Abstract

   To elucidate the lipidomic and metabolomic alterations associated with
   hypertrophic cardiomyopathy (HCM) pathogenesis, we utilized cmybpc3-/-
   zebrafish model. Fatty acid profiling revealed variability of 10 fatty
   acids profiles, with heterozygous (HT) and homozygous (HM) groups
   exhibiting distinct patterns. Hierarchical cluster analysis and
   multivariate analyses demonstrated a clear separation of HM from HT and
   control (CO) groups related to cardiac remodeling. Lipidomic profiling
   identified 257 annotated lipids, with two significantly dysregulated
   between CO and HT, and 59 between HM and CO. Acylcarnitines and
   phosphatidylcholines were identified as key contributors to group
   differentiation, suggesting a shift in energy source. Untargeted
   metabolomics revealed 110 and 53 significantly dysregulated
   metabolites. Pathway enrichment analysis highlighted perturbations in
   multiple metabolic pathways in the HM group, including nicotinate,
   nicotinamide, purine, glyoxylate, dicarboxylate, glycerophospholipid,
   pyrimidine, and amino acid metabolism. Our study provides comprehensive
   insights into the lipidomic and metabolomic unique signatures
   associated with cmybpc3-/- induced HCM in zebrafish. The identified
   biomarkers and dysregulated pathways shed light on the metabolic
   perturbations underlying HCM pathology, offering potential targets for
   further investigation and potential new therapeutic interventions.

Supplementary Information

   The online version contains supplementary material available at
   10.1038/s41598-024-72863-5.

   Keywords: Hypertrophic cardiomyopathy, Lipidomics, Metabolomics,
   MYBPC3, Zebrafish

   Subject terms: Genomics, Genotype, Cardiovascular biology, Cardiac
   hypertrophy, Cardiovascular diseases, Cardiovascular genetics,
   Genetics, Cardiology

Background

   Hypertrophic Cardiomyopathy (HCM) is a disorder with an estimated
   prevalence of 1 in 500, clinically characterized by left ventricular
   myocardial wall hypertrophy in the absence of other systemic,
   metabolic, or cardiac diseases^[48]1–[49]3. It has a critical
   complication of sudden death due to cardiac failure^[50]4, especially
   in asymptomatic athletes and younger patients^[51]5. HCM is a
   heterogeneous disease caused by pathogenic variation in genes encoding
   sarcomeric, Z-disc, and calcium-controlling proteins^[52]1. The most
   prevalent genetic variants impact sarcomeric-encoding proteins
   (40–60%), affecting the contractility of the heart. These include
   myosin heavy chain 7 (MYH7), myosin binding protein C3 (MYBPC3),
   cardiac troponin T2 (TNNT2) and cardiac troponin I3
   (TNNI3)^[53]6–[54]9.

   With a frequency of ~ 30–40%, pathogenic variants in MYBPC3 are the
   most common cause of HCM^[55]10,[56]11. This is attributed to its vital
   role in sarcomeric structural configuration and function through
   modulation of myofilament sliding velocity and its key contribution to
   the actin-myosin interaction, which is responsible for
   contractility^[57]12,[58]13. Pathogenic MYBPC3 variants are typically
   frameshift, non-sense, or splice site variants, which lead to premature
   termination codons (PTC)^[59]14. Consequently, PTCs are degraded
   through non-sense mediated RNA decay, which could lead to allelic loss
   of function, impacting the function of the protein and interaction with
   other associated proteins^[60]12,[61]15.

   Despite the significant contribution of genetic variants in
   understanding the etiology of HCM, they have been proven insufficient
   in fully capturing the overall associated clinical and pathological
   features related to clinical presentations and severity^[62]16. Thus,
   the clinical picture of ~ 40% of HCM patients remains unresolved when
   assessed through clinical examination and genetic
   testing^[63]17,[64]18.

   Changes in cardiac structure and function in HCM often correlate with
   changes in energy consumption as a compensation mechanism in
   HCM^[65]19. In the heart, ATP is the primary source of energy and
   adaptation to energy demands throughout the day due to physiological
   and nutritional changes is crucial. ATP can be sourced from metabolites
   including amino acids, lipids, and carbohydrates. Disturbances in ATP
   supply could thus lead to alterations in cardiac activity and eventual
   cardiovascular diseases (CVDs)^[66]20. Cellular studies have depicted
   that HCM-associated variants increase the demand for utilizing ATP,
   thus emphasizing a higher requirement for myocardial energy in HCM
   patients^[67]4. As there is insufficient ATPase activity in such
   patients, it is hypothesized that the higher demand for energy leads to
   energy stress and adverse remodeling within the heart^[68]4. Thus,
   understanding the changes in cardiac metabolic activity could provide
   an avenue for further understanding of CVDs, including HCM. Through the
   integration of metabolomics and lipidomics profiling, the
   identification of distinct metabolite features and changes associated
   with a genotype, environmental factors, including diet and physical
   activity could lead to a wider understanding of HCM etiology.

