Integrated gene expression and alternative splicing analysis in human and mouse models of Rett Syndrome

doi:10.21203/rs.3.rs-4895075/v1

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Research Article

Integrated gene expression and alternative splicing analysis in human and mouse models of Rett Syndrome

https://doi.org/10.21203/rs.3.rs-4895075/v1

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Background

Mutations of the MeCP2 gene lead to Rett syndrome (RTT), a rareX-linked developmental disease causing severe intellectual and physical disability. How the loss or defective function of MeCP2 mediates RTT is still poorly understood. MeCP2 is a global gene expression regulator, acting at transcriptional and post-transcriptional levels. Although several transcriptomic studies have been performed in human RTT biosamples and Mecp2mutant mouse models, few genes or pathways have been consistently associated with MeCP2 mutations. Despite the known regulatory role of MeCP2 in splicing mechanisms, the contribution of alternative splicing dysregulation to RTT pathophysiology has received little attention. To gain insight into common molecular pathways that might be dysregulated in RTT, we explore and integrate publicly available RNA sequencing (RNA-seq) data from human RTT patients and Mecp2-mutant mouse models, processing data for gene expression and alternative splicing.

Methods

We downloaded from the Sequence Read Archive 100 samples (SRA-experiments) from 5 independent BioProjects on human Rett Syndrome patients, and 130 samples from 9 independent BioProjects on MeCP2 mutant mouse models. We performed a massive bioinformatics re-analysis of raw data, applying single, standardized pipelines for differential gene expression and alternative splicing analysis.

Results

Our comparative study across datasets indicates common differentially expressed genes (DEGs) and differentially alternatively spliced (DAS) genes shared by human or mouse datasets. We observed that genes dysregulated either in their expression or splicing are involved in two main functional categories: cell-extracellular matrix adhesion regulation and synaptic functions, the first category more significantly enriched in human datasets. A low overlap between human and mouse DEGs and DAS genes was observed.

Limitations

The main limitation of our analysis is the inclusion in the study of highly heterogeneous RNA-seq datasets, deriving from various RTT tissues and cells, and carrying different MeCP2 mutations.

Conclusions

Our massive bioinformatics study indicates for the first time a significant dysregulation of alternative splicing in human RTT datasets, suggesting the crucial contribution of altered RNA processing to the pathophysiology of Rett syndrome. Additionally, we observed that human and mouse DEGs and DAS genes converge into common functional categories related to cell-extracellular matrix adhesion and synaptic signaling.

Rett syndrome

Autism

RNA-seq

alternative splicing

Rett syndrome (RTT, OMIM #312750) is a rare X-linked neurodevelopmental disorder caused in over 95% of cases by mutations in the Methyl-CpG-binding Protein 2 (MeCP2) gene [1, 2]. MeCP2 is a multifunctional protein, implicated in transcriptional and post-transcriptional regulation of gene expression, including splicing of precursor RNAs [3–7]. In the brain, MeCP2 is considered an integration hub for diverse neuronal processes [8, 9]. Two hundred unique RTT-causing mutations of MeCP2 have been identified (RettBASE; http://mecp2.chw.edu.au/), located throughout the entire MeCP2 gene [10]. Although the clinical presentation of the syndrome is highly heterogeneous [11], features of typical RTT are distinctive, and characterized by developmental regression, including the loss of speech and hand skills, after six months of normal development [12]. Despite the extensive preclinical and clinical research, it is still unclear how MeCP2 dysfunction leads to the disease and no specific therapy is available [6, 9, 13]. Several RNA-sequencing (RNA-seq) transcriptomic studies have been performed in patient biosamples [14–16] as well as in RTT mouse models, revealing dysregulation in the expression of coding and non-coding genes [17–23]. However, there is little agreement among the studies. Mouse models of RTT recapitulate many of the symptoms observed in human patients, including developmental, motor, cognitive, and social impairments. Although significant differences between human and mouse biology, many conserved biological processes exist, with most highly expressed genes in humans also being highly expressed in mice [24]. Therefore, the comparison of RNA-seq-based data across species and independent studies might be a powerful tool to highlight convergent biological processes and candidate genes affected by the disease. Human and mouse orthologous genes have conserved exonic structures and lengths, though mouse introns are generally shorter [24]. Indeed, alternative splicing events are poorly conserved, especially in the brain and neuronal cells [25, 26]. Continuous development and refinement of bioinformatics tools enable the extraction of novel insights from the re-analysis of public raw RNA-seq data, improving the interpretation of large-scale datasets [27, 28]. Herein, we re-analyzed on a High-Performance Computing platform [29] primary data from 100 human and 130 mouse RTT publicly archived RNA-seq experiments. We applied the same up-to-date transcriptomic computational workflow to primary data (Fig. 1), to avoid methodological differences in computational data analysis across studies and to obtain a more comparable set of results. Taking into account recent studies showing anomalous patterns of alternative splicing (AS) in RTT [21,30–32], we also computationally processed the RNA-seq raw data to detect AS alterations, creating added-value data resources on RTT-related splicing defects.

