Characterization of six clinical drugs and dietary intervention in the non-obese CDAA-HFD mouse model of MASH and progressive fibrosis.

The choline-deficient L-amino acid defined-high fat diet (CDAA-HFD) mouse model is widely used in preclinical metabolic dysfunction-associated steatohepatitis (MASH) research. To validate the CDAA-HFD mouse, we evaluated disease progression and responsiveness to dietary and pharmacological interventions with semaglutide, lanifibranor, elafibranor, obeticholic acid (OCA), firsocostat and resmetirom.Disease phenotyping was performed in C57BL/6J mice fed CDAA-HFD for 3-20 weeks and ranked using the MASLD Human Proximity Score (MHPS). Semaglutide, lanifibranor, elafibranor, OCA, firsocostat or resmetirom were profiled as treatment intervention for 8 weeks, starting after 6 weeks of CDAA-HFD feeding. Semaglutide and lanifibranor were further evaluated as early (preventive) therapy for 9 weeks, starting 3 weeks after CDAA-HFD diet feeding. Additionally, benefits of dietary intervention (chow reversal) for 8 weeks were characterized following 6 weeks of CDAA-HFD feeding. CDAA-HFD mice demonstrated a non-obese phenotype with fast onset and progression of MASH and fibrosis, high similarity to human MASH-fibrosis, and tumor development after 20 weeks of diet-induction. Semaglutide and lanifibranor partially reversed fibrosis when administered as prevention, but not as treatment intervention. Elafibranor was the only interventional drug to improve fibrosis. In comparison, chow-reversal resulted in complete steatosis regression with improved liver inflammation and fibrosis in CDAA-HFD mice. CDAA-HFD mice recapitulate histological hallmarks of advanced MASH with progressive severe fibrosis, however, in the absence of a clinical translational obese dysmetabolic phenotype. CDAA-HFD mice are suitable for profiling drug candidates directly targeting hepatic lipid metabolism, inflammation, and fibrosis. The timing of pharmacological intervention is critical for determining antifibrotic drug efficacy in the model.

Differential regulation of the proteome and phosphoproteome along the dorso-ventral axis of the early Drosophila embryo.

The initially homogeneous epithelium of the early Drosophila embryo differentiates into regional subpopulations with different behaviours and physical properties that are needed for morphogenesis. The factors at top of the genetic hierarchy that control these behaviours are known, but many of their targets are not. To understand how proteins work together to mediate differential cellular activities, we studied in an unbiased manner the proteomes and phosphoproteomes of the three main cell populations along the dorso-ventral axis during gastrulation using mutant embryos that represent the different populations. We detected 6111 protein groups and 6259 phosphosites of which 3398 and 3433 were differentially regulated, respectively. The changes in phosphosite abundance did not correlate with changes in host protein abundance, showing phosphorylation to be a regulatory step during gastrulation. Hierarchical clustering of protein groups and phosphosites identified clusters that contain known fate determinants such as Doc1, Sog, Snail, and Twist. The recovery of the appropriate known marker proteins in each of the different mutants we used validated the approach, but also revealed that two mutations that both interfere with the dorsal fate pathway, Toll10B and serpin27aex do this in very different manners. Diffused network analyses within each cluster point to microtubule components as one of the main groups of regulated proteins. Functional studies on the role of microtubules provide the proof of principle that microtubules have different functions in different domains along the DV axis of the embryo.

phuEGO: A Network-Based Method to Reconstruct Active Signaling Pathways From Phosphoproteomics Datasets.

