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Results for “Systems Biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2020

Identification of COVID-19-relevant transcriptional regulatory networks and associated kinases as potential therapeutic targets

Identification of transcriptional regulatory mechanisms and signaling networks involved in the response of host to infection by SARS-CoV-2 is a powerful approach that provides a systems biology view of gene expression programs involved in COVID-19 and may enable identification of novel therapeutic targets and strategies to mitigate the impact of this disease. In this study, we combined a series of recently developed computational tools to identify transcriptional regulatory networks involved in the response of epithelial cells to infection by SARS-CoV-2, and particularly regulatory mechanisms that are specific to this virus. In addition, using network-guided analyses, we identified signaling pathways that are associated with these networks and kinases that may regulate them. The results identified classical antiviral response pathways including Interferon response factors (IRFs), interferons (IFNs), and JAK-STAT signaling as key elements upregulated by SARS-CoV-2 in comparison to mock-treated cells. In addition, comparing SARS-Cov-2 infection of airway epithelial cells to other respiratory viruses identified pathways associated with regulation of inflammation (MAPK14) and immunity (BTK, MBX) that may contribute to exacerbate organ damage linked with complications of COVID-19. The regulatory networks identified herein reflect a combination of experimentally validated hits and novel pathways supporting the computational pipeline to quickly narrow down promising avenue of investigations when facing an emerging and novel disease such as COVID-19.

systems biology↗

bioRxiv2021

Hierarchical Harmonization of Atom-Resolved Metabolic Re-actions Across Metabolic Databases

Metabolic models have been proven to be useful tools in system biology and have been successfully applied to various research fields in a wide range of organisms. A relatively complete metabolic network is a prerequisite for deriving reliable metabolic models. The first step in constructing metabolic network is to harmonize compounds and reactions across different metabolic databases. However, effectively integrating data from various sources still remains a big challenge. Incomplete and inconsistent atomistic details in compound representations across databases is a very important limiting factor. Here, we optimized a subgraph isomorphism detection algorithm to validate generic compound pairs. Moreover, we defined a set of harmonization relationship types between compounds to deal with inconsistent chemical details while successfully capturing atom-level characteristics, enabling a more complete enabling compound harmonization across metabolic databases. In total, 15,704 compound pairs across KEGG (Kyoto Encyclopedia of Genes and Genomes) and MetaCyc databases were detected. Furthermore, utilizing the classification of compound pairs and EC (Enzyme Commission) numbers of reactions, we established hierarchical relationships between metabolic reactions, enabling the harmonization of 3,856 reaction pairs. In addition, we created and used atom-specific identifiers to evaluate the consistency of atom mappings within and between harmonized reactions, detecting some consistency issues between the reaction and compound descriptions in these metabolic databases.

systems biology↗

bioRxiv2020

Environmental flexibility does not explain metabolic robustness

Cells show remarkable resilience against genetic and environmental perturbations. However, its evolutionary origin remains obscure. In order to leverage methods of systems biology for examining cellular robustness, a computationally accessible way of quantification is needed. Here, we present an unbiased metric of structural robustness in genome-scale metabolic models based on concepts prevalent in reliability engineering and fault analysis. The probability of failure (PoF) is defined as the (weighted) portion of all possible combinations of loss-of-function mutations that disable network functionality. It can be exactly determined, if all essential reactions, synthetic lethal pairs of reactions, synthetic lethal triplets of reactions etc., are known. In theory, these minimal cut sets (MCSs) can be calculated for any network, but for large models the problem remains computationally intractable. Herein, we demonstrate that even at the genome scale only the lowest-cardinality MCSs are required to efficiently approximate the PoF with reasonable accuracy. We analysed the robustness of 489 E. coli, Shigella, Salmonella, and fungal genome-scale metabolic models (GSMMs). In contrast to the popular "congruence theory", which explains the origin of genetic robustness as a byproduct of selection for environmental flexibility, we found no correlation between network robustness and the diversity of growth-supporting environments. On the contrary, our analysis indicates that amino acid synthesis rather than carbon metabolism dominates metabolic robustness.

