell cultures using proteomic and phosphoproteomic analyses. In HEK293T cells, MAGED2 knockdown induces prominent changes in protein phosphorylation rather than changes in protein abundance. MAGED2 is expressed in mouse embryonic kidneys and its expression declines during development. MAGED2 interacts with G-protein alpha subunit (GNAS), suggesting a role in G-protein coupled receptors (GPCR) signalling. In kidney collecting duct cell lines, Maged2 knockdown subtly modulated vasopressin type 2 receptor (V2R)-induced cAMP-generation kinetics, rewired phosphorylation-dependent signalling, and phosphorylation of CREB. Maged2 knockdown resulted in a large increase in aquaporin-2 abundance during long-term V2R activation. The increase in aquaporin-2 protein was mediated transcriptionally. Taken together, we link MAGED2 function to cellular signalling as a desensitizer of V2R-induced aquaporin-2 expression. SIGNIFICANCE: In most forms of Bartter Syndrome, the underlying cause of the disease is well understood. In contrast, the role of MAGED2 mutations in a newly discovered form of Bartter Syndrome (BARTS5) is unknown. In our manuscript we could show that MAGED2 modulates vasopressin-induced protein and phosphorylation patterns in kidney cells, providing a broad basis for further studies of MAGED2 function in development and disease., (Copyright © 2021. Published by Elsevier B.V.)

The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with ∼400 clinical oncology drugs, to study a tumor-specific drug mechanism of action. Here, this resource serves as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. Dose-response and perturbational profiles for 32 kinase inhibitors are provided to 21 teams who are blind to the identity of the compounds. The teams are asked to predict high-affinity binding targets of each compound among ∼1,300 targets cataloged in DrugBank. The best performing methods leverage gene expression profile similarity analysis as well as deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessments of drug mechanisms of action., Competing Interests: A.C. is founder, equity holder, and consultant of DarwinHealth, Inc., a company that has licensed the PANACEA database used in this manuscript from Columbia University. Columbia University is also an equity holder in DarwinHealth, Inc. J.S.-R. has received funding from GSK and Sanofi and personal fees from Travere Therapeutics. B.S. received consultation fees from Turbine, Ltd. All other authors declare no competing interests., (© 2021.)

Mouse models are frequently used to study chronic liver diseases (CLDs). To assess their translational relevance, we quantified the similarity of commonly used mouse models to human CLDs based on transcriptome data. Gene-expression data from 372 patients were compared with data from acute and chronic mouse models consisting of 227 mice, and additionally to nine published gene sets of chronic mouse models. Genes consistently altered in humans and mice were mapped to liver cell types based on single-cell RNA-sequencing data and validated by immunostaining. Considering the top differentially expressed genes, the similarity between humans and mice varied among the mouse models and depended on the period of damage induction. The highest recall (0.4) and precision (0.33) were observed for the model with 12-months damage induction by CCl 4 and by a Western diet, respectively. Genes consistently up-regulated between the chronic CCl 4 model and human CLDs were enriched in inflammatory and developmental processes, and mostly mapped to cholangiocytes, macrophages, and endothelial and mesenchymal cells. Down-regulated genes were enriched in metabolic processes and mapped to hepatocytes. Immunostaining confirmed the regulation of selected genes and their cell type specificity. Genes that were up-regulated in both acute and chronic models showed higher recall and precision with respect to human CLDs than exclusively acute or chronic genes. Conclusion: Similarly regulated genes in human and mouse CLDs were identified. Despite major interspecies differences, mouse models detected 40% of the genes significantly altered in human CLD. The translational relevance of individual genes can be assessed at https://saezlab.shinyapps.io/liverdiseaseatlas/., (© 2021 The Authors. Hepatology Communications published by Wiley Periodicals LLC on behalf of American Association for the Study of Liver Diseases.)