Signaling networks are critical for virtually all cell functions. Our current knowledge of cell signaling has been summarized in signaling pathway databases, which, while useful, are highly biased toward well-studied processes, and do not capture context specific network wiring or pathway cross-talk. Mass spectrometry-based phosphoproteomics data can provide a more unbiased view of active cell signaling processes in a given context, however, it suffers from low signal-to-noise ratio and poor reproducibility across experiments. While progress in methods to extract active signaling signatures from such data has been made, there are still limitations with respect to balancing bias and interpretability. Here we present phuEGO, which combines up-to-three-layer network propagation with ego network decomposition to provide small networks comprising active functional signaling modules. PhuEGO boosts the signal-to-noise ratio from global phosphoproteomics datasets, enriches the resulting networks for functional phosphosites and allows the improved comparison and integration across datasets. We applied phuEGO to five phosphoproteomics data sets from cell lines collected upon infection with SARS CoV2. PhuEGO was better able to identify common active functions across datasets and to point to a subnetwork enriched for known COVID-19 targets. Overall, phuEGO provides a flexible tool to the community for the improved functional interpretation of global phosphoproteomics datasets.

Limited oxygen in standard cell culture alters metabolism and function of differentiated cells.

The in vitro oxygen microenvironment profoundly affects the capacity of cell cultures to model physiological and pathophysiological states. Cell culture is often considered to be hyperoxic, but pericellular oxygen levels, which are affected by oxygen diffusivity and consumption, are rarely reported. Here, we provide evidence that several cell types in culture actually experience local hypoxia, with important implications for cell metabolism and function. We focused initially on adipocytes, as adipose tissue hypoxia is frequently observed in obesity and precedes diminished adipocyte function. Under standard conditions, cultured adipocytes are highly glycolytic and exhibit a transcriptional profile indicative of physiological hypoxia. Increasing pericellular oxygen diverted glucose flux toward mitochondria, lowered HIF1α activity, and resulted in widespread transcriptional rewiring. Functionally, adipocytes increased adipokine secretion and sensitivity to insulin and lipolytic stimuli, recapitulating a healthier adipocyte model. The functional benefits of increasing pericellular oxygen were also observed in macrophages, hPSC-derived hepatocytes and cardiac organoids. Our findings demonstrate that oxygen is limiting in many terminally-differentiated cell types, and that considering pericellular oxygen improves the quality, reproducibility and translatability of culture models.

Integrated systems biology approach identifies gene targets for endothelial dysfunction.

Endothelial dysfunction (ED) is critical in the development and progression of cardiovascular (CV) disorders, yet effective therapeutic targets for ED remain elusive due to limited understanding of its underlying molecular mechanisms. To address this gap, we employed a systems biology approach to identify potential targets for ED. Our study combined multi omics data integration, with siRNA screening, high content imaging and network analysis to prioritise key ED genes and identify a pro- and anti-ED network. We found 26 genes that, upon silencing, exacerbated the ED phenotypes tested, and network propagation identified a pro-ED network enriched in functions associated with inflammatory responses. Conversely, 31 genes ameliorated ED phenotypes, pointing to potential ED targets, and the respective anti-ED network was enriched in hypoxia, angiogenesis and cancer-related processes. An independent screen with 17 drugs found general agreement with the trends from our siRNA screen and further highlighted DUSP1, IL6 and CCL2 as potential candidates for targeting ED. Overall, our results demonstrate the potential of integrated system biology approaches in discovering disease-specific candidate drug targets for endothelial dysfunction.

Highlights from the 1st European cancer dependency map symposium and workshop.

The systematic identification of tumour vulnerabilities through perturbational experiments on cancer models, including genome editing and drug screens, is playing a crucial role in combating cancer. This collective effort is known as the Cancer Dependency Map (DepMap). The 1st European Cancer Dependency Map Symposium (EuroDepMap), held in Milan last May, featured talks, a roundtable discussion, and a poster session, showcasing the latest discoveries and future challenges related to the DepMap. The symposium aimed to facilitate interactions among participants across Europe, encourage idea exchange with leading experts, and present their work and future projects. Importantly, it sparked discussions on future endeavours, such as screening more complex cancer models and accounting for tumour evolution.

Large-scale phage-based screening reveals extensive pan-viral mimicry of host short linear motifs.