systems biology↗

bioRxiv2020

Internetwork connectivity of molecular networks across species of life

BackgroundMolecular interactions have been studied as independent complex networks in systems biology. However, molecular networks dont exist independently of each other. In a network of networks approach (called multiplex), we uncover the design principles for the joint organization of transcriptional regulatory network (TRN) and protein-protein interaction (PPI) network. ResultsWe find that TRN and PPI networks are non-randomly coupled in the TRN-PPI multiplex across five different eukaryotic species. Gene degrees in TRN (number of downstream genes) are positively correlated with protein degrees in PPI (number of interacting protein partners). Gene-gene interactions in TRN and protein-protein interactions in PPI also non-randomly overlap in the multiplex. These design principles are conserved across the five eukaryotic species. We show that the robustness of the TRN-PPI multiplex is dependent on these design principles. Further, functionally important genes and proteins, such as essential, disease-related and those involved in host-pathogen PPI networks, are preferentially situated in essential parts of the human multiplex with highly overlapping interactions. ConclusionWe unveil the multiplex architecture of TRN and PPI networks across different species. Multiplex architecture may thus define a general framework for studying molecular networks across the different species of life. This approach may uncover the building blocks of the hierarchical organization of molecular interactions.

systems biology↗

bioRxiv2020

gapseq: Informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models

Microbial metabolic processes greatly impact ecosystem functioning and the physiology of multi-cellular host organisms. The inference of metabolic capabilities and phenotypes from genome sequences with the help of reference biomolecular knowledge stored in online databases remains a major challenge in systems biology. Here, we present gapseq: a novel tool for automated pathway prediction and metabolic network reconstruction from microbial genome sequences. gapseq combines databases of reference protein sequences (UniProt, TCDB), in tandem with pathway and reaction databases (MetaCyc, KEGG, ModelSEED). This enables the prediction of an organism’s metabolic capabilities from sequence homology and pathway topology criteria. By incorporating a novel LP-based gap-filling algorithm, gapseq facilitates the construction of genome-scale metabolic models that are suitable for metabolic phenotype predictions by using constraint-based flux analysis. We validated gapseq by comparing predictions to experimental data for more than 3, 000 bacterial organisms comprising 14, 895 phenotypic traits that include enzyme activity, energy sources, fermentation products, and gene essentiality. This large-scale phenotypic trait prediction test showed, that gapseq yields an overall accuracy of 81% and thereby outperforms other commonly used reconstruction tools. Furthermore, we illustrate the application of gapseq-reconstructed models to simulate biochemical interactions between microorganisms in multi-species communities. Altogether, gapseq is a new method that improves the predictive potential of automated metabolic network reconstructions and further increases their applicability in biotechnological, ecological, and medical research. gapseq is available at https://github.com/jotech/gapseq.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗

bioRxiv2021

Stop Bickering! Reconciling Signaling Pathway Databases with Network Topologies

A major goal of molecular systems biology is to understand the coordinated function of genes or proteins in response to cellular signals and to understand these dynamics in the context of disease. Signaling pathway databases such as KEGG, NetPath, NCI-PID, and Panther describe the molecular interactions involved in different cellular responses. While the same pathway may be present in different databases, prior work has shown that the particular proteins and interactions differ across database annotations. However, to our knowledge no one has attempted to quantify their structural differences. It is important to characterize artifacts or other biases within pathway databases, which can provide a more informed interpretation for downstream analyses. In this work, we consider signaling pathways as graphs and we use topological measures to study their structure. We find that topological characterization using graphlets (small, connected subgraphs) distinguishes signaling pathways from appropriate null models of interaction networks. Next, we quantify topological similarity across pathway databases. Our analysis reveals that the pathways harbor database-specific characteristics implying that even though these databases describe the same pathways, they tend to be systematically different from one another. We show that pathway-specific topology can be uncovered after accounting for database-specific structure. This work present the first step towards elucidating common pathway structure beyond their specific database annotations.

systems biology↗

bioRxiv2020

Single Cell-type Integrative Network Modeling IdentifiedNovel Microglial-specific Targets for the Phagosome inAlzheimer's disease

SummaryLate-Onset Alzheimer’s Disease (LOAD) results from a complex pathological process influenced by genetic variation, aging and environment factors. Genetic susceptibility factors indicate that myeloid cells such as microglia play a significant role in the onset of LOAD. Here, we developed a computational systems biology approach to construct probabilistic causal and predictive network models of genetic regulatory programs of microglial cells under LOAD diagnosis by integrating two independent brain transcriptome and genome-wide genotype datasets from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP) and Mayo Clinic (MAYO) studies in the AMP-AD consortium. From this network model, we identified and replicated novel microglial-specific master regulators predicted to modulate network states associated with LOAD. We experimentally validated three microglial master regulators (FCER1G, HCK and LAPTM5) in primary human microglia-like cells (MDMi) by demonstrating the molecular impact these master regulators have on modulating downstream genomic targets identified by our top-down/bottom-up method and the causal relations among the three key drivers. These master regulators are involved in phagocytosis, a process associated with LOAD. Thus, we propose three new master regulator (key driver) genes that emerged from our network analyses as robust candidates for further evaluation in LOAD therapeutic development efforts.Competing Interest StatementThe authors have declared no competing interest.View Full Text

systems biology↗

bioRxiv2020

Elucidating Human Milk Oligosaccharide biosynthetic genes through network-based multi-omics integration

