Characterization of the molecular, cellular, and behavioral changes caused by exposure to a saline-alkali environment in the Chinese mitten crab, Eriocheir sinensis.

In the context of global warming, the accelerated evaporation of seawater will lead to a continuous expansion of saline-alkali land area. As an important economic freshwater crustacean, investigation on the mechanism of damage to Eriocheir sinensis (E. sinensis) under saline-alkali environment will provide a valuable precedent for understanding the detrimental effect of climate change on crustaceans. In this study, histopathological analysis and integrative omics analysis were employed to explore the injury mechanism on the cerebral nervous system of E. sinensis exposure to saline-alkali stress. Our findings revealed that under this stress E. sinensis exhibited behavioral disorders and damage to cerebral neurosecretory cells and key organelles. Additionally, several pathways related to detoxification metabolism, neurotransmitter synthesis, and antioxidant defense were significantly down-regulated. Collectively, these results show, for the first time, that saline-alkali stress can induce neurodegenerative disease-like symptoms in E. sinensis, and provide critical information for understanding the harmful effects of saline-alkali environments.

Integrated multi-omics profiling of nonfunctioning pituitary adenomas.

PURPOSE: Genetic and epigenetic alterations are involved in pituitary adenoma pathogenesis, however the molecular basis of proliferative nonfunctioning pituitary adenomas (NFPAs) remains unclear. Here, we analyzed integrated multi-omics profiling including copy number variation (CNV), DNA methylation and gene expression of 8 NFPAs. METHODS: We collected 4 highly proliferative (hpNFPA, Ki-67 ≥ 3) and 4 lowly proliferative (Ki-67 ≤ 1) NFPAs, and comprehensively assessed CNV, DNA methylation, and gene expression by Illumina HumanMethylation450 BeadChip and Affymetrix GeneChip PrimeView Human Gene Expression Array. We performed Ingenuity Pathway Analysis (IPA) for differentially expressed genes to illustrate aberrant pathways and delineated protein-protein networks of selected key genes in dysregulated pathways. RESULTS: Aberrant arm level CNV, dysregulated DNA methylation, and associated impacts on gene expressions were observed in 2 early occurring hpNFPAs. Chromosomal losses were associated with attenuated expression of DNA methyltransferases, further altering global methylation in these 2 samples. Correlation analysis between DNA methylation and gene expression in 8 NFPAs indicates methylation in promoter and gene body regions are both involved in gene regulation. IPA showed PPARα/RXRα, dopamine receptor signaling, cAMP-mediated signaling, and calcium signaling were all activated, while p38 MAPK and ERK5 signaling were inhibited in hpNFPAs. Moreover, selected key gene networks in hpNFPAs exhibited concurrent methylation status and expression levels of adenylate cyclase genes, G protein subunits, HLA genes, CXCL12, and CCL2. CONCLUSION: This study presents comprehensive multi-omics views of CNV, DNA methylation, and gene expression in 8 NFPAs. Pathway analysis and network maps of key genes provide clues to elucidate the molecular basis of hpNFPA.

Flexible Data Trimming Improves Performance of Global Machine Learning Methods in Omics-Based Personalized Oncology.

(1) Background: Machine learning (ML) methods are rarely used for an omics-based prescription of cancer drugs, due to shortage of case histories with clinical outcome supplemented by high-throughput molecular data. This causes overtraining and high vulnerability of most ML methods. Recently, we proposed a hybrid global-local approach to ML termed floating window projective separator (FloWPS) that avoids extrapolation in the feature space. Its core property is data trimming, i.e., sample-specific removal of irrelevant features. (2) Methods: Here, we applied FloWPS to seven popular ML methods, including linear SVM, k nearest neighbors (kNN), random forest (RF), Tikhonov (ridge) regression (RR), binomial naïve Bayes (BNB), adaptive boosting (ADA) and multi-layer perceptron (MLP). (3) Results: We performed computational experiments for 21 high throughput gene expression datasets (41-235 samples per dataset) totally representing 1778 cancer patients with known responses on chemotherapy treatments. FloWPS essentially improved the classifier quality for all global ML methods (SVM, RF, BNB, ADA, MLP), where the area under the receiver-operator curve (ROC AUC) for the treatment response classifiers increased from 0.61-0.88 range to 0.70-0.94. We tested FloWPS-empowered methods for overtraining by interrogating the importance of different features for different ML methods in the same model datasets. (4) Conclusions: We showed that FloWPS increases the correlation of feature importance between the different ML methods, which indicates its robustness to overtraining. For all the datasets tested, the best performance of FloWPS data trimming was observed for the BNB method, which can be valuable for further building of ML classifiers in personalized oncology.