Viruses mimic host short linear motifs (SLiMs) to hijack and deregulate cellular functions. Studies of motif-mediated interactions therefore provide insight into virus-host dependencies, and reveal targets for therapeutic intervention. Here, we describe the pan-viral discovery of 1712 SLiM-based virus-host interactions using a phage peptidome tiling the intrinsically disordered protein regions of 229 RNA viruses. We find mimicry of host SLiMs to be a ubiquitous viral strategy, reveal novel host proteins hijacked by viruses, and identify cellular pathways frequently deregulated by viral motif mimicry. Using structural and biophysical analyses, we show that viral mimicry-based interactions have similar binding strength and bound conformations as endogenous interactions. Finally, we establish polyadenylate-binding protein 1 as a potential target for broad-spectrum antiviral agent development. Our platform enables rapid discovery of mechanisms of viral interference and the identification of potential therapeutic targets which can aid in combating future epidemics and pandemics.

Pan-Cancer landscape of protein activities identifies drivers of signalling dysregulation and patient survival.

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.

Phosphoproteomics data-driven signalling network inference: Does it work?

The advent of global phosphoproteome profiling has led to wide phosphosite coverage and therefore the opportunity to predict kinase-substrate associations from these datasets. However, the regulatory kinase is unknown for most substrates, due to biased and incomplete database annotations. In this study we compare the performance of six pairwise measures to predict kinase-substrate associations using a data driven approach on publicly available time resolved and perturbation mass spectrometry-based phosphoproteome data. First, we validated the performance of these measures using as a reference both a literature-based phosphosite-specific protein interaction network and a predicted kinase-substrate (KS) interactions set. The overall performance in predicting kinase-substrate associations using pairwise measures across both these reference sets was poor. To expand into the wider interactome space, we applied the approach on a network comprising pairs of substrates regulated by the same kinase (substrate-substrate associations) but found the performance to be equally poor. However, the addition of a sequence similarity filter for substrate-substrate associations led to a significant boost in performance. Our findings imply that the use of a filter to reduce the search space, such as a sequence similarity filter, can be used prior to the application of network inference methods to reduce noise and boost the signal. We also find that the current gold standard for reference sets is not adequate for evaluation as it is limited and context-agnostic. Therefore, there is a need for additional evaluation methods that have increased coverage and take into consideration the context-specific nature of kinase-substrate associations.

Use of viral motif mimicry improves the proteome-wide discovery of human linear motifs.

Linear motifs have an integral role in dynamic cell functions, including cell signaling. However, due to their small size, low complexity, and frequent mutations, identifying novel functional motifs poses a challenge. Viruses rely extensively on the molecular mimicry of cellular linear motifs. In this study, we apply systematic motif prediction combined with functional filters to identify human linear motifs convergently evolved also in viral proteins. We observe an increase in the sensitivity of motif prediction and improved enrichment in known instances. We identify >7,300 non-redundant motif instances at various confidence levels, 99 of which are supported by all functional and structural filters. Overall, we provide a pipeline to improve the identification of functional linear motifs from interactomics datasets and a comprehensive catalog of putative human motifs that can contribute to our understanding of the human domain-linear motif code and the associated mechanisms of viral interference.

Identification of phenotype-specific networks from paired gene expression-cell shape imaging data.

The morphology of breast cancer cells is often used as an indicator of tumor severity and prognosis. Additionally, morphology can be used to identify more fine-grained, molecular developments within a cancer cell, such as transcriptomic changes and signaling pathway activity. Delineating the interface between morphology and signaling is important to understand the mechanical cues that a cell processes in order to undergo epithelial-to-mesenchymal transition and consequently metastasize. However, the exact regulatory systems that define these changes remain poorly characterized. In this study, we used a network-systems approach to integrate imaging data and RNA-seq expression data. Our workflow allowed the discovery of unbiased and context-specific gene expression signatures and cell signaling subnetworks relevant to the regulation of cell shape, rather than focusing on the identification of previously known, but not always representative, pathways. By constructing a cell-shape signaling network from shape-correlated gene expression modules and their upstream regulators, we found central roles for developmental pathways such as WNT and Notch, as well as evidence for the fine control of NF-kB signaling by numerous kinase and transcriptional regulators. Further analysis of our network implicates a gene expression module enriched in the RAP1 signaling pathway as a mediator between the sensing of mechanical stimuli and regulation of NF-kB activity, with specific relevance to cell shape in breast cancer.