AO_SCPLOWBSTRACTC_SCPLOWHuman Milk Oligosaccharides (HMOs) are abundant carbohydrates fundamental to infant health and development. Although these oligosaccharides were discovered more than half a century ago, their biosynthesis in the mammary gland remains largely uncharacterized. Here, we used a systems biology framework that integrated glycan and RNA expression data to construct an HMO biosynthetic network and predict glycosyltransferases involved. To accomplish this, we constructed models describing the most likely pathways for the synthesis of the oligosaccharides accounting for >95% of the HMO content in human milk. Through our models, we propose candidate genes for elongation, branching, fucosylation, and sialylation of HMOs. We further explored selected enzyme activities through kinetic assay and their co-regulation through transcription factor analysis. These results provide the molecular basis of HMO biosynthesis necessary to guide progress in HMO research and application with the ultimate goal of understanding and improving infant health and development. SO_SCPLOWIGNIFICANCEC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWSTATEMENTC_SCPLOWWith the HMO biosynthesis network resolved, we can begin to connect genotypes with milk types and thereby connect clinical infant, child and even adult outcomes to specific HMOs and HMO modifications. Knowledge of these pathways can simplify the work of synthetic reproduction of these HMOs providing a roadmap for improving infant, child, and overall human health with the specific application of a newly limitless source of nutraceuticals for infants and people of all ages.

systems biology↗

bioRxiv2014

Predicting growth conditions from internal metabolic fluxes in an in-silico model of E. coli

A widely studied problem in systems biology is to predict bacterial phenotype from growth conditions, using mechanistic models such as flux balance analysis (FBA). However, the inverse prediction of growth conditions from phenotype is rarely considered. Here we develop a computational framework to carry out this inverse prediction on a computational model of bacterial metabolism. We use FBA to calculate bacterial phenotypes from growth conditions in E. coli, and then we assess how accurately we can predict the original growth conditions from the phenotypes. Prediction is carried out via regularized multinomial regression. Our analysis provides several important physiological and statistical insights. First, we show that by analyzing metabolic end products we can consistently predict growth conditions. Second, prediction is reliable even in the presence of small amounts of impurities. Third, flux through a relatively small number of reactions per growth source (~10) is sufficient for accurate prediction. Fourth, combining the predictions from two separate models, one trained only on carbon sources and one only on nitrogen sources, performs better than models trained to perform joint prediction. Finally, that separate predictions perform better than a more sophisticated joint prediction scheme suggests that carbon and nitrogen utilization pathways, despite jointly affecting cellular growth, may be fairly decoupled in terms of their dependence on specific assortments of molecular precursors.

Systems Biology↗

bioRxiv2023

ModelSEED v2: High-throughput genome-scale metabolic model reconstruction with enhanced energy biosynthesis pathway prediction

Since the release of ModelSEED in 2010, the systems biology research community has used the ModelSEED genome-scale metabolic model reconstruction pipeline to build over 200,000 draft metabolic reconstructions that support hundreds of publications. Here we describe the first comprehensive update to this reconstruction tool, with new features such as (i) a dramatically improved representation of energy metabolism, which ensures that models produce accurate amounts of ATP per mol of nutrient consumed; (ii) a new template for Archaea model reconstruction; and (iii) a significantly improved curation of all metabolic pathways with mappings to RAST subsystems annotations. We applied the improved pipeline to build and analyze core and genome-scale models for Archaea and Bacteria genomes in KEGG. The new ModelSEED pipeline generates larger models that possess more reactions and genes and require fewer gap-filled reactions. In addition, we see conserved patterns in the ATP biosynthesis mechanism across phylogeny, and identify clades where our understanding of energy biosynthesis is still poor. ModelSEED v2 is available through KBase and the ModelSEED website (https://modelseed.org/), which supports reconstruction, gap-filling, flux balance analysis and model export through a modern web interface.