Big data in yeast systems biology.

Systems biology uses computational and mathematical modeling to study complex interactions in a biological system. The yeast Saccharomyces cerevisiae, which has served as both an important model organism and cell factory, has pioneered both the early development of such models and modeling concepts, and the more recent integration of multi-omics big data in these models to elucidate fundamental principles of biology. Here, we review the advancement of big data technologies to gain biological insight in three aspects of yeast systems biology: gene expression dynamics, cellular metabolism and the regulation network between gene expression and metabolism. The role of big data and complementary modeling approaches, including the expansion of genome-scale metabolic models and machine learning methodologies, are discussed as key drivers in the rapid advancement of yeast systems biology.

Molecular disease presentation in diabetic nephropathy.

Diabetic nephropathy, as the most prevalent chronic disease of the kidney, has also become the primary cause of end-stage renal disease with the incidence of kidney disease in type 2 diabetics continuously rising. As with most chronic diseases, the pathophysiology is multifactorial with a number of deregulated molecular processes contributing to disease manifestation and progression. Current therapy mainly involves interfering in the renin-angiotensin-aldosterone system using angiotensin-converting enzyme inhibitors or angiotensin-receptor blockers. Better understanding of molecular processes deregulated in the early stages and progression of disease hold the key for development of novel therapeutics addressing this complex disease. With the advent of high-throughput omics technologies, researchers set out to systematically study the disease on a molecular level. Results of the first omics studies were mainly focused on reporting the highest deregulated molecules between diseased and healthy subjects with recent attempts to integrate findings of multiple studies on the level of molecular pathways and processes. In this review, we will outline key omics studies on the genome, transcriptome, proteome and metabolome level in the context of DN. We will also provide concepts on how to integrate findings of these individual studies (i) on the level of functional processes using the gene-ontology vocabulary, (ii) on the level of molecular pathways and (iii) on the level of phenotype molecular models constructed based on protein-protein interaction data.

Micro-/nano-plastics as vectors of heavy metals and stress response of ciliates using transcriptomic and metabolomic analyses.

The escalating presence of microplastics and heavy metals in marine environments significantly jeopardizes ecological stability and human health. Despite this, research on the combined effects of microplastics/nanoplastics (MPs/NPs) and heavy metals on marine organisms remains limited. This study evaluated the impact of two sizes of polystyrene beads (approximately 2 µm and 200 nm) combined with cadmium (Cd) on the ciliate species Euplotes vannus. Results demonstrated that co-exposure of MPs/NPs and Cd markedly elevated reactive oxygen species (ROS) levels in ciliates while impairing antioxidant enzyme activities, thus enhancing oxidative damage and significantly reducing carbon biomass in ciliates. Transcriptomic profiling indicated that co-exposure of MPs/NPs and Cd potentially caused severe DNA damage and protein oxidation, as evidenced by numerous differentially expressed genes (DEGs) associated with mismatch repair, DNA replication, and proteasome function. Integrated transcriptomic and metabolomic analysis revealed that DEGs and differentially accumulated metabolites (DAMs) were significantly enriched in the TCA cycle, glycolysis, tryptophan metabolism, and glutathione metabolism. This suggests that co-exposure of MPs/NPs and Cd may reduce ciliate abundance and carbon biomass by inhibiting energy metabolism and antioxidant pathways. Additionally, compared to MPs, the co-exposure of NPs and Cd exhibited more severe negative effects due to the larger specific surface area of NPs, which can carry more Cd. These findings provide novel insights into the toxic effects of MPs/NPs and heavy metals on protozoan ciliates, offering foundational data for assessing the ecological risks of heavy metals exacerbated by MPs/NPs.

Fully defined NGN2 neuron protocol reveals diverse signatures of neuronal maturation.

