Introduction

Nearly 1% of infants worldwide are born with a congenital heart defect (CHD) [1]. The causes for more than half of CHDs are unknown [2], but it is believed that alterations in  active developmental pathways during the first 8 weeks of life account for the majority. Most research on heart development has been focused on this rather early period. Little is known about how factors beyond this period may contribute to the progression of cardiac defects.

Cardiac defects are often present as either “left-sided” or “right-sided”, where pathology predominantly affects a single chamber. In many of these instances, it can be difficult to ascertain primary pathology from secondary consequences from altered flow through developing heart. For instance, Hypoplastic Left Heart Syndrome (HLHS), a very severe CHD requiring multiple palliative surgeries followed by a heart transplant during a child’s life [3, 4], involves a spectrum of malformations characterized by the underdevelopment of left-sided structures including the left ventricle (LV) [5]. In some babies, HLHS is fully manifest by 12 weeks of gestation. In other instances, HLHS evolves during fetal life from primary aortic stenosis [6, 7]. Conversely, in Tetralogy of Fallot, a defect characterized by “right-sided” pathology, an anteriorly malaligned conal septum can result in significant hypertrophy of the right ventricle (RV) and is associated with RV outflow tract abnormalities such as pulmonary valve or branch pulmonary artery stenoses [8]. In both conditions, the typical pattern of intracardiac blood flow is disrupted, potentially exacerbating abnormalities in development. These reflect the importance of understanding the baseline molecular differences between the developing RV and LV for greater insight into the pathogenesis of CHD.

Prior studies have shown differential oxygen exposure and cardiac output between the fetal RV and LV [9, 10], which could also result in modulation of varied metabolic pathways or responses to oxygen content for each chamber [11,12,13,14]. Additionally, sex can interact significantly with the prevalence and severity of CHDs [15, 16]. For example, HLHS is significantly associated with Turner Syndrome, with a slight predominance in males [17,18,19]. Little is known about possible differences in these attributes in the developing RV versus LV and how they may contribute to the development or the progression of CHDs. These unique in utero physiologic milieus and possible inherent differences between RV and LV development could play a part in both the natural and un-natural (i.e., CHD) development of the ventricles. Hence, understanding potential differences in the molecular pathways functioning in each ventricle during development could lead to better understanding of observed CHD pathology.

In this study, we performed mRNA-seq analysis to gain insight into ventricle-specific gene expression changes during gestation. Our study included 11 unaffected hearts and one heart with HLHS. We interpreted ventricle-specific transcriptome differences using multivariate, gene ontology over-representation, network enrichment, and systematic literature mining. In the process, we found notable differences in the expression of immune-related genes between the RV and LV which revolved around the ‘positive regulation of CD4 T cells’, ‘upregulation of IFN \(\gamma\) production and signaling pathway’, and ‘positive regulation of TH17 differentiation and response’ (representative GO terms italicized). Finally, we examined putative molecular contributors to the pathogenesis of HLHS in an index case.

Material and Methods

mRNA-Sequencing

Total RNA samples were provided by the University of Washington Birth Defects Research Lab (BDRL, IRB STUDY00000380) and prepared for Illumina HiSeq 2500 Sequencing using the SMARTer Ultra Low RNA kit (Takara-Clontech) according to manufacturer’s protocol. Sequencing was performed using single-end reads (50 bases) and at a read depth of 30 M per sample. Basecalls and demultiplexing were performed with Illumina’s bcl2fastq software and a custom python demultiplexing program with a maximum of one mismatch in the indexing read. mRNA-seq reads were aligned to GRCh38 (GENCODE 20) with STAR version 2.0.4b. Fetal mouse heart mRNA-seq data (C57Bl6/J; E14; n = 5, contributing 3 RV and 2 LV) were obtained from a previous study [20]. A count per million threshold equivalent to ∼10 raw expression value was applied to remove all lowly expressed genes and only genes above the threshold and having ≥ 5 samples were kept. Due to the small sample size, no outliers (> 1.5 SD from the means) were removed from the human dataset and no outliers were identified for the mouse dataset. Filtered transcripts were then annotated to gene symbols using org.Hs.eg.db and org.Mm.eg.db. A table of individual subject characteristics can be found in the online resources (Online Resource 10).