Our integrated analysis of transcriptional and AS changes in RTT revealed convergent dysregulated cellular pathways across tissues and conserved between mice and humans, relevant interspecies transcriptional and AS divergences were also observed.

Data download

The selected datasets were downloaded from the Sequence Read Archive (SRA, https://www.ncbi.nlm.nih.gov/sra) using the SRA toolkit v. 2.9.6 (https://github.com/ncbi/sra-tools/wiki/01.-Downloading-SRA-Toolkit). The metadata are reported in Table 2.

RNA-seq data processing

Quality control and trimming

A read quality control was performed on the FASTQ files using FASTQC [33]. Low-quality reads and read adapters were removed using Trimmomatic [34]. For adapter trimming were used the TrueSeq adapters distributed within the software:

>PrefixPE/1

TACACTCTTTCCCTACACGACGCTCTTCCGATCT

>PrefixPE/2

GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT

>PE1

TACACTCTTTCCCTACACGACGCTCTTCCGATCT

>PE1_rc

AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTA

>PE2

GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT

>PE2_rc

AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC

For single end:

>TruSeq3_IndexedAdapter

AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC

>TruSeq3_UniversalAdapter

AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTA

The parameters used are: LEADING:3, TRAILING:3, SLIDINGWINDOW:4:15, MINLEN:36.

Read alignment and annotation

The HISAT2 aligner software (version 2.1.0) [35] was employed for aligning the RNA-Seq reads to the reference UCSC genome assembly for Homo Sapiens (v. hg38) and Mus musculus (v. mm10). The reference genomic indexes were downloaded within HISAT2's suite. The alignment of RNA-Seq reads to the reference genome was performed using the HISAT2 main command and adding the parameter. The resulting alignment file in SAM format was converted to the binary BAM format for efficient storage and subsequent analysis and to facilitate downstream analysis, the BAM file was sorted by genomic coordinates. The conversion was performed using the SAMtools [36]. When the experiment had multiple runs, the BAM files for each lane were merged with Picard tools (v. 2.3.0, http://broadinstitute.github.io/picard/). The gene expression level quantification was performed using Stringtie (version 1.3.4) [37]. This step involved assigning reads to genes and counting the number of reads associated with each gene. The genome annotation GTF file was obtained from the UCSC hg38/mm10 release. The prepDe.py python script (https://github.com/gpertea/stringtie/blob/master/prepDE.py) was used to generate the gene count matrices for subsequent differential expression analysis, as described in the StringTie documentation.

Differential gene expression and GO enrichment analysis

The post-processing analyses were conducted using the R programming language with R-studio (v. 4.0.5, RStudio Team (2020). RStudio: Integrated Development for R. RStudio, PBC, Boston, MA URL http://www.rstudio.com/). The raw count data obtained from Stringtie were used as input for prepDE.py script, followed by differential gene expression analysis. The DESeq2 R package (version 1.36.0) [38] was used to normalize the raw counts, estimate size factors, and perform statistical tests to identify differentially expressed genes (DEGs). The analysis considered relevant factors such as genotype, replicates, and experimental design. Significant DEGs were determined based on at least 0.05 q-values and 0.58 log2 fold change thresholds, in up- or down-regulation. RTT samples in each dataset were always compared to the internal WT control samples belonging to the same BioProject. Samples from different BioProjects were never combined together in the transcriptomic re-analysis. Upsetplots to visualize DEG list overlaps were built using ComplexHeatmap (v. 2.15.4), R package [39].