NGN2-driven induced pluripotent stem cell (iPSC)-to-neuron conversion is a popular method for human neurological disease modeling. In this study, we present a standardized approach for generating neurons utilizing clonal, targeted-engineered iPSC lines with defined reagents. We demonstrate consistent production of excitatory neurons at scale and long-term maintenance for at least 150 days. Temporal omics, electrophysiological, and morphological profiling indicate continued maturation to postnatal-like neurons. Quantitative characterizations through transcriptomic, imaging, and functional assays reveal coordinated actions of multiple pathways that drive neuronal maturation. We also show the expression of disease-related genes in these neurons to demonstrate the relevance of our protocol for modeling neurological disorders. Finally, we demonstrate efficient generation of NGN2-integrated iPSC lines. These workflows, profiling data, and functional characterizations enable the development of reproducible human in vitro models of neurological disorders.

MCMV-infected maize attracts its insect vector Frankliniella occidentalis by inducing ß-myrcene.

Maize lethal necrosis is attributed to the accumulation of maize chlorotic mottle virus (MCMV), an invasive virus transmitted by insect vectors. The western flower thrips (WFT) can shift host to maize, thus promoting the spread of MCMV. However, our understanding of the characteristics and interactions involved in the transmission of MCMV is still limited. This study finds that non-viruliferous WFTs showed a 57.56% higher preference for MCMV-infected maize plants compared to healthy maize plants, while viruliferous WFTs showed a 53.70% higher preference for healthy maize plants compared to MCMV-infected maize plants. We also show for the first time that both adults and larvae of WFT could successfully acquire MCMV after 1 min of acquisition access period (AAP), and after 48 h of AAP, WFT could transmit MCMV in an inoculation access period of 1 h without a latent period. Both adults and larvae of WFT can transmit MCMV for up to 2 days. Furthermore, the decreasing number of viruliferous WFTs and transmission rates as time progressed, together with the transcriptomic evidence, collectively suggest that WFTs transmit MCMV in a semi-persistent method, a mode of transmission requiring minutes to several hours for acquisition access and having a retention time of several hours to a few days. Additionally, ß-myrcene can attract WFTs significantly and is detected in Nicotiana benthamiana plants transiently expressing MCMV CP (coat protein), which is consistent with results in MCMV-infected maize plants through the metabolomic profiling and the preference analyses of WFT. Therefore, this study demonstrates the indirect interaction between MCMV and WFT by inducing maize to synthesize ß-myrcene to attract insect vectors. The exploration of specific interactions between MCMV and WFT could help to expand the mechanism studies of virus-vector-host plant interaction and put forward a new insight for the combined control of MCMV and WFT through the manipulation of plant volatiles and key insect genes.

Defining multiple layers of intratumor heterogeneity based on variations of perturbations in multi-omics profiling.

BACKGROUND: Intratumor heterogeneity (ITH) plays a crucial role in tumor progression, relapse, immune evasion, and drug resistance. Existing ITH quantification methods based on a single molecular level are inadequate to capture ITH evolving from genotype to phenotype. METHODS: We designed a set of information entropy (IE)-based algorithms for quantifying ITH at the genome (somatic copy number alterations and mutations), mRNA, microRNA (miRNA), long non-coding RNA (lncRNA), protein, and epigenome level, respectively. We evaluated the performance of these algorithms by analyzing the correlations between their ITH scores and ITH-associated molecular and clinical features in 33 TCGA cancer types. Moreover, we evaluated the correlations between the ITH measures at different molecular levels by Spearman correlation and clustering analysis. RESULTS: The IE-based ITH measures had significant correlations with unfavorable prognosis, tumor progression, genomic instability, antitumor immunosuppression, and drug resistance. The mRNA ITH showed stronger correlations with the miRNA, lncRNA, and epigenome ITH than with the genome ITH, supporting the regulatory relationships of miRNA, lncRNA, and DNA methylation towards mRNA. The protein-level ITH displayed stronger correlations with the transcriptome-level ITH than with the genome-level ITH, supporting the central dogma of molecular biology. Clustering analysis based on the ITH scores identified four subtypes of pan-cancer showing significantly different prognosis. Finally, the ITH integrating the seven ITH measures displayed more prominent properties of ITH than that at a single level. CONCLUSIONS: This analysis provides landscapes of ITH at various molecular levels. Combining the ITH observation from different molecule levels will improve personalized management for cancer patients.

Exploiting Multi-Omics Profiling and Systems Biology to Investigate Functions of TOMM34.