   The zebrafish model has been reported to possess a similar metabolism
   to humans^[69]21, making it an increasingly common target for
   metabolomic studies^[70]22. Zebrafish cmybpc3 knockout line has been
   previously employed and proven as an efficient model to study the
   association of genetic variants and clinical
   presentation^[71]23–[72]25.

   To shed more light on the relationship between metabolic activity and
   HCM, with the aim to improve diagnosis efficiency and patient
   stratification, lipidomics, metabolomics, and fatty acid signatures,
   analysis of heterozygous and homozygous established zebrafish
   MYBPC3-HCM models is performed. The ultimate goal is to tackle the
   pathophysiological interactions of biomarkers related to HCM
   progression.

Results

Sample collection

   We analyzed 21 zebrafish homogenate samples through lipidomics and
   metabolomics to study HCM signatures in order to identify, relatively
   quantify, and statistically compare lipids and metabolites between the
   experimental groups of transgenic reporter Tg myl7:eGFP line as a
   control (CO), cmybpc3+/-::myl7:eGFP heterozygous (HT), and
   cmybpc3-/-::myl7:eGFP homozygous (HM) (Fig. [73]1) using liquid
   chromatography-high resolution mass spectrometry. In total, we have
   analyzed 7 replicates for each group of CO, HT and HM; each replicate
   included pooled 150 larvae. Each sample’s performance was monitored
   with the addition of isotopically labeled internal standards through
   analysis. These internal standards were monitored for mass accuracy,
   peak area response, and retention time variability. All parameters were
   within accepted criteria of 3ppm, +/- 30% of the median and +/- 2% of
   the median, respectively. The selection of features for analysis was
   designed to capture the metabolic and lipidomic alterations associated
   with HCM. Diverse data sources, including an in-house MS2 library and
   mzCloud for metabolomics and LipidSearch for lipidomics, were
   incorporated to provide wide coverage of potential biomarkers and
   metabolic pathways.

Fig. 1.

   [74]Fig. 1
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   Representative images of the zebrafish larvae at 5 days old. (A)
   Representative lateral view of zebrafish larvae (control (CO),
   cmybpc3+/-::myl7:eGFP heterozygous (HT), and cmybpc3-/-::myl7:eGFP
   homozygous (HM))Transgenic reporter line myl7:egfp expressing green
   fluorescent two-chambered heart (500 μm scale bar). (B) A ventral view
   of HM heart at 168x high-magnification image captured using Axiozoom
   Zeiss stereomicroscope equipped with Axiocam camera, scale bar 50 μm.

   Fatty acids, acylcarnitines and lipidomic changes associated with
   cmybpc3-HCM zebrafish model.

   A total of 10 fatty acids were detected in the zebrafish lysate that
   showed variability in fatty acids profile between the different groups.
   Hierarchical cluster analysis revealed that both HT and HM groups
   showed variability in concentrations of detected fatty acids
   (Fig. [76]2).

Fig. 2.

   [77]Fig. 2
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   Hierarchical cluster analysis of fatty acids. Z-score clustering
   results for fatty acids are shown as a heatmap for all samples using
   MetaboAnalyst 5.0. The distance measure was Euclidean, and clustering
   method was Ward. The rows and columns are ordered so that similar
   groups and similar metabolites are close to one another. The fatty
   acids are present in the rows, and the sample groups in columns. Dark
   blue to dark red color gradient denotes lower to higher levels. On the
   top of the heatmap, the homozygous sample group is represented in blue,
   the heterozygous group in green and the control group in red.

   PCA showed the clustering of the three examined groups with the first
   two principal components explaining 81.8% of the variability. The HM
   group showed a relatively distinct separation in comparison to the CO
   and HT groups, despite the 95% confidence region overlapping between HM
   and the other groups (Fig. [79]3A).

Fig. 3.

   [80]Fig. 3
   [81]Open in a new tab

   Principal components analysis (PCA) graph. The percentages on the axes
   represent the variation in the data set of the first two components.
   The points in the shaded regions represent the 95% confidence interval
   of each group and any overlaps between groups. The PCAs were generated
   using MetaboAnalayst 5.0 comparing between sample groups (control (CO),
   cmybpc3+/-::myl7:eGFP heterozygous (HT), and cmybpc3-/-::myl7:eGFP
   homozygous (HM). (A) Fatty Acids, (B) Acycarnitines, (C) Lipids and (D)
   Metabolites.

   The PLS-DA differentiated metabolic phenotypes with the first component
   explaining 67.5% of the variability (Supplementary Figure [82]S1). The
   results highlight 9,12,15-octadecatrienoic acid as the variable showing
   the highest VIP score of 1.95 for component 1, followed by hexadecanoic
   acid (Supplementary Figure [83]S1). Particularly, 13-Octadecanoic acid
   (C18:1), 9,12-Octadecadienoic acid, 9,12,15-octadecatrienoic acid,
   Hexadecanoic acid, 14-methyl-, and 5,8,11,14,17-Eicosapentaenoic acid,
   were significant between the control group (CO) and cmybpc3-/-
   homozygous group (HM) (Supplementary Figure [84]S1).

