Biology subjects

Garrido-Rodriguez, M.

Publications and source records attributed to Garrido-Rodriguez, M..

14 recordsLinked to original sources

bioRxiv2026

Multi-omics and biophysical phosphoproteomics upon BRAF inhibition uncover functional networks of BRAFV600E-driven signaling

Dysregulated kinase activity drives oncogenic signaling, disrupts cellular homeostasis, and promotes tumour progression. The BRAFV600E mutation constitutively activates the MAPK pathway and is a key therapeutic target in melanoma and other cancers, but the functional relevance of most downstream phosphorylation events and mechanisms of drug resistance remain unclear. To address this, a global multi-omic model of BRAF inhibition response was established in BRAFV600E-mutant cells by integrating time-resolved and biophysical phosphoproteomics, transcriptomics, and thermal proteome profiling. Ultradeep phosphoproteomics revealed extensive phosphorylation changes upon BRAF inhibitor treatment, while biophysical phosphoproteomics identified phosphorylation events linked to altered protein solubility and subcellular localization, suggesting changes in nucleic acid interactions and nuclear reorganisation. Network-based integration of these datasets prioritized functionally relevant phosphorylation sites and kinases. Experimental validation identified CDK9, CLK3, and TNIK as critical regulators of BRAFV600E signaling and candidate targets for combinatorial inhibition capable of re-sensitising resistant cells. The transcription factor ETV3 emerged as a previously unrecognised effector of BRAF signaling. Biophysical proteomics data confirmed that ETV3 phosphorylation modulates DNA-binding, while functional assays combining knockdown, metabolomics, and drug screening demonstrated its role in coordinating transcriptional and metabolic adaptations to BRAF inhibition. This study provides a systems-level framework linking phosphorylation dynamics to protein function and phenotype, identifies ETV3 as a new node in oncogenic BRAF signaling, and illustrates how integrated, site-resolved models can reveal mechanisms of kinase-driven oncogenesis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=43 SRC="FIGDIR/small/704793v1_ufig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@1df1401org.highwire.dtl.DTLVardef@9a77a5org.highwire.dtl.DTLVardef@124f819org.highwire.dtl.DTLVardef@1c6b57_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LITime- and cell type-resolved phosphoproteomics maps BRAF inhibition dynamics C_LIO_LIBiophysical phosphoproteomics, combining quantitative phosphoproteomics with solubility profiling or nuclear fractionation, reveals phosphorylation-driven changes of protein solubility and localization C_LIO_LIIntegration of abundance and biophysical phosphoproteomics data identifies functionally relevant phosphorylation events of BRAFV600E signaling C_LIO_LINetwork integration of multimodal phosphoproteomic, transcriptomic and thermal proteome profiling data links signaling to protein function and cellular phenotypes C_LIO_LIBiophysical evidence improves models and identifies non-canonical kinases driving BRAF signaling as well as novel downstream regulators such as ETV3 C_LIO_LIFollow-up experiments reveal a ETV3-GLUT3-mediated metabolic adaptation in BRAFV600E cells C_LI

systems biology↗

bioRxiv2025

ScAPE: A lightweight multitask learning baseline method to predict transcriptomic responses to perturbations

Predicting biological responses to perturbations is essential for advancing drug development efforts. While high-throughput technologies have greatly enhanced our ability to study perturbation effects at the molecular level, accurately modeling these responses remains challenging due to the complex underlying molecular interactions, the vast dimensionality of cellular response, the limited amount of data, and technical limitations in data collection. Machine learning (ML) approaches have emerged as powerful tools for addressing these challenges. However, the observation in recent benchmarks that complex models do not consistently outperform simpler ones highlights the need for robust and efficient baselines for evaluating new methods. We present ScAPE (Single Cell Analysis of Perturbational Effects), an ML method for predicting drug-induced gene expression changes. ScAPE leverages only aggregated gene-level statistics across cell types and perturbations as input features, yet achieves good performance despite this simplicity. Its effectiveness was demonstrated in the Single-cell Perturbation Prediction NeurIPS 2023 challenge, where it ranked among the top-performing methods. We describe the core model and introduce extensions that enhance its utility as a baseline, including a multitask learning extension for simultaneous prediction of multiple transcriptional response metrics. A software API is provided to support adoption and integration into research pipelines. Benchmarking against other leading challenge entries and general-purpose ML approaches, using the original challenge metric, supports ScAPE as a simple, lightweight baseline for evaluating methods that predict perturbation effects at the gene-expression level. ScAPE is built using Keras 3, enabling support for different backends such as TensorFlow, JAX, or PyTorch, and freely available as an open-source package at github.com/scapeML/scape.