Transcription factors: Bridge between cell signaling and gene regulation.

Transcription factors (TFs) are key regulators of intrinsic cellular processes, such as differentiation and development, and of the cellular response to external perturbation through signaling pathways. In this review we focus on the role of TFs as a link between signaling pathways and gene regulation. Cell signaling tends to result in the modulation of a set of TFs that then lead to changes in the cell's transcriptional program. We highlight the molecular layers at which TF activity can be measured and the associated technical and conceptual challenges. These layers include post-translational modifications (PTMs) of the TF, regulation of TF binding to DNA through chromatin accessibility and epigenetics, and expression of target genes. We highlight that a large number of TFs are understudied in both signaling and gene regulation studies, and that our knowledge about known TF targets has a strong literature bias. We argue that TFs serve as a perfect bridge between the fields of gene regulation and signaling, and that separating these fields hinders our understanding of cell functions. Multi-omics approaches that measure multiple dimensions of TF activity are ideally suited to study the interplay of cell signaling and gene regulation using TFs as the anchor to link the two fields.

Suppression of insulin-induced gene 1 (INSIG1) function promotes hepatic lipid remodelling and restrains NASH progression.

OBJECTIVE: Non-alcoholic fatty liver disease (NAFLD) is a silent pandemic associated with obesity and the metabolic syndrome, and also increases cardiovascular- and cirrhosis-related morbidity and mortality. A complete understanding of adaptive compensatory metabolic programmes that modulate non-alcoholic steatohepatitis (NASH) progression is lacking. METHODS AND RESULTS: Transcriptomic analysis of liver biopsies in patients with NASH revealed that NASH progression is associated with rewiring of metabolic pathways, including upregulation of de novo lipid/cholesterol synthesis and fatty acid remodelling. The modulation of these metabolic programmes was achieved by activating sterol regulatory element-binding protein (SREBP) transcriptional networks; however, it is still debated whether, in the context of NASH, activation of SREBPs acts as a pathogenic driver of lipotoxicity, or rather promotes the biosynthesis of protective lipids that buffer excessive lipid accumulation, preventing inflammation and fibrosis. To elucidate the pathophysiological role of SCAP/SREBP in NASH and wound-healing response, we used an Insig1 deficient (with hyper-efficient SREBPs) murine model challenged with a NASH-inducing diet. Despite enhanced lipid and cholesterol biosynthesis, Insig1 KO mice had similar systemic metabolism and insulin sensitivity to Het/WT littermates. Moreover, activating SREBPs resulted in remodelling the lipidome, decreased hepatocellular damage, and improved wound-healing responses. CONCLUSIONS: Our study provides actionable knowledge about the pathways and mechanisms involved in NAFLD pathogenesis, which may prove useful for developing new therapeutic strategies. Our results also suggest that the SCAP/SREBP/INSIG1 trio governs transcriptional programmes aimed at protecting the liver from lipotoxic insults in NASH.

Cell Surface Mechanics Gate Embryonic Stem Cell Differentiation.

Cell differentiation typically occurs with concomitant shape transitions to enable specialized functions. To adopt a different shape, cells need to change the mechanical properties of their surface. However, whether cell surface mechanics control the process of differentiation has been relatively unexplored. Here we show that membrane mechanics gate exit from naive pluripotency of mouse embryonic stem cells. By measuring membrane tension during early differentiation, we find that naive stem cells release their plasma membrane from the underlying actin cortex when transitioning to a primed state. By mechanically tethering the plasma membrane to the cortex by enhancing Ezrin activity or expressing a synthetic signaling-inert linker, we demonstrate that preventing this detachment forces stem cells to retain their naive pluripotent identity. We thus identify a decrease in membrane-to-cortex attachment as a new cell-intrinsic mechanism that is essential for stem cells to exit pluripotency.

CEN-tools: an integrative platform to identify the contexts of essential genes.