Gene Ontology (GO) enrichment analysis was performed using the clusterProfiler R package (version 4.7.1.003) [40] to gain insights into the functional annotation and enrichment of differentially expressed genes. The analysis aimed to identify overrepresented GO terms associated with the genes of interest. The gene list used for GO analysis consisted of genes that showed significant differential expression between experimental conditions, as determined by the differential expression analysis using DESeq2. If necessary, the gene identifiers in the input gene list were converted to a common identifier system, such as Entrez Gene IDs or official gene symbols, using available R package annotation databases (version 3.8.2) [41, 42]. The gene list was annotated with GO terms using the enrichGO function from clusterProfiler. The annotation was based on the specific organism's GO database (org.Hs.eg.db or org.Mm.eg.db). The enrichGO function was used to identify significantly enriched GO terms within the gene list. This function performed a hypergeometric test to determine if a particular GO term was overrepresented in the input gene list compared to the genome background. The p-values were adjusted for multiple testing using the Benjamini-Hochberg correction method. The results of the GO enrichment analysis were visualized using various plotting functions from clusterProfiler. To remove redundancy of enriched GO terms we processed data with the simplify function from the simplifyEnrichment R package [43], with cut-off 0.7. This step provided a more concise and interpretable representation of the enriched GO terms. To perform KEGG pathway enrichment analysis the clusterProfiler package was used following the same steps of the GO enrichment analysis, loading the “KEGG.db” downloaded from the create_kegg_db ("") command, using the “createKEGGdb” R package.

To compare mouse and human DEG lists and execute a GO enrichment analysis across species, ClusterProfiler was used with accessory R packages useful for data frame manipulation and plotting: tidyverse [44], dplyr (10.32614/CRAN.package.dplyr) and ComplexHeatmap [39].

Protein Protein Interaction (PPI) network analysis was built using the web app STRING (v12.0) [45] using the core set of shared DEGs as input.

Differential Alternative Splicing Analysis

To investigate differential AS events between experimental conditions (RTT and WT), MAJIQ software suite (v. 2.2) [46] was employed. Required dependencies and reference GFF3 annotations, mm10.ensGene.gff3 and hg38.ensGene.gff3, were obtained according to MAJIQ official documentation. Two MAJIQ configuration files, one for mouse and one for human, were created to specify the experimental design and sample information, including details such as the input folders and BAM files for each condition, sample groupings, and experimental factors. The MAJIQ "build" command was used to detect and quantify AS events from the aligned RNA-Seq data in BAM format, thus producing .voila files and LSV quantification for each sample. The output of this process is a .majiq file for each sample, which is then used as input for the "majiq deltapsi" command to identify differentially spliced events between conditions.

The differential splicing analysis output included the differential splicing events, their psi values (percent spliced-in), and statistical significance. MAJIQ calculates differential AS over local splicing variations (LSVs) rather than the entire gene. Even small LSV variations (20%) can significantly affect differential splicing, so this filtering criterion, combined with a q-value threshold of < 0.05, was used to produce filtered lists of LSVs. Various visualization tools and packages were employed, such as Voila, a visualization package that combines the output of MAJIQ Builder and MAJIQ Quantifier using interactive D3 components and HTML5 [47]. Additionally, a python script was used to plot and categorize different splicing event types between RTT and WT samples.

To gain insights into the biological functions and pathways associated with differential alternative splicing (DAS) genes, GO Enrichment Analysis was performed on DAS shared between bioprojects. DAS genes were linked to their corresponding gene annotations or functional databases to determine enriched terms or pathways. Chordplots were generated with chordDiagram R function from circos visualization utilities (http://circos.ca/intro/tabular_visualization/.)