systems biology↗

bioRxiv2021

Interactions of host defense and hyper-keratinization in psoriasis

ObjectivesTo understand the crosstalk between the host and microbiota in psoriatic skin, using a systems biology approach based on transcriptomics and microbiome profiling. MethodsWe collected the skin tissue biopsies and swabs in both lesion and non-lesion skin of 13 patients with psoriasis (PsO), 15 patients with psoriatic arthritis (PsA), and healthy skin from 12 patients with ankylosing spondylitis (AS). We performed transcriptome sequencing and metagenomics profiling on the local skin sites to study the similarities and differences in the molecular profiles between the three conditions, and the associations between the host defense and microbiota dynamic. ResultsWe found that lesion and non-lesional samples were remarkably different in terms of their transcriptome profiles. Functional annotation of differentially expressed genes (DEGs) showed a major enrichment in neutrophil activation. By using coexpression gene networks, we identified a gene module that was associated with local psoriasis severity at the site of biopsy. From this module, we extracted a "core" set of genes that were functionally involved in neutrophil activation, epidermal cell differentiation and response to bacteria. Skin microbiome analysis revealed that the abundance of Enhydrobacter, Micrococcus and Leptotrichia were significantly correlated with the "core network" of genes. ConclusionsWe identified a core network that regulates inflammation and hyper-keratinization in psoriatic skin, and is associated with local disease severity and microbiome composition.

systems biology↗

bioRxiv2020

Inference and validation of an integrated regulatory network of autism

Autism is a complex neurodevelopmental disorder. Functional roles of several non-coding transcripts including long noncoding RNAs (lncRNAs) have been shown to influence the pathobiology of autism. We hypothesized that there are more autism-associated lncRNAs to be discovered. Here, we utilized a systems biology approach to identify novel lncRNAs that might play a role in the molecular pathogenesis of autism. Based on the data provided by the Simons Foundation Autism Research Initiative (SFARI), a three-component regulatory network comprising mRNAs, microRNAs (miRNAs) and lncRNAs was constructed. Functional enrichment analysis was performed to identify molecular pathways potentially mediated by components of the network. The potential association of four candidate lncRNAs with autism was investigated experimentally by developing and verifying a valproic acid (VPA)-exposed mouse model of autism. We composed a network of 33 mRNA, 25 miRNA and 4 lncRNA nodes associated with neurologically-relevant pathways and functions. We then verified the differential expression of four candidate lncRNAs: Gm10033, 1500011B03Rik, A930005H10Rik and Gas5 in the brain of VPA-exposed mice. We furthermore identified a novel splice variant of Gm10033, designated as Gm10033-{Delta}Ex2, which was expressed in various mouse tissues. The integrative approach, we utilized, combines the analysis of a three-component regulatory network with experimental validation of targets in an animal model of autism. As a result of the analysis, we prioritized a set of candidate autism-associated lncRNAs. These links add to the common understanding of the molecular and cellular mechanisms that are involved in disease etiology, specifically in the autism.

systems biology↗

bioRxiv2020

Data-independent Acquisition-based Proteome and Phosphoproteome Profiling across Six Melanoma Cell Lines Reveals Determinants of Proteotypes

Human cancer cell lines are widely used in pharmacological and systems biological studies. The rapid documentation of the steady-state gene expression landscape of the cells used in a particular experiment may help to improve the reproducibility of scientific research. Here we applied a data-independent acquisition mass spectrometry (DIA-MS) method, coupled with a peptide spectral-library free data analysis workflow, to measure both proteome and phosphoproteome of a melanoma cell line panel with different metastatic properties. For each cell line, the single-shot DIA-MS detected 8,100 proteins and almost 40,000 phosphopeptides in the respective measurement of two hours. Benchmarking the DIA-MS data towards the RNA-seq data and tandem mass tag (TMT)-MS results from the same set of cell lines demonstrated comparable qualitative coverage and quantitative reproducibility. Our data confirmed the high but complex mRNA~protein and protein~phospsite correlations. The results successfully established DIA-MS as a strong and competitive proteotyping approach for cell lines. The data further showed that all subunits of Glycosylphosphatidylinositol (GPI)-anchor transamidase complex were overexpressed in metastatic melanoma cells and identified altered phosphoprotein modules such as BAF complex and mRNA splicing between metastatic and primary cells. This study provides a high-quality resource for calibrating DIA-MS performance, benchmarking DIA bioinformatic algorithms, and exploring the metastatic proteotypes in melanoma cells.