An emerging theme from large-scale genetic screens that identify genes essential for cell fitness is that essentiality of a given gene is highly context-specific. Identification of such contexts could be the key to defining gene function and also to develop novel therapeutic interventions. Here, we present Context-specific Essentiality Network-tools (CEN-tools), a website and python package, in which users can interrogate the essentiality of a gene from large-scale genome-scale CRISPR screens in a number of biological contexts including tissue of origin, mutation profiles, expression levels and drug responses. We show that CEN-tools is suitable for the systematic identification of genetic dependencies and for more targeted queries. The associations between genes and a given context are represented as dependency networks (CENs), and we demonstrate the utility of these networks in elucidating novel gene functions. In addition, we integrate the dependency networks with existing protein-protein interaction networks to reveal context-dependent essential cellular pathways in cancer cells. Together, we demonstrate the applicability of CEN-tools in aiding the current efforts to define the human cellular dependency map.

Dynamic regulation of hypoxia-inducible factor-1α activity is essential for normal B cell development.

B lymphocyte development and selection are central to adaptive immunity and self-tolerance. These processes require B cell receptor (BCR) signaling and occur in bone marrow, an environment with variable hypoxia, but whether hypoxia-inducible factor (HIF) is involved is unknown. We show that HIF activity is high in human and murine bone marrow pro-B and pre-B cells and decreases at the immature B cell stage. This stage-specific HIF suppression is required for normal B cell development because genetic activation of HIF-1α in murine B cells led to reduced repertoire diversity, decreased BCR editing and developmental arrest of immature B cells, resulting in reduced peripheral B cell numbers. HIF-1α activation lowered surface BCR, CD19 and B cell-activating factor receptor and increased expression of proapoptotic BIM. BIM deletion rescued the developmental block. Administration of a HIF activator in clinical use markedly reduced bone marrow and transitional B cells, which has therapeutic implications. Together, our work demonstrates that dynamic regulation of HIF-1α is essential for normal B cell development.

Prediction of Signed Protein Kinase Regulatory Circuits.

Complex networks of regulatory relationships between protein kinases comprise a major component of intracellular signaling. Although many kinase-kinase regulatory relationships have been described in detail, these tend to be limited to well-studied kinases whereas the majority of possible relationships remains unexplored. Here, we implement a data-driven, supervised machine learning method to predict human kinase-kinase regulatory relationships and whether they have activating or inhibiting effects. We incorporate high-throughput data, kinase specificity profiles, and structural information to produce our predictions. The results successfully recapitulate previously annotated regulatory relationships and can reconstruct known signaling pathways from the ground up. The full network of predictions is relatively sparse, with the vast majority of relationships assigned low probabilities. However, it nevertheless suggests denser modes of inter-kinase regulation than normally considered in intracellular signaling research. A record of this paper's transparent peer review process is included in the Supplemental Information.

Systems analysis of RhoGEF and RhoGAP regulatory proteins reveals spatially organized RAC1 signalling from integrin adhesions.

Rho GTPases are central regulators of the cytoskeleton and, in humans, are controlled by 145 multidomain guanine nucleotide exchange factors (RhoGEFs) and GTPase-activating proteins (RhoGAPs). How Rho signalling patterns are established in dynamic cell spaces to control cellular morphogenesis is unclear. Through a family-wide characterization of substrate specificities, interactomes and localization, we reveal at the systems level how RhoGEFs and RhoGAPs contextualize and spatiotemporally control Rho signalling. These proteins are widely autoinhibited to allow local regulation, form complexes to jointly coordinate their networks and provide positional information for signalling. RhoGAPs are more promiscuous than RhoGEFs to confine Rho activity gradients. Our resource enabled us to uncover a multi-RhoGEF complex downstream of G-protein-coupled receptors controlling CDC42-RHOA crosstalk. Moreover, we show that integrin adhesions spatially segregate GEFs and GAPs to shape RAC1 activity zones in response to mechanical cues. This mechanism controls the protrusion and contraction dynamics fundamental to cell motility. Our systems analysis of Rho regulators is key to revealing emergent organization principles of Rho signalling.