Dataset description and RNA-seq analysis

To identify differential gene expression and AS signatures in RTT, we integrated previously published human and mouse datasets on RNA expression, available from the SRA portal. We interrogated SRA for “MeCP2 Rett syndrome RNA-seq”, and then we selected data based on the following priority criteria: i) include at least six samples with a minimum of 3 biological replicates per condition; ii) include a WT control group; iii) include only samples from drug-free human or animals biosamples (except for human post-mortem brain tissues and blood). From now on, we will use the term "dataset" to refer to a selected Bioproject experiment from SRA. This process resulted in the identification of 5 human and 9 mouse BioProjects, with 8 and 15 datasets respectively, for a total of 100 and 130 SRA experiments (Table 1). Data description and metadata are summarized in Table 2. The human BioProjects were highly heterogeneous in terms of associated MeCP2 mutations (MeCP2 mutations are indicated in Table 2 and schematized in Additional file 1-Fig. S1): large exon 3–4 deletion in the methyl-CpG binding domain (MBD) and the transcriptional repression domain (TRD) (PRJNA509687) [15, 48], R133C missense mutation in the MBD (PRJNA482372) [16] and E235fs frameshift mutation in the TRD domain (PRJNA419983) [14, 18]. Data from blood (PRJNA383878) [49] or post-mortem brain tissues (Cingulate Cortex and Temporal Cortex, PRJNA527289) [50] were obtained from a pool of patients with different MeCP2 mutations. Mouse datasets were from large MeCP2 deletion genotypes: exon 3 deletion (PRJNA379366) [20, 51], or exon 3–4 deletion (PRJNA278144, PRJNA279421, PRJNA355642, PRJNA448674, PRJNA451208, PRJNA531153) [17, 18, 21, 22, 26, 27]. All mouse models were global mutants, only one was a conditional mutant (PRJNA448674) [52, 53]. The PRJNA526675 was done on mutant mice carrying R306C mutation MeCP2 in the TRD [22, 54].

Table 1

Human and Mouse datasets included in this Study. Information for each bioprojects are provided. PE( paired end); SE (single end); n.a. not available
Organism	Bioproject	Reference	Layout	# SRA experiment	Size (GB)	Platform
Human	PRJNA509687	Rodrigues et al., 2020 PMID: 32209477	PE	36	113	Illumina Genome Analyzer II
	PRJNA383878	Chen et al., 2017 PMID: 28525759	SE	11	9.5	Illumina HiSeq 2500
	PRJNA482372	Xiang et al., 2020 PMID:32526163	PE	6	48	Illumina HiSeq 4000
	PRJNA527289	Aldinger et al., 2020 PMID: 32561870	SE	16	115	Illumina HiSeq 2000
	PRJNA419983	Ohashi et al., 2018. PMID:29742391	PE	31	469	Illumina HiSeq 2000
Mouse	PRJNA278144	Chen et al., 2015 PMID: 25870282	PE	6	600	Illumina HiSeq 2000
	PRJNA355642	Zhao et al., 2017 PMID: 28367307	PE	12	71	Illumina HiSeq 2500
	PRJNA379366	Pacheco et al., 2017 PMID:29090078	PE	8	32	Illumina HiSeq 2500
	PRJNA414468	Raman et al., 2018 PMID: 30104565	PE	6	400	Illumina HiSeq 2000
	PRJNA448674	n.a.	PE	16	393	Illumina HiSeq 2000
	PRJNA451208	Osenberg et al., 2018 PMID:29769330	PE	24	212	Illumina HiSeq 2500
	PRJNA526675	Boxer et al., 2020 PMID: 31784358	SE	40	270	NextSeq 500
	PRJNA531153	Sanfeliu et al., 2019 PMID: 31110484	PE	12	147	Illumina HiSeq 2000
	PRJNA279421	Gabel et al., 2015 PMID:25762136	SE	6	11	Illumina HiSeq 2000

To compare RNA-seq datasets from independent studies, raw data from the selected datasets were individually re-analyzed by applying the same standardized transcriptomic and alternative splicing computational workflows (Fig. 1).