Mechanism-based risk assessment is urged to advance and fully permeate into current safety assessment practices, possibly at early phases of drug safety testing. Toxicogenomics is a promising source of mechanisms-revealing data, but interpretative analysis tools specific for the testing systems (e.g. hepatocytes) are lacking. In this study, we present the TXG-MAPr webtool (available at https://txg-mapr.eu/WGCNA_PHH/TGGATEs_PHH/ ), an R-Shiny-based implementation of weighted gene co-expression network analysis (WGCNA) obtained from the Primary Human Hepatocytes (PHH) TG-GATEs dataset. The 398 gene co-expression networks (modules) were annotated with functional information (pathway enrichment, transcription factor) to reveal their mechanistic interpretation. Several well-known stress response pathways were captured in the modules, were perturbed by specific stressors and showed preservation in rat systems (rat primary hepatocytes and rat in vivo liver), with the exception of DNA damage and oxidative stress responses. A subset of 87 well-annotated and preserved modules was used to evaluate mechanisms of toxicity of endoplasmic reticulum (ER) stress and oxidative stress inducers, including cyclosporine A, tunicamycin and acetaminophen. In addition, module responses can be calculated from external datasets obtained with different hepatocyte cells and platforms, including targeted RNA-seq data, therefore, imputing biological responses from a limited gene set. As another application, donors' sensitivity towards tunicamycin was investigated with the TXG-MAPr, identifying higher basal level of intrinsic immune response in donors with pre-existing liver pathology. In conclusion, we demonstrated that gene co-expression analysis coupled to an interactive visualization environment, the TXG-MAPr, is a promising approach to achieve mechanistic relevant, cross-species and cross-platform evaluation of toxicogenomic data., (© 2021. The Author(s).)

Computational models of biological systems can exploit a broad range of rapidly developing approaches, including novel experimental approaches, bioinformatics data analysis, emerging modelling paradigms, data standards and algorithms. A discussion about the most recent advances among experts from various domains is crucial to foster data-driven computational modelling and its growing use in assessing and predicting the behaviour of biological systems. Intending to encourage the development of tools, approaches and predictive models, and to deepen our understanding of biological systems, the Community of Special Interest (COSI) was launched in Computational Modelling of Biological Systems (SysMod) in 2016. SysMod's main activity is an annual meeting at the Intelligent Systems for Molecular Biology (ISMB) conference, which brings together computer scientists, biologists, mathematicians, engineers, computational and systems biologists. In the five years since its inception, SysMod has evolved into a dynamic and expanding community, as the increasing number of contributions and participants illustrate. SysMod maintains several online resources to facilitate interaction among the community members, including an online forum, a calendar of relevant meetings and a YouTube channel with talks and lectures of interest for the modelling community. For more than half a decade, the growing interest in computational systems modelling and multi-scale data integration has inspired and supported the SysMod community. Its members get progressively more involved and actively contribute to the annual COSI meeting and several related community workshops and meetings, focusing on specific topics, including particular techniques for computational modelling or standardisation efforts., (© The Author(s) 2021. Published by Oxford University Press.)

In recent years, network-based methods have become an attractive analytical approach for toxicogenomics studies. They can capture not only the global changes of regulatory gene networks but also the relationships between their components. Among them, a causal reasoning approach depicts the mechanisms of regulation that connect upstream regulators in signaling networks to their downstream gene targets. In this work, we applied CARNIVAL, a causal network contextualisation tool, to infer upstream signaling networks deregulated in drug-induced liver injury (DILI) from gene expression microarray data from the TG-GATEs database. We focussed on six compounds that induce observable histopathologies linked to DILI from repeated dosing experiments in rats. We compared responses in vitro and in vivo to identify potential cross-platform concordances in rats as well as network preservations between rat and human. Our results showed similarities of enriched pathways and network motifs between compounds. These pathways and motifs induced the same pathology in rats but not in humans. In particular, the causal interactions "LCK activates SOCS3, which in turn inhibits TFDP1" was commonly identified as a regulatory path among the fibrosis-inducing compounds. This potential pathology-inducing regulation illustrates the value of our approach to generate hypotheses that can be further validated experimentally., Competing Interests: Declaration of Competing Interest JSR received funding from GSK and Sanofi and consultant fees from Travere Therapeutics., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