Although modern biology is now in the post-genomic era with vastly increased access to high-quality data, the set of human genes with a known function remains far from complete. This is especially true for hundreds of mitochondria-associated genes, which are under-characterized and lack clear functional annotation. However, with the advent of multi-omics profiling methods coupled with systems biology algorithms, the cellular role of many such genes can be elucidated. Here, we report genes and pathways associated with TOMM34, Translocase of Outer Mitochondrial Membrane, which plays role in the mitochondrial protein import as a part of cytosolic complex together with Hsp70/Hsp90 and is upregulated in various cancers. We identified genes, proteins, and metabolites altered in TOMM34-/- HepG2 cells. To our knowledge, this is the first attempt to study the functional capacity of TOMM34 using a multi-omics strategy. We demonstrate that TOMM34 affects various processes including oxidative phosphorylation, citric acid cycle, metabolism of purine, and several amino acids. Besides the analysis of already known pathways, we utilized de novo network enrichment algorithm to extract novel perturbed subnetworks, thus obtaining evidence that TOMM34 potentially plays role in several other cellular processes, including NOTCH-, MAPK-, and STAT3-signaling. Collectively, our findings provide new insights into TOMM34's cellular functions.

Patient-derived gene and protein expression signatures of NGLY1 deficiency.

N-Glycanase 1 (NGLY1) deficiency is a rare and complex genetic disorder. Although recent studies have shed light on the molecular underpinnings of NGLY1 deficiency, a systematic characterization of gene and protein expression changes in patient-derived cells has been lacking. Here, we performed RNA-sequencing and mass spectrometry to determine the transcriptomes and proteomes of 66 cell lines representing four different cell types derived from 14 NGLY1 deficient patients and 17 controls. Although NGLY1 protein levels were up to 9.5-fold downregulated in patients compared with parents, residual and likely non-functional NGLY1 protein was detectable in all patient-derived lymphoblastoid cell lines. Consistent with the role of NGLY1 as a regulator of the transcription factor Nrf1, we observed a cell type-independent downregulation of proteasomal genes in NGLY1 deficient cells. In contrast, genes involved in ribosome biogenesis and mRNA processing were upregulated in multiple cell types. In addition, we observed cell type-specific effects. For example, genes and proteins involved in glutathione synthesis, such as the glutamate-cysteine ligase subunits GCLC and GCLM, were downregulated specifically in lymphoblastoid cells. We provide a web application that enables access to all results generated in this study at https://apps.embl.de/ngly1browser. This resource will guide future studies of NGLY1 deficiency in directions that are most relevant to patients.

New insights into aging-associated characteristics of female subcutaneous adipose tissue through integrative analysis of multi-omics data.

Aging could be critical in limiting the application of subcutaneous adipose tissue (SAT) in tissue repair and reconstruction. However, no systematic study on the characteristics of SAT aging has been conducted. In this study, a scanning electronic microscope was used to detect the structural and compositional changes of SAT collected from nine females in three age groups. Multi-omics data of SAT from 37 females were obtained from Gene Expression Omnibus database, and 1860 genes, 56 miRNAs, and 332 methylated genes were identified as being differentially expressed during aging among non-obese females. Using Weighted Correlation Network Analysis (WGCNA), 1754 DEGs were defined as aging-associated genes for non-obese females, distributed among ten co-expression modules. Through Gene Ontology enrichment analysis and Gene Set enrichment analysis on those aging-associated DEGs, SAT aging was observed to be characterized by variations in immune and inflammatory states, mitochondria, lipid and carbohydrate metabolism, and regulation of vascular development. SUPV3L1, OGT, and ARPC1B were identified as conserved and core SAT-aging-related genes, as verified by RT-qPCR among 18 samples in different age groups. Multi-omics regulatory networks of core aging-associated biological processes of SAT were also constructed. Based on WGCNA, we performed differential co-expression analysis to unveil the differences in aging-related co-expression patterns between obese and non-obese females and determined that obesity could be an important accelerating factor in aging processes. Our work provides a landscape of SAT aging, which could be helpful for further research in fields such as repair and reconstruction as well as aging.

Multi-omics profiling identifies C1QA/B+ macrophages with multiple immune checkpoints associated with esophageal squamous cell carcinoma (ESCC) liver metastasis.