Gene expression analysis of human datasets

The overall transcriptomic analysis of the 8 human RTT datasets uncovered a total of 6367 significantly changed genes ( Log2 fold change > 0.58 or <-0.58, and q-value < 0.05, Table 2). The intersection of the DEG lists from each dataset indicated 96 common genes significantly regulated in 3 or more datasets (Fig. 2A, Additional file 2: Supplementary Table 1). Among these common DEGs, 5 genes were dysregulated in 50% of the input datasets (Fig. 2B, Additional fie 2: Table S1): CD44, SHISA6, PDLIM1, SLC16A3, and SOCS3. CD44 is a class I transmembrane glycoprotein, regulator of dendrite morphogenesis [55]; SHISA6 is an auxiliary subunit of the AMPAR [56]; SLC16A3 is an integral component of post-synaptic density membrane [57]; PDLIM1 is a cytoskeletal protein [58]; SOCS3 is a major regulator of infections and inflammation [59]. The expression of CD44 and SHISA6 was significantly increased in all 4 datasets, while the expression of SLC16A3 and SOCS3 was increased in neuronal samples and decreased in the blood. The observed changes in PDLIM1 expression were not consistent among datasets (Fig. 2B). Then, we functionally characterized the 96 common human DEGs, recognizing 7 ASD-risk genes, according to the SFARI database (https://gene.sfari.org) (AHNAK, BCL11A, CACNA1G, IRF2BPL, RIMS1, SLC24A2, SLC7A3), 10 transcription factors (TF) (BCL11A, BCL11B, EGR1, ETS1, HES4, ONECUT2, PRDM5, SALL4, STAT4, ZNF439) and one RNA binding protein (RBP) (CPEB3). BCL11A is a candidate gene of chromosome 2p16.1-p15 deletion syndrome [60] (Additional file 1: Supplementary Fig. 2, Additional file 2: Table S1). Considering the high heterogeneity of human RTT datasets and the low DEG intersection observed, we reasoned that dysregulated genes, diverse across biosamples, might participate in similar functional networks. Therefore, we performed a comparative analysis to identify GO terms and Kegg pathways enriched across datasets. Interestingly, we observed a significant enrichment of the ‘Integrin Binding’ GO term (Table S1), and of the Kegg pathways ‘ECM-receptor interaction’, ‘Focal adhesion’, and ‘Calcium Signaling’ (Fig. 2C). To identify potential networks of interactions among the 96 common human DEGs, we performed a Protein-Protein Interaction (PPI) analysis finding 88 nodes and 33 edges, with a PPI enrichment p-value 0.0051 (Additional file 1: Fig. S3). The functional enrichment analysis in the network indicated Compartment GO terms related to the cell periphery and junction, and the Kegg pathway ‘ECM-receptor interaction’ (Additionalfile 2: Table S1).

To summarize, gene functional analysis of human common DEGs underlined a significant dysregulation of genes implicated in cell-extracellular matrix adhesion, cell communication, and synaptic signaling.

Gene expression analysis of mouse datasets

The overall transcriptome analysis of the 13 datasets obtained from MeCP2 mutant mice revealed a total of 3011 DEGs (log2 fold change > 0.58 or <-0.58 and q-value < 0.05, Table 2). DEG intersection among all datasets revealed 113 common genes significantly regulated in at least 3 datasets (Fig. 3A and Additional file 3: Table S2). Among these common DEGs the expression of Cacna1g, Irak1, and Sdk1 was found significantly altered in more than 50% of the input datasets (Fig. 3B). Cacna1g is a low-voltage-activated subgroup of voltage-sensitive calcium channels [61]; Irak1 is a serine/threonine-protein kinase that plays a critical role in initiating innate immune response against foreign pathogens [62]; Sdk1 is a homophilic adhesion molecule, involved in cell junction organization, axon guidance, and synapse formation [63]. The expression changes of these highly recurring DEGs was consistent among datasets (Fig. 3B). 3 out of the 113 common mouse DEGs are SFARI genes: Cacna1g, Oxtr and Sema5a (Additional file 1: Fig. S4).

A comparative analysis to match GO terms and Kegg pathways across mouse datasets indicated as enriched GO terms ‘Gated channel activity’, ‘Channel regulatory activity’, and ‘Receptor ligand activity’ (Fig. 3C). Moreover, ‘Neuroactive ligand-receptor interaction’ was the only Kegg pathway significantly enriched among datasets (Additional file 3: Table S2).

PPI network analysis of the 113 common mouse DEGs highlighted 109 nodes and 35 edges with a PPI enrichment p-value: 5E-9 (Additional file 1: Fig. S5). Functional enrichment in the network indicated the ‘Neuroactive ligand-receptor interaction’ Kegg pathway and ‘Extracellular space’ as Cellular Component GO term (Additional file 3: Table S2).

To recapitulate, our gene functional analysis of mouse common DEGs revealed a significant enrichment of ligand and voltage-gated ion channel activities. Furthermore, our network analysis highlighted a functional enrichment for components of cell periphery and ECM receptors.