Differential Gene Expression Analysis

The DEseq2 package was used to determine differentially expressed genes (DEGs) between two groups. When necessary, FDRtool was utilized to correct over-estimation of the assumed null distribution variance by Wald Test, which is indicated by a hill–shaped p-value histogram. In brief, the z-scores returned by DEseq2 was used as input to FDRtool to re-estimate the p-values that were then adjusted for multiple comparisons by the Benjamini–Hochberg method. Genes were considered differentially expressed if they had a |log2FC|> 2 for human or |log2FC|> 1.5 for the mouse dataset and a padj < 0.05 for all contrasts. To evaluate the immune components of the DEGs identified, we retrieved comprehensive lists of immune-related genes in humans from InnateDB [21], ImmPort, and IRIS databases. A list of 6231 genes was used to identify the immune-related genes in this study.

Gene Ontology (GO) Enrichment Analysis

The list of DEGs was submitted, as a single-gene set per comparison, to query for GO terms associated with biological processes via the gene over-representation test (enrichGO), against the background gene set, using the package ClusterProfiler. The minimal gene set size of three and an FDR < 0.05 cutoff was set to determine significance. The enriched GO terms were then reduced for term redundancy using the function simplify(), with dispensability cutoff = 0.7. We then functionally re-clustered select lists of DEGs to refine the over-representation GO enrichment test as described by Huang et al. [22]. In brief, the semantic similarity of the DEGs were calculated using the GoSemSim package [23] with the Wang method. The correlation matrix was obtained with method = ”spearman” and hclust method = “complete”. Clusters were then determined using dynamicTreeCut package [24], with a minimum gene size set at 30 and 15 for the human and mice dataset, respectively, method = ”hybrid”, and deepSplit = 0. The functional clusters of DEGs were then re-submitted for GO enrichment analysis using “compareCluster()” with the aforementioned parameters and the enriched GO terms reduced for semantic redundancy. Visual summary of representative subset of terms was performed with Revigo [25].

Network Enrichment Analysis

A list of 278 DEGs unique to the unaffected right vs left ventricle comparison were further subjected to network analysis with GO Biological Processes and GO Immune System Process databases and ClueGO were used to determine and visualize the enrichment networks. The following parameters were used to construct the enrichment network: min/max GO level = 4–8, Number of Genes = 3, Min Percentage = 4, Kappa Score Threshold = 0.45, Sharing Group Percentage = 50 and the statistical significance was set at Benjamini–Hochberg adjusted p-value < 0.05.

Literature Mining—Acumenta Literature Lab (LitLabTM)

LitLab™ (Acumenta Biotech, USA) allows the identification of biological and biochemical terms that are significantly associated with the literature from a gene set. The analysis provides additional meaning to experimentally derived gene and protein data [26]. The LitLab™ database contains current gene, biological, and biochemical references in every indexed PubMed abstract and are updated quarterly (> 35 million abstracts, as of January 2023). LitLab™ calculates the frequencies of the input genes with > 86,000 terms in the Literature Lab™ database (as of December 2022) and compares the values with that of a 1000 random gene sets, containing the same number of genes and literature volume profile as the experimental set, to determine statistical significance (cutoff at p-value < 0.0228). LitLab™ is composed of four main applications: Term Viewer, PLUS, Editor, and Gene Retriever.

Results

Characteristics of the Overall Transcriptomic Profiling

Among the 12 examined RNA samples, eight were female (one with HLHS with likely aortic stenosis and mitral stenosis variety based on gross examination) and four were male. The average gestational age was at 88 days or 12 weeks + 4 days (range 78 to 98 days or 11 weeks + 1 to 14 weeks). On average, 27 M read depth was achieved, with the alignment rate of uniquely mapped reads averaging at 65%, a median mapped read of 12 M, and the median number of transcripts detected at 15,712 (Online Resource 1a–b). The relatively low alignment rate is expected for a single-end short fragment library (50 bp) and is deemed appropriate for gene expression analysis [27]. The count density distributions were found to be homogeneous and peaked at 8log2 counts per million among the samples (Online Resource 1c). A total of 14,586 genes remained after gene-level filtering and conversion to gene symbols using org.Hs.eg.db. Annotation to gene biotype revealed predominately protein-coding genes (89%) (Online Resource 1d). The normalized expressions of the select housekeeping genes (GAPDH, GUSB, IPO8, POLR2A, and YWHAZ) [28] were not significantly different between the left and right ventricles (Online Resource 2a). Principal component analysis of the overall gene expression indicated that the inter-individual difference contributed to 29% of the variance (PC1) and partly trended with gestational age (Online Resource 2b). Taking the top 1000 highly variable genes among the ventricles highlighted a clear separation by sex across PC2 (13%) (Online Resource 2c–d). Thus, we considered sex as a potential confounder and performed downstream analyses while segregating by sex.