systems biology↗

bioRxiv2023

Essential Gene Knockdowns Reveal Genetic Vulnerabilities and Antibiotic Sensitivities in Acinetobacter baumannii

The emergence of multidrug-resistant Gram-negative bacteria underscores the need to define genetic vulnerabilities that can be therapeutically exploited. The Gram-negative pathogen, Acinetobacter baumannii, is considered an urgent threat due to its propensity to evade antibiotic treatments. Essential cellular processes are the target of existing antibiotics and a likely source of new vulnerabilities. Although A. baumannii essential genes have been identified by transposon sequencing (Tn-seq), they have not been prioritized by sensitivity to knockdown or antibiotics. Here, we take a systems biology approach to comprehensively characterize A. baumannii essential genes using CRISPR interference (CRISPRi). We show that certain essential genes and pathways are acutely sensitive to knockdown, providing a set of vulnerable targets for future therapeutic investigation. Screening our CRISPRi library against last-resort antibiotics uncovered genes and pathways that modulate beta-lactam sensitivity, an unexpected link between NADH dehydrogenase activity and growth inhibition by polymyxins, and anticorrelated phenotypes that underpin synergy between polymyxins and rifamycins. Our study demonstrates the power of systematic genetic approaches to identify vulnerabilities in Gram-negative pathogens and uncovers antibiotic-essential gene interactions that better inform combination therapies. ImportanceAcinetobacter baumannii is a hospital-acquired pathogen that is resistant to many common antibiotic treatments. To combat resistant A. baumannii infections, we need to identify promising therapeutic targets and effective antibiotic combinations. In this study, we comprehensively characterize the genes and pathways that are critical for A. baumannii viability. We show that genes involved in aerobic metabolism are central to A. baumannii physiology and may represent appealing drug targets. We also find antibiotic-gene interactions that may impact the efficacy of carbapenems, rifamycins, and polymyxins, providing a new window into how these antibiotics function in mono- and combination therapies. Our studies offer a useful approach for characterizing interactions between drugs and essential genes in pathogens to inform future therapies.

systems biology↗

bioRxiv2025

The MicroMap is a network visualisation resource for microbiome metabolism

The human microbiome plays a crucial role in metabolism and thereby influences health and disease. Constraint-based reconstruction and analysis (COBRA) has proven an attractive framework to generate mechanism-derived hypotheses along the nutrition-host-microbiome-disease axis within the computational systems biology community. Unlike for human, no large-scale visualisation resource for microbiome metabolism has been available to date. To address this gap, we created the MicroMap, a manually curated microbiome metabolic network visualisation, which captures the metabolic content of over a quarter million microbial genome-scale metabolic reconstructions. The MicroMap contains 5,064 unique reactions and 3,499 unique metabolites, including for 98 drugs. The MicroMap allows users to intuitively explore microbiome metabolism, inspect microbial metabolic capabilities, and visualise computational modelling results. Further, the MicroMap shall serve as an educational tool to make microbiome metabolism accessible to broader audiences beyond computational modellers. For example, we utilised the MicroMap to generate a comprehensive collection of 257,429 visualisations, corresponding to the entire scope of our current microbiome reconstruction resources, to enable users to visually compare and contrast the metabolic capabilities for diaerent microbes. The MicroMap seamlessly integrates with the Virtual Metabolic Human (VMH, www.vmh.life) and the COBRA Toolbox (opencobra.github.io), and is freely accessible at the MicroMap dataverse (https://dataverse.harvard.edu/dataverse/micromap), in addition to all the generated reconstruction visualisations.

systems biology↗

bioRxiv2025

Percolation and lifestyle transition in microbial metabolism

Microbial species exhibit a remarkable diversity in their metabolic properties, genome composition, and ecological distribution. A central challenge of systems biology is to understand the relationships between genomic, metabolic, and phenotypic properties of bacteria. However, it is currently not well understood how the structure of metabolic network defines and reflects the lifestyle of diverse bacterial species across the tree of life. By analyzing thousands of genome-scale metabolic models of bacteria, we found a percolation-like transition in their ability to grow on independent carbon sources at around 800 metabolic reactions or about 2000 protein-coding genes. The observed transition is characterized by significant changes in metabolic network functional connectivity primarily associated with the completion of central carbon metabolism and the TCA cycle. Strikingly, experimentally observed phenotypic properties of bacteria also exhibit two markedly different regimes below and above the transition. Species with metabolic network sizes below the transition are typically obligate symbionts and require complex minimal media for their growth. In contrast, species with networks above the transition are primarily free- living generalists. The observed percolation transition is also reflected in multiple other genomic properties, such as a substantial decrease in the fraction of regulatory genes below the transition and higher evolvability for new metabolic phenotypes above the transition. Furthermore, we find that the distribution of bacterial genome sizes from unbiased environmental metagenomic sequencing also reflects genomic clusters corresponding to the observed transition. Overall, our work identifies two qualitatively different regimes in microbial metabolism and lifestyles characterized by distinct structural and functional properties of their metabolic networks.