Comparative functional analysis of human and mouse DEGs

To perform a cross-species functional analysis, we compared human and mouse GO terms. Interestingly, our analysis revealed that the GO term ‘Gated channel activity’, ‘Calmodulin binding’, ‘Actin binding’, and Extracellular-matrix structural components’ are significantly enriched in at least two human and mouse datasets (Additional file 1: Fig. S6, Additional file 4: Table S3).

To summarize, interspecies comparative functional analysis confirmed in both human and mouse RTT datasets a dysregulation of cell-surface-associated components and the ECM, as well as biological processes regulated by calcium signaling.

Alternative splicing analysis of human Rett datasets

Data processing of primary data for AS allowed us to identify statistically significant aberrant splicing events in all human datasets. A total of 940 DAS genes were identified in the human datasets (Table 2).

DAS intersection analysis among human datasets showed that 20 genes were differentially spliced in at least 3 datasets (common DAS) (Fig. 4A and Fig. 4C). MeCP2 mutations affect various types of AS events, including exon skipping (ES), alternative 3’ splice site (A3SS), alternative 5’ splice site (A5SS), and intron retention (IR). Consistently with previous studies, we observed a significant increase of local splicing variatios (LSV) in Rett compared to WT for ES and IR ( Fig. 4B) [21]. Common DAS, identified in neuronal datasets only (Fig. 4A), are protein-coding genes with multiple annotated transcripts. The majority of them are related to neurodevelopmental disorders (Fig. 4C): 9 of them have been associated with ASD [64–72], one to microcephaly [73] or developmental and epileptic encephalopathy [74], 5 are Sfari genes (Fig. 4C). CD47 and ANKRD36C are the most recurring DAS, present in 50% of datasets (Fig. 4C). Both genes generate by AS multiple protein isoforms. CD47 (alias IAP) is an integrin-associated transmembrane protein, that mediates cell-to-cell interactions [75]. Overexpression of CD47 is associated with brain overgrowth and 16p11.2 deletion syndrome [65]. ANKRD36C, belongs to the ankyrin repeat-containing protein family and is involved in protein-protein interactions and ion channel inhibitor activity [76]. To evaluate the relevance for synaptic localization and/or function of human common DAS genes, we performed a GO-analysis using SynGO, a curated database of synaptic genes [77]. Interestingly, we observed a significant enrichment of GO terms related to synaptic organization and function (Fig. 4D). Human DAS gene functional protein association network analysis indicated an enrichment of ‘Vesicle’ GO component and ‘Cell junction’ UniProt Keyword (Fig. S6 and Additional file 5: Table S4).

Alternative splicing analysis of mutant MeCP2 mouse datasets

AS analysis of mouse datasets indicated DAS genes in all datasets except one (Table 2). In the overall we identified 394 DAS. DAS intersection analysis of the 13 mouse datasets revealed 11 DAS genes common to at least 3 datasets (Fig. 5A and Fig. 5C). Intriguingly, our AS analysis on mouse datasets identified as mostly frequent DAS genes the non-coding RNA (lncRNA) genes Snhg14 and Firre in 8 and 6 datasets, respectively (Fig. 5C). Both genes undergo extensive alternative splicing. Snhg14 is associated in human with Angelman and Prader-Willi Syndromes [78]. The gene is located within the Prader-Willi critical region and produces a long paternally imprinted RNA that hosts small nucleolar RNA, C/D box 115 and 116 clusters [78]. Firre is an X-linked gene and its transcript anchors the inactive X chromosome near the nucleolus [79]. Common mouse DAS genes also include the protein-coding genes Sptan1 and Ap4S1, associated to Early Infantile Epileptic Encephalopathy and Spastic paraplegia, respectively [80, 81], the TF Etv1 and the ribosomal protein Rpl7a (Fig. 5C).

SynGO functional analysis of the 10 common mouse DAS genes (excluding MeCP2 from our analysis) indicated significant enrichment of terms related to intrinsic and extrinsic synaptic components, with ‘Translation at pre- and post-synapse’ as the most significant term (Fig. 5D, Additional file 6: Table S5). The overall splicing pattern changes are similar in human and mouse as reported in Fig. 4B and Fig. 5B, however the number of estimated DAS in human is almost 10 times higher than in mouse (number of DAS normalizing over library sizes).

In summary, human and mouse AS analysis indicated a significant perturbation of splicing. Nevertheless, none of the DAS genes were conserved between mouse and human. However, our functional analysis by SynGO recognized both in mouse and human datasets splicing dysregulation of genes involved in synaptic structure and function.