bioinformatics↗

bioRxiv2025

High-throughput peptide-centric local stability assay extends protein-ligand identification to membrane proteins, tissues, and bacteria

Systematic mapping of protein-ligand interactions is essential for understanding biological processes and drug mechanisms. Peptide-centric local stability assay (PELSA) is a powerful tool for detecting these interactions and localizing potential binding sites. However, its original workflow is limited in throughput, sample compatibility and accessible protein targets. Here, we introduce a high-throughput adaptation - HT-PELSA - that increases sample processing efficiency 100-fold while maintaining high sensitivity and reproducibility. HT-PELSA substantially extends the capabilities of the original method by enabling sensitive protein-ligand profiling in crude cell, tissue and bacterial lysates, making it possible to identify membrane protein targets in diverse biological systems. We demonstrate that HT-PELSA can precisely and accurately determine binding affinities of small molecule inhibitors, sensitively detect direct and allosteric ATP binding, and reveal off-target interactions of a marketed kinase inhibitor in heart tissue. By enhancing scalability, reducing costs, and enabling system-wide drug screening across a wide range of sample types, HT-PELSA - when combined with next-generation mass spectrometry - offers a powerful platform poised to accelerate both drug discovery and basic biological research.

cell biology↗

bioRxiv2024

NetworkCommons: bridging data, knowledge and methods to build and evaluate context-specific biological networks

SummaryWe present NetworkCommons, a platform for integrating prior knowledge, omics data, and network inference methods, facilitating their usage and evaluation. NetworkCommons aims to be an infrastructure for the network biology community that supports the development of better methods and benchmarks, by enhancing interoperability and integration. Availability and ImplementationNetworkCommons is implemented in Python and offers programmatic access to multiple omics datasets, network inference methods, and benchmarking setups. It is a free software, available at https://github.com/saezlab/networkcommons. Contactsaezlab@ebi.ac.uk, martin.garrido@embl.de Supplementary DataContribution guidelines, additional figures, and descriptions for data, knowledge, methods, evaluation strategies and their implementation are available in the Supplementary Data and in the NetworkCommons documentation at https://networkcommons.readthedocs.io/.

bioinformatics↗

bioRxiv2024

Unified knowledge-driven network inference from omics data

Analysing omics data requires computational methods to effectively handle its complexity and to derive meaningful hypotheses about molecular mechanisms. While data-driven statistical and machine learning methods can identify patterns from omics data across multiple samples, they typically require a large number of samples and they often lack interpretability and alignment with existing biological knowledge. In contrast, knowledge-based network methods integrate molecular data with prior knowledge to provide results that are biologically interpretable, but they lack both a unified mathematical framework, leading to ad-hoc solutions specific to particular data types or prior knowledge, limiting their generalisability, and a common modelling interface for programmatic manipulation, restricting method extensions. Furthermore, existing methods generally cannot perform joint network inference across multiple samples or conditions, which restricts their capacity to capture shared mechanisms, making these methods more sensitive to noise and prone to overfitting. To address these limitations, we introduce CORNETO (Constrained Optimisation for the Recovery of NETworks from Omics), a unified framework for knowledge-driven network inference. CORNETO redefines the joint inference task as a constrained optimisation problem with a penalty that induces structured sparsity, allowing for simultaneous network inference across multiple samples. The framework is highly flexible and supports a wide variety of prior knowledge networks--undirected, directed and signed graphs, as well as hypergraphs--enabling the generalisation and improvement of many network inference methods, despite their seemingly different assumptions. We demonstrate its utility by presenting novel extensions of methods for signalling, metabolism and protein-protein interactions. We show how these new methods improve the performance of traditional techniques on a diverse set of biological tasks using simulated and real data. CORNETO is available as an open-source Python package (github.com/saezlab/corneto), facilitating researchers in extending, reusing, and harmonising methods for network inference.

