IntroductionTranscription factors (TFs) are central regulators of various biological processes, integrating multiple extracellular and intracellular signals to modulate transcriptional activity. This integration of signals is influenced by the intricate interplay between protein-protein interactions, epigenetic modifications, and post-translational modifications, which give rise to complex coordinated regulatory networks that underlie cell specificity.1,2 The misregulation or dysfunction of these TFs and their networks can lead to a wide range of diseases.3 However, only a small subset of the TFs expressed in cells is necessary to establish cell-type-specific programs. Consequently, estimating changes in TF activity dynamics may be a more effective approach than merely focusing on the top differentially regulated individual genes to identify target perturbations that can reverse diseases. This approach will facilitate the identification of more precise and effective mechanisms for therapeutic interventions targeting TFs,4 which remains a significant challenge. In this study, we utilized the BITFAM model,5 which identifies disease-relevant TFs across diverse datasets by revealing regulatory drivers of cell states in diseased tissue. This model integrates single-cell (sc) RNA-seq data with high-confidence chromatin immunoprecipitation (ChIP)-seq data, leveraging Bayesian inference to extract the most variably expressed genes across conditions and clusters. Notably, BITFAM was demonstrated to be well-suited for TFs that are lowly expressed, addressing a common challenge in scRNA-seq data analysis.6We employed this method to investigate progressive hypertrophic remodeling, a critical intermediate stage between disease trigger and heart failure, which is a leading cause of death worldwide. Applying this approach revealed Krüppel-like factor 15 (KLF15) as a major transcriptional regulator that loses its repressive control over stress-responsive genes under pathological conditions, consistent with decreased KLF15 expression and impaired transcriptional homeostasis. KLF15, a member of the 17-family members encompassing Krüppel-like factors with conserved zinc finger domains, plays a critical role in cardiac homeostasis by regulating lipid metabolism and energy balance. Its dysregulation is associated with human cardiomyopathies and has been linked to the development of heart failure in mouse models.7 Overexpression of KLF15 has been suggested to reduce cardiomyocyte hypertrophy,8,9,10,11,12,13,14,15 but its role as a transcriptional key node and its ability to restore cell-specific homeostasis in severe cardiac hypertrophy models and human cells remain unresolved.Without biological validation, computationally derived insights remain descriptive and risk misinterpreting statistical model fits as true biological mechanisms.16 Although TF targeting remains challenging, the combination of catalytically dead Cas9 derivatives of CRISPR-Cas9 systems with precise DNA-binding at gene regulatory domains has substantially advanced the therapeutic modulation of TF activity.17,18 One version of this system is CRISPR activation (CRISPRa), which enables activation of endogenous gene expression and was applied in this study to validate our bioinformatic identification of KLF15 dysregulation in hypertrophic cardiomyocytes. CRISPRa systems have been progressively optimized for enhanced transcriptional upregulation evolving from first-generation dCas9-VP64 fusions (a tetramer of the herpesvirus VP16 activation domain) to the tripartite VPR activator (VP64, p65, and Rta), and further to highly potent synergistic activation mediator (SAM) and SunTag platforms.19,20 Together, these systems allow for precise and controlled modulation of gene expression. Despite their proven efficacy in cell culture and transgenic models,21,22,23 systemic delivery of these CRISPRa systems to post-mitotic cardiomyocytes remains challenging. While CRISPR editing advances clinical trials for genetic diseases,24 other systems, including CRISPRa systems, face significant challenges in therapeutic translation, particularly for non-genetic diseases. Although in vivo CRISPRa studies in the cardiovascular system remain scarce, we previously successfully established proof-of-concept using a cardiomyocyte-specific mouse model (Myh6 (Myosin heavy chain 6)-Streptococcus pyogenes (Spy)dCas9VPR-tdTomato (CRISPRa)) in neonatal mice. AAV9 facilitated effective delivery of single guide (sg) RNA, allowing for dosage-titratable expression. This approach enables fine-tuned expression levels, a key advantage over traditional overexpression systems that yield supraphysiological gene expression by introducing exogenous transgenes.21 A recent study used CRISPRa based on a VP64 activator to restore haploinsufficiency in a genetic cardiomyopathy. This positions CRISPRa as a powerful tool for regulating endogenous gene expression in potential in vivo therapeutic applications.25 CRISPRa holds promise for restoring dysregulated transcriptional networks in diseased cells programming a healthy transcriptional landscape. While this offers significant potential, CRISPRa applications for disease management, particularly within the cardiovascular system, remain limited.26,27Our study marks a major advancement in the field by combining the ability to establish TF activity dynamics, distinguishing between healthy and stressed cardiomyocytes at single-cell (sc) resolution, with the targeted use of the CRISPRa technology. Our detailed functional perturbation assays advance CRISPRa's effectiveness in generating a healthy transcriptional environment in stressed cardiomyocytes across various models and species. THis also uncovers new therapeutic potential: targeting cardiomyocyte gene expression profiles can also reprogram fibroblast responses toward a healthier state. This intervention proved sufficient to preserve cardiac homeostasis and prevent heart failure progression. Lastly, this study establishes an AAV-compatible delivery system for CRISPRa in human myocardium, enabling effective gene modulation and supporting clinical translation.ResultsSingle-cell transcriptomics defines a hierarchy of TF activity controlling cellular cardiomyocyte response to afterload stress progressionWe hypothesized that restoring the balance of endogenous key transcriptional programs disrupted by cellular stress in specific cell types may be sufficient to mitigate maladaptive cellular responses in cardiomyocytes. To test this hypothesis, we conducted an analysis to identify key transcriptional nodes that control critical processes distinguishing healthy and disease conditions during progressive hypertrophic remodeling induced by transaortic constriction (TAC) at the sc level. This analysis included ventricular mouse cardiomyocytes at distinct stages of heart failure progression: 5 days (compensatory), 8 weeks (pathological remodeling), and 16 weeks (failing) after TAC, as well as corresponding age-matched sham controls (Dataset-1) (Fig. 1a). Before sequencing, mice were subjected to echocardiography analysis to confirm ongoing cardiac remodeling (Supplementary Fig. 1a–c). Sequencing was performed using a full-length transcript method.28 After cell clustering, cardiomyocytes were scored for stress levels and further subclustered into five distinct populations (CM0-CM4) (Fig. 1b and Supplementary Fig. 1d). The CM2 cluster exhibited the highest stress score, characterized by the expression of hypertrophy-associated genes, including Nppa (Atrial natriuretic peptide), Nppb (B-type natriuretic peptide), Ankrd1 (Ankyrin repeat domain-containing protein 1), and Acta1 (Alpha skeletal muscle actin). Consistent with this, CM2 was enriched in TAC hearts across all stages and was characterized by transcripts related to hypertrophic and dilated cardiomyopathy. In contrast, CM0 and CM1 were predominantly represented under sham conditions (Fig. 1c and Supplementary Fig. 1e). Gene ontology (GO) analysis revealed that highly expressed transcripts in CM0 were primarily associated with mitochondrial respiration, while CM1 transcripts were categorized under fatty acid beta-oxidation, both reflecting biological functions of healthy cardiomyocytes. Furthermore, CM3 transcripts represented mRNA splicing processes, and CM4 transcripts were associated with branched-chain amino acid (BCAA) metabolism (Supplementary Fig. 2a). We then inferred TF activity in different conditions and cell clusters using BITFAM,5 leveraging its advantages in accurately identifying changes in activity of lowly expressed TFs, as seen in cardiomyocytes. This analysis generated cell cluster-specific TF activity profiles that distinguished healthy from pathological cell types (selected top 10 TFs are depicted in Fig. 1d). Using a random forest model, we examined the hierarchical importance of TF activity and found that KLF15 was the TF with the highest inferred activity in the TAC condition across all disease stages (Fig. 1e). Notably, the highest fold change in KLF15 activity was observed in the stressed CM2 population, which was present in all time points and experimental conditions (Supplementary Fig. 2b). In line with this, genes that are normally repressed by KLF15, including Acta1, Myh7, Ankrd1, and Nppa were upregulated and Aldehyde dehydrogenase (Aldh) 2, which is normally activated by KLF15 was downregulated in CM2 (Fig. 1f). Further validating these finding, a significant reduction in Klf15 expression in response to TAC along with decreased Aldh2 and increased Nppa expression was confirmed by RT-qPCR (Fig. 1g). RNA-seq data further confirmed regulation of Klf15 and its target genes upon TAC (Supplementary Fig. 2c). This aligns with the prediction from the BITFAM model, which suggests that if a TF is known to be a repressor, an increase in its inferred activity indicates that the TF is less effective at repressing its target genes. Since KLF15 exhibits a dual function as both a transcriptional promoter and repressor of hypertrophic genes, we referred to this phenomenon as a degree of decreased repressive control in KLF15’s activity (Supplementary Fig. 2d). Additionally, a similar pattern of expression was observed in the myocardium of Klf15−/− (KO)8 compared to control wild-type mice indicating their dependency on KLF15 (Supplementary Fig. 2e). This finding was further validated with a publicly available dataset229 (Dataset-2: baseline, 2, 5, and 11 weeks after TAC) using the same pipeline of evaluation, which revealed a significant change in KLF15 activity within the top three TFs in stressed cells (Supplementary Fig. 3a–e). Similar to dataset-1, repressed gene weights attributed to KLF15 activity were upregulated (Myh7, Nppa, and Nppb) (Supplementary Fig. 3f).Fig. 1Time-resolved, single-cell (sc) transcription factor (TF) activity analysis and intervention in pressure overload in vivo. a Schematic showing the study design (Dataset-1). Mice undergo either SHAM or transverse aortic constriction (TAC) surgery to induce pathological cardiac hypertrophy. Hearts were harvested at multiple timepoints (5 days (5D, n = 5 TAC and SHAM, total 10), 8 weeks (8 W, n = 3 TAC and Sham, total 6), and 16 weeks (16 W, n = 3 TAC and SHAM, total 6) post-surgery) to capture progressive stages from homeostasis to compensated hypertrophy and pathological remodeling. ScRNA sequencing (scRNA-seq) was performed, and TF activity changes were analyzed in different cardiomyocyte (CM) subpopulations. b Uniform Manifold Approximation and Projection (UMAP) of the cardiomyocyte clusters (CM0-4) in all conditions, along with stress score defined by the expression of Nppa, Nppb, Acta, and Ankrd1. c Relative cardiomyocyte subcluster (CM0-CM4) distribution between SHAM and TAC groups. d Heatmap showing the inferred TF activities using the scRNA-seq dataset from cardiomyocytes in different stages of heart remodeling. e Barplot with the TF activity importance using a Random Forest classification model. f Fold changes of transcript expression positively (Aldh2) and negatively (Nppa, Ankrd1, Mhy7 Acta1) regulated by KLF15 in the identified cardiomyocyte subclusters (CM0-4). g RT-qPCR validation of a subset of genes in (f) (n = 3 per group). h Pseudobulk expression of KLF15 in cardiomyocytes from human hearts of non-failing donors (NF, n = 16) and patients with dilated cardiomyopathy (DCM, n = 11) and hypertrophic cardiomyopathy (HCM, n = 