Differentially Expressed Genes Identified Between RV and LV

To explore the inherent gene expression differences between the developing ventricles, we performed differential gene expression analysis, excluding the single HLHS case, between the following groups: 1) All RV vs LV (n = 11 vs 11), 2) Female RV vs Female LV (n = 7 vs 7) and 3) Males RV vs Males LV (n = 4 vs 4). We defined the significant DEGs as those with an absolute log2 fold change (FC) > 2 and a padj < 0.05 (full list provided as Online Resource 11). We observed a total of 529 (332 up- and 197 down-regulated), 563 (300 up- and 263 down-regulated), and 219 (140 up- and 79 down-regulated) DEGs in the aforementioned group comparisons, respectively (Fig. 1 a–c). Differences in sample size likely account for the lower number of DEGs identified when looking only at males. Of note, sex determination was further confirmed by assessing the expression of sex biomarkers XIST, TSIX (female) and DDX3Y (male) [29] as well as differentially expressed markers CD99 (higher in males) [30] and KDM6A (higher in females) [31] (data not shown). Majority of the DEGs were unique to the three different comparisons, as shown by the intersection analysis (Fig. 1d, Online Resource 12). The effect of sex was examined within each of the ventricle types and returned relatively small lists of DEGs for RV (43; 30 up- and 13 down-regulated) and LV (31; 19 up- and 12 down-regulated) when comparing Males vs Females (n = 4 vs 7) (Online Resource 12). Overall, we did not observe differential expression of genes involved in cardiac asymmetry or chamber/arch development (sources: Reactome R-HSA-1181150, MSigDB M10039, and [32,33,34,35,36,37,38,39]) (data not shown).

Fig. 1
figure 1

Significant DEGs between the right and left ventricles exhibit a high proportion of uniqueness. Differential gene expression analysis comparing ventricle sides. (a) among healthy (i.e., unaffected) hearts, (b) within female subject only, and (c) within male subjects only. (d) Summary and intersection of DEGs identified among all comparisons as indicated. Each gene is represented by a dot. Significance of the DEGs, red dots, were defined by the following thresholds: |log2FC|> 2 and p.adjust < 0.05

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Biological Modulations Inherent to the Ventricles Clearly Implicated Differential Immune Functions

GO Enrichment Analysis Using the Lists of DEGs Directly as Inputs

To gain knowledge of the biological processes ascribed to the differentially expressed genes, we performed GO enrichment analyses via the over-representation test and using individual lists of DEGs as inputs. The analysis returned 34 significantly enriched GO terms for the All RV vs LV group and 4 terms, relating to hydrolase and peptidase activities, for the Female RV vs Female LV comparison (Online Resource 13). There were no GO term enrichments returned for any of the other comparisons, namely: Males, RV vs LV; RV, Males vs Females; and LV, Males vs Females. The lack of GO enrichment suggests that not enough related DEGs were identified to enrich a particular biological/molecular pathway. Additionally, for the Males vs Females comparisons, some of the DEGs are known sexually dimorphic genes (e.g., XIST, TIST, DDX3Y) where their differential expression is driven by chromosome bias [40]. However, we can’t rule out the effect of the reduced group size of the comparisons (7 Females vs 4 Males) for obtaining a small number of DEGs and the lack of GO term enrichment.

Prior Functional Re-Clustering of the Lists of DEGs Improved GO Enrichment Results

To improve the sensitivity and coverage of the enriched biological processes, we subclustered the lists of DEGs on the basis of the semantic similarity of their associated GO terms prior to re-analysis. We increased the enriched GO terms to four and eightfold for the All RV vs LV and Female RV vs LV comparisons, respectively (Fig. 2a–b). In addition, eight terms were found enriched for the Males RV vs LV comparison, which previously had no significant enrichments (Fig. 2c). For the All RV vs LV comparison, the GO terms can be clustered into three board categories: (1) regulation of hemostasis, retinol and triglyceride metabolism, and HDL remodeling, (2) T cell-mediated immunity, and cellular stress response and detoxification of metal ion, and (3) negative regulation of neurogenesis, positive regulation of JAK-STAT pathway, pituitary gland development, and positive regulation of hormone metabolic process. Regulation and metabolism of small biomolecules including vitamins, lipids, and hormones, stood out as the predominant processes among sex-specific groups, i.e., the processes were enriched in sex-specific RV vs LV comparisons but not seen in the top GO enriched from all sex combined RV vs LV. (Fig. 2b–c). Since the enrichment of biological processes is dependent on the input DEGs list, it is expected to see more granularity with the larger contrast, All RV vs LV (n = 22), which recapitulate findings from both female and male only comparisons. Interestingly, ‘retinol metabolic process’ was persistently enriched across all comparisons (female, male, or altogether) and was primarily driven by the differential expression of cytochrome P450 complex proteins and an infant-specific metabolic enzyme (ADH1A). To help summarize the categories of GO terms, we reduced and visualized the enriched terms obtained from the All RV vs LV comparison using Revigo [25] (Fig. 2d). With this approach, it was easier to appreciate additional subsets of enriched GO terms; the representative term for each subset is indicated in the plot (see Online Resource 13 for full lists).