bioinformatics↗

bioRxiv2024

Evaluating signaling pathway inference from kinase-substrate interactions and phosphoproteomics data

Cellular signaling plays a vital role in how cells communicate and adapt to both environmental and internal cues. At the molecular level, signaling is largely driven by phosphorylation cascades controlled by kinases. Because of this, kinase-driven signaling pathways are used as a conceptual framework to interpret molecular data across biological contexts. However, signaling pathways were created using limited throughput technologies. As knowledge of kinase-substrate interactions grows through novel computational and experimental approaches, and phosphoproteomic methods improve their coverage and accuracy, traditional signaling pathways need to be revisited. In this study, we critically assess context-specific signaling pathway reconstruction using phosphoproteomics and kinase-substrate networks. We first integrate literature, protein language models, and peptide array data to create a state-of-the-art kinase-substrate network. Focusing on epidermal growth factor (EGF), we conduct a meta-analysis of recent short-term response phosphoproteomics studies, which we complement with three own datasets, representing the most comprehensive characterization of the EGF response available to date. Using three alternative computational methods, we infer kinase-driven pathways, which we compare to multiple ground truth sets, including the canonical pathway, experimentally validated interactions, and correlation supported interactions. Our findings reveal that literature-curated networks, when combined with network propagation, yield the best recovery of ground truth interactions. We found that up to 90% of data-supported direct interactions are absent from current ground truth sets, indicating many unexplored, but data supported kinase interactions. Our results challenge traditional views on signaling pathways and illustrate how to develop new mechanistic hypotheses using phosphoproteomics and network methods.

systems biology↗

bioRxiv2024

Dynamic multi-omics and mechanistic modeling approach uncovers novel mechanisms of kidney fibrosis progression

Kidney fibrosis, characterized by excessive extracellular matrix deposition, is a progressive disease that, despite affecting 10% of the population, lacks specific treatments and suitable biomarkers. This study presents a comprehensive, time-resolved multi-omics analysis of kidney fibrosis using an in vitro model system based on human kidney PDGFR{beta}+ mesenchymal cells aimed at unraveling disease mechanisms. Using transcriptomics, proteomics, phosphoproteomics, and secretomics we quantified over 14,000 biomolecules across seven time points following TGF-{beta} stimulation. This revealed distinct temporal patterns in the expression and activity of known and potential kidney fibrosis markers and modulators. Data integration resulted in time-resolved multi-omic network models which allowed us to propose mechanisms related to fibrosis progression through early transcriptional reprogramming. Using siRNA knockdowns and phenotypic assays, we validated predictions and regulatory mechanisms underlying kidney fibrosis. In particular, we show that several early-activated transcription factors, including FLI1 and E2F1, act as negative regulators of collagen deposition and propose underlying molecular mechanisms. This work advances our understanding of the pathogenesis of kidney fibrosis and provides a resource to be further leveraged by the community. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=71 SRC="FIGDIR/small/618507v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@d2bfaborg.highwire.dtl.DTLVardef@257d34org.highwire.dtl.DTLVardef@13f06f3org.highwire.dtl.DTLVardef@e16e6c_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