15). Each point represents one patient (aggregated cardiomyocyte nuclei). i Number of human cardiovascular diseases that are significantly associated with gain or loss of binding of a particular TF, which identified KLF15 in the loss-of-function category. j Schematic overview of in vivo disease modeling (TAC and SHAM) and experimental setup to modulate Klf15 expression with CRISPRa. Delivery of AAV9 containing Klf15 (gRNA), non-targeting control NT (gRNA), or only saline injection in CRISPRa and CRISPRa/Klf15 homozygous (Klf15−/−) knock-out (KO) mice. k Representative images of whole hearts of the different experimental conditions and treatments showing heart dimensions as well as expression of tdTomato (dCas9VPR) and EGFP (under CK8 promoter control in the AAV9 construct used for gRNA delivery). Scale bar = 5 mm. Saline group depicted in Fig. S5a. l Klf15 expression in whole hearts as measured by RT-qPCR in the NT and Klf15 (gRNA) groups (SHAM NT, n = 16 (light blue); TAC NT n = 13 (light red); SHAM Klf15 n = 7 (dark blue); TAC Klf15 n = 10 (dark red)). m Cardiomyocyte cross-sectional area (CSA) in NT and Klf15 (gRNA) groups upon TAC or SHAM and corresponding quantification. n ≥ 40 cells per mouse, from 3 mice per condition, scale bar = 50 µm. n–o Echocardiography analysis. Fractional shortening (FS) (n) and left ventricular inner diameter in systole (LVIDs) (o) are shown for NT and Klf15 (gRNA) groups upon TAC or SHAM. n = 5-11 per group. (p) Survival rate in NT and Klf15 (gRNA) groups upon TAC, as well as Klf15 (gRNA) in Klf15 −/− KO mice and all SHAM groups. All the SHAM groups are grouped together, showing no differences between the groups. SHAM group, n = 22; TAC NT (gRNA); n = 40, TAC Klf15 (gRNA); n = 15, TAC Klf15 −/− KO with Klf15 (gRNA) n = 6. Mean ± standard error of the mean (SEM) in (g). Mean ± 95% confidence interval (box) and minimum/maximum (whiskers) in (h), (l)–(o). Student’s t-test was used to test for statistical significance in (g). DESeq2-normalized counts with genome-wide Benjamini–Hochberg correction were applied in (h). Two-way ANOVA and Tukey’s post hoc test were used for (l)–(o). Mantel-Cox log-rank test was used to test for statistical significance in (p)Full size imageTo further evaluate the transcriptional dynamics of KLF15 in human cardiovascular diseases, we analyzed publicly available datasets derived from single-nucleus profiling of human dilated (DCM) and hypertrophic cardiomyopathy (HCM) as well as non-failing (NF) donors30 and detected a significant reduction of KLF15 in cardiomyocytes of DCM and HCM patients compared to NF controls (Fig. 1h). Additionally, we applied a statistical approach to identify single-nucleotide variants (SNVs) that affect transcription factor binding.31 Analysis of cardiovascular genome-wide association studies (GWAS) showed that regulatory (r)SNVs associated KLF15 with its loss of function in four cardiovascular diseases (Fig. 1i). Collectively, these findings suggest that altered KLF15 transcriptional activity is a key driver of the phenotypic and pathological remodeling of cardiomyocytes in an evolutionarily conserved manner.CRISPRa normalizes cell-specific perturbed transcriptional activity by KLF15 restoration and prevents heart failure progressionWe hypothesized that normalizing the perturbed transcriptional activity of KLF15, as a key TF, will maintain homeostasis upon stress. Consequently, we tested CRISPRa (using dCas9VPR) as a safer, physiologically relevant strategy to normalize TF activity. Unlike classical open reading frame (ORF) overexpression (OE), which induced aberrant cytosolic KLF15 accumulation in vitro, CRISPRa preserved its native nuclear localization (Supplementary Fig. 4a), as previously observed.21 We used a mouse model with constitutive dCas9VPR expression restricted to cardiomyocytes (Myh6-dCas9VPR-tdTomato).21 We first validated robust expression of dCas9VPR, as indicated by the tdTomato reporter and the delivery of gRNAs shown by the expression of EGFP encoded by the AAV construct as well as by immunobloting in the adult heart (Supplementary Fig. 4b, c). To activate Klf15 expression, we administered validated gRNAs complementary to the 5’ transcriptional start site (TSS) upstream region of the Klf15 gene body via intravenous injection of recombinant AAV 2/9 (AAV9) at a dose of 1E + 12 viral genomes (vg) per mouse. This resulted in a significant induction of Klf15 expression at 2 and 8 weeks after AAV9 injection in ventricular tissue compared to saline-injected controls (Supplementary Fig. 4d). CRISPRa transgenic mice showed normal cardiac function and Klf15 expression (Supplementary Fig. 4e, f) as well as a similar response to pressure overload compared to wild-type littermates (Supplementary Fig. 4g–j). Next, we performed Klf15 re-expression experiments in CRISPRa adult mice, which were injected with either AAV9 Klf15 gRNAs (CRISPRa-Klf15), non-targeting (NT) control gRNAs (CRISPRa-NT), or saline and subjected to TAC or sham surgery eight weeks post-injection to ensure robust AAV transduction (Fig. 1j). Klf15 KO8 were used to generate a double CRISPRa/Klf15−/− mouse line, which served as a negative control. AAV9 transduction efficiency and dCas9VPR-tdTomato expression were similar in sham and TAC groups, as demonstrated by reporter expression. CRISPRa-Klf15 mice showed smaller hearts after TAC as compared to respective controls 16 weeks post-surgery. CRISPRa/Klf15 KO mice subjected to TAC resulted in an exacerbated cardiac dilation as expected,10,11 which was not rescued by injection of AAV9 Klf15 gRNAs (Fig. 1k and Supplementary Fig 5a, b). This indicated that the rescue mediated by CRISPRa depends specifically on KLF15. Echocardiography analysis confirmed comparable heart function among the groups prior to TAC, except for CRISPRa/Klf15 KO mice, which showed reduced systolic function prior to surgery. Successful and uniform pressure overload over all TAC groups was confirmed (Supplementary Fig. 5d, e). At 16 weeks post-TAC, RT-qPCR analysis revealed reduced Klf15 expression in all TAC groups, except for the CRISPR-Klf15 group, which showed normalized Klf15 expression (Fig. 1l and saline shown in Supplementary Fig 5c). After TAC, CRISPRa-Klf15 showed reduced cardiomyocyte cross-sectional area (CSA), reduced functional decline as shown by fractional shortening (FS), better survival, and reduced ventricular internal dimension at systole (LVIDs) as compared to all control TAC conditions (Saline and CRISPRa-NT) (Fig. 1m–p and Supplementary Fig 5f). Enhanced transcript levels of Klf15 positively correlated with FS (R2 = 0.69) and negatively correlated with cardiac mass (R2 = 0.77) in TAC CRISPRa-Klf15 mice (Supplementary Fig. 5g). These results establish CRISPRa-mediated Klf15 reactivation as a viable strategy to counteract pathological cardiac tissue remodeling and to restore functional homeostasis in the stressed heart.Activation of Klf15 restores a homeostatic transcriptional landscape in cardiomyocytes upon stress in vivoBulk transcriptomic analysis of hearts after 16 weeks of TAC revealed a significant downregulation of metabolic and maturation genes, including Klf15 and its direct targets (e.g., Aldh2, Adhfe1 (Alcohol dehydrogenase, iron containing 1), and Cacna1g (Calcium voltage-gated channel subunit alpha1 G))32 as well as upregulation of genes associated with stress and cardiomyocyte de-differentiation (e.g., Nppa, Myh7, Dstn (Destrin, actin depolymerizing factor), Ccnd2 (Cyclin D2), Tbx20 (T-box transcription factor 20), Bmp4 (Bone morphogenetic protein 4), and Runx1 (RUNX family transcription factor 1)) compared to control sham groups. Expression of non-cardiomyocyte stress markers (Shisa3 (Shisa family member 3), Emcn (Endomucin), and Postn (Periostin))8 was also increased in TAC hearts. However, the expression of these genes was largely normalized in CRISPRa-Klf15 hearts upon TAC (Supplementary Fig. 6a). To focus on cell-specific responses, cells were isolated and subjected to scRNA-seq analysis (Dataset-3, Fig. 2a and Supplementary Fig 6b). Cell clusters were classified based on previously described cell markers and differentially regulated genes were analyzed (Supplementary Fig. 6c).33 GO term analysis of genes enriched in cardiomyocytes of TAC CRISPRa-Klf15 compared to TAC CRISPRa-NT hearts categorized to homeostatic metabolic processes, such as fatty acid oxidation, while downregulated genes were largely classified to glycolytic and stress response (Supplementary Fig. 6d). TF perturbation bioinformatic analysis of increased transcripts in TAC CRISPRa-Klf15 revealed KLF15 as the highest related TF to these genes compared to TAC CRISPRa-NT (Supplementary Fig. 6e), confirming the transcriptional activation of Klf15 in this condition. To evaluate the direct effect of Klf15-induced expression, cardiomyocytes were subclustered, resulting in four cardiomyocyte subclusters (CM0-CM3) (Fig. 2b). CM0 was over-represented under TAC conditions, and accordingly, an increased stress score was observed in this cluster. The CM1 population was more abundant under sham conditions. CM2 was particularly abundant in TAC CRISPRa-Klf15 and showed the most significant expression of Klf15 and EGFP reporter representing the AAV9 transduced population. CM3 showed a similar representation in all conditions (Fig. 2c–e). Transcripts enriched in CM0 were categorized into cell differentiation, growth, and response to ischemia. CM1 exhibited enrichment of processes related to fatty acid oxidation, mitochondrial activity, and muscle contraction. CM2 was enriched in fatty acid oxidation metabolism, BCAA metabolism, endothelial cell proliferation, cellular detoxification, and viral entry into host cells. CM3 showed enrichment in RNA processes and muscle contraction (Fig. 2f). Cells expressing EGFP, indicative of AAV9 transduction, positively correlated with higher levels of Klf15 within the CRISPRa-Klf15 group (Supplementary Fig. 7a). Notably, expression of predicted gRNA off-targets with 1-2 mismatches showed no differential expression between TAC CRISPRa-Klf15 and NT groups (Supplementary Fig. 7b).Fig. 2Cardiomyocyte transcriptome landscape upon CRISPRa Klf15. a Schematic showing the study design. CRISPRa mice with NT or Klf15 (gRNA) underwent either SHAM or TAC surgery to induce pathological cardiac hypertrophy. The hearts were harvested for scRNA-seq analysis (Dataset-3). b UMAP of cardiomyocyte subclusters (CM0-CM3) and stress score of single cardiomyocytes in the SHAM NT (gRNA), TAC NT or Klf15 (gRNA) hearts as defined by the expression of Nppa, Nppb, Acta, and Ankrd1. c Relative distribution of cardiomyocyte subclusters in the different conditions (SHAM NT (gRNA), TAC NT or Klf15 (gRNA) (CM0: pink; CM1: green; CM2: blue and CM3: violet). d UMAP of cardiomyocyte subclusters per condition. e Expression levels of Klf15 and EGFP reporter transcripts in the different cardiomyocyte subclusters (CM0-CM4). EGFP expression indicates AAV9 delivery. f Gene ontology (GO) of biological processes analysis of the different cardiomyocyte subclusters based on the significantly enriched genes (Log2FC ≥ 0.25, P-value ≤ 0.05) in each subcluster. g Violin plot showing expression levels of KLF15 target Aldh2 and reprogramming marker Acta2, differentially regulated in the TAC NT and Klf15 (gRNA) groups. h Dotplot comparing KLF15-dependent network of genes contributing to Wnt and developmental signaling activation, hypertrophic pathways, Klf15 targets, and metabolic pathways, which are known to be controlled by KLF15, in different conditions (SHAM NT (gRNA), TAC NT and Klf15 (gRNA) groups) as well as in the different cardiomyocyte subsets (CM0-CM3). Different dot colors indicated a subcluster of CMs. Dot size indicates the proportion of cells expressing the gene, and color reflects the average z-scored expression level. i Representative immunofluorescence images of α-SMA and ACTN2 expression in SHAM NT (gRNA), TAC NT and Klf15 (gRNA) groups. α-SMA (red), ACTN2 (green), nuclei (DAPI, blue), scale bar = 20 μm. j Heatmap of proteomics data comparing the same groups depicting proteins representing main remodeling processes with corresponding gene ontology analysis (k). The Wilcoxon rank sum test was used for statistical significance in (e) and (g)Full size imageConsistent with the Klf15 levels, cardiomyocyte expression of Aldh2, a direct metabolic target gene of KLF15, was decreased in TAC hearts