Fig. 2
figure 2

Semantic similarity clustering of the DEGs’ function prior to GO enrichment analysis increased sensitivity and coverage by four to eightfold. The diagrams summarize the GO terms enriched, separated into clusters based on semantic similarity of the biological processes, and with the number of DEGs that contribute to the GO enrichment indicated in parentheses. (a) The RV vs LV comparison among all the unaffected hearts revealed three major clusters, (b) RV vs LV comparison among females returned two clusters, and (c) RV vs LV comparison among males returned two clusters with markedly less enriched terms. (d) Visual summary of representative subsets of terms enriched when comparing RV vs LV DEGs in unaffected hearts using a term similarity-based clustering. REVIGO determines subset ‘Representative’ terms by prioritizing the terms with higher ‘uniqueness’ while also considering enrichment p-values to guide the selection

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Network Mapping of GO Terms and DEGs Highlight a Putative Role for the Th1 and Th17 Pathways

The significant enrichment of immune-related genes between the RV and LV was further investigated by performing the enrichment analysis with only the DEGs uniquely identified when comparing All RV vs LV (278 genes); illustrated in Online Resource 5a. GO enrichment analysis returned 28 GO terms (Online Resource 5b). This comparison provided enhanced granularity about the putative immune processes involved, such as interferon gamma signaling pathway, Th17cell-mediated immunity, interleukin-17 production, and T cell activation and cytotoxicity. This result suggests that the differential expression of genes relating to immune processes between RV and LV are not influenced by sex. To gain insights into processes specifically influenced by sex, we conducted an enrichment analysis on DEGs exclusively identified when comparing RV vs LV in females (399 genes) and males (182 genes) separately (Online Resource 6a). Among females, the distinctive genes enriched 9 GO terms related to ‘negative regulation of peptidase’ and ‘RAS protein signal transduction,’ neither are known to exhibit sex-specific functions (Online Resource 6b). Conversely, in males, the unique genes did not yield enrichment for any GO terms. Using a network approach, we mapped the interactions between GO terms and DEGs and found an intricate cluster of T cell-related function (Fig. 3). In our analysis, IFNg takes a central role in the network and, along with IL12R and TBX21 (which encodes the transcription factor T-bet), promotes the positive regulation of CD4 T cells and Th1 differentiation; PRKCQ can contribute to Th1 bias by inducing IL-2 and T-bet expression [41, 42]. Regulation of Th17 immune response is also prominent with PRKCQ having a known role in Th17 differentiation [42] and IL-17 production [43]; PKC-theta leads to activation of SRC1 which then interacts with RORgT and induces IL-17A production [44]. OSM signals through its receptor and activates STAT family proteins. Interaction with OSM type 1 receptor, especially, induces STAT3 expression which has been shown essential for Th17 differentiation [45]. Collectively, these findings imply that Th1 and Th17 pathways play a differential role in the developing ventricles.

Fig. 3
figure 3

GO and DEGs interaction network emphasizes the differential role of immune functions between the RV and LV of the developing heart. The node color indicates biologically similar reactions and the size reflects the number of genes contributing to the pathway. If the reaction pathway shares 50% or more of the contributing genes, then they are connected by an edge. The representative nodes (based on statistical significance) are indicated by the colored texts. Genes are denoted by small dots with color matching the GO terms to which they belong. The font color for the gene represents up– (red) or down– (blue) regulation

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Comparison with Mouse Model Data