   We analyzed the total lipids to identify lipidomic profiling to relate
   key lipid biomarkers in the cmybpc3-HCM model. Lipid profiling
   determined a total of 221 lipid annotations where fragmentation spectra
   were matched against LipidSearch software (Supplementary Table [85]S1).
   The annotated lipid compounds covered several lipid classes:
   acylcarnitines, carnitine esters, ceramides, diacylglycerols,
   hexosylceramides, lysophosphotidylcholines,
   lysophophatidylethanolamines, phosphatidylcholines,
   phosphatidylethanolamines, phophatidylglycerol phosphatidylinositol,
   phosphatidylmethanol, sphingomyelin, triacylglycerol and coezyme-Q10. A
   total of two lipids were significantly dysregulated between the CO and
   HT; and 48 lipids between CO and HM groups using One way ANOVA analysis
   with Tukey as post-hoc test (Supplementary Table [86]S2 and S3) with
   false discovery rate (FDR) < 0.05. The first two principal components
   explained 54.3% of the variability in cmybpc3-HCM models (Fig. [87]3B).

   Sparse Partial Least Squares-Discriminant Analysis (sPLS-DA) using five
   components with 10 variables per component established a clear
   separation between the three groups based on lipidomic profiles
   (Supplementary Figure [88]S2). The results demonstrate that
   acylcarnitines and phosphatidylcholines contribute the most to the
   differentiation between the three examined groups (Supplementary Figure
   [89]S2). The results of the hierarchical clustering indicated that
   within the homozygous group, there is observed variability in the
   concentrations of lipids that form a distinct signature in the mutant
   HCM model (Supplementary Figure [90]S2).

   To discriminate between the three groups, we compared the groups using
   acylcarnitines profiles. The first two components, acylcarnitines (18:3
   and 20:4), explained 88.2% of this variability between CO, HT and HM
   samples. The HM samples clustered separately from the CO, HT groups
   (Fig. [91]3C). The hierarchical cluster analysis demonstrated that the
   HM group clusters separately with an elevation in acylcarnitine levels
   (Supplementary Figure S3).

   The PLS-DA model revealed significant discrimination in the metabolic
   phenotypes of acylcarnitines. The acylcarnitines with the highest VIP
   scores, contributing to the differentiation of the HM group from the
   other HT and CO groups, were highlighted in Supplementary Figure S3B
   and S3C.

   To understand the underlying metabolic perturbation of cmybc3-/-
   related pathological HCM, untargeted metabolomics was employed to
   characterize the differences between the examined groups. A total of
   110 and 53 metabolites were annotated under positive and negative ESI
   modes, respectively with an MS2 fragmentation pattern match in an
   in-house library or mzCloud software (Supplementary Table S4). A total
   of 13 metabolites were significantly dysregulated between HM and CO
   (Supplementary Table S5), while none were significant between HT and
   CO, with FDR < 0.05.

   PCA did not show clear clustering between the groups with the first two
   components explaining 48.4% of the variability (Fig. [92]3D). Further
   analysis using sPLS-DA with 10 variables per component on the top 5
   components showed a more distinct separation of the HM samples and a
   unique metabolic signature related to both HM and HT groups
   (Supplementary Figure S4).

   To explore the significant metabolites distinguishing HCM phenotype, we
   compared between two groups: HM and CO groups, HM and HT groups, HT and
   CO groups. Metabolites were considered significant if they had a log2
   fold change > 1 and FDR < 0.05. Comparing HM and CO groups, among the
   163 identified metabolites, 16 were significant, with 6 showing
   downregulation and 10 upregulation (Supplementary Figure S4). The
   OPLS-DA revealed a clear and distinct clustering of the HM and CO
   groups with R^[93]2Y (model interpretation rate) of 87.7% and Q^[94]2
   (model predictive ability) of 74.5% (Supplementary Figure S4).
   Metabolites with VIP > 1 were selected as important metabolites with
   asparagine showing the highest VIP score, followed by stachydrine
   (Supplementary Figure S4). Furthermore, Random Forest model
   demonstrates robust discriminatory power with AUROC curve (Area Under
   the Receiver Operating Characteristic curve) of 0.995 (CI 1–1) using 15
   features (Fig. [95]4A) and predictive accuracy of 93%. The metabolites
   were mapped to pathways to determine pathways enriched in the HM group
   compared to CO group. The enrichment analysis shows that 8 pathways
   were significantly dysregulated with FDR < 0.05 (Fig. [96]5A and
   Supplementary Table S6).

Fig. 4.