compared to sham control but normalized in TAC CRISPRa-Klf15 hearts. This was validated by RT-qPCR (Supplementary Fig. 7c). Expression of Acta2 (Actin Alpha 2, Smooth Muscle), a marker of fetal cardiomyocytes and a stress-induced reprogramming, was reduced in TAC CRISPRa-Klf15 cardiomyocytes compared to TAC CRISPRa-NT cardiomyocytes (Fig. 2g and Supplementary Fig. 7c). Further analysis of KLF15-dependent transcriptional networks revealed an increase in the expression of developmental and hypertrophic genes that are negatively regulated by KLF15, including Mef2 (Myocyte enhancer factor 2), Gata4 (GATA binding protein 4), Wnt signaling, and myocardin-mediated serum response factor (SRF) target genes Nppa and Myh7 along with downregulation of genes involved in BCAA and lipid metabolism, which are positively regulated by KLF1512,13,14 in TAC CRISPRa-NT hearts particularly in stressed cardiomyocytes (CM0). Expression of these transcripts was largely normalized in CRISPRa-Klf15 cardiomyocytes, particularly in CM2 (Fig. 2h). The regulation of α-SMA (Actin Alpha 2, Smooth Muscle), the protein product of the Acta2 gene, was further corroborated by immunofluorescence, which showed co-localization with cTnT (Cardiac Troponin T)-expressing cells indicating de-differentiation of cardiomyocytes (Fig. 2i and Supplementary Fig. 7d). Further cardiomyocyte de-differentiation hallmarks, including increased expression of Tnni1 (slow skeletal muscle troponin I) and decreased expression of Tnni3 (cardiac-specific troponin I) were observed in TAC CRISPRa-NT hearts but not in TAC CRISPRa-Klf15 hearts (Supplementary Fig. 7e).Next, we performed bulk proteomic analysis. Although this approach lacks sc resolution and may obscure cell-specific effects, it offers valuable validation of our findings beyond the transcript level. This data showed that levels of proteins depending on KLF15 transcription, including hypertrophic factors α-SMA, MYH7, and ANP (atrial natriuretic peptide, the product of Nppa) were increased in TAC CRISPRa-NT hearts but were attenuated in TAC CRISPRa-Klf15 hearts. Metabolic and calcium handling proteins, including ALDH2, were more abundant in TAC CRISPRa-Klf15 compared to TAC CRISPRa-NT. Additionally, the levels of pro-fibrotic proteins were also attenuated in TAC CRISPRa-Klf15 compared to TAC CRISPRa-NT (Fig. 2j). Accordingly, GO processes analysis of more abundant proteins in TAC CRISPRa-Klf15 compared to TAC CRISPRa-NT revealed enrichment in gene transcription, while less abundant proteins were categorized to glycosylation and stress signaling, including Wnt and TGF-β signaling (Fig. 2k and Supplementary Fig. 7f). Thus, CRISPRa-enhanced expression of Klf15 normalized its downstream homeostatic transcriptional network in a subset of cardiomyocytes, which is sufficient to prevent transcriptional fetal reprogramming and maintain cardiac performance.CRISPRa-mediated induction of Klf15 in cardiomyocytes triggers a cell non-autonomous protective functionEncouraged by the proteomic data showing a reduced fibrotic response in TAC CRISPRa-Klf15 hearts, we investigated potential cell non-autonomous mechanisms mediated by Klf15-induced expression in cardiomyocytes. Analysis of the cell-cell interaction landscape by CellChat34 using the scRNA-seq data (dataset-3, Fig. 3a) revealed that TGF-β and POSTN signaling were reduced in TAC CRISPRa-Klf15 hearts compared to TAC CRISPRa-NT hearts (Supplementary Fig. 8a). Moreover, fibroblasts in TAC CRISPRa-Klf15 hearts exhibited significantly decreased incoming interactions compared to those in TAC CRISPRa-NT hearts (Fig. 3b). Accordingly, a failing fibroblast population as well as its activation score, which showed elevation in TAC compared to sham hearts, were attenuated in TAC CRISPRa-Klf15 hearts (Fig. 3c, d and Supplementary Fig. 8b). Similarly, the extent of tissue fibrosis was reduced in TAC CRISPRa-Klf15 hearts compared to TAC CRISPRa-NT hearts (Fig. 3e). We next analyzed signals derived from cardiomyocytes towards fibroblasts, which exhibited enriched pathological processes, including the TGF-β signaling pathway in TAC CRISPRa-NT hearts but not in TAC CRISPRa-Klf15 (Fig. 3f). Consistently, fibroblasts in TAC CRISPRa-Klf15 hearts showed normalization of TGF-β target gene expression, including Acta2, and Postn while Tgfβ2 (Transforming Growth Factor Beta 2) remained unchanged (Fig. 3g and Supplementary Fig. 8c). This data indicated reduced TGF-β signaling activation and supports the hypothesis that cardiomyocyte-derived signals induced by CRISPRa-mediated Klf15 activation modulate fibroblast activation. We reasoned that the most highly regulated transcripts related to TGF-β signaling and fibrosis with the potential to generate a protective secretome from CRISPRa-Klf15-activated cardiomyocytes may contribute to the antifibrotic mechanism. Therefore, we specifically focused our search within the CM2 population in the dataset-3, where CRISPRa-Klf15 exerted its strongest effect. This search identified a significant upregulation of Alpha-2-glycoprotein 1, zinc-binding (Azgp1) in CM2 as well as in TAC CRISPRa-Klf15 bulk cardiomyocytes compared to sham and TAC CRISPRa-NT (Supplementary Fig. 8d). AZGP1 is a secreted protein with antifibrotic effects in the kidney and elevated levels have been associated with a lower risk of mortality and cardiovascular events.35,36 In line, Azgp1 expression was substantially reduced in TAC hearts compared to sham, while its expression was significantly increased in TAC CRISPRa-Klf15 cardiomyocytes (Fig. 3h and Supplementary Fig. 8e). Analysis of the single-nucleus profiling of human DCM and HCM presented in Fig. 130 revealed significant reduction of AZGP1 in cardiomyocytes of DCM and HCM patients compared to NF controls highlighting its clinical relevance (Fig. 3i). Moreover, Azgp1 expression was significantly increased in CRISPRa-Klf15 cardiomyocytes in normal non-stressed hearts indicating that KLF15 is a promotor of its expression (Supplementary Fig. 8f). To further investigate the mechanism in human cells, we used human induced pluripotent stem cell (hiPSC) derived cardiomyocytes and performed KLF15 Chromatin Immunoprecipitation (ChIP)-seq using lentivirally transduced cells with FLAG- and HA-tagged constructs (Supplementary Fig. 9a). This revealed an enrichment of KLF15 at the promoter region of AZGP1 (Fig. 3j and Supplementary Fig. 9b) as well as significant induction of AZGP1 expression in these cardiomyocytes (Supplementary Fig. 9c). To validate this finding, we used hiPSC-cardiomyocytes stably expressing the CRISPRa system37,38 and transduced them with lentiviral particles expressing validated KLF15 gRNAs (validation in Supplementary Fig. 9d, e). CRISPRa-induced expression of KLF15 resulted in significantly increased expression of AZGP1 (Fig. 3k). Moreover, increased expression of AZGP1 by CRISPRa KLF15 or AZGP1 OE as a positive control (validated in Supplementary Fig. 9f, g) resulted in significantly increased secretion of AZGP1 from hiPSC-cardiomyocytes as determined by ELISA (Fig. 3l). Next, conditioned media of these hiPSC-cardiomyocytes were used to treat hiPSC-derived fibroblasts. This resulted in a reduction of basal expression of ACTA2 with consequently reduced levels of α-SMA in hiPSC-fibroblasts as confirmed by RT-qPCR analysis and immunofluorescence, respectively (Fig. 3m, n). Furthermore, treating hiPSC-fibroblasts with conditioned media from hiPSC-cardiomyocytes, in which AZGP1 was knocked down via lentiviral expression of short hairpin (sh) RNA against AZGP1, resulted in increased fibrotic marker expression (Supplementary Fig. 9h). Overall, these results revealed a major and novel contribution of AZGP1 to the antifibrotic effects mediated by KLF15 activation in cardiomyocytes.Fig. 3Cardiomyocyte-dependent KLF15 regulation modulates fibrosis in a cell non-autonomous manner in vivo. a Schematic showing the study design. CellChat analysis was applied to the scRNA-seq data (Dataset-3). b Scatter plot demonstrating outgoing and incoming interaction strength between the different cell types in TAC NT and Klf15 (gRNA) hearts. Macrophage (MC), endothelial cells (EC), fibroblasts (FB), pericytes (PC), and cardiomyocytes (CM). c Stacked bar plot illustrating the proportion of fibroblast subtypes (failing and non-failing) and d violin plot showing the fibroblast activation score based on the expression of Acta2, Postn, and Cadherin 11 (Cdh11) in TAC NT and Klf15 (gRNA) hearts compared to SHAM NT (gRNA). e Longitudinal section of the whole heart with Sirius red staining (scale bar = 2 mm), indicating interstitial and perivascular ventricular fibrosis in TAC NT (gRNA) compared to TAC Klf15 (gRNA) hearts and SHAM NT (gRNA). f Corresponding GO pathways analysis of the cardiomyocyte (CM) signaling towards fibroblasts (FB) based on ligand-receptor signaling enriched in each condition. g Violin plots showing the expression levels of TGF-β activation target genes Acta2 and Postn in fibroblasts of TAC NT (gRNA) and Klf15 (gRNA) hearts compared to SHAM. h Expression levels of the anti-fibrotic factor Azgp1 in cardiomyocytes of TAC NT and Klf15 (gRNA) hearts compared to SHAM NT (gRNA). i Pseudobulk expression of AZGP1 in cardiomyocytes from human hearts of non-failing donors (NF, n = 16) and patients with dilated cardiomyopathy (DCM, n = 11), and hypertrophic cardiomyopathy (HCM, n = 15). Each point represents one patient (aggregated cardiomyocyte nuclei). j Scheme illustrating the experiments of KLF15 open reading frame (ORF) overexpression (OE) fused to triple FLAG and single HA epitopes in hiPSC-derived cardiomyocytes for Chromatin Immunoprecipitation (ChIP)-seq. KLF15 ChIP-seq traces identified occupancy at the AZGP1 promoter in hiPSC-cardiomyocytes (pink traces, n = 1). IgG (black traces) served as a pulldown control. k Proof-of-concept CRISPRa KLF15 in hiPSC-cardiomyocytes. Induced expression of KLF15 compared to NT (gRNA) was validated by RT-qPCR (n = 5-6), which resulted in a concomitant transcriptional upregulation of AZGP1. l Increased AZGP1 secretion in CRISPRa-KLF15 (gRNA) hiPSC-cardiomyocytes as detected by ELISA compared to NT (gRNA) control and positive control AZGP1 (OE) (n = 3-4). m Schematic of experimental setup for hiPSC-cardiomyocyte (conditions in l) and hiPSC-fibroblast crosstalk conditioned media experiment along with the RT-qPCR analysis for ACTA2 expression in hiPSC-fibroblasts upon different treatments (n = 3 per condition, 5 days). n Representative immunofluorescence images and quantification of α-SMA expression in hiPSC-fibroblasts normalized by Vimentin expression upon treatment of the conditioned media obtained in (l). α-SMA (red), Vimentin (green), nuclei (DAPI, blue), scale bar = 50 μm. n = 6-7 field of views per condition. Mean ± 95% confidence interval (box) and minimum/maximum (whiskers) in (i), (k), and (n). Mean ± standard error of the mean (SEM) in (l) and (m). Wilcoxon rank sum test was used for statistical significance in (d), (g), and (h). DESeq2-normalized counts with genome-wide Benjamini–Hochberg correction were applied in (i). Student’s t-test in (k), One-ANOVA and Tukey’s post hoc test were used to test for statistical significance in (l)-(n)Full size imageCRISPRa-mediated transcriptional normalization of KLF15 represses cardiomyocyte pathological reprogramming in stressed hiPSC- cardiomyocytesDespite well-established evidence of KLF15 downregulation in human cardiomyocytes, its precise mechanistic and functional roles in human cardiac cells remain largely unexplored. To address this, we differentiated cardiomyocytes using the CRISPRa hiPSC line and transduced them with KLF15 or NT gRNA. Similar transduction efficiencies were demonstrated by TurboGFP reporter (Supplementary Fig. 10a). These cells were used to generate 3D engineered human myocardium (EHM) tissues designed to mimic afterload-induced stress (Fig. 4a, upper panel).39,40 Stress was induced in EHM upon isometric contractions by culturing them on fixed (Fix = stress) metal poles. Control tissues were cultured under auxotonic contractions on flexible silicone poles (Flex = Control (CT)). Stressed EHM exhibited increased tissue stiffness (Young’s modulus), brittleness, and reduced strain tolerance compared to control tissues, confirming pathological tissue remodeling (Supplementary Fig. 10b, c). EHM showed homogeneous and sustained expression of dCas9VPR-tdTomato and gRNAs (TurboGFP) over