Mammalian models such as mouse are by far the most practical means to study fetal development both for ethical and technical reasons. However, molecular and mechanistic studies in mice have rarely been directly compared to humans [46], especially when comparing ventricle-specific gene regulation. We re-analyzed mRNA-seq data from of C57Bl6/J mice collected at embryonic age E14 that was generated from a previous study [20] and are available at NCIBI GEO under dataset ID GSE157522. The total number of genes expressed that remained after filtering and gene symbol annotation was 12,813; among which 93% were found to be protein-coding. Normalized expressions of Gapdh, Ppia, and Pgk1, robust housekeeping genes for mouse fetal heart, were not significantly different between the two ventricles. We found 82 DEGs (|log2FC| > 1.5 and padj < 0.05) between the RV and LV in our mouse dataset (Online Resource 7a, Online Resource 11). The resulting enrichment of GO terms highlighted processes such as cell adhesion and leukocytes and T cells migration/proliferation (Online Resource 7b). We observed a lack of granularity among the GO terms which can in part be due to the small sample size, yielding a smaller number of informative DEGs. Key differences compared with the biological process enrichment obtained in the human samples were hydrogen peroxide metabolism, gas transport, and regulation of muscle fiber development.

HLHS

Repression of Epigenetic/DNA Organization and Upregulation of Muscle Development in HLHS

To examine the differential gene expression in the heart with HLHS, we used RV and LV as independent samples and compared the HLHS heart to the equivalent aggregate of unaffected hearts. Thus, 147 (51 up– and 96 down–regulated) DEGs were obtained comparing HLHS (n = 2) vs. unaffected hearts (n = 22) (Online Resource 8a, Online Resource 11). The DEGs enriched 30 GO terms (Online Resource 8b) which increased to 35 after functionally clustering the DEGs (Fig. 4a). The GO enrichment showed major downregulation of genes relating to epigenetic regulation of gene expression, chromatin organization, protein-DNA complex organization, and DNA conformation change. As well, major upregulation of genes involved in muscle organ development, contraction, and response to mechanical stimulus (Fig. 4b). We generated a network of the genes, along with their log2FC, and GO terms (Fig. 4c), to provide further insights on the key factors that drive the differences in the biological process. Several histone genes (e.g., H1-1, H1-2, H1-5 H4C4, H4C9) and replication regulators (e.g., CDC45, H3K27me3, CDT1, GINS2, ASF1B) were significantly downregulated. On the other hand, some genes related with muscle development have relatively unknown mechanisms (e.g., FHL1, XIRP2), and yet have several diseases associations. Other genes (ACTA1, ANKRD2, ANKRD1, RYR1) support upregulation of muscle formation. An increased prevalence of HLHS has been documented in males and females with Turner syndrome. To determine this possibility, we manually curated gene biomarkers of Turner syndrome in female from the literature [47, 48] and compared the gene expression in the HLHS case vs all the other hearts. We found no significant differences, suggesting Turner’s syndrome was likely not in play in our index case of HLHS.

Fig. 4
figure 4

Major molecular changes in the HLHS case pertain to DNA organization and muscle development compared with that of the combined healthy left and right ventricles. (a) The diagram summarizes the GO terms enriched, separated into clusters based on semantic similarity of the biological processes, and with the number of DEGs that contribute to the GO enrichment indicated in parentheses. (b) Enriched GO terms are plotted based on their −log10(qvalue) and Zscore. This score was calculated from the sums of up– and down–regulated DEGs divided by the square root of the total number of genes which contributed to the enrichment of the term. (c) The network shows the GO terms enriched as yellow nodes and the genes contributing to the enrichment, linked by edges, are depicted by small dot colored based on their log2FC

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DEGs–Literature Associations Highlight PAI-1 Upregulation in HLHS

To gain further perspective about the biological and biochemical processes associated with our list of 147 DEGs, we utilized two literature mining solutions, LitLab™ and Gene Retriever™ (Fig. 5). This approach identified Medical Subject Headings (MeSH) terms significantly associated with our DEG list, providing information derived from peer-reviewed publications. Seven clusters of terms were identified, including dilated cardiomyopathy, hypertrophic cardiomyopathy, cardiac calcium regulation, plasminogen activator inhibitor-1 (PAI-1), beta-catenin, and WNT signaling pathways. The involvement of antifibrinolytic pathways provided an interesting novel avenue to explore. In the literature, PAI-1 deficiency in 12 to 20 weeks old mice has been associated with upregulation of TGFb expression from cardiomyocytes and contribute to the development of cardiac fibrosis [49, 50]. In 12-week-old mice, expression of PAI-1 has been shown to provide a protective effect to cardiac stress response in a model of cardiac hypertrophy and fibrosis [51]. In our data, SERPINE1, the gene that encodes for PAI-1, is significantly upregulated by 15-fold in the RV of the HLHS case compared with the unaffected (data not shown). This surprising observation could represent a compensatory mechanism.