Background: Esophageal squamous cell carcinoma (ESCC) is a highly lethal malignant tumor lacking effective treatments; 20% of ESCC patients develop liver metastasis with an extremely short survival time of ≈5 months. The tumor microenvironment (TME) plays a crucial role in tumor homeostasis, but the relationship between the ESCC TME and liver metastasis is still unknown. Methods: To identify potential cell populations contributing to ESCC liver metastasis, single-cell RNA (scRNA) sequencing data were analyzed to identify the major cell populations within the TME. Each of the major cell populations was re-clustered to define detailed cell subsets. Thereafter, the gene set variation analysis (GSVA) score was calculated for the bulk RNA-seq data based on the gene signatures of each cell subset. The relationship between the GSVA score of each cellular subset and clinical outcome was further analyzed to identify the cellular subset associated with ESCC liver metastasis, which was validated by multiplex immunohistochemistry. Results: C1QA/B+ tumor-associated macrophages (TAMs) acted as the central regulator of the ESCC TME, closely associated with several key cell subsets. Several immune checkpoints, including CD40, CD47 and LGALS9, were all positively expressed in C1QA/B+ macrophages, which may exert central regulatory control of immune evasion by ESCC via these immune checkpoints expressions. Conclusions: Our results comprehensively revealed the landscape of tumor-infiltrating immune cells associated with ESCC prognosis and metastasis, and suggest a novel strategy for developing immunotherapies for ESCC liver metastasis by targeting C1QA/B+ TAMs.

Why Single-Cell Sequencing Has Promise in MDS.

Myelodysplastic syndromes (MDS) are a heterogeneous group of diseases characterized by ineffective hematopoiesis. The risk of MDS is associated with aging and the accumulation of somatic mutations in hematopoietic stem cells and progenitors (HSPC). While advances in DNA sequencing in the past decade unveiled clonal selection driven by mutations in MDS, it is unclear at which stage the HSPCs are trapped or what prevents mature cells output. Single-cell-sequencing techniques in recent years have revolutionized our understanding of normal hematopoiesis by identifying the transitional cell states between classical hematopoietic hierarchy stages, and most importantly the biological activities behind cell differentiation and lineage commitment. Emerging studies have adapted these powerful tools to investigate normal hematopoiesis as well as the clonal heterogeneity in myeloid malignancies and provide a progressive description of disease pathogenesis. This review summarizes the potential of growing single-cell-sequencing techniques, the evolving efforts to elucidate hematopoiesis in physiological conditions and MDS at single-cell resolution, and discuss how they may fill the gaps in our current understanding of MDS biology.

Double-jeopardy: scRNA-seq doublet/multiplet detection using multi-omic profiling.

The computational detection and exclusion of cellular doublets and/or multiplets is a cornerstone for the identification the true biological signals from single-cell RNA sequencing (scRNA-seq) data. Current methods do not sensitively identify both heterotypic and homotypic doublets and/or multiplets. Here, we describe a machine learning approach for doublet/multiplet detection utilizing VDJ-seq and/or CITE-seq data to predict their presence based on transcriptional features associated with identified hybrid droplets. This approach highlights the utility of leveraging multi-omic single-cell information for the generation of high-quality datasets. Our method has high sensitivity and specificity in inflammatory-cell-dominant scRNA-seq samples, thus presenting a powerful approach to ensuring high-quality scRNA-seq data.

Multi-omics profiling: the way towards precision medicine in metabolic diseases.

Metabolic diseases including type 2 diabetes mellitus (T2DM), non-alcoholic fatty liver disease (NAFLD), and metabolic syndrome (MetS) are alarming health burdens around the world, while therapies for these diseases are far from satisfying as their etiologies are not completely clear yet. T2DM, NAFLD, and MetS are all complex and multifactorial metabolic disorders based on the interactions between genetics and environment. Omics studies such as genetics, transcriptomics, epigenetics, proteomics, and metabolomics are all promising approaches in accurately characterizing these diseases. And the most effective treatments for individuals can be achieved via omics pathways, which is the theme of precision medicine. In this review, we summarized the multi-omics studies of T2DM, NAFLD, and MetS in recent years, provided a theoretical basis for their pathogenesis and the effective prevention and treatment, and highlighted the biomarkers and future strategies for precision medicine.

Genomic and Transcriptomic Characterization of Sporadic Medullary Thyroid Carcinoma.