systems biology↗

bioRxiv2024

Comprehensive evaluation of phosphoproteomic-based kinase activity inference

Kinases play a central role in regulating cellular processes, making their study essential for understanding cellular function and disease mechanisms. To investigate the regulatory state of a kinase, numerous methods have been, and continue to be, developed to infer kinase activities from phosphoproteomics data. These methods usually rely on a set of kinase targets collected from various kinase-substrate libraries. However, only a small percentage of measured phosphorylation sites can usually be attributed to an upstream kinase in these libraries, limiting the scope of kinase activity inference. In addition, the inferred activities from different methods can vary making it crucial to evaluate them for accurate interpretation. Here, we present a comprehensive evaluation of kinase activity inference methods using multiple kinase-substrate libraries combined with different inference algorithms. Additionally, we try to overcome the coverage limitations for measured targets in kinase substrate libraries by adding predicted kinase-substrate interactions for activity inference. For the evaluation, in addition to classical cell-based perturbation experiments, we introduce a tumor-based benchmarking approach that utilizes multi-omics data to identify highly active or inactive kinases per tumor type. We show that while most computational algorithms perform comparably regardless of their complexity, the choice of kinase-substrate library can highly impact the inferred kinase activities. Hereby, manually curated libraries, particularly PhosphoSitePlus, demonstrate superior performance in recapitulating kinase activities from phosphoproteomics data. Additionally, in the tumor-based evaluation, adding predicted targets from NetworKIN further boosts the performance, while normalizing sites to host protein levels reduces kinase activity inference performance. We then showcase how kinase activity inference can help in characterizing the response to kinase inhibitors in different cell lines. Overall, the selection of reliable kinase activity inference methods is important in identifying deregulated kinases and novel drug targets. Finally, to facilitate the evaluation of novel methods in the future, we provide both benchmarking approaches in the R package benchmarKIN. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC="FIGDIR/small/601117v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@44de1org.highwire.dtl.DTLVardef@9510a3org.highwire.dtl.DTLVardef@775225org.highwire.dtl.DTLVardef@1b2256f_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2024

Etrinabdione (VCE-004.8), a B55a activator, promotes angiogenesis and arteriogenesis in critical limb ischemia.

BackgroundVasculogenic therapies explored for the treatment of peripheral artery disease (PAD) have encountered minimal success in clinical trials. Addressing this, B55, an isoform of protein phosphatase 2A (PP2A), emerges as pivotal in vessel remodeling through activation of hypoxia-inducible factor 1 (HIF-1). This study delves into the pharmacological profile of VCE-004.8 (Etrinabdione) and evaluates its efficacy in a preclinical model of critical limb ischemia, with a focus on its potential as a PP2A/B55 activator to induce angiogenesis and arteriogenesis. MethodsVascular endothelial cells were used for in vitro experiments. Aorta ring assay was performed to explore sprouting activity. Matrigel plug-in assay was used to assess the angiogenic potential. Critical limb ischemia (CLI) in mice was induced by double ligation in the femoral arteria. Endothelial vascular and fibrotic biomarkers were studied by immunohistochemistry and qPCR. Arteriogenesis was investigated by microvascular casting and micro-CT. Proteomic analysis in vascular tissues was analyzed by LC-MS/MS. Ex-vivo expression of B55 and biomarkers were investigated in artery samples from PAD patients. ResultsVCE-004.8 exhibited the ability to induce B55 expression and activate the intersecting pathways B55/AMPK/Sirtuin 1/eNOS and B55/PHD2/HIF-1. VCE-004.8 prevented OxLDL and H2O2-induced cytotoxicity, senescence, and inflammation in endothelial cells. Oral VCE-004.8 increased aorta sprouting in vitro and angiogenesis in vivo. In CLI mice VCE-004.8 improved collateral vessel formation and induced endothelial cells proliferation, angiogenic gene expression and prevented fibrosis. The expression of B55, Caveolin 1 and Sirtuin-1 is reduced in arteries from CLI mice and PAD patient, and the expression of these markers was restored in mice treated with VCE-004.8. ConclusionsThe findings presented in this study indicate that Etrinabdione holds promise in mitigating endothelial cell damage and senescence, while concurrently fostering arteriogenesis and angiogenesis. These observations position Etrinabdione as a compelling candidate for the treatment of PAD, and potentially other cardiovascular disorders. Novelty and SignificanceO_ST_ABSWhat Is Known?C_ST_ABSO_LIThe phosphatase PPA2/B55 stabilizes endothelial cells (ECs) in response to cell stress conditions, thereby protecting ECs from apoptosis and promoting angiogenesis. C_LIO_LIEtrinabdione (VCE-004.8) functions as a potent activator of PPA2/B55 inducing PHD2 dephosphorylation at ser125 and fostering HIF activation. C_LIO_LIVCE-004.8 prevents vascular damage in preclinical models of systemic and cardiac fibrosis and alleviates blood-brain barrier disruption in neuroinflammatory conditions. C_LIO_LIVCE-004.8 is also a dual agonist of PPAR{psi} and CB2 receptors and shows antiinflammatory activity. C_LIO_LIOral VCE-004.8 has meet the primary endpoints of safety and tolerability in a Phase IIa clinical trial with systemic sclerosis patients (clinicaltrial.gov: NCT03745001). C_LI What New Information Does This Article Contribute?O_LIEtrinabdione induces HIF-1 expression in endothelial cells through a novel pathway that potentially involves two axes: B55/PHD2 and B55/AMPK/Sirt1 signaling that may converge on HIF stabilization. C_LIO_LIEtrinabdione prevented endothelial cell damage and senescence, while inducing arteriogenesis and angiogenesis in CLI mice. C_LIO_LIIn arteries of patients with PAD and in CLI mouse models, the expression levels of B55, Caveolin 1, and Sirtuin 1 are diminished. However, treatment with Etrinabdione specifically in CLI mice prompts an increase in the levels of these proteins. C_LIO_LIEtrinabdione triggers neovascularization and angiogenesis specifically within hypoxic tissue in a critical ischemia model, with no impact on healthy tissue. C_LI