a time course of four weeks (Fig. 4a, lower panel and Supplementary Fig. 10d). The transcriptional reduction of KLF15 was recapitulated under stress conditions in EHM with hiPSC-cardiomyocytes derived from CRISPRa and wild-type lines, offering a suitable model for KLF15 gene re-activation (Supplementary Fig. 10e). CRISPRa-mediated restoration of KLF15 expression was confirmed in stressed EHM treated with KLF15-targeting gRNAs (CRISPRa-KLF15) compared to NT controls (CT, CRISPRa-NT) (Fig. 4b). Reduced force of contraction was observed in CRISPRa-NT stressed tissues validating the model. In contrast, force of contraction was maintained in the CRISPRa-KLF15 stress group and was comparable to controls (CT, CRISPRa-NT) (Fig. 4c). Similar to the in vivo finding, increased expression of α-SMA was observed in CRISPRa-NT stress group, while this was normalized in the CRISPRa-KLF15 EHM upon stress (Fig. 4d and Supplementary Fig. 10f).Fig. 4CRISPRa-mediated transcriptional normalization of KLF15 in hiPSC-cardiomyocytes under stress. a Scheme depicting the in vitro human myocardial disease model using engineered human myocardium (EHM) with fixed metal poles (stress) for mechanical loading compared to flexible (control, CT) silicone poles representing the control condition. Representative images of EHM generated with CRISPRa hiPSC-cardiomyocytes expressing dCas9VPR (demonstrated by tdTomato, red) and NT or KLF15 (gRNA) (demonstrated by TurboGFP, green) in different conditions. Scale bar = 2 mm. b KLF15 expression in EHM measured by RT-qPCR in CT and stress EHM (corresponding wild-type hiPSC-cardiomyocyte EHM are depicted in Supplementary Fig. 10d) (n = 7-15 per group from six independent hiPSC-cardiomyocyte differentiations and three independent EHM generations). c Force-Ca2+ concentration-response curves for functional analysis of EHM comparing stress NT (gRNA) (light red); CT NT (gRNA) (light blue); stress KLF15 (gRNA) (dark red) and CT KLF15 (gRNA) (dark blue) (n = 3-5 per group). d Immunoblot demonstrating α-SMA, ACTN2, and TurboGFP protein levels in CT/stress NT (gRNA) and CT/stress KLF15 (gRNA) and corresponding densitometry quantification showing recovery of repression upon stress KLF15 (gRNA) (n = 3-4 per group, additional immunoblot used for quantification in Supplementary Fig. 10f). GAPDH was used as a loading control. e Principal component (PC) analysis and of CT NT (gRNA) (n = 5); stress NT (gRNA) (n = 3); and stress KLF15 (gRNA) (n = 5) tissues subjected to bulk transcriptome analysis and f corresponding heatmap of transcript expression involved in cardiac metabolism, developmental/hypertrophic signaling, fibrosis, and structural and Ca2+ handling genes. g Validation of a subset of genes presented in the heatmap as measured by NanoString. Selected genes (ACTA2, BMP4, and SLC8A1) that showed normalized expression upon KLF15 re-expression (further metabolic genes are presented in Supplementary Fig. 11c). n = 5-10 per group. h Selection of the highest significantly enriched GO pathways analysis for the indicated conditions (control NT (gRNA) versus stress NT (gRNA); stress NT (gRNA) versus stress KLF15 (gRNA)) and i enriched GO pathways analysis for NT (gRNA) versus KLF15 (gRNA) at baseline. GO pathway analysis was done based on the significantly differentially expressed genes (Log2FC ≥ 0.25, P-value ≤ 0.05) in each comparison for up- and downregulated genes. j Relative expression of AZGP1 in NT (gRNA) versus KLF15 (gRNA) at baseline conditions in EHM (n = 4). k KLF15 expression measured by RT-qPCR in hiPSC-cardiomyocytes exposed to indicated concentrations of TGF-β1 (n = 3-8, 5 days), as well as α-SMA expression evaluated by l immunoblot and m immunofluorescence in cTnT positive hiPSC-cardiomyocytes. Vehicle (Veh) exposure served as a control group. Vinculin was used as a loading control. α-SMA (red), cTnT (green), nuclei (DAPI, blue), scale bar = 50 µm. n RT-qPCR of hiPSC-cardiomyocyte with TGF-β1 exposure (final concentration: 100 pM, 5 days) showing relative KLF15 expression along with ACTN2 and de-differentiation markers (ACTA2 and CCND1) (n = 4-7 per group). Vehicle (Veh) exposure served as the control group. o Representative immunoblot images and quantification of α-SMA and ACTN2 protein expression level in hiPSC-cardiomyocytes upon treatment with indicated concentrations of TGF-β1 (n = 3-5 per group, 5 days). Vehicle (Veh) exposure served as the control group. Vinculin was used as a loading control. p Immunoblot of downstream mediators of TGFβ signaling pathway activation, SMAD2/3, p38, and HSP27 (p-: phosphorylated) in hiPSC-cardiomyocytes upon TGF-β1 exposure (final concentration: 10 pM, 2 h, n = 3) with respective inhibitors: SB431542 (SMAD2/3 inhibitor (inhib), final concentration: 10 µM, n = 4) and SB203580 (p38 inhibitor (inhib), final concentration: 10 µM, n = 4). Vehicle only condition (Veh) was used a negative control (n = 3). Vinculin was used as a loading control. q RT-qPCR analysis (n = 11-12 per condition) of hiPSC-cardiomyocytes treated with TGF-β1 (final concentration: 10 pM, 5 days) with respective inhibitors: SB431542 (SMAD2/3 inhibitor (inh), final concentration: 10 µM) and SB203580 (p38 inhibitor (inh), final concentration: 10 µM). Vehicle (Veh) exposure served as control group. Mean ± 95% confidence interval (box) and minimum/maximum (whiskers) in (b), (g), (j), (n), and (q). Mean ± standard error of the mean (SEM) in (d), (k), and (o). Two-way ANOVA and Tukey’s post hoc test were used for (b), (d), and (g). Student’s t-test was used in (j) and (n). One-way ANOVA and Tukey’s post-hoc test were used to test for statistical significance in (k), (o), and (q)Full size imageThese tissues were subjected to next-generation sequencing (NGS). Principal component analysis and hierarchical clustering revealed three distinct gene expression profiles according to the experimental groups. Activation of KLF15 altered the gene expression profile in stressed tissues towards that of control conditions (Fig. 4e and Supplementary Fig. 11a, b). Specifically, CRISPRa-NT tissues upon stress showed reduced expression of KLF15, metabolic targets, and structural genes, along with the upregulation of developmental and hypertrophic markers. Moreover, TGF-β dependent target genes, including ACTA2, POSTN, CCL2 (C-C Motif Chemokine Ligand 2), CCN2 (connective tissue growth factor), and TAGLN (Transgelin) were upregulated along with reduced expression of antifibrotic genes BMP7 (bone morphogenetic protein 7) and AZGP1 in CRISPRa-NT upon stress. These changes were reversed in CRISPRa-KLF15 stress EHM (Fig. 4f). NanoString analysis validated increased expression of genes that can support cardiac function, including SLC8A1 (Solute Carrier Family 8 Member A1), GLUT4 (Glucose Transporter Type 4), and CKM (creatine Kinase, Muscle), as well as decreased expression of ACTA2 and BMP4 in CRISPRa-KLF15 EHM upon stress (Fig. 4g and Supplementary Fig. 11c).41 GO analysis of upregulated genes in CRISPRa-NT stress tissues compared to CRISPRa-NT control tissues was related to myocardial stress, including cell cycle activation and HIF-1 signaling, while the downregulated genes classified to channel activities involved in membrane potential. In CRISPRa-KLF15 stress tissues, upregulated genes clustered to mitochondrial respiration, whereas downregulated genes categorized to stress processes such as the TGF-β signaling pathway compared to CRISPRa-NT stress tissues (Fig. 4h). Additionally, control EHM with CRISPRa-KLF15 showed decreased TGF-β signaling pathway activity along with increased AZGP1 expression compared to NT control independent from the stress condition (Fig. 4i, j, and Supplementary Fig. 11d). Altogether, we demonstrated for the first time a reduction in KLF15 expression in a human model subjected to mechanical load. This was accompanied by cardiomyocyte reprogramming, a pro-fibrotic response, and functional decline, all of which were reversed by CRISPRa-mediated induction of KLF15.Stress-induced signals regulate KLF15 to maintain cellular homeostasis by modulating cellular maturation in hiPSC-cardiomyocytesWe further aimed to investigate how KLF15 is regulated in hiPSC-cardiomyocytes under stress conditions. Increased secretion of TGF-β by various cell types is a characteristic feature of hypertrophic remodeling induced by pressure overload and has been shown to downregulate Klf15 expression in animal models.12 Among the three TGF-β isoforms, TGF-β1 is the most extensively implicated in pathological cardiac remodeling.42 Therefore, we treated hiPSC-cardiomyocytes with TGF-β1, which resulted in a dose-dependent downregulation of KLF15 along with increased expression of the dedifferentiation marker α-SMA, specifically in cTnT-positive cells (Fig. 4k–m). Upregulation of reprogramming hallmark genes was observed, including ACTA2 and the cell cycle marker CCND1 (Cyclin D1), along with the reduced expression of mature sarcomeric ACTN2 (alpha-actinin-2) at the transcript and protein level (Fig. 4n, o). We also tested the effect of TGF-β2 in hiPSC-cardiomyocytes, which resulted in downregulation of KLF15 only at high concentrations. In contrast to the effect triggered by TGF-β1, TGF-β2 treatment resulted in ACTA2 and BMP7 downregulation, while CCND1 levels remained unchanged (Supplementary Fig. 12a, b). Next, we investigated the TGF-β1-mediated cascade regulating KLF15. Consequently, we blocked the TGF-β1 canonical activation of SMAD2/3 using the inhibitor SB431542, which prevented the TGF-β1-mediated reduction of KLF15, as well as the upregulation of ACTA2, CCND1, and α-SMA. Blocking of p38 activity (non-canonical TGF-β1 signaling) with the inhibitor SB203580 did not affect TGF-β1-mediated reduction of KLF15 or activation of ACTA2, CCND1, and α-SMA (Fig. 4p, q and Supplementary Fig. 12c). Of note, p38 inhibition resulted in accumulation rather than reduction of its phosphorylation. This is explained by the SB203580 mode of action, which does not block p38 MAPK phosphorylation itself but prevents its kinase activity, thereby preventing the phosphorylation of downstream substrates such as HSP27, which was demonstrated in our analysis.43 Thus, downregulation of KLF15 is a cell-specific downstream effector of the canonical TGF-β1 pathway triggering fetal reprogramming in human cardiomyocytes.To investigate the dependency of KLF15 expression on fetal reprogramming, we generated double transgenic CRISPRa hiPSC KLF15 homozygous (−/−) or heterozygous (+/−) KO lines (validation in Supplementary Fig. 12d-f). These cells were differentiated into cardiomyocytes and cultured for 60 days. KLF15-deficient cardiomyocytes showed increased α-SMA expression and cell size, with the most pronounced effect in KLF15−/− cells compared to KLF15+/+ cells (Fig. 5a). Immunoblot confirmed increased levels of α-SMA along with another cardiomyocyte remodeling marker, TAGLN, in KLF15+/− and KLF15−/− cardiomyocytes. CRISPRa-mediated elevation of KLF15 in cardiomyocytes showed reduced α-SMA and TAGLN in KLF15+/− cells but not in KLF15−/− compared to NT controls (Fig. 5b). RT-qPCR confirmed decreased ALDH2 levels and increased ACTA2 expression in KLF15+/− and KLF15−/− cardiomyocytes. This reduction was significantly rescued in KLF15+/− CRISPRa-KLF15 cardiomyocytes (Fig. 5c). This data indicated that loss of KLF15 is sufficient to induce a fetalization program in cardiomyocytes.Fig. 5KLF15 gene dose dependently alters cardiomyocyte de-differentiation, contractility and mitochondria network complexity. a Schematic showing the generated KLF15 genetic deletions (KO), heterozygous (+/−) and homozygous (−/−) in CRISPRa hiPSC used for cardiomyocyte differentiation, followed by treatment of control NT or KLF15 (gRNA). Representative immunofluorescence images of α-SMA and cTnT expression in KLF15+/− and KLF15−/− hiPSC-cardiomyocytes, along with the NT (gRNA) and KLF15 (gRNA). α-SMA (red), cTnT (gray), nuclei (DAPI, blue), scale bar = 50 µm. b Immunoblot of cardiomyocyte de-differentiation makers (α-SMA, TAGLN) in hiPSC-cardiomyocytes KLF15+/+, KLF15+/−, and KLF15−/− with the lentiviral transduction of NT or KLF15 (gRNA). Vinculin was used as a loading control. n = 2 per condition. c RT-qPCR analyses for determining KLF15, ALDH2, and ACTA2 expression in KLF15+/+, KLF15+/−, and KLF15−/− hiPSC-cardiomyocytes transduced with NT or KLF15 (gRNA). n = 3-8 per condition. d Ca2+ handling was assessed. Amplitudes of