   [97]Fig. 4
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   Receiver Operating Characteristics (ROC) curve for significant
   metabolites. ROC was generated by the Random Forest model using
   MetaboAnalyst 6.0, employing different number of features (5, 10, 15,
   25, 50 and 100). The corresponding Area Under the Curve (AUC) value and
   confidence interval (CI) were determined for each set of features. (A)
   ROC curves of the cmybpc3-/-::myl7:eGFP homozygous (HM) vs. control
   (CO) model. (B) ROC curves of the cmybpc3+/-::myl7:eGFP heterozygous
   (HT) vs. HM model.

Fig. 5.

   [99]Fig. 5
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   Pathway enrichment analysis. Pathway enrichment analysis was done using
   MetaboAnalyst 5.0 and 6.0 using KEGG for species zebrafish (Danio
   rerio)^[101]63. The Y-axis shows the matched pathways according to the
   p-values from the pathway enrichment analysis and X-axis pathway impact
   values from the pathway topology analysis. The node color of each
   pathway is determined by the p-value (red = lowest p-value and highest
   statistical significance), and the node radius (size) is based on the
   pathway impact factor, with the biggest indicating the highest impact.
   Pathway enrichment analysis was done comparing (A) Homozygous and
   Control groups. (B) Homozygous and Heterozygous groups. Nicotinate and
   nicotinamide metabolism was significant in both Homozygous compared to
   Control and Heterozygous groups with p-values of 6.28E-6 and 2.65E-4,
   respectively.

   The pathways involved the metabolism of nicotinate, nicotinamide,
   purine, glyoxylate, dicarboxylate, glycerophospholipid, pyrimidine,
   pantothenate, CoA, sphingolipid, alanine, aspartate, and glutamate
   metabolism. Comparing HM to HT group, 26 metabolites displayed
   significantly different levels, 24 upregulated and 2 downregulated
   (Supplementary Figure S4). The Random Forest model effectively
   differentiates between these groups with AUROC of 1 (1–1) using 10
   features (Fig. [102]4B) and a predictive accuracy of 98%. Pathway
   enrichment analysis revealed significant pathways involving cysteine,
   methionine, nicotinate, nicotinamide, phenylalanine, tyrosine, and
   tryptophan metabolism (Fig. [103]5B and Supplementary Table S7).
   Comparison between HT and CO groups revealed that only 3 metabolites
   showed significantly different levels of nicotinic acid, ornithine, and
   prolinamide. Between these two groups, no pathways were highlighted
   with an FDR < 0.05.

Discussion

   There is limited research on HCM metabolic alterations in cardiac
   remodeling. Herein, we performed a comprehensive multi-omics assessment
   at the early stages of cardiac remodeling in HCM utilizing the
   zebrafish model. Our integrative omics analysis reveals unique
   metabolic and lipidomic alterations implying the impact on critical
   molecular and biochemical pathways contributing to HCM pathophysiology.
   We collected 150 larvae in 7 replicates of cmybpc3 HCM zebrafish model
   at 5 days post-fertilization (dpf), as the heart is fully developed by
   3dpf. In the zebrafish, at 1dpf, the heart is a valveless tube, at
   1.5dpf cardiac looping and chamber ballooning occur, followed by the
   formation of the two-chamber heart at 2dpf, and finally at 3dpf
   ventricular trabeculation occurs^[104]26.

   This study revealed several significant differences in fatty acid,
   lipid and metabolite profiles detected in zebrafish homogenate samples
   between the examined CO, HT, and HM groups. The main alterations were
   seen with acylcarnitines, redox regulation via NAD/NADH, pyrimidine,
   and purine metabolism. These differences further prove the
   pathogenicity of MYBPC3 genetic variation^[105]4,[106]27,[107]28.

   The rate of acylcarnitine metabolism plays an essential role in
   maintaining a balance between lipid metabolism and levels of
   intracellular glucose^[108]29. Acylcarnitines function as activated
   long-chain fatty acid carriers, transporting fatty acids into the
   mitochondria for beta-oxidation, producing acetyl CoA, and subsequently
   providing cellular energy through the citric acid cycle^[109]30. As
   well, acylcarnitines have been associated with the production of ketone
   bodies and the peroxidation of fatty acids^[110]31. Furthermore,
   acylcarnitines have been linked as potential biomarkers for HCM
   severity and other cardiovascular diseases^[111]27,[112]32–[113]34.