pharmacology and toxicology↗

bioRxiv2024

Pervasive sublethal effects of agrochemicals as contributing factors to insect decline

Insect biomass is declining across the globe at an alarming rate. Climate change and the widespread use of pesticides have been hypothesized as two underlying drivers. However, the lack of systematic experimental studies across chemicals and species limits our causal understanding of this problem. Here, we employed a chemical library encompassing 1024 different molecules--including insecticides, herbicides, fungicides, and plant growth inhibitors --to investigate how insect populations are affected by varying concentrations of pesticides, focusing on sublethal doses. Using a controlled laboratory pipeline for Drosophila melanogaster, we found that 57% of these chemicals affect the behavior of larvae at sublethal concentrations, and an even higher proportion compromises long-term survivability after acute exposure. Consistent with these results, we observed that exposure to chemicals at doses orders of magnitude below lethality induced widespread phosphorylation changes across the larval proteome. The effects of agrochemicals were amplified when the ambient temperature was increased by four degrees. We also tested the synergistic effects of multiple chemicals at doses found widely in nature and observed fitness-reducing changes in larval developmental time, behavior, and reproduction. Finally, we expanded our investigation to additional fly species, mosquitos, and butterflies and detected similar behavioral alterations triggered by pesticides at sublethal concentrations. Our results provide experimental evidence that strongly suggests sublethal doses of agrochemicals coupled with changes in environmental temperatures are contributing to the global decline in insect populations. We anticipate that our assays can contribute to improving chemical safety assessment, better protect the environment, secure food supplies, and safeguard animal and human health, as well as understand our rapidly changing world.

ecology↗

bioRxiv2023

Assessing the impact of transcriptomics data analysis pipelines on downstream functional enrichment results