Although the genetic etiology of the RTT is largely associated with MeCP2 mutations, linking clinical symptoms to specific molecular mechanisms is still an unsolved issue. This may depend on the fact that MeCP2 molecular functions are still partially understood. MeCP2 was initially recognized as a methylated DNA binder [4]. It interacts with a wide spectrum of chromatin components and regulators and shows RNA binding activity [3, 5, 6, 82]. Recent experimental evidence strongly indicates MeCP2 contribution to RNA maturation [21, 30, 31, 83–86]. The current view of MeCP2 is of a multifaceted epigenetic driver, involved in multiple layers of co-transcriptional regulation, some still elusive. Therefore, a deeper understanding of its functions is essential for a better comprehension of RTT molecular mechanisms and the development of new clinical interventions.

As recently stated, transcriptomic studies represent an excellent unbiased readout of RTT molecular mechanisms [87]. However, till now no genes or molecular pathways have been consistently associated with MeCP2 pathological mutations. Methodological differences in computational data analysis across studies represent one important issue in comparative transcriptomics. Therefore, to perform a retrospective comparative analysis of RTT human and mouse RNA-seq published studies, we re-analyzed input raw data by applying unique pipelines for gene expression. Despite the known implication of MeCP2 in RNA processing [21, 30, 31, 83–86], much less attention has been devoted to AS variation in RTT, and AS profiling in RTT human biosamples is still missing. AS is an important source of protein sequence variation and functions in the mammalian nervous system [25], and it might be relevant for the pathophysiology of RTT [21, 30], as demonstrated in many other neurological disorders [88]. Our data mining for AS revealed significant alterations in splicing profiles of RTT datasets. The lack of overlap between human and mouse DAS gene lists could be ascribed to the rapid evolution of RNA processing mechanisms and the low conservation from rodents to primates of splicing cis-regulatory sequences of orthologous genes [24, 25, 89]. Detecting the AS dysregulation of human genes relevant to the disease, our analysis pinpoints the relevance of splicing investigation for the understanding of the pathobiology of RTT. Interestingly, our gene expression and AS splicing analysis revealed that both DEGs and DAS genes converge onto common cellular pathways. Functional analysis of human DEGs and DAS genes indicates a significant dysregulation of genes and pathways related to cell-extracellular matrix communication, and synapse structure and function. Mouse DEGs and DAS genes functionally converge to synaptic structure and function. Overall, synaptic dysregulation was observed in human and mouse transcriptomic analysis. Accordingly, in 50% of human datasets we observed a dysregulation of CD44 and CD47, at their expression and AS levels. Both genes codify for transmembrane glycoprotein genes that mediate cellular response to the microenvironment. CD44 is involved in cell-cell interactions, cell adhesion, and migration. In the brain, CD44 regulates structural and functional plasticity of dendritic spines and coordinates neural circuit development [80, 91]. CD47, also known as a ‘‘don’t eat me’’ signal, promotes dendritic and axonal development in hippocampal neurons and prevents excess pruning during postnatal development [92, 93]. Both CD44 and CD47 are autism-associated genes [64, 92]. It was shown that MeCP2 binds CD44 precursor RNA and regulates its splicing [30]. However, no altered CD44 splicing was observed in our analysis. Moreover, among highly frequent DEG and DAS genes in human datasets, we identified ion channel regulators and subunits, such as SHISA6 and ANKRD36C. CACNA1G, a voltage-sensitive calcium channel, is the only common DEG identified in 3 and 7 mouse datasets, respectively. CACNA1G is associated with various neurological disorders: autosomal-dominant cerebellar ataxia [95], idiopathic generalized epilepsy [96], spinocerebellar and cerebellar ataxia [97], and autism [98]. Recently, CACNA1G has been reported as a pathological variant in RETT patients [99]. Changes in the expression of Irak1 were observed in the majority of MeCP2 KO mouse datasets, in agreement with previous studies [22, 100]. It should be pointed out that the Irak1 gene is located ~ 3 kb downstream of Mecp2, in the same transcriptional orientation. As previously discussed, changes in its expression might be a direct consequence of the large deletion in the MeCP2 KO mouse line [53] (Mecp2^tm1.Bird carries exon 3 and 4 large gene deletion), causing an altered local chromatin conformation or the deletion of an Irak1 negative regulator [100]. No changes in Irak1 expression were observed in R306C MeCP2 mutant mice.