   Fatty acids and associated lipids play an important role in
   cardiomyocyte structure and function^[114]35. Changes occur in fatty
   acids regulation during cardiac growth and development^[115]35. In HCM
   patients, reports of impaired myocardial fatty acid oxidation were
   identified through various multi-omics analyses^[116]28,[117]36. This
   was linked to a decrease in acylcarnitine levels that affects the
   supply of efficient cellular energy in the heart^[118]4. Studies in
   patients with HCM have identified an inverse relationship between
   myocardial and circulating acylcarnitines levels^[119]37,[120]38. In
   our zebrafish HCM model, a higher abundance of acylcarnitines was noted
   in HM group compared to HT and CO, suggesting higher use of
   beta-oxidation of fatty acids as an energy source. Moreover, plasma
   acylcarnitines were associated as one of the top metabolites that can
   draw the distinction between mild and severe forms of HCM in age and
   sex-matched patients^[121]39, highlighting the relevance of monitoring
   their levels in efficient HCM diagnosis.

   Cardiac hypertrophy is induced by pressure overload with changes in
   fatty acids that were seen in our HM HCM model. The
   9,12,15-octadecatrienoic acid, also known as alpha-linolenic acid
   (ALA), was highlighted as the highest VIP score distinguished among the
   three examined zebrafish groups with a higher level in HM and a lower
   level in HT compared to the control group. ALA is a polyunsaturated
   fatty acid that was found to play a crucial role as a structural
   component in cell membranes, affecting permeability, flexibility and
   fluidity along with other omega-3 fatty acid^[122]40. Also, these fatty
   acids have been shown to have highly protective effects in
   cardiovascular disease-associated risks by suppressing pro-inflammatory
   cytokines^[123]41–[124]43.

   In regard to saturated fatty acids, our analysis demonstrated that
   hexadecenoic (palmitic acid) was highlighted as the second highest VIP
   score fatty acids distinguishing the examined profiles. Long fatty acid
   chains, such as palmitic acid and S-59-adenosyl-methionine through the
   catechol-O-methyltransferase biosynthesize palmitic acid methyl ester,
   have been shown earlier to activate voltage-dependent Ca^2+ channels
   that are associated with HCM^[125]44–[126]46.

   The fatty acid levels showed opposite trends in our zebrafish model,
   with higher levels in HM group and lower levels in HT group in
   comparison to the control group. This might be due to the fact that HT
   group resembles the human autosomal dominant HCM disease, while
   truncating variants were reported to be lethal^[127]47. Cardiomyocytes
   generate two-thirds of ATP by the oxidation of fatty acids and
   one-third by glucose^[128]48. In end-stage heart failure patients, a
   decrease in fatty acid oxidation and a shift towards increased glucose
   metabolism leading to fatty acids accumulation was
   reported^[129]49,[130]50, where as in our zebrafish HM group, a similar
   signature was observed. In contrast, our results suggest that the HT
   group fatty acid levels appear to be compensating for energy demands
   through the oxidation of fatty acids.

   Importantly, oxidative stress is implicated in various cardiac
   diseases, including HCM and heart failure. It plays a key role in HCM
   remodeling and dysfunction^[131]51. The initiation of HCM occurs via
   the Ca^2+-dependent pathway, activated by mitochondrial reactive oxygen
   species (ROS), triggering the cardiac Na+/Ca^2+
   exchanger^[132]52,[133]53. Additionally, serum biomarkers indicative of
   oxidative stress were observed in patients with HCM^[134]51. Similar to
   previous findings, metabolic pathways associated with oxidative stress
   were significantly dysregulated in our zebrafish cmybpc3-/- HM compared
   to cmybpc3+/- HT group. Methionine, S-adenosylmethionine and cysteine
   were significantly dysregulated in the HM, suggesting an imbalance
   between the production of ROS and the ability to detoxify them within
   the model.

   Notably, nicotinate, nicotinamide and tryptophan metabolites were
   upregulated in cmybpc3-/- HM compared to CO and HT groups. NAD + is
   involved in many metabolic processes, including energy metabolism and
   stress response, in which significant pathophysiological alterations
   have been observed in HCM patients^[135]54,[136]55. NAD + can be
   synthesized either through the de novo pathway using tryptophan or
   through the salvage pathway using nicotinamide riboside, nicotinamide
   mononucleotide, nicotinic acid, nicotinamide, or nicotinamide ribose.
   Our observation is consistent with earlier studies that reported a
   depletion of cellular NAD + levels in HCM models and patients,
   suggesting an increased effort to synthesize more NAD + in response to
   severe oxidative stress^[137]56–[138]58.

   Furthermore, we identified disruptions in pathways involving purines,
   pyrimidines, and glycerophospholipids, comparing the HM to CO zebrafish
   groups. Consistent with previous reports of HCM models and patients’
   data^[139]37,[140]59,[141]60, a link between purine and pyrimidine
   metabolite alterations was demonstrated in our zebrafish model. In
   addition, according to the New York Heart Association categorization, a
   significant relationship was identified between purine and pyrimidine
   metabolites indicative of a worse prognosis in HCM patients^[142]16. As
   well, HCM patients’ lipidomics analysis showed altered levels of
   glycerophospholipids and triglycerides^[143]60.