Transcriptomics, and in particular RNA-Seq, has become a widely used approach to assess the molecular state of biological systems. To facilitate its analysis, many tools have been developed for different steps, such as filtering lowly expressed genes, normalisation, differential expression, and enrichment. While numerous studies have examined the impact of method choices on differential expression results, little attention has been paid to their effects on further downstream functional analysis using enrichment of gene sets, such as pathways, which typically provides the basis for interpretation and follow-up experiments. To address this gap, we introduce FLOP (FunctionaL Omics Processing), a comprehensive nextflow-based workflow that combines various methods for preprocessing and downstream enrichment analysis, allowing users to perform end-to-end analyses of count level transcriptomic data. We illustrate FLOP capabilities on diverse datasets comprising samples from end-stage heart failure patients and cancer cell lines in both basal and drug-perturbed states. We found that the correlation between gene set enrichment analysis results can vary significantly for alternative pipelines. Additionally, we observed that not filtering the data had the highest impact on the correlation between pipelines in the gene set space, especially in settings with limited statistical power. Overall, our results underscore the impact of carefully evaluating the consequences of the choice of preprocessing methods on downstream enrichment analyses. We envision FLOP as a valuable tool to measure the robustness of functional analyses, ultimately leading to more reliable and conclusive biological findings. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=45 SRC="FIGDIR/small/557538v1_ufig1.gif" ALT="Figure 1"> View larger version (14K): org.highwire.dtl.DTLVardef@1b35ee7org.highwire.dtl.DTLVardef@11bd02aorg.highwire.dtl.DTLVardef@ff7b18org.highwire.dtl.DTLVardef@ea8d2c_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Deep quantitative glycoproteomics reveals gut microbiome induced remodeling of the brain glycoproteome

HighlightsO_LIHigh throughput glycoproteomics method with multiplexed quantification C_LIO_LI25-fold improvement of the mouse brain glycoproteome coverage C_LIO_LIStructural features dictate level of glycosite micro-heterogeneity C_LIO_LIGut microbiome composition extensively impacts the brain glycoproteome C_LIO_LIModulation of glycosylation is site-specific C_LI Protein glycosylation is a highly diverse post-translational modification, modulating key cellular processes such as cell signaling, adhesion and cell-cell interactions. Its deregulation has been associated with various pathologies, including cancer and neurological diseases. Methods capable of quantifying glycosylation dynamics are essential to start unraveling the biological functions of protein glycosylation. Here we present Deep Quantitative Glycoprofiling (DQGlyco), a method that combines high-throughput sample preparation, high-sensitivity detection, and precise multiplexed quantification of protein glycosylation. We used DQGlyco to profile the mouse brain glycoproteome, in which we identify 158,972 and 15,056 unique N- and O-glycopeptides localized on 3,199 and 2,365 glycoproteins, respectively - this amounts to 25-fold more glycopeptides identified compared to previous studies. We observed extensive heterogeneity of glycoforms and determined their functional and structural preferences. The presence of a defined gut microbiota resulted in extensive remodeling of the brain glycoproteome when compared to that of germ-free animals, exemplifying how the gut microbiome may affect brain protein functions.

molecular biology↗

bioRxiv2023

Integration of Thermal Proteome Profiling with phosphoproteomic and transcriptomic data via mechanistic network models decodes the molecular response to PARP inhibition

The deregulation of complex diseases often spans multiple molecular processes. A multimodal functional characterization of these processes can shed light on the disease mechanisms and the effect of drugs. Thermal Proteome Profiling (TPP) is a mass-spectrometry based technique assessing changes in thermal protein stability that can serve as proxies of functional changes of the proteome. These unique insights of TPP can complement those obtained by other omics technologies. Here, we show how TPP can be integrated with phosphoproteomics and transcriptomics in a network-based approach using COSMOS, a framework for causal integration of multi-omics, to provide an integrated view of transcription factors, kinases and proteins with altered thermal stability. This allowed us to recover known mechanistic consequences of PARP inhibition in ovarian cancer cells on cell cycle and DNA damage response in detail and to uncover new insights into drug response mechanisms related to interferon and hippo signaling. We found that TPP complements the other omics data and allowed us to obtain a network model with higher coverage of the main underlying mechanisms. These results illustrate the added value of TPP, and more generally the power of network models to integrate the information provided by different omics technologies. We anticipate that this strategy can be used to broadly integrate functional proteomics with other omics to study complex molecular processes. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=63 SRC="FIGDIR/small/553354v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@af952eorg.highwire.dtl.DTLVardef@16b1d1borg.highwire.dtl.DTLVardef@1440787org.highwire.dtl.DTLVardef@14a4227_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2019