Fig. 5
figure 5

Literature association analysis reinforces the GO term enrichment results comparing ventricles from unaffected vs HLHS hearts. We used Literature Lab (LitLab™) to perform an association analysis of the 147 DEGs between ventricles from HLHS and unaffected hearts with the current body of scientific literature. Briefly, LitLab™ queried the PubMed database (> 35 million abstracts, as of January 2023) for articles associated with the gene list of interest and returned the tagged Medical Subject Headings (MeSHs) with statistical significance. This analysis yielded seven MeSH terms clusters

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IL-3 and IL-36, Downstream Targets of IL-17A, are Significantly Upregulated in HLHS

To contrast immune gene expression in our data, we interrogated the ImmPort database [52] which is a curated list of 4677 immunologically relevant genes. We identified four genes and pseudogenes each that were significantly upregulated in HLHS compared with normal hearts: IL3, TRAV16, IL36B, CD300LD, IGLV8OR8-1, FKBP1AP3, CEACAMP2, GLYATL1P4. The expression of those 8 genes is mostly absent in unaffected heart while they are elevated (between 4 and 16-fold) in our HLHS heart. A seeded-network analysis approach was used to infer interacting protein and deduced enrichment in the IL-3 signaling pathway, autophagy/aggrephagy, and tyrosine phosphorylation. IL36 and IL3 are induced by IL-17A and positive synergistic and negative regulatory effects, respectively [53, 54]. The TRAV16 gene codes for a V region of the variable domain of T cell receptor alpha chain [55]. The CD300 family is composed of membrane receptor proteins that modulate a broad and diverse array of immune cell processes such as the positive regulation of IL-6 and TNFa production [56]. Recently, CD300LD was identified as a receptor of norovirus entry [57]. IGLV8OR8-1, CEACAMP2, GLYATL1P4 are pseudogenes. Also, FKBP1AP3 is a pseudogene of FKBP12, a major T cell immunoregulator of TGFb and ryanodine receptors (RYR) activity. Of note, RYR1 is significantly upregulated (10.8-fold) in HLHS compared with unaffected heart in our analysis.

TGFb, IL-17, and p53 Pathways Enriched by Differentially Expressed ISGs in HLHS

In a single-cell analysis of unaffected hearts, Suryawnshi et al. identified 18 cell types, including 6 types of immune cells. When contrasted to a heart with congenital heart block, the authors found that all cell types overexpressed interferon-stimulated genes (ISGs) and ‘interferon signaling’ was also identified as the most up-regulated biological process according to pathway analysis of the overall DEGs. In our data, contrasting ISGs (sources: MSigDB M15615 and M13453) expression between HLHS vs unaffected hearts revealed 14 differentially expressed genes (p < 0.05); TRIM26, CSRP3 (involved in myogenesis), PTPN11 (implicated in Noonan syndrome), RBBP4, CASP8, SF3A1, HADHB, BBC3 (regulate by p53, binds Bcl-2), SMAD4 (TGFb signaling), SRSF2, SKP1, FOSL1, CYCS (cytochrome C), DDX17. Interestingly, TGFb, IL-17, and apoptosis/p53 pathways were enriched by those 14 genes using PROGENy (data not shown), a bioinformatic approach that infer signaling activity of pathways from gene expression while taking into consideration post-translational modification [58].

Discussion

Over the last three decades, tremendous insights have been gained regarding early developmental changes in the embryonic heart, leading to the formation of the well-recognized four chambered structure [59, 60]. Much less, however, is known about the activities that occur over the ensuing time that prepares the heart for postnatal circulation. To our knowledge, our report is the first to systematically examine DEGs between the left and right ventricles. The interpretation of our results can be mapped to a time point where key developmental events (e.g., septation) have already completed. Nevertheless, in humans, the months that follows completion of cardiac morphogenesis can still allow cardiac defects to develop due to hemodynamic remodeling.

Our study reveals several notable findings that differentiate the developing RV and LV. First, sex was a factor affecting gene expression profiles, a novel finding that aligns with the known differences in both propensity toward certain defects [15, 61] as well as manifestations of response to circulatory effects of CHD [62, 63]. Our finding also aligns at the molecular level with the study by Synnergren et al. where they compared RA vs LV in paired adult (58–80 years old) males and females and identified more protein-coding genes involved in immune function in females than in males (12). Second, our results showed major differential expression of immune genes between RV and LV, perhaps suggestive of differential activity of remodeling between the two ventricles during development. Thus far, there have been only a few investigations exploring the role of the immune system in cardiac development. Lastly, our examination of an unique case of HLHS highlights the potential role of histone modifiers and chromatin access as well as cardiomyocyte contractile activity. Additionally, our findings point to a dysregulation of the Th1/Th17 balance via upregulation of genes promoting IL-17 production in the HLHS case, perhaps reflective of a compensatory mechanism in response to other primary pathology within the developing LV. Given the constraint of having only one HLHS case, this aspect of our study is inherently exploratory, and as such, will require further validation with a larger cohort.