systolic Ca2+ transients, rates of Ca2+ transient upstroke velocity (time to peak), and Ca2+ elimination kinetics (Relaxation time 50%) were assessed in KLF15+/+ and KLF15−/− hiPSC-cardiomyocytes. n = 30 per condition. e Mitochondria (Mito)-stress test in KLF15+/+, KLF15+/−, and KLF15−/− hiPSC-cardiomyocytes evaluated for functional measurement of mitochondrial respiration (OCR) along with quantification of ATP production, basal respiration, and maximal respiration. All the measurements were normalized by total protein level (n = 8-12). Experiments in d and e were done in two hiPSC-cardiomyocyte differentiations. f High-resolution AiryScan confocal imaging was used to evaluate mitochondria networks in the indicated hiPSC-cardiomyocyte cultures. Mean mitochondrial volume was increased upon KLF15 activation and reduced in KLF15+/− NT (gRNA) and both KLF15−/− NT and KLF15 (gRNA) conditions. Branches per mitochondria were decreased in cardiomyocytes lacking KLF15 but increased upon KLF15 activation. n = 8-12 field of views per condition. g RT-qPCR assessing the levels of KLF15 expression upon TGFβ1 exposure (final concentration: 10 pM, 5 days) in hiPSC-cardiomyocytes transduced with NT and KLF15 gRNA. Vehicle condition (Veh) was used as a negative control, n = 3-5 per condition. h Ca2+ handling evaluation and i Mito-stress were done in conditions mentioned in (g). Experiments in h and i were done in two hiPSC-cardiomyocyte differentiations. j RT-qPCR analyses for AZGP1 expression in KLF15+/+, KLF15+/−, and KLF15−/− hiPSC-cardiomyocytes transduced with NT or KLF15 (gRNA). n = 3-8 per condition. k Conditioned media from hiPSC-cardiomyocytes (conditions presented in 5a) were used to treat wild-type (WT) hiPSC-fibroblasts (FB) along with conditioned media from AZGP1 overexpressing (OE) hiPSC-cardiomyocytes. Quantification of α-SMA expression normalized to Vimentin is depicted. n = 4-5 field of views per condition. l Scheme showing TGFβ1 exposure (final concentration: 10 pM, 24 hours) to hiPSC-fibroblasts (FBs) with SB431542 (SMAD2/3 inhibitor (inhib), final concentration: 10 µM, n = 2), human recombinant AZGP1 protein (final concentration: 1 µg/ml, n = 2) or BMP7 protein (final concentration: 400 ng/ml, n = 2) exposure (24 h) along with immunoblot of fibrosis markers (POSTN, α-SMA and CTGF). Vehicle (Veh) exposure served as control group (n = 2). Alpha-Tubulin was used as a loading control. m Immunoblot of downstream mediators of TGFβ signaling pathway activation, SMAD2/3, p38, AKT, and ERK1/2 (p-: phosphorylated) in hiPSC-fibroblasts with the conditions mentioned in l with 2 hours exposure time. Vinculin was used as a loading control, n = 2 per condition. n Scheme showing open reading frame overexpression of KLF15 fused to triple FLAG and one HA epitope in hiPSC-cardiomyocytes used for ChIP and Immunoprecipitation (IP) for mass spectrometry (MS) analysis. o GO pathways analysis performed on promoters of protein-coding genes identified by ChIP-seq of FLAG-HA-KLF15 compared to input showing significantly enriched GO terms. Input served as ChIP analysis control. p KLF15 ChIP-seq traces identified occupancy at the ALDH2, ENO1, SLC25A42, PLORMT, and POLG2 promoter in hiPSC-cardiomyocytes (pink traces, n = 1). q Heatmap of ChIP-seq identified KLF15 target genes in EHM transduced with NT or KLF15 (gRNA) derived from bulk RNA-seq. n = 3 per condition. r Volcano plot depicting significantly enriched proteins from KLF15 IP experiments (FLAG pull-down) showing confirmation of KLF15 pull-down and identifying CSRP3 as a novel KLF15 binding partner in hiPSC-cardiomyocytes. IgG was used as a pull-down control. FC fold change. s Heatmap of known downstream targets of CSRP3 in EHM transduced with NT or KLF15 (gRNA) derived from bulk RNA-seq. n = 3 per condition. Mean ± standard error of the mean (SEM) in (c), (g), and (j). Mean ± 95% confidence interval (box) and minimum/maximum (whiskers) in (d), (e), (f), (h), and (k). Two-way ANOVA and Tukey’s post hoc test were used for (c), (f), (g), (j), and (k). Student’s t-test was used in (d). One-way ANOVA and Tukey’s post-hoc test were used to test for statistical significance in (e) and (h)Full size imageTo gain insights into the functional consequences of KLF15 deficiency, we analyzed excitation-contraction (EC) coupling in KLF15+/− and KLF15−/− hiPSC-cardiomyocytes. Compared to controls, a complete loss of KLF15−/− reduced the amplitudes of systolic calcium transients and slowed the rates of calcium increase (time to peak) and decay (relaxation time 50%) compared to wild-type cells (Figs. 5d and S13a). Since EC coupling is tightly coupled to mitochondrial function through mechano-energetic coupling,44 we also determined mitochondrial and metabolic function of KLF15-deficient cardiomyocytes. In fact, complete loss of KLF15−/− decreased basal and maximal oxygen consumption rate (OCR) as well as ATP-linked respiration, indicating compromised mitochondrial function, while the presence of one KLF15 allele (KLF15+/−) mitigated this impairment (Fig. 5e). However, additional rescue attempts via CRISPRa-KLF15 yielded no statistically significant alterations (Supplementary Fig. 13b, c). Accordingly, mitochondrial structure was impaired in KLF15+/− and KLF15−/− cardiomyocytes, resembling a more immature state characterized by decreased mitochondrial branches and junctions, indicative of a disrupted network, while the mitochondrial counts remained unchanged compared to KLF15+/+ cells. Conversely, CRISPRa-KLF15 significantly increases mitochondrial branching in KLF15+/− but not in KLF15−/− cardiomyocytes (Fig. 5f and Supplementary Fig. 13d, e). These data characterized a pathological mitochondrial phenotype upon genetic loss of KLF15, which was reversed by its transcriptional activation in heterozygous cells. Major sarcomeric structural alterations were not observed (Supplementary Fig. 13f).To investigate the impact of KLF15-deficiency in a pathological context, we tested the effect of CRISPRa-KLF15-mediated induction after TGF-β1 treatment (Fig. 5g). Treatment with TGF-β1 increased systolic calcium levels and accelerated the kinetics of calcium transients, indicating a hyperdynamic condition as can be observed in various forms of cardiac hypertrophy. Accordingly, maximal mitochondrial respiration was also increased in response to TGF-β1. These TGF-β1-induced alterations in EC coupling and mitochondrial energetics were all prevented by the CRISPRa-KLF15-mediated induction (Fig. 5h, i and Supplementary Fig. 13g). These data suggest that initial TGF-β-mediated reduction in KLF15 levels triggers a compensatory response, however, persistent suppression of KLF15 may contribute to the progression of heart failure.Effects of KLF15 on differential regulation of fibrotic responsesWe further evaluated the paracrine effects of the cells under different doses of KLF15 expression. In line with the data in Fig. 3, CRISPRa-KLF15 significantly induced the expression of AZGP1 in pathological conditions upon TGF-β1 treatment (Supplementary Fig. 14a) as well as in KLF15+/+ and KLF15+/- but not in KLF15−/− hiPSC-cardiomyocytes (Fig. 5j). We next investigated whether KLF15+/− and KLF15−/− hiPSC-cardiomyocytes produce a pathological secretome that can affect fibroblasts, which may be rescued by induction of KLF15 by CRISPRa. Conditioned medium from KLF15+/− and KLF15−/− hiPSC-cardiomyocytes with and without CRISPRa-KLF15 or AZGP1 OE as a positive control was used to treat hiPSC-fibroblasts. Loss of KLF15 expression in cardiomyocytes yielded conditioned media that induced α-SMA elevation in fibroblasts. In contrast, supernatant from KLF15+/− hiPSC-cardiomyocytes with CRISPRa-KLF15 or AZGP1 OE significantly ameliorated α-SMA activation in fibroblast, whereas this was not the case with medium from CRISPRa-KLF15 in KLF15−/− hiPSC-cardiomyocytes (Fig. 5k and Supplementary Fig. 14b). Interestingly, BMP7 expression was also increased in KLF15+/− hiPSC-cardiomyocytes with CRISPRa-KLF15 but not in KLF15+/+ cardiomyocytes (Supplementary Fig. 14c). To evaluate the effects of these two potential antifibrotic factors induced upon stress in CRISPRa-KLF15 cardiomyocytes, we employed human recombinant AZGP1 and BMP7 proteins on hiPSC-fibroblasts treated with TGF-β1. AZGP1 exhibited a similar effect to SMAD2/3 inhibition, resulting in amelioration of the fibrotic response as evidenced by reduced levels of POSTN, α-SMA, and CTGF (Connective Tissue Growth Factor) following TGF-β1 treatment (Fig. 5l). AZGP1 abolished SMAD2/3 phosphorylation induced by TGF-β1, while BMP7 only reduced its phosphorylation. AZGP1 treatment did not significantly affect the phosphorylation levels of p38, AKT, and ERK1/2 (Fig. 5m). To determine if AZGP1 mediates the effects of KLF15 in cardiomyocytes, we analyzed hiPSC-cardiomyocytes with AZGP1 OE. Our analysis revealed no significant changes in the expression levels of genes associated with reprogramming or fibrosis, including ACTA2, KLF15, ACTN2, and BMP7. Additionally, we observed no alterations in α-SMA levels (Supplementary Fig. 14d, e). In addition, unlike in fibroblasts, AZGP1 OE in hiPSC-cardiomyocytes was unable to modulate upregulated ACTA2 expression upon TGF-β1 treatment (Supplementary Fig. 14f). These data indicate that AZGP1 does not contribute to cardiomyocyte reprogramming but instead primarily affects the fibroblast response. Finally, direct CRISPRa-KLF15 induction in hiPSC-fibroblasts did not alter the expression of AZGP1 and did not ameliorate the fibrotic response induced by TGF-β1. (Supplementary Fig. 14g). Altogether, these findings indicate that KLF15 regulates the fibrotic response through cardiomyocytes, where loss of KLF15 induces a pathological secretome that activates fibroblasts. This phenotype can be rescued by CRISPRa-mediated KLF15 restoration, with AZGP1 acting as a major downstream contributor.KLF15 acts through distinct downstream pathways within cardiomyocyte transcriptional networksTo elucidate the KLF15-mediated cell-autonomous regulatory landscape establishing homeostasis in cardiomyocytes, lentiviral transduction of FLAG- and HA-tagged KLF15 was performed, followed by ChIP-seq and mass spectrometry (MS) analysis (Fig. 5n). ChIP-seq was validated by identification of KLF motifs in the enriched sequences (Supplementary Fig. 15a). Recruitment of KLF15 was identified at promoters of metabolic, mitochondrial, and developmental genes, indicating a direct regulation of KLF15 in these processes. Specifically, KLF15 enrichment was identified in SLC25A42 (Solute Carrier Family 25 Member 42), POLRMT (RNA Polymerase Mitochondrial), and POLG (DNA Polymerase Gamma, Catalytic Subunit), important for energy supply (Fig. 5o, p). Validating this finding, EHM generated from CRISPRa-KLF15 hiPSC-cardiomyocytes exhibited upregulated expression of these genes compared to EHM with CRISPRa-NT controls, as determined by NGS. These data validated KLF15’s activating role on these direct targets. (Fig. 5q). Moreover, protein pulldown followed by MS identified CSRP3 (Muscle LIM protein) as the most significantly enriched protein in the KLF15 pulldown (Fig. 5r and Supplementary Fig. 15b–e). CSRP3 is associated with hypertrophic cardiomyopathy and regulation of hypertrophic signaling, including NPPA, ACTA2, and structural proteins.45,46 Consistently, EHM generated with CRISPRa-KLF15 hiPSC-cardiomyocytes showed regulation of these genes as compared to CRISPRa-NT control EHM (Fig. 5s). The genes encoding these proteins showed no significant enrichment of KLF15 in their promoter regions (Supplementary Fig. 15f). These data indicate that KLF15 exerts direct and indirect effects on metabolic, sarcomeric, and mitochondrial gene regulation.An engineered therapeutic AAV-delivery vector is effective for CRISPRa-mediated KLF15 transcriptional restorationFinally, we developed a cardiomyocyte-specific delivery vector suitable for translating the CRISPRa approach to clinical settings. To achieve this, we engineered an AAV-compatible CRISPR/dCas9VPR-containing vector with a comparable efficiency to the transgenic Spy-dCas9VPR systems by screening various mini-dCas9VPR constructs. They included Sauri-Cas9 (3.3 kbp, PAM: NNGG), Sauri-Cas9KKH (3.3 kbp, PAM: NNRG), SaSauri-Cas9 (3.3 kbp, PAM: NNGG), and Slug-Cas9HF (3.3 kbp, PAM: NNGG).47,48 These present a smaller coding sequence compared to that of SpyCas9 (4.5 kbp, PAM: NGG) with reportedly high-fidelity properties