   The present study, while providing valuable insights into cardiac
   remodeling in HCM model, is not without its limitations. While the
   zebrafish model is advantageous for its genetic tractability and
   experimental manipulability, caution must be exercised when
   extrapolating findings to the complexities of the human cardiovascular
   system. One notable limitation pertains to the intrinsic anatomical
   disparity between the zebrafish heart, with its two-chambered
   structure, and the human heart, which is characterized by a more
   complex four-chambered configuration. Additionally, using laboratory
   animal models, while beneficial for controlled experimental conditions,
   introduces some limitations related to these optimal conditions. The
   multifactorial nature of cardiovascular health, influenced by
   lifestyle, dietary habits, and environmental factors, is inherently
   challenging to fully replicate in a controlled laboratory setting.
   Furthermore, we utilized whole larvae lysate samples as the process of
   dissecting the zebrafish hearts while preserving sample integrity poses
   a technical challenge that could impact the reliability of results. To
   address this, we opted to analyze the entire zebrafish lysate,
   believing this approach may unveil unique and significant differences
   in metabolomics and lipidomics profiles, providing a comprehensive
   perspective on cardiac physiology in this model organism. Overall,
   while this study provides valuable insights into the metabolic
   perturbations underlying HCM, addressing these limitations for future
   research directions will not only enhance our understanding of HCM but
   also pave the way for developing more effective therapeutic
   interventions. This potential impact should instill hope in the future
   of HCM research and treatment.

   In conclusion, our study provides a comprehensive multi-omics
   assessment of hypertrophic cardiomyopathy (HCM) using the cmybpc3-/-
   zebrafish model, highlighting significant alterations in lipidomic and
   metabolomic profiles associated with the disease. The findings
   underscore the importance of acylcarnitines, phosphatidylcholines, and
   various metabolic pathways, including nicotinate, nicotinamide, purine,
   pyrimidine, and amino acid metabolism, in HCM pathophysiology. These
   findings are significant and should be a cornerstone for future
   research in this field.

   Future research should investigate the mechanistic roles of these
   identified metabolites and lipids in HCM progression, validate our
   findings in mammalian models, and conduct longitudinal studies to
   monitor changes over time. Additionally, exploring the dysregulated
   pathways as potential therapeutic targets and developing drug models to
   test in zebrafish and other systems could offer new treatment
   strategies.

Materials and methods

Zebrafish care and maintenance

   Zebrafish (Danio rerio) Tg myl7:eGFP line as a control (CO),
   cmybpc3+/-::myl7:eGFP heterozygous (HT) and cmybpc3-/-::myl7:eGFP
   homozygous (HM) adults were maintained in standard conditions according
   to the Ministry of Public Health (MOPH), Qatar animal research
   guidelines and under an approved protocol by the Institutional Animal
   Care Committee (Qatar Foundation, protocol IACUC 2020 − 1132) and
   adhered to the ARRIVE guidelines. The adult zebrafish were setup for
   breeding, the embryos were collected and incubated in E3 egg water
   media (5.0mM NaCl, 0.17mM KCl, 0.16mM MgSO[4]-7H[2]O, 0.4mM
   CaCl[2]-2H[2]O in ddH[2]O) at 28.5 °C.

Zebrafish sample lysis

   For each experimental group, CO, HT, and HM at 5 days old, 150 larvae
   were pooled in one tube with a total of 7 replicates for each group to
   obtain more information on fatty acids, metabolomic and lipidomic
   profiles. All deformed larvae were excluded prior to collection; no
   criteria were set for exclusion during the experiment. Excess media in
   the pooled larvae tubes was removed, then the tubes were snap frozen in
   liquid nitrogen then stored in -80 °C freezer until further processing.
   All samples were processed as previously described^[144]61 with some
   modifications; the samples were homogenized in chilled 400 µL methanol
   (Fisher A456-500) using a Bel-Art pestle tube homogenizer (SCIENCEWARE
   F19923-0000)., and kept on ice for 20 min; then vortexed at 2000 rpm,
   4 °C for three cycles of 10 s with a 15 s break between each cycle
   using an Eppendorf thermomixer. Then, it was centrifuged at 14,000 g
   for 5 min at 4 °C, and 400 µL of the supernatant was collected in glass
   vials (Waters 186007201 C). The supernatant was further processed and
   analyzed by three methods for fatty acid profiling, metabolomics and
   lipidomics. The different groups were de-identified throughout
   analysis.

Fatty acid methyl ester analysis

   Fatty acid analysis was conducted on 50 µL of the zebrafish homogenate
   supernatant after converting total lipids to fatty acid methyl esters
   through transesterification. The samples were transferred to 10 mL
   Reacti-vial vials (Thermo Scientific TS13225) and 2 mL of 25mL/L
   H[2]SO[4] (Sigma Aldrich 339741) in methanol was added. The mixture was
   mixed and then placed in an oven for 2 h at 60 °C. Once the tubes were
   cooled at room temperature, 2 mL of saturated NaCl (Sigma Aldrich
   31434) in H[2]O was added and mixed, followed by 1 mL of hexane
   (Scharlau HE02342500). Following centrifugation, the hexane layer was
   transferred to a glass vial and any trace amounts of water were removed
   by mixing a small amount of Na[2]SO[4] (Sigma Aldrich 798592) to the
   solution. The sample was then transferred to an autosampler vial and
   injected into a gas chromatograph coupled to a single quadrupole mass
   spectrometer in Scan mode (Agilent Technologies, Santa Clara, CA).