The development of the heart left–right asymmetry has been well studied across species during embryonic life. In vertebrates, asymmetry is caused by nodal cilia and coordinated differential expression of genes, especially transcription factors [35, 59, 64]. In the fetal heart, some pathways, such as the Nodal pathway, have been linked to asymmetry and cardiac defects [32, 34, 65]. Perhaps not surprisingly, in our dataset, genes belonging to the NODAL-signaling (source: R-HSA-1181150) and cardiac chamber/arch development pathways [66, 67] were not differentially expressed between ventricles except for a significantly lower TBX5 expression in RV than in LV (data not shown). This may reflect the lesser involvement of these genes during post-septation phase of development. TBX5 is a gene encoding a transcriptional activator involved in atrial natriuretic factor (ANF) gene regulation. In mice, at E11.5 (equivalent to 7 weeks + 1 to 7 weeks + 3 in human [46]), Tbx5 is expressed in the myocardium of RA, LA, AVC, and LV, while expression of both Tbx5 and ANF is virtually absent from the RV and outflow track [66]. In this regard, our findings aligned with prior reports, given our samples correspond to a later developmental stage (11 weeks + 1 to 14 weeks).

Oxygen content in the heart must be tightly regulated as hypoxia induces essential growth factors but a severe lack of oxygen causes irreversible damage [68]. Further, unique circulation leads to differential blood flow with different oxygen contents through the developing heart. Therefore, differential gene expression may affect development by modulating oxygen availability or responses to hypoxia. To explore the relationship between ventricles and oxygen-responsive pathway, we contrasted the HIF1a, VEGF, “response to oxygen species”, and “response to oxygen levels” pathways (sources: hsa04066, hsa04370, GO:0000302, and MSigDB GO:0070482, respectively) between ventricles. In our data, there were no differences in the expression of genes involved in these pathways between ventricles.

Left to right asymmetric expression of immune genes has been demonstrated in adult hearts in animal models. Greater expression of immune genes has been reported in the RV of adult male rats compared with LV in both normoxic and hypoxic conditions [13]. Differential expression of immune genes between RV and LV has been shown in 8–10 weeks old mouse hearts exposed to cold ischemic conditions [14]. As part of the present study, we compared RV and LV from fetuses of mice at embryonic age E14 and found enrichment of cell–cell adhesion and leukocytes and T cells migration/proliferation in the RV. In humans, differential expression of immune-related gene has been reported between the RA and LV of the adult male hearts [12]. However, no studies have been performed looking at spatial differences of immune-related gene expression and how this may impact cardiac development.

In our study, when comparing RV vs LV from the unaffected hearts, IL-17 production was significantly upregulated in the RV. IL-17A is known to induce expression of IL-36 [69] and IL-3 [70]. Interestingly, we observed upregulation of IL-36 and IL-3 (8 and 12-fold) expression in the RV of our HLHS case than in its LV and to all the ventricles of the unaffected hearts (data not shown). IL-36 and IL-17A have been shown to synergistically induce inflammation with TNFα in human endothelial cells and keratinocytes [53, 69, 71]. On the other hand, IL-3 may act as a negative regulator of the Th17 pathway, preventing the development of autoimmune response [54] and has been shown to be essential for cardiac development in zebrafish [72]. Additionally, several genes promoting the Th17 differentiation pathway (source: hsa04659) – such as IL-23, IL-23R, IL-17F/A, IL-1b, and RORC – had markedly elevated expression in RV of our HLHS case compared with the other hearts (Online Resource 9). Notably, the expression of the RORC gene (coding for RORy/RORyT which is involved in upregulation of IL17A production [73]) and NCOA1 gene (encoding for SRC-1, a co-activator of RORy activity [74]) were six–eightfold higher in both ventricles of our HLHS case compared with the unaffected heart ventricles (data not shown). To summarize the findings regarding DEGs relevant to the Th1 and Th17 pathways, we constructed a network diagram highlighting the key genes significantly upregulated in the HLHS case compared to the healthy (Fig. 6). The source of IL-17 is another very interesting topic and could arise from a unique embryonically-derived subset of γδT17 cells [75], where RORyT, along with STAT3, are critical for gdT17 differentiation during the embryonic phase [76, 77]. In short, the increased expression of IL17 may either reflect a bystander effect (i.e., increased inflammation in the neighborhood leads to increased expression of IL-17) or a key contributor to evolving the pathology in HLHS.