Prostaglandin E 2 (PGE 2 ) promotes an immunosuppressive microenvironment in cancer, partly by signaling through four receptors (EP 1 , EP 2 , EP 3 , and EP 4 ) on T cells. Here, we comprehensively characterized PGE 2 signaling networks in helper, cytotoxic, and regulatory T cells using a phosphoproteomics and phosphoflow cytometry approach. We identified ~1500 PGE 2 -regulated phosphosites and several important EP 1–4 signaling nodes, including PKC, CK2, PKA, PI3K, and Src. T cell subtypes exhibited distinct signaling pathways, with the strongest signaling in EP 2 -stimulated CD8 + cells. EP 2 and EP 4 , both of which signal through G αs , induced similar signaling outputs, but with distinct kinetics and intensity. Functional predictions from the observed phosphosite changes revealed PGE 2 regulation of key cellular and immunological processes. Last, network modeling suggested signal integration between the receptors and a substantial contribution from G protein–independent signaling. This study offers a comprehensive view of the different PGE 2 -regulated phosphoproteomes in T cell subsets, providing a valuable resource for further research on this physiologically and pathophysiologically important signaling system.

IgA nephropathy (IgAN) is the most common glomerulonephritis. It is characterized by the deposition of immune complexes containing immunoglobulin A (IgA) in the kidney's glomeruli, triggering an inflammatory process. In many patients, the disease has a progressive course, eventually leading to end-stage kidney disease. The current understanding of IgAN's pathophysiology is incomplete, with the involvement of several potential players, including the mucosal immune system, the complement system, and the microbiome. Dissecting this complex pathophysiology requires an integrated analysis across molecular, cellular, and organ scales. Such data can be obtained by employing emerging technologies, including single-cell sequencing, next-generation sequencing, proteomics, and complex imaging approaches. These techniques generate complex "big data," requiring advanced computational methods for their analyses and interpretation. Here, we introduce such methods, focusing on the broad areas of bioinformatics and artificial intelligence and discuss how they can advance our understanding of IgAN and ultimately improve patient care. The close integration of advanced experimental and computational technologies with medical and clinical expertise is essential to improve our understanding of human diseases. We argue that IgAN is a paradigmatic disease to demonstrate the value of such a multidisciplinary approach., (© 2021. The Author(s).)

Recent technological developments allow us to measure the status of dozens of proteins in individual cells. This opens the way to understand the heterogeneity of complex multi-signaling networks across cells and cell types, with important implications to understand and treat diseases such as cancer. These technologies are, however, limited to proteins for which antibodies are available and are fairly costly, making predictions of new markers and of existing markers under new conditions a valuable alternative. To assess our capacity to make such predictions and boost further methodological development, we organized the Single Cell Signaling in Breast Cancer DREAM challenge. We used a mass cytometry dataset, covering 36 markers in over 4,000 conditions totaling 80 million single cells across 67 breast cancer cell lines. Through four increasingly difficult subchallenges, the participants predicted missing markers, new conditions, and the time-course response of single cells to stimuli in the presence and absence of kinase inhibitors. The challenge results show that despite the stochastic nature of signal transduction in single cells, the signaling events are tightly controlled and machine learning methods can accurately predict new experimental data., (© 2021 The Authors. Published under the terms of the CC BY 4.0 license.)

We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective., (© 2021 The Authors. Published under the terms of the CC BY 4.0 license.)

Detailed maps of the molecular basis of the disease are powerful tools for interpreting data and building predictive models. Modularity and composability are considered necessary network features for large-scale collaborative efforts to build comprehensive molecular descriptions of disease mechanisms. An effective way to create and manage large systems is to compose multiple subsystems. Composable network components could effectively harness the contributions of many individuals and enable teams to seamlessly assemble many individual components into comprehensive maps. We examine manually built versions of the RAS-RAF-MEK-ERK cascade from the Atlas of Cancer Signalling Network, PANTHER and Reactome databases and review them in terms of their reusability and composability for assembling new disease models. We identify design principles for managing complex systems that could make it easier for investigators to share and reuse network components. We demonstrate the main challenges including incompatible levels of detail and ambiguous representation of complexes and highlight the need to address these challenges., (© The Author(s) 2021. Published by Oxford University Press.)