for gene editing purposes and similar PAM sequence requirements. The RUVC and HNH endonuclease domains of Sauri-Cas9 and derivatives as well as Slug-Cas9 were identified by amino acid sequence homology comparisons and single amino acids encoded in these regions were exchanged based on Spy-dCas9 as a blueprint (Fig. 6a). Transactivation domains (VP64, p65 transactivation domain, and Epstein-Barr virus R transactivator (Rta) domain, VPR)49,50,51 were fused to the mini-dCas9 variants. Next, they were embedded in an AAV expression construct under a cardiomyocyte-specific human TNNT2 (Cardiac Troponin T) promoter52 containing all regulatory elements needed for effective expression as well as a cassette for single gRNA expression that included the U6 human Pol III promoter (Fig. 6b and Supplementary Fig. 16a). A suitable gRNA targeting the KLF15 promoter was cloned into the construct and the efficiency to activate KLF15 expression was tested in HEK293T cells compared to that of an NT gRNA-expressing construct. We found significant activation of KLF15 expression by Sauri-dCas9VPR (Supplementary Fig. 16b). Expression of Sauri-dCas9 as well as localization within the nucleus was confirmed by immunoblot probing the HA-epitope tag encoded at the C-terminus of Sauri-dCas9VPR in HEK293T cells (Fig. 6c). Expression of the Sauri-dCas9VPR transcription activator was likewise confirmed in hiPSC-cardiomyocytes by immunoblot (Fig. 6d). The catalytical inactivity was confirmed by comparing EGFP editing upon Sauri-Cas9 or Sauri-dCas9 transfections in HEK293T cells lentivirally transduced with an EGFP expression vector (Fig. 6e). Expression of Sauri-dCas9VPR under TNNT2 promoter and KLF15 CRISPR activation were further validated in hiPSC-cardiomyocytes using AAV9 transduction confirming all-in-one CRISPRa vector mediated gene activation specifically in cardiomyocytes (Fig. 6f).Fig. 6Engineering and validation of a small dCas9VPR variant for endogenous gene activation in cardiomyocytes. a Comparison of Streptococcus pyogenes (Spy) and Staphylococcus auricularis (Sauri) Cas9 peptide sequences of the RUVC and HNH domains, identifying possible single amino acid that render Cas9 nuclease inactive. Homology comparison identified SpyCas9 D10 and H840 equivalent amino acids in SauriCas9. b Schematic overview of an all-in-one engineered AAV9 vector for simultaneous Sauri-dCas9VPR expression under TNNT2 promoter control and a single gRNA under U6 promoter control. c Expression of CMV-driven Sauri-dCas9VPR in HEK293T cells, which localized in the cytoplasmic and nuclear compartments. GAPDH and c-Myc were used as loading controls for cytoplasmic and nuclear compartments, respectively. n = 2 per condition. Untransfected cells were used as a control. d Expression of Sauri-dCas9VPR of tagged (HA) dCas9 was detected by immunoblotting in transfected HEK293T cells. GAPDH was used as a loading control. n = 2 per condition, Control = untransfected cells. e HEK293T cells constitutively expressing EGFP were used to test the nuclease activity of the engineered Sauri-dCas9 compared to the original Sauri-Cas9 with gRNAs targeting the EGFP open reading frame. Enzymatic inactivity was confirmed by PCR. f Expression of Sauri-dCas9VPR as well as KLF15 activation was confirmed in AAV9-transduced hiPSC-cardiomyocytes. n = 3-4 per condition. g Schematic representation of biomimetic cultures of human myocardium transduced with AAV9 EGFP (control vector), Sauri-dCas9VPR-NT, or KLF15 under the TNNT2 promoter indicated successful transduction of cardiomyocytes in human cardiac slice cultures (h). Scale bar = 50 μm. i dCas9VPR and KLF15 expression by RT-qPCR (n = 3-5 per condition) along with j contractile force of Sauri-dCas9VPR-KLF15-transduced tissues (n = 3-5 per group) compared to control tissues (EGFP or NT (gRNA) group). k Scheme summarizing the KLF15 upstream and downstream mechanisms and the proposed therapeutic approach presented in this study. Mean ± 95% confidence interval (box) and minimum/maximum (whiskers) in (f), (i), and (j). One-way ANOVA and Tukey’s post hoc test in (f) and (i) or Student’s t-test in (i) and (j) were used to test for statistical significanceFull size imageFinally, we tested the efficiency of the TNNT2-Sauri-dCas9VPR construct and KLF15 activation in precision-cut human myocardium53 as a model for physiological cell context and transducibility of human heart tissue. We first determined the ability of AAV9 particles to transduce the human myocardium slices using the EGFP reporter by culturing them for 11 days (Fig. 6g, h and Supplementary Fig. 16c). We used the biomimetic culture of human myocardium from patients with cardiomyopathies to transduce AAV9-TNNT2-Sauri-dCas9VPR containing the validated KLF15 gRNA or corresponding NT control. Hearts from three patients were prepared to derive slices for KLF15 or NT gRNA delivery. Tissue biomechanics were characterized for 11 days of culture. Expression of Sauri-dCas9VPR as well as KLF15 was confirmed by RT-qPCR (Fig. 6i). Cultured slices showed increased contractility over the time of culture in slices treated with AAV9 KLF15 gRNA compared to NT control (Fig. 6j). Cardiac specificity of the TNNT2 promoter was validated in vivo (Supplementary Fig. 16d). These results validated a method for delivery of a CRISPRa platform into human cardiomyocytes that does not exceed the payload of AAVs and that efficiently modulated KLF15 expression, an approach compatible with clinical translation.DiscussionA major challenge in developing therapies for non-genetic diseases lies in the limited understanding of causative targets. Although TFs represent attractive candidates for developing novel disease treatments by orchestrating the regulation of multiple cellular pathways, they remain difficult therapeutic targets.54,55 Thus, our study addressed this gap by identifying and targeting a cell-type-specific TF activity relevant for heart failure progression. Despite limited in vivo validations, the predictive power of TF activity inference was demonstrated by identifying Nuclear Receptor Subfamily 4 Group A Member 1 (NR4A1) as a pro-inflammatory TF in wound monocytes/macrophages using BITFAM, confirmed by Nr4a1 knockout mice showing impaired tissue repair. This provides a strong rationale for targeting monocytes/macrophages TF networks to modulate inflammation and enhance wound repair outcomes.6 In this study, we identified KLF15 as the TF with the highest activity in distinguishing failing from healthy cardiomyocytes, despite its reduced expression. This is consistent with the BITFAM model,5 linking increased “repressor” activity to diminished repressive efficacy, underscoring the importance of carefully evaluating the weighted genes contributing to the inferred activity. Pivotal studies link reduced KLF15 expression to human cardiomyopathies and heart failure, establishing it as a repressor of hypertrophy and fibrosis while enhancing cardiac energetics and suppressing inflammation.8,9,11,12,13,14,56 Our study dissected the role of KLF15 at the sc resolution and identified a dual role of KLF15, encompassing a cardiomyocyte-autonomous inhibition of fetal gene reprogramming and a non-autonomous repressor of cardiac fibrosis in the homoestatic heart. Under stress, KLF15 is downregulated via TGF-β pathway activation, leading to a dysregulation of a homoeostatic transcriptional network in cardiomyocytes. Therapeutic KLF15 restoration using CRISPRa collectively preserved cardiac homeostasis under stress conditions, preventing heart failure development (Fig. 6k).Successful applications of CRISPR/Cas9 have been demonstrated mainly for genetic diseases,27 but our study marks the first instance of effective CRISPRa use to treat a non-genetic, progressive disease, such as heart failure. A recent study demonstrated CRISPRa for treating genetic cardiomyopathy induced by a Filamin-C (FLNC) truncating variant in mice. This involved the dSaCas9-VP64 activator, which effectively restored FLNC haploinsufficiency and normalized electrophysiological abnormalities.25 However, using stronger activators like VPR might improve effectiveness for more challenging target genes, including TFs. Based on our prediction model, we aimed at enhancing reduced Klf15 endogenous expression in adult stressed cardiomyocytes by systemically delivering AAV9-gRNAs targeting the Klf15 promoter in a cardiomyocyte-specific CRISPRa mouse model.21 Sc transcriptomics identified the targeted AAV9-Klf15 gRNA cardiomyocyte subpopulation with Klf15 activation exhibiting a healthy transcriptional profile that characterized the TAC CRISPRa-Klf15 treated hearts. Notably, this restoration of Klf15 expression upon sustained stress was sufficient to prevent heart failure progression, underscoring the critical role of KLF15 in maintaining homeostasis. In line with KLF15 roles, KLF15-dependent metabolic and pathological hypertrophic pathways, dysregulated in stressed cardiomyocytes were normalized in CRISPRa-Klf15-targeted cells upon stress. This rescue was not observed in a Klf15 KO model, confirming the specificity of KLF15 for this process. We also demonstrated that mechanical load in a 3D human tissue engineered model recapitulated the endogenous reduction of KLF15 as observed in mouse and human hearts upon stress. Consistent with the in vivo findings, cardiomyocyte-specific enhancement of KLF15 expression prevented pathological reprogramming and functional decline under mechanical strain in this model, highlighting its role in human cardiomyocytes.Cardiomyocyte de-differentiation, characterized by fetal sarcomeric isoform activation and reduced lipid metabolism, precedes terminal heart failure. KLF15 normalization prevented this reprogramming via two distinct mechanisms: direct regulation of metabolic/mitochondrial genes and cofactor interactions balancing fetal/adult cytoskeleton protein expression. We demonstrated KLF15 enrichment on key genes coordinating mitochondrial maturation and fatty acid beta-oxidation, such as SLC25A42, POLRMT, and POLG, which ensure proper mitochondrial function and energy supply in the heart. Consistently, loss of function of these genes leads to mitochondrial dysfunction and heart failure.57,58 Furthermore, we discovered KLF15-CSRP3 interaction by pulldown assays. CSRP3 regulates transcription, cytoskeleton, and mechanotransduction in cardiac/skeletal muscle. It also binds myogenic bHLH (Basic Helix-Loop-Helix) factors (cooperating with MRTFs/SRF) and links mechanical cues to regulation of fetal troponin levels and hypertrophy programs.59,60,61 Similar to our disease modeling with KLF15 reduction, CSRP3-deficient human embryonic stem cell-derived cardiomyocytes display increased expression of hypertrophic signaling genes, including NPPA, ACTA2, TAGLN, and fetal troponin isoforms.46 While KLF15-myocardin interactions suggest that CSRP3 scaffolds structural protein fine-tuning, this warrants further investigation.In line with these findings, KLF15 loss in human cardiomyocytes impairs mitochondrial respiration and Ca²⁺ handling, mirroring heart failure phenotypes in Klf15 KO mice,8 but a single KLF15 allele suffices to maintain metabolic function and contractility. Differently, TGF-β signaling, which downregulates KLF15, initially elicits adaptive hypertrophy via enhanced Ca²⁺ transients and mitochondrial respiration, boosting acute performance. This is consistent with an acute, adaptive hypertrophic response to TGF-β1 stimulation,62 which progresses to decompensation long-term. Our in vitro model may only reflect the acute effects of cardiac pathophysiology, rather than the long-term effects seen with the genetic deletion of KLF15. Our in vivo findings with KLF15 enhancement prevented compensatory cardiac hypertrophic remodeling while preserving cardiac function, suggesting that KLF15 can prevent the harmful transition from compensated hypertrophy to decompensated heart failure.KLF15 normalization yields dual benefits, preventing cardiomyocyte de-differentiation while promoting an anti-fibrotic cardiac environment. In all our models, CRISPRa-mediated KLF15 enhancement significantly attenuated TGF-β-driven stress responses, indicating that pathological effects of TGF-β, including fibrosis, largely stem from downstream KLF15 downregulation in cardiomyocytes. KLF15 is known to suppress cardiac and lung fibroblast activation,56,63 yet our model targeted cardiomyocytes