   GC-MS analyses were carried out on an Agilent 7890 C gas chromatograph
   with a 5977 A EI-MSD (Agilent Technologies, Wilmington, DE).
   Chromatography was carried out using a 60 m x 0.25 mm TR-FAME capillary
   column with a film thickness of 0.25 μm (Thermo Scientific, Waltham,
   MA). Helium was used as the carrier gas at a flow rate of 1.0 mL/min
   with constant flow compensation. The GC inlet were held at 290 °C, and
   the MS transfer line was maintained at 260 °C. Sample injections of 0.5
   µL were performed with a split ratio of 5:1. After a solvent delay of
   8.7 min, the oven temperature was programmed from 30 to 200 °C at a
   rate of 5 °C/min and then to 220 °C at 1 °C/min and to 260 °C at
   6.5 °C/min with a final hold of 2 min. Full-scan acquisition with
   electron impact ionization carried out using 70 eV was used for
   quantification based on the extracted ion chromatogram. All mass
   spectra were acquired over the m/z range of 30–500. Spectra were
   matched with a FAME standard mix (37 Component FAME Mix, Supelco,
   Whippany, NJ) or putatively annotated by comparison with NIST EI
   library (2017 release).

Metabolomics analysis

   An aliquot of the zebrafish homogenate supernatant (125 µL) was
   transferred to glass vials and 20 µL of isotopically labelled QC
   standard mix (MSK-QC-KIT, Metabolomics QC Kit, Cambridge Isotope
   Laboratories, Tewsbury, MA) was added. The mixture was evaporated in a
   vacuum centrifuge (Genevac EZ-2 Plus, Genevac Ltd, Ipswich, UK) to
   dryness. The residue was then stored in − 80 °C freezer until analysis.
   After reconstitution in 90 µL of 1% methanol in water (Fisher W6500),
   20 µL of each sample was mixed to prepare a sample mix used for
   compound identification. Each sample was injected individually (2 µL
   injection volume) on an ultra-high pressure liquid chromatograph
   coupled to an orbitrap tribrid mass spectrometer (Vanquish LC and
   Fusion Lumos, Thermo Scientific, Waltham, MA). The column utilized was
   an Accucore C18 + reverse phase column (27101–152130, 2.1 × 150 mm,
   1.5 μm, Thermo Scientific, Waltham, MA) held at 45 °C and using a
   mobile phase gradient of H[2]O and methanol with 0.1% formic acid
   (Fisher A1171-AMP) additive (MPA and MPB respectively) at a flow rate
   of 0.2 mL/min. Initial mobile phase conditions were 1% MPB rising to
   100% MPB over 12 min. The heated electrospray ion source (NGS-HESI) was
   operated with a sheath gas flow of 40 AU, auxiliary gas flow rate of 8
   AU, sweep gas flow of 1 AU and spray voltage of 3.5 kV or 3 KV in
   positive and negative mode respectively. The vaporizer and ion transfer
   tube temperature settings were 320 °C and 275 °C respectively.
   Automatic gain control target was set to 1E5 ions and with an auto
   setting of maximum injection time and RF lens setting of 35%. Each
   sample was injected four times with varied method parameters: (1)
   positive ESI polarity with low mass range (59–250 m/z), (2) positive
   ESI polarity with high mass range (250–1200 m/z), (3) negative ESI
   polarity with low mass range, and (4) negative ESI polarity with high
   mass range, all in full scan mode with orbitrap mass resolution of
   240 K (FWHM at m/z 200) with internal calibration of all scans with
   fluoranthene ion. The sample mix was injected numerous times with
   AcquireX deep scan mode to enable maximal MS2 fragmentation with HCD
   fragmentation mode of detected peaks above an intensity threshold of
   2E4. The data-dependent MS/MS mode utilized an orbitrap mass resolution
   of 60 K in MS1 and 30 K in MS2 and stepped collision energies of 20, 35
   and 50%. Dynamic exclusion was utilized with isotope exclusion and
   feature exclusion for 4 s after one MS2 scan with a mass tolerance
   setting of 3ppm.