Background: Sporadic medullary thyroid carcinoma (MTC) is a relatively uncommon neuroendocrine malignancy and the molecular tumorigenesis of its sporadic type (sMTC) is only partially understood. In this study, we performed a study focusing on the genomic and transcriptomic characterization of sMTC. Methods: Twenty-nine sMTC patients were included. Whole-exome sequencing (WES) was carried out in 18 patients, including both tumor samples and matched noncancerous tissues. Whole transcriptome sequencing (RNA-Seq) was performed in all 29 tumors. WES, RNA-Seq, and copy number alteration (CNA) data were analyzed. A Cell Counting Kit-8 (CCK-8) assay was used to evaluate cell proliferation. Results: Among the somatic mutations, RET was the only recurrently cancer-related mutated gene (5/18, 27.8%). In the germline, FAT1 and FAT4, two members of the FAT gene family, were identified as the two most common mutated genes. CNA analysis found that FAT1 and FAT4, both located on chromosome 4q, were also two of the genes most commonly affected by somatic chromosomal deletions (4/18, 22.2%). Using TT and MZ-CRC-1 cell lines, the CCK-8 assay showed that FAT1 and FAT4 knockdown could promote MTC cell proliferation. Based on the gene expression profile, patients were clustered into two molecular subtypes: the mesenchymal-like subtype is characterized by epithelial-mesenchymal transition, while the proliferative-like subtype is associated with enrichment of cell cycle pathways. Most events of structural recurrence (80%) occurred in the proliferative-like subtype. Conclusion: In addition to RET, these findings demonstrate that FAT1/FAT4 genomic alterations appear to be frequent in sMTC. Two molecular subtypes of sMTC with distinct biological behavior could be identified. However, these results need to be validated by larger samples and more comprehensive experiments in the future, especially for the frequency and function of FAT1/FAT4 germline variants.

Toward Systems Biomarkers of Response to Immune Checkpoint Blockers.

Immunotherapy with checkpoint blockers (ICBs), aimed at unleashing the immune response toward tumor cells, has shown a great improvement in overall patient survival compared to standard therapy, but only in a subset of patients. While a number of recent studies have significantly improved our understanding of mechanisms playing an important role in the tumor microenvironment (TME), we still have an incomplete view of how the TME works as a whole. This hampers our ability to effectively predict the large heterogeneity of patients' response to ICBs. Systems approaches could overcome this limitation by adopting a holistic perspective to analyze the complexity of tumors. In this Mini Review, we focus on how an integrative view of the increasingly available multi-omics experimental data and computational approaches enables the definition of new systems-based predictive biomarkers. In particular, we will focus on three facets of the TME toward the definition of new systems biomarkers. First, we will review how different types of immune cells influence the efficacy of ICBs, not only in terms of their quantification, but also considering their localization and functional state. Second, we will focus on how different cells in the TME interact, analyzing how inter- and intra-cellular networks play an important role in shaping the immune response and are responsible for resistance to immunotherapy. Finally, we will describe the potential of looking at these networks as dynamic systems and how mathematical models can be used to study the rewiring of the complex interactions taking place in the TME.

Danon Disease-Associated LAMP-2 Deficiency Drives Metabolic Signature Indicative of Mitochondrial Aging and Fibrosis in Cardiac Tissue and hiPSC-Derived Cardiomyocytes.

Danon disease is a severe X-linked disorder caused by deficiency of the lysosome-associated membrane protein-2 (LAMP-2). Clinical manifestations are phenotypically diverse and consist of hypertrophic and dilated cardiomyopathies, skeletal myopathy, retinopathy, and intellectual dysfunction. Here, we investigated the metabolic landscape of Danon disease by applying a multi-omics approach and combined structural and functional readouts provided by Raman and atomic force microscopy. Using these tools, Danon patient-derived cardiac tissue, primary fibroblasts, and human induced pluripotent stem cells differentiated into cardiomyocytes (hiPSC-CMs) were analyzed. Metabolic profiling indicated LAMP-2 deficiency promoted a switch toward glycolysis accompanied by rerouting of tryptophan metabolism. Cardiomyocytes' energetic balance and NAD+/NADH ratio appeared to be maintained despite mitochondrial aging. In turn, metabolic adaption was accompanied by a senescence-associated signature. Similarly, Danon fibroblasts appeared more stress prone and less biomechanically compliant. Overall, shaping of both morphology and metabolism contributed to the loss of cardiac biomechanical competence that characterizes the clinical progression of Danon disease.