exclusively, with CRISPRa-KLF15 expression confined to these cells. We showed that KLF15 induction in cardiomyocytes generated an antifibrotic secretome, prominently featuring AZGP1. AZGP1 was shown to exhibit anti-fibrotic effects by interfering with TGF-β signaling at the transcriptional and receptor levels.36 We demonstrated that AZGP1 is a novel transcriptional target of KLF15, produced by cardiomyocytes, reducing TGF-β canonical signaling in fibroblasts. This revealed a novel KLF15-AZGP1 axis mediating cardiomyocyte–fibroblast crosstalk. Notably, neither fibroblast KLF15 activation nor cardiomyocyte AZGP1 overexpression alone recapitulated the full anti-fibrotic/cardioprotective effects of cardiomyocyte KLF15 activation.Our study demonstrated that TGF-β1 directly downregulates KLF15 in hiPSC-cardiomyocytes via its canonical SMAD2/3 pathway, driving de-differentiation and reducing AZGP1 expression. Other studies have shown TGF-β-mediated KLF15 repression via p38 phosphorylation in rat cardiomyocytes,12 indicating a species-specific regulation. Furthermore, homozygous and heterozygous genetic deletions of KLF15 unequivocally demonstrated that cardiomyocyte de-differentiation and AZGP1 regulation depend on KLF15. This implies that downregulation of KLF15 is a downstream effect of the TGF-β pathway in human cardiomyocytes under stress, resulting in hallmarks of cardiac remodeling. Remarkably, the sole normalization of KLF15 has the potential to prevent this pathological remodeling. In this context, TGF-β suppression in cardiomyocytes protects against maladaptive remodeling in response to pressure overload, as evidenced by cardiomyocyte-specific TGF-βR2 knockdown, which presents a challenge for therapeutic targeting.64 Thus, KLF15 can be leveraged for cell-selective modulation of TGF-β downstream signaling, for optimally preventing pathological human cardiac remodeling.From a therapeutic perspective, both CRISPR/Cas9 and AAV9 have been FDA-approved for clinical use, enabling rapid translation to patients.65,66 However, application of effective CRISPRa systems requires the co-administration of two different AAV vectors due to their limited packaging capacity. Here, we engineered Sauri-dCas9VPR as a single-AAV-compatible platform for gene activation, largely retaining SpCas9 PAM requirements under cardiomyocyte-specific TNNT2 promoter control, achieving efficient activation in human cells and functional rescue in KLF15-targeted myocardium. Importantly, we validated the engineered CRISPRa system in primary human myocardial slices, establishing a pioneering therapeutic proof-of-concept for a well-characterized in vivo target. This connects preclinical research with clinically relevant human tissue models, creating a robust pipeline for validation of pathophysiologically relevant targets. In conclusion, this discovery establishes a foundation for cardiac therapies, as KLF15 is implicated in various conditions, including atherosclerosis, aortic lesions, and diabetic cardiomyopathy,7 while demonstrating the broader utility of CRISPRa for targeting transcriptional disease mechanisms. Our compact, cardiomyocyte-specific system enables precise endogenous gene upregulation offering first-in-class potential for clinical translation, cross species research, and mechanistic studies. This approach overcomes the limitations of classical overexpression while preserving the native genomic context..MethodsEthics statementsAll mouse experiments in this study were approved by the Lower Saxony State Office for Consumer Protection and Food Safety (LAVES), animal research project AZ-G 15-1840 and AZ-G 33.9-42502-04-20/3434. Approval for the use of human material was received from the local ethics committee of the Justus Liebig University Giessen (AZ 184/24). Cardiac tissue specimens obtained from patients with cardiomyopathies were used to prepare myocardial slices. All procedures conducted in this study adhered to the principles outlined in the Declaration of Helsinki. Informed consent was signed by all tissue donors.Experimental animal modelMice (Myh6-dCas9VPR)21 were crossed with Klf15/Klf15 knockout (KO) mice10 to obtain double transgenic Myh6-dCas9VPR/Klf15 KO mice. Wild-type C57BL/6N animals were obtained from Charles River Laboratories. Animals were fed ad libitum and housed in a temperature- and humidity-controlled environment in a 12 h day/night cycle. Both male and female mice were included in the study. Four- to six-month-old mice were injected via the tail vein with 1 × 1012 vg/mouse rAAV2/9-Klf15 (KLF15 gRNAs A, B, C) or rAAV2/9-NT (NT gRNAs 1, 2, 3) diluted in 80 µL of 0.9% NaCl. Injected adult mice underwent transverse aortic constriction surgery (TAC) using a 26 G blunt cannula or sham surgery (control) as previously described.67 To determine the level of pressure overload by aortic ligation, a high-frequency Doppler probe was used to measure the ratio between blood flow velocities in the right and left carotid arteries. TAC mice with a blood flow gradient < 60% were excluded. Echocardiography was performed 4 and 8 weeks post-surgery, and hearts were harvested 16 weeks post-surgery. Mice were anesthetized with 2% isoflurane inhalation, and ventricular measurements were performed with a VisualSonics Vevo 2100 Imaging System equipped with an MS400, 30 MHz MicroScan transducer. The observer was unaware of the genotypes and treatments. Images of excised organs were taken with a Stereo Lumar.V12 stereomicroscope (Carl Zeiss). Surgeries and echocardiography were performed and analyzed in a blinded manner with the support of the CRC1002 service unit.Molecular cloningPlasmids, the design of gRNA for the validation of human KLF15 gRNA, and the TRISPR vector used for the delivery of gRNAs were described previously.21 A list of gRNAs is provided in Table 1. The CMV promoter was exchanged with human TNNT2 promoter in pAAV-CMV-SauriCas9, pAAV-CMV-SauriCas9-KKH-puro, and pAAV-CMV-SaSauriCas9 (a gift from Yongming Wang, Addgene plasmid #135964, #135966, #135967) using XbaI and BshTI sites (both Thermo Fisher Scientific).48 SauriCas9 was exchanged with SlugCas9 derived from pAAV-CMV-SlugCas9 (a gift from Yongming Wang, Addgene plasmid #163793)47 using In-Fusion cloning (Takara) and BshTI and BamHI restriction sites (Thermo Fisher Scientific). Mutations in the RUVC and HNH domains were introduced by homology comparison and mutagenesis PCRs. The transactivation domain, HA-tag, and mini-polyA were introduced by a gene block (Eurofins Genomics) using the BamHI restriction site (Thermo Fisher Scientific). Sanger sequencing (MicroSynth Seqlab) and ITR integrity checks were performed using XmaI and KpnI sites (Thermo Fisher Scientific). Nuclease deficiency was checked by transfecting CMV-SauriCas9 or CMV-Sauri-dCas9 plasmids co-expressing the following EGFP targeting gRNAs: EGFP gRNA1: 5’-GTCAAGGAGGACGGCAACATC-3’, EGFP gRNA2: 5’-GCGACCAGGATGGGCACCACC-3’ into HEK293T cells transduced with an EGFP lentiviral expression plasmid. DNA was isolated, and PCRs were performed to check for deletion events using the following primer pair: EGFP fwd: 5’-GAGCAAGGGCGAGGAGCT-3’; EGFP rev: 5’-TTGTACAGCTCGTCCATGCC-3’.Single-cell preparation for transcriptome analysisCardiac cells were isolated from whole mouse hearts using a modified Langendorff perfusion protocol. Briefly, hearts were perfused with perfusion buffer containing 150 mM NaCl, 5 mM HEPES, 5.4 mM KCl, 10 mM glucose, 2 mM Na-pyruvate, 1.2 mM MgCl2, 10 mM taurine, and 12.38 mM 2,3-butanedione monoxime for 2 min at 37 °C followed by perfusion with perfusion buffer supplemented with 25 mM CaCl2 and 210 µg/mL Liberase DH (Roche) for 10 min at 37 °C. Reactions were stopped by adding perfusion buffer supplemented with 0.5% w/v bovine serum albumin (BSA) and 200 µM CaCl2. Hearts were minced and homogenized into a single cell suspension. For sc imaging, glass coverslips were coated with Matrigel (Corning) for 1 hour at 37 °C and cells were seeded on prepared coverslips. Cells attached within 30 min at room temperature. Live-cell imaging was performed on an Axio Imager M2 with ZEN software (Carl Zeiss).Whole single-cell RNA sequencingSamples were prepared and processed for sc transcriptomics at the Next Generation Sequencing Facility, Institute of Human Genetics, University Medical Center Göttingen. Briefly, cells were distributed on 5184 nanowell chips ICELL8 250 v Chip (ICELL8 System, Takara Bio). Single live cells were identified using Hoechst 33342 and propidium iodide staining (NucBlue Cell Stain Reagent, Thermo Fisher Scientific) and the CellSelect Software (Takara Bio). Complementary DNA synthesis was performed by oligo-dT priming in a one-step RT-PCR reaction. P5 indexing primers, Terra Polymerase, and Reaction Buffer were added for library preparation. Transposase enzyme and reaction buffer (Tn5 mixture) were added to each well. P7 indexing primers were dispensed to wells. Final libraries were amplified and pooled as they were extracted from the chip. Pooled libraries were purified, and size was selected with Agencourt AMPureXP magnetic beads (Beckman Coulter) to obtain an average library size of 500 bp. A typical yield for a library comprised of ~1300 cells was 15 nM. Libraries were sequenced with a HiSeq4000 (Illumina) to obtain, on average, ~500K reads per cell (single-end, 50 bp). Raw sequencing files (bcl-files) were converted into a single fastq file using Illumina bcl2fastq software (v2.20.0.422) for each platform. Each fastq file was demultiplexed and analyzed using the Cogent NGS analysis pipeline (CogentAP) from Takara Bio (v1.0). In brief, the “cogent demux” wrapper function was used to allocate the reads to the cells based on the cell barcodes provided in the well-list files. Subsequently, the “cogent analyze” wrapper function was used to perform read trimming with cutadapt (version 3.2), genome alignment to Mus musculus gene annotation version 102 from ENSEMBL was done using STAR (version 2.7.7a), read counting for exonic, genomic, and mitochondrial regions was done using featureCounts (version 2.0.1), and a gene matrix was generated with the number of reads expressed for each cell in each gene.Single-cell transcriptomic analysisRaw gene matrices underwent quality control (QC) filtering for cells and genes using the following parameters: (a) for cells, only those with at least 10,000 reads associated with at least 300 different genes, and (b) for genes, only those containing at least 100 reads mapped to them from at least three different cells. Mitochondrial and ribosomal content were calculated for each cell to ensure data quality. They were then filtered based on total counts and mitochondrial content based on five median absolute deviations. Only those genes with at least 200 reads mapped from a minimum of 3 different cells were considered, and mitochondrial, ribosomal, and hemoglobin genes were systematically removed. The Seurat package (version 4.1.2) was utilized in this analysis. Following QC, scRNA-seq data were normalized with NormalizeData. Variable features were identified using FindVariableFeatures, and data were scaled while excluding mitochondrial gene expression. Principal component analysis (PCA) was performed with RunPCA, and the optimal number of principal components was determined using an ElbowPlot. The normalized data were then used to identify cell clusters through FindNeighbors and FindClusters. Different clustering resolutions (0.1–0.5) were explored to find the most suitable resolution. Uniform Manifold Approximation and Projection (UMAP) was applied with RunUMAP for visualizing cell clusters. The Seurat package’s integration method was employed to correct for batch effects. The scRNA-seq data were split by sample using SplitObject, and each subset was transformed using SCTransform with the generalized linear model approach and a Poisson distribution. Integration features were selected with SelectIntegrationFeatures, and data were prepared for integration with PrepSCTIntegration. Integration anchors were identified using FindIntegrationAnchors, and data integration was performed with IntegrateData. The integrated dataset underwent additional dimensionality reduction, clustering, and visualization using PCA, UMAP, FindNeighbors, FindClusters, and DimPlot. Differential gene expression analysis was conducted on the integrated dataset to identify markers associated with different biological conditions, utilizing