Lipidomics analysis

   Another aliquot of the zebrafish homogenate supernatant (125 µL) was
   transferred to glass vials and 20 µL of isotopically labelled lipid QC
   mix (330707, Splash Lipidomix, Avanti Polar Lipids, Alabaster, AL) and
   805 µL of H[2]O: CHCl[3]:methanol (24.8:31.1:44.1) was added and the
   mixture mixed. Another 250 µL of CHCl[3] was then added and the mixture
   was mixed, followed by an aliquot of 250 µL of H[2]O and mixing. The
   mixture was centrifuged at 4000 rpm at 15 °C for 15 min to produce a
   bilayer. The bottom layer (400 µL) was transferred to a glass vial and
   evaporated in a vacuum centrifuge and the residue stored at − 80 °C
   until analysis. On the day of analysis, the sample was reconstituted in
   100 µL of isopropanol: methanol: CHCl[3] (1:1:1, isopropanol Fisher
   lA461-500, CHCl3 Sigma Aldrich 650498) and 20 µL of each sample mixed
   to make a sample mix. Each sample (1 µL injection volume) was injected
   as above with a column temperature setting of 70 °C using mobile phases
   acetonitrile: water (6:4, MPA) and acetonitrile: isopropanol (1:9,
   MPB), each with 0.1% formic acid and 10 mM ammonium acetate as
   additives. The initial mobile phase condition was 10% MPB and rose to
   100% over 32 min. The HESI source was set to sheath gas flow 40 AU,
   auxiliary gas flow 7 AU, sweep gas flow 1 AU and spray voltages 3.5 kV
   and 2.8 kV in positive and negative modes, respectively, with vaporizer
   temperature of 300 °C and ion transfer tube temperature 350 °C.
   Automatic gain control target was set to 2E5 ions, with an auto setting
   of maximum injection time and RF lens setting of 60%. Each sample was
   injected twice in ESI positive and negative scan mode at 240 K orbitrap
   mass resolution with a full scan mass range of 200–2000 m/z. The sample
   mix was injected several times with AcquireX deep scan mode to enable
   MS2 fragmentation in HCD fragmentation mode utilizing an orbitrap mass
   resolution of 120 K in MS1 and 15 K in MS2 with stepped collision
   energies of 25, 30 and 35% and a method that triggered CID
   fragmentation at 32% and 35% collision energies after the detection of
   the phosphocholine headgroup (m/z 184.0733) and the fragment ions
   formed by the neutral loss of M + NH[4] adducts of common fatty acids
   respectively.

Data processing and analysis

   The processing of data files was conducted using Compound Discoverer
   (version 3.3), where features were aligned and grouped with a mass
   tolerance of 3 and 5 ppm for metabolomics and lipidomics samples,
   respectively, with a retention time maximum shift of 0.2 min.
   Lipidomics samples were annotated with LipidSearch (version 4.23),
   allowing for [M + H]^+, [M + NH[4]]^+, [M + Na]^+, [M + H-H[2]O]^+,
   [M-H]^−, [M + HCOO]^− and [M + CH[3]COO]^− ions with a mass tolerance
   of 5 ppm and fragment mass tolerance of 10 ppm. Features in the
   metabolomics data files were annotated with the following online
   databases: (1) in-house MS2 library of over 950 metabolite compounds
   where match factors were elemental composition, retention time and MS2
   spectrum and (2) mzCloud where match factors were elemental composition
   and MS2 spectrum, both with a parent mass tolerance of 5 ppm and
   fragment mass tolerance of 10 ppm. All discovered compounds were
   integrated for peak area in each sample and used for statistical
   analysis.

   The added isotopically-labeled internal standards were monitored in
   each sample for retention time fluctuations, peak abundance and mass
   accuracy. The monitored peaks were within acceptable limits of ± 2% of
   median retention time, ± 30% of median peak abundance and ± 3 ppm mass
   accuracy.

   MetaboAnalyst v5.0 and v6.0^[145]62Superscript> were used for
   multivariate statistics. Log10 data transformation and Pareto scaling
   were applied to all data. Initially, the variance in the data was
   analysed using Principal Components Analysis (PCA). Following this, a
   Partial Least Squares - Discriminant Analysis (PLS-DA) was applied and
   variable importance was assessed using VIP (variable importance in
   projection) scores. Sparse PLS-DA was used for metabolomics and
   lipidomics data to effectively reduce the number of variables. In this
   analysis, five components were used with 10 variables per component.
   Hierarchical clustering heatmaps were generated using Euclidean
   distance measure and Ward clustering method. Pathway enrichment
   analysis was performed with global Ancova with topology analysis set to
   relative-betweeness centrality. ROC curve analysis was performed using
   Random Forests with Monte-Carlo cross-validation where within each
   iteration, the data was split randomly into two-thirds training and
   one-third validation sets. This multivariate study on lab-grown
   zebrafish in a highly controlled environment where comparisons were
   made with an experimental control group has minimal confounders.

Electronic supplementary material

   Below is the link to the electronic supplementary material.
   [146]Supplementary Material 1^ (126MB, xlsx)
   [147]Supplementary Material 2^ (3.3MB, docx)

Acknowledgements