Tetrahydrocannabinolic Acid A (THCA-A) Reduces Adiposity And Prevents Metabolic Disease Caused By Diet-Induced Obesity

Cannabis has remarkable therapeutic potential, but its clinical use is limited by the psychotropic activity of {Delta}9-tetrahydrocannabinol ({Delta}9-THC). Surprisingly, the biological profile of the non-narcotic native precursor of {Delta}9-THC ({Delta}9-THC acid A, {Delta}9-THCA-A) is still largely unexplored. We present evidence that {Delta}9-THCA-A is a partial and selective PPAR{gamma} modulator, endowed with lower adipogenic activity than the full PPAR{gamma} agonist rosiglitazone (RGZ) and with an enhanced osteoblastogenic activity in human mesenchymal stem cells. Docking and in vitro functional assays indicated that {Delta}9-THCA-A binds to and activates PPAR{gamma} by acting at both the canonical and the alternative sites of the ligand-binding domain. Transcriptomic signatures at inguinal white adipose tissue (iWAT) from mice treated with {Delta}9-THCA-A confirmed its mode of action on PPAR{gamma}. Administration of {Delta}9-THCA-A in a mouse model of high fat diet (HFD)-induced obesity significantly reduced fat mass and body weight gain, markedly ameliorating glucose intolerance and insulin resistance, and largely preventing liver steatosis, adipogenesis and macrophage infiltration in fat tissues. Additionally, immunohistochemistry, transcriptomic, and plasma biomarker analyses showed that treatment with {Delta}9-THCA-A caused browning of iWAT and displayed potent anti-inflammatory actions in HFD mice. Altogether, our data validate the potential of {Delta}9-THCA-A as a low adipogenic PPAR{gamma} agonist, capable of substantially improving the symptoms of obesity-associated metabolic syndrome and inflammation. These findings suggest that {Delta}9-THCA-A, and perhaps non-decarboxylated Cannabis sativa extracts, are worth considering for addition to our inventory of cannabis medicines.\n\nSIGNIFICANCE STATEMENTThe medicinal use of Cannabis is gaining momentum, despite the adverse psychotropic effects of {Delta}9-THC, the decarboxylation product of its naturally occurring and non-psychotropic precursor {Delta}9-THCA-A. We present evidence that {Delta}9-THCA-A is a partial ligand agonist of PPAR{gamma} with lower adipogenic activity compared to the full PPAR{gamma} agonist rosiglitazone (RGZ). Moreover, chronic administration of {Delta}9-THCA-A in a mouse model of high fat diet (HFD)-induced obesity significantly reduced body weight gain and fat mass, improved glucose intolerance and insulin resistance, and prevented liver steatosis and macrophage infiltration in fat tissues, additionally inducing white adipose tissue browning. Collectively, these observations qualify {Delta}9-THCA-A, a compound devoid of psychotropic effects, as an efficacious pharmacological agent to manage metabolic syndrome and obesity-associated inflammation.\n\nHighlights- {Delta}9-THCA-A is a partial PPAR{gamma} ligand agonist with low adipogenic activity\n- {Delta}9-THCA-A enhances osteoblastogenesis in bone marrow derived mesenchymal stem cells.\n- {Delta}9-THCA-A reduces body weight gain, fat mass, and liver steatosis in HFD-fed mice\n- {Delta}9-THCA-A improves glucose tolerance, insulin sensitivity, and insulin profiles in vivo\n- {Delta}9-THCA-A induces browning of iWAT and has a potent anti-inflammatory activity

pharmacology and toxicology↗