PrepSCTFindMarkers and FindMarkers. The packages dplyr, ggplot2, tidyverse, EnhancedVocano, and dittoseq were used for data visualization and manipulation.68,69,70 Gene ontology analysis was performed by ShinyGO 0.82 and Enrichr.71,72 For BITFAM TF inference, cardiomyocyte samples were extracted from the two selected datasets,28,29, and their count matrices were subjected to basic BITFAM analysis to obtain a list containing matrices with the inferred TF activity and gene weights. The inferred activity matrix was extracted using the BITFAM_activities tool, and a data frame with the barcoded cells, the inferred activity, and the conditions was constructed for the Random Forest classification. The results were used to plot the TF importance. The BITFAM_weights tool was used to extract the downstream weights for each TF.Human-induced pluripotent stem cellsKLF15 loss of function in CRISPRa hiPSCs (RUCDRi002-A-15) was generated by ribonucleoprotein-based CRISPR/Cas9 targeting exon 2 of the KLF15 gene, as described previously.38 Transgenic hiPSCs containing the CRISPRa system (RUCDRi002-A-15) were previously described.37,38 Differentiation protocols for cardiomyocytes, fibroblasts, and EHM were previously described.39,73 Human induced pluripotent stem cells were cultured in culture dishes coated with Matrigel (Corning) in Stem MACS iPS Brew XF, human (Miltenyi Biotec).For hiPSC-cardiomyocyte differentiation, hiPSCs were grown until 70% confluency and washed twice with basal medium (RPMI 1640 + GlutaMAX, 1 mM sodium pyruvate (Gibco), 200 μM L-ascorbic acid, 100 U/mL penicillin, and 100 μg/mL streptomycin (Gibco)). For mesoderm induction, basal medium was supplemented with 2% v/v B27 no insulin (Thermo Fisher Scientific), 1 μM CHIR99021 (Merck Chemicals GmbH), 5 ng/mL BMP4 (Bio-Techne), 5 ng/mL FGF-2 (Peprotech), and 9 ng/mL Activin-A (R&D Systems), and cells were cultured for 3 days. Cells were washed twice with basal medium and cardiomyocyte differentiation was initiated for 9 days in basal medium supplemented with 2% v/v B27 supplement (Thermo Fisher Scientific) and 5 μM IWP-4 (ReproCELL Europe Ltd.). Cardiomyocytes were metabolically selected in RPMI 1640, no glucose, 2.2 mM Na-lactate, 100 μM β-mercaptoethanol (Gibco), 100 U/mL penicillin, and 100 μg/mL streptomycin (Gibco) for 5 days. Cells were detached using StemPro Accutase Cell Dissociation Reagent (Gibco) for 30 min and Versene (Gibco) for 5 min at room temperature and reseeded on Matrigel-coated flasks in base medium supplemented with 5 µM ROCK inhibitor Y-27632 (Stemgent). For the TGFβ treatments, hiPSC-cardiomyocytes were differentiated and treated with different concentrations of TGFβ1 or TGFβ2 (Peprotech) (final concentration: 0.001, 0.01, 0.1, 10, 100, and 1000 pM) for 5 days, and cells were harvested for analysis. For TGFβ inhibitor treatment, cells were pretreated for 1 h with respective inhibitors: SB431542 (SMAD2/3 inhibitor, final concentration: 10 µM, Peprotech) and SB203580 (p38 inhibitor, final concentration: 10 µM, Thermo Fisher Scientific) before adding TGFβ1 (final concentration: 10 pM, 1 h or 5 days). The medium was changed daily.To differentiate fibroblasts, hiPSCs were plated on Matrigel-precoated flasks 4 days before starting the differentiation and maintained in Stem MACS iPS Brew XF, human (Miltenyi Biotec) with daily medium changes. On the day of differentiation, medium was replaced with RPMI 1640 + GlutaMax (Gibco) supplemented with 2% v/v B27 no insulin (Thermo Fisher Scientific), 1 µmol/L CHIR99021 (Stemgent), 5 ng/mL bFGF (Peprotech), 5 ng/mL BMP4 (R&D Systems), and 9 ng/mL Activin-A (R&D Systems), and refreshed after 24 h for mesoderm induction. After 2 days, growth factor composition was switched to the addition of 5 µmol/L IWP4 (Stemgent). 24 h later, cells were passaged on laminin (final concentration 0.9 µg/cm², BioLamina) coated flasks using TrypLE Express (Gibco) and replated in RPMI 1640 + GlutaMax, supplemented with 2% v/v B27 supplement (Thermo Fisher Scientific), 1 µmol/L CHIR00921, 50 ng/mL BMP4, and 4 µM retinoic acid (Sigma-Aldrich) to induce formation of epicardial cells. On day 10, fibroblast differentiation was induced by the addition of 25 ng/mL VEGFA (Peprotech) and 50 ng/mL bFGF in KnockOut DMEM (Gibco) supplemented with 10% v/v KnockOut serum (Gibco) and 2 mmol/L L-glutamine (Gibco). Cells were again passaged on day 14 and maintained in culture on vitronectin (final concentration: 0.9 µg /cm2, Invitrogen) coated flasks for up to seven passages with weekly passaging intervals in the fibroblast culture medium (10 ng/mL bFGF, 5 ng/mL EGF (Peprotech), 20 ng/mL IGF-1 (Peprotech), 0.5 ng/mL VEGFA, 10% v/v fetal bovine serum, and 500 μM L-ascorbic acid (Sigma) in DMEM, low glucose, GlutaMAX™ Supplement (Gibco)). Medium was changed every 2 days.Supernatant/recombinant treatmentFor hiPSC-cardiomyocytes supernatant experiments for assessing the effect of AZGP1 in hiPSC-fibroblasts, supernatant from cardiomyocytes was collected daily for 5 days. To assess the concentration of AZGP1 in the supernatant, the human ZAG/AZGP1 ELISA Kit (Proteintech) was used by following the manufacturer’s instructions. The hiPSC-fibroblasts were seeded in culture vessels, and a medium containing 50% of fibroblast culture medium and 50% of supernatant from hiPSC-cardiomyocytes was added. Cells were collected 5 days after seeding for analysis. The medium was changed daily.For human recombinant AZGP1 and BMP7 protein treatment, hiPSC-fibroblasts were cultured in the fibroblast culture medium and the cells were treated with TGFβ1 (final concentration: 10 or 100 pM, Peprotech) and human recombinant AZGP1 (final concentration: 1 µg/mL, R&D Systems) or BMP7 (final concentration: 400 ng/mL, Peprotech) for 2, 24 h or 5 days with the additional Zinc2+ supplement (final concentration: 1.5 nM, MERCK) in the medium. The cells were cultured in the fibroblast culture medium without FBS for 16 h before TGFβ1 and recombinant treatment.Human myocardial slicesFor long-term cultivation of human myocardium, the MyoDish-1 tissue culture system (InVitroSys) was employed, following the protocol adapted from Fischer et al.53 Briefly, tissue specimens obtained from patients with cardiomyopathies were cleared of connective tissue and intramuscular fat and subsequently trimmed to a 1 × 1 cm2 cross-sectional area. Tissue blocks were embedded in 4% w/v low-melt agarose (Roth), mounted on the cooled (4 °C) stage of a vibratome (VT1200S, Leica Biosystems), and cut in the epicardium-tangential plane (2 mm vibration amplitude, 0.06 mm/s feed rate). Resulting 300 µm-thick slices were affixed (Histoacryl, B. Braun) to small plastic triangles with aligned longitudinal fiber orientation. Subsequently, slices were transferred to biomimetic culture chambers (InVitroSys) containing 2.4 mL pre-warmed (37 °C) culture medium (Medium 199, Gibco) supplemented with 100 U/mL penicillin/100 µg/mL streptomycin, 1% insulin/transferrin/selenite 100-x, 20 nM dexamethasone, 50 µM 2-mercaptoethanol) and placed in a standard incubator with 37 °C and 5% CO2. Electrical pacing (0.5 Hz, 50 mA, 3 ms) was initiated via the MyoDish software. After 24 h of cultivation, the preload was set to 1 mN and remained constant throughout the subsequent culture period. To verify the effectiveness of AAV9 particles in transducing human myocardium, cultivated slices were incubated for two days with the reporter gene EGFP (pAAV-TNNT2-EGFP) at a concentration of 1 × 1011 vg/slice. After 8 additional days of culturing, expression of EGFP in the human tissue slices was examined using confocal microscopy. After confirmation of AAV9 transduction in human tissue, cultured slices were transduced with 1 × 1011 vg/slice TNNT2-Sauri-dCas9VPR containing the validated KLF15 gRNA or the corresponding NT gRNA controls. The contractile performance of transduced tissue slices was recorded for 11 days via the MyoDish software and analyzed with the “LabChart Reader” software (AD Instruments). Upon completion of the culture period, the tissue sections were extracted from the chambers and stored at −80 °C for subsequent immunoblotting and qPCR analysis.Generation of viral particles and transductionFor lentiviral production, 3 × 106 HEK293T cells (Takara) were transfected with 1.23 µg pMD2, 2.27 µg psPAX2 (both kindly provided by Didier Trono, Addgene plasmid #12259 and #12260) and 3.5 µg pGIPZ-KLF15-gRNA (KLF15 gRNAs A, D, E)/pGIPZ-NT-gRNA (NT gRNAs 1, 2, 3)/LentiORF AZGP1 (horizon)/pGIPZ AZGP1 shRNA (horizon)/pGIPZ-FLAG-HA-KLF15/pGIPZ-FLAG-HA-EGFP transfer vectors using Turbofect (Thermo Fisher Scientific). Medium was exchanged the following day and subsequently collected for 3 days. The lentiviral particle-containing supernatant was concentrated using Amicon Ultra-15, 100 kDa MWCO (Sigma Aldrich), and the lentiviral particle concentration was estimated using Lenti-X GoSticks Plus (Takara). For hiPSC-cardiomyocyte or hiPSC-fibroblasts transduction, cells were seeded in the respective culture vessel. The cells were left to recover for three days (hiPSC-cardiomyocyte) or one day (hiPSC-fibroblasts) before transduction. At day 0, the culture medium was changed, and the virus was added to the culture medium. The following day, the media were changed. Transduction efficiency was assessed by fluorescence microscopy. Images were analyzed with ImageJ.AAV9 preparation was carried out essentially as previously described.21,74 For the generation of recombinant AAV9, shuttle vectors for these recombinants were co-transfected with AAV9 helper, pDG-9 (a gift from Dr. Roger Hajjar), into HEK293T cells (AAVPro 293T Cells, Takara 632273) to produce virus. To prepare the recombinant AAV9, HEK293T cells were plated at a density of 8 × 106 per T-175 flask and maintained in DMEM/F12 (Gibco) containing 10% v/v FBS and 100 U/mL penicillin/100 µg/mL streptomycin at 37 °C and 5% CO2. For each virus preparation, 48 flasks were used. Twenty-four hours after plating, cultures were transfected using Polyethylenimine “Max” (MW 40,000, Polysciences) as follows: for each T175 flask, 15 µg of helper plasmid and 5 µg of recombinant plasmid were mixed with 1 mL of DMEM/F12 (no antibiotics) and 160 µL of polyethylenimine (0.517 mg/mL), vortexed for 30 s, and incubated for 15 min at room temperature. This was then mixed with 18 mL DMEM/F12 containing 2% v/v FBS and 100 U/mL penicillin/100 µg/mL streptomycin, which was then used to replace the media on the cultures. The cultures were then rocked intermittently for 15 min before incubation. Three days after transfection, the cells collected from six T-175 flasks were centrifuged at 500 g for 10 min and then resuspended in 10 mL of lysis buffer (150 mM NaCl, 50 mM Tris-–HCl). The resuspended cells were then subjected to three rounds of freeze–thaw, followed by treatment with benzonase (Novagen) and 1 mM MgCl2 at 37 °C for 30 min. The cell debris was collected by centrifugation at 3400×g for 20 min. The supernatant obtained from six T-175 flasks containing the AAV9 was then purified on an iodixanol gradient composed of the following four phases: 7.3 mL of 15%, 4.9 mL of 25%, 4 mL of 40%, and 4 mL of 60% iodixanol (Optiprep, SigmaAldrich) overlayed with 10 mL of cell supernatant. The gradients were centrifuged in a 70Ti rotor (Beckman Coulter) at 69,000 rpm for 1 h using OptiSeal Polyallomer Tubes (Beckman Coulter). Virus was collected by inserting a needle 2 mm below the 40–60% interface and collecting 4 or 5 fractions (~4 mL) of this interface and most of the 40% layer. The fractions were analyzed for viral content and purity by examining 10 µL of each fraction on a 12% SDS–PAGE gel (BioRad), followed by staining with InstantBlue (Expedeon) to visualize the viral capsid proteins VP1, VP2, and VP3. The virus was then collected from the fractions of several gradients, and the buffer was exchanged with lactated Ringer’s using an ultrafiltration device, Vivaspin 20, 100 kDa MWCO (GE Healthcare). The final viral preparation was then fractionated on a 12% SDS-PAGE gel, stained with InstantBlue, and then compared with a similarly stained gel of a virus with a known titer. Quantification of AAV genomic titers was done by RT-qPCR using gene-specific primers or polyA-specific primers on purified AAV vectors after density gradient ultracentrifugation.Statistical analysisGraphPad Prism 11 was used for statistical analyses unless otherwise specified. Differences with P