Background: The protein 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase-3 (PFKFB3) is a key stimulator of glycolytic flux. Systemic, partial PFKFB3 inhibition previously decreased total plaque burden and increased plaque stability. However, it is unclear which cell type conferred these positive effects. Myeloid cells play an important role in atherogenesis, and mainly rely on glycolysis for energy supply. Thus, we studied whether myeloid inhibition of PFKFB3-mediated glycolysis in Ldlr -/- LysMCre +/- Pfkfb3 fl/fl ( Pfkfb3 fl/fl ) mice confers beneficial effects on plaque stability and alleviates cardiovascular disease burden compared to Ldlr -/- LysMCre +/- Pfkfb3 wt/wt control mice ( Pfkfb3 wt/wt )., Methods and Results: Analysis of atherosclerotic human and murine single-cell populations confirmed PFKFB3/Pfkfb3 expression in myeloid cells, but also in lymphocytes, endothelial cells, fibroblasts and smooth muscle cells. Pfkfb3 wt/wt and Pfkfb3 fl/fl mice were fed a 0.25% cholesterol diet for 12 weeks. Pfkfb3 fl/fl bone marrow-derived macrophages (BMDMs) showed 50% knockdown of Pfkfb3 mRNA. As expected based on partial glycolysis inhibition, extracellular acidification rate as a measure of glycolysis was partially reduced in Pfkfb3 fl/fl compared to Pfkfb3 wt/wt BMDMs. Unexpectedly, plaque and necrotic core size, as well as macrophage (MAC3), neutrophil (Ly6G) and collagen (Sirius Red) content were unchanged in advanced Pfkfb3 fl/fl lesions. Similarly, early lesion plaque and necrotic core size and total plaque burden were unaffected., Conclusion: Partial myeloid knockdown of PFKFB3 did not affect atherosclerosis development in advanced or early lesions. Previously reported positive effects of systemic, partial PFKFB3 inhibition on lesion stabilization, do not seem conferred by monocytes, macrophages or neutrophils. Instead, other Pfkfb3 -expressing cells in atherosclerosis might be responsible, such as DCs, smooth muscle cells or fibroblasts., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Tillie, De Bruijn, Perales-Patón, Temmerman, Ghosheh, Van Kuijk, Gijbels, Carmeliet, Ley, Saez-Rodriguez and Sluimer.)

5-Fluorouracil (5-FU) is a widely used chemotherapeutical that induces acute toxicity in the small and large intestine of patients. Symptoms can be severe and lead to the interruption of cancer treatments. However, there is limited understanding of the molecular mechanisms underlying 5-FU-induced intestinal toxicity. In this study, well-established 3D organoid models of human colon and small intestine (SI) were used to characterize 5-FU transcriptomic and metabolomic responses. Clinically relevant 5-FU concentrations for in vitro testing in organoids were established using physiologically based pharmacokinetic simulation of dosing regimens recommended for cancer patients, resulting in exposures to 10, 100 and 1000 µM. After treatment, different measurements were performed: cell viability and apoptosis; image analysis of cell morphological changes; RNA sequencing; and metabolome analysis of supernatant from organoids cultures. Based on analysis of the differentially expressed genes, the most prominent molecular pathways affected by 5-FU included cell cycle, p53 signalling, mitochondrial ATP synthesis and apoptosis. Short time-series expression miner demonstrated tissue-specific mechanisms affected by 5-FU, namely biosynthesis and transport of small molecules, and mRNA translation for colon; cell signalling mediated by Rho GTPases and fork-head box transcription factors for SI. Metabolomic analysis showed that in addition to the effects on TCA cycle and oxidative stress in both organoids, tissue-specific metabolic alterations were also induced by 5-FU. Multi-omics integration identified transcription factor E2F1, a regulator of cell cycle and apoptosis, as the best key node across all samples. These results provide new insights into 5-FU toxicity mechanisms and underline the relevance of human organoid models in the safety assessment in drug development., (© 2021. The Author(s).)