Fig. 6
figure 6

Network diagram summarizing the differential regulation of the Th1 and Th17 pathways in the HLHS ventricles compared to all the unaffected ventricles. DEGs analysis between the RV and LV identified upregulation of several immune genes (indicated in bold and underlined text within circle). Based on the associations with GO terms and known functions reported in the literature, arrows indicating induction (green pointy arrows) and repression (brown blunt arrow) are depicted between the genes and GO terms. The expression of IL-3, IL-36, and SRC-1/RORyT genes were significantly upregulated in the HLHS case compared to the unaffected hearts and are highlighted by red circles. Significantly enriched GO terms are depicted by blue ovals with blue shading. Closely related GO terms have a connecting dark blue line between them

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Using an underdeveloped left ventricle and HLHS newborn-derived iPSC-endocardial cells, Miao et al. [78] showed that intrinsic endocardial defects and an abnormal endocardial population could contribute to the pathogenesis of HLHS. Potential indirect involvement of the immune system in cardiac development has been hinted in the past. Cui et al. carried out scRNA-seq of 18 human embryos, spanning 5 to 25 weeks gestational age [59]. Although focused on the whole-heart as opposed to RV versus LV, they identified three types of immune cells in the developing heart: mast cells, macrophages, and B/T cells. Previously, CCR2- macrophages were shown to remodel the coronary plexus by modulating blood vessel expansion and expression of growth factors [67, 79]. Additionally, endocardially derived tissue macrophages (EcTM) have been shown to be essential in the remodeling of aortic and mitral valves [80]. Comparing the enriched gene signature of EcTM in our data revealed elevated expression of CD44 (lymphocyte activation, recirculation, and homing; cell adhesion; cancer) and CD74 (antigen presentation MCHII) in the HLHS heart compared with the unaffected hearts. Cell surface CD74 serves as a receptor for macrophage migration inhibitory factor (MIF) on many cell types; MIF binding to CD74 induces a signaling cascade that results in regulation of cell proliferation and survival. These findings support a role for the immune system in HLHS, perhaps as part of local tissue remodeling [81].

Apart from the immune system, we also identified genes involved in retinol metabolism, metal ion detoxification, and neuronal differentiation between RV and LV of the unaffected hearts. The rational for these differential enrichments is less clear. Atypical retinol metabolism has been linked to many developmental issues in mice [82,83,84] and is perhaps related to altered retinoic acid regulation of neural crest cell migration [85,86,87,88,89]. In our dataset, three metallothionein protein-coding genes (MT1G, MT1M, and MT1H), a family of cysteine-rich metal chelators which bind and decrease levels of free metal ions, were upregulated in the unaffected RV compared with LV. Metal ions have been implicated in the regulation of immune function, although the mechanisms remain largely unknown [90]. Alternatively, these differences may reflect the physiologic differences between the RV and LV in utero (e.g., the RV provides a higher cardiac output than the LV). Overall, from our results, we are unable to determine in what way retinol metabolism, metal ion detoxification, and neuronal differentiation are affecting functions in the ventricles, and these findings require further exploration.

The study has certain limitations that warrant consideration. The lack of sufficient clinical information prevents us from obtaining a detailed knowledge regarding the HLHS case (e.g., absence or presence of endocardial fibroelastosis or exact status of the mitral or aortic valve or other associated abnormal histologic abnormalities). Additionally, analytical constraints arise from the reliance on a single-case for comparison with an unaffected group. Inferences were made from combining ventricles of the HLHS case to allow statistical testing at the expense of losing ventricle-specific information. Despite the exploratory nature of this approach and limitations, the study revealed unique gene expression patterns associated with HLHS and underscores the need for further validation with a larger cohort. This approach aligns with other research leveraging single-case scenarios to generate hypotheses and advance understanding in heart development and pathology [91, 92].

Conclusion

Little is known about differential gene expression in the developing ventricles following septation and how alteration of the associated molecular pathways may contribute to the pathophysiology of CHDs. Our findings suggest a major part of the differently enriched signaling pathways between the right and left developing ventricles relates to immune function, previously not reported. Our analyses also bring novel evidence for possible implication of Th17 cells in HLHS in addition to genes related to chromatin organization and muscle contraction. The detailed ventricle-specific information in our data enriches the existing knowledge base, providing a foundation for deeper exploration into the intricacies of human heart development and disease pathogenesis.