systems biology↗

bioRxiv2018

RNA-Seq and Protein Mass Spectrometry in Microdissected Kidney Tubules Reveal Signaling Processes that Initiate Lithium-Induced Diabetes Insipidus

ABSTRACT1Lithium salts, used for treatment of bipolar disorder, frequently induce nephrogenic diabetes insipidus (NDI), limiting therapeutic success. NDI is associated with loss of expression of the molecular water channel, aquaporin-2, in the renal collecting duct (CD). Here, we use the methods of systems biology in a well-established rat model of lithium-induced NDI to identify signaling pathways activated at the onset of polyuria. Using single-tubule RNA-Seq, full transcriptomes were determined in microdissected cortical CDs of rats 72 hrs after initiation of lithium chloride (LiCl) administration (vs. time-controls without LiCl). Transcriptome-wide changes in mRNA abundances were mapped to gene sets associated with curated canonical signaling pathways, showing evidence for activation of NF-{kappa}B signaling with induction of genes coding for multiple chemokines as well as most components of the Major Histocompatibility Complex (MHC) Class I antigen-presenting complex. Administration of antiinflammatory doses of dexamethasone to LiCl-treated rats countered the loss of aquaporin-2 protein. RNA-Seq also confirmed prior evidence of a shift from quiescence into the cell cycle with arrest. Time course studies demonstrated an early (12 hrs) increase in multiple immediate early genes including several transcription factors. Protein mass spectrometry in microdissected cortical CDs provided corroborative evidence but also identified decreased abundance of several anti-oxidant proteins. Integration of new data with prior data about lithium effects at a molecular level leads to a signaling model in which lithium increases ERK activation leading to induction of NF-{kappa}B signaling and an inflammatory-like response that represses Aqp2 gene transcription.

systems biology↗

bioRxiv2024

ARID1A-induced transcriptional reprogramming rewires signalling responses to drug treatment in melanoma

Resistance to BRAF and MAPK inhibitors is a significant challenge in melanoma treatment, driven by adaptive and acquired mechanisms that allow tumour cells to evade therapy. Here, we examined early signalling responses to single and combined BRAF and MAPK inhibition in a BRAFV600E, drug-sensitive melanoma cell line and a drug-resistant ARID1A-knockout (KO) derivative. ARID1A, frequently mutated in melanoma, is associated with resistance and immune evasion. Using an innovative systems biology approach that integrates transcriptomics, proteomics, phosphoproteomics, and functional kinomics through matrix factorization and network analysis, we identified key signalling alterations and resistance mechanisms. We found that ARID1A-KO cells exhibited transcriptional rewiring, sustaining MAPK1/3 and JNK activity post-treatment, bypassing feedback sensitivity observed in parental cells. This rewiring suppressed PRKD1 activation, increased JUN activity--a central resistance network node--and disrupted PKC dynamics through elevated basal RTKs (e.g., EGFR, ROS1) and Ephrin receptor activity post-treatment. ARID1A mutations also reduced HLA-related protein expression and enriched extracellular matrix components, potentially limiting immune infiltration and reducing immunotherapy efficacy. Our graph-theoretical multi-omics approach uncovered novel resistance-associated signalling pathways, identifying PRKD1, JUN, and NCK1 as critical nodes. While receptor activation redundancies complicate single-target therapies, they also present opportunities for combination strategies. This study highlights ARID1As role in reshaping signalling and immune interactions, offering new insights into melanoma resistance mechanisms. By identifying actionable targets, including JUN and immune pathways, we provide a foundation for developing integrated therapeutic strategies to overcome resistance in BRAF/MAPK inhibitor-treated melanoma. One sentence summaryThis study reveals how ARID1A-mediated transcriptional rewiring drives resistance to MAPK inhibitors in melanoma by altering signalling pathways, immune interactions, and receptor dynamics, highlighting potential targets for combinatorial therapies.

systems biology↗