Our finding of the altered AS of Snhg14 and Firre ncRNAs in many mouse datasets is puzzling. Snhg14 is a host gene for the small nucleolar RNA genes Snord115 Snord116 [78], however their expression was not altered in our mouse gene expression analysis. No changes of SNHG14 and FIRRE AS or expression were observed in human datasets. Both the mouse and human SNHG14/Snhg14 loci are imprinted and only expressed from the paternally inherited chromosome [78]. Large deletions of the SNHG14 locus, as well as microdeletions of the SNORD116 locus, lead in human to the neurodevelopmental genetic disorder Prader–Willi syndrome (PWS) [78]. The relatively poor conservation of the IPW gene between human and mouse [101] and the fact that a paternally inherited deletion that includes the Ipw gene appears asymptomatic in mice [102] argue against any biological relevance for this ncRNA gene in PWS [103]. Syntenically conserved lncRNA Firre displays different expression and localization patterns in human and mouse [104]. In general, lncRNAs are rapidly evolving, thus typically poorly conserved in their sequences and this rapid evolution might affect potential functions of lncRNAs [104]. This should be the case of Snhg14 and Firre. We hypothesize that the observed altered splicing of Snhg14 and Firre might depend on a local chromatin conformation resulting from MeCP2 loss of function.

Limitations

The major limitation of this study is the inclusion in our massive analysis of RNA-seq datasets generated from highly heterogeneous biosamples. Transcriptomic data were obtained from biosamples carrying different MeCP2 mutations, in some cases RNA-seq were generated from multiple patients with different MeCP2 mutations. Although MeCP2 mutant mice analyzed have more similar genetic backgrounds, RNA-seq data were obtained from various brain regions, primary neurons, and blood. This genetic and biological heterogeneity might explain the low number of shared DEG and DAS identified. Nonetheless, we were able to detect common dysregulated genes, relevant to the disease. Future studies will be required to clarify their role in the pathophysiology of RTT. In the future, single-cell transcriptomics analysis will help elucidate gene dysregulation specific to different types of neurons and brain regions. Additionally, our AS analysis is exploratory, aimed at investigating the overall dysregulation of AS in RTT. Detailed AS analysis of RTT-relevant genes will be required to molecularly characterize the functional impact of the altered processing of primary transcripts and the potential implication for the disease.

Although we are far from fully understanding the molecular mechanisms underlying RTT, our findings provide novel insights into RTT dysfunctional pathways and genes associated with the disease. Our global transcriptomic analysis highlights dysregulation in RTT of molecular processes related to cell-extracellular matrix adhesion and synaptic signaling in both in human and mouse. Furthermore, our study underlines the critical role of altered AS programs in RTT, suggesting that alterations in both gene expression and AS could contribute to the pathophysiology of RTT. Therefore, integrating gene expression and splicing analysis may be important for gaining a better understanding of the downstream effects of MeCP2 mutations.

Our findings may have significant implications for a better understanding of the disease mechanisms, but they could also offer new opportunities for improving RTT therapeutic intervention.

RTT

Rett syndrome

MeCP2

Methyl-CpG-binding Protein 2

RNA-seq

RNA sequencing

lncRNA

long non coding RNA

DEG

Differentially expressed genes

DAS genes\

Differentially alternative spliced genes

Alternative splicing

Knockout

iPSC

Induced pluripotent stem cells

NPC

Neuronal progenitor cells

Cingulate cortex (human datasets), whole cortex (mouse datasets)

Temporal cortex

Neuron

Blood

Cerebellum

Hypotalamus

Forebrain

Hippocampus

Microglia

Visual Cortex

cortical neurons

Gene ontology

Exon skipping

Intron retention

5’ASS

5’ Alternative splice site

3’ASS

3’ Alternative splice site

Acknowledgements

We acknowledge the support of ELIXIR-IT and CINECA (HPC@CINECA) for the provision of high-performance computational resources. In particular, the ELIXIR-ITA HPC@CINECA and ELIXIR-IIB HPC@CINECA initiatives for providing HPC resources to our project (P.I. Cecilia Mannironi, Call ELIXIR-IT HPC@CINECA 2018-2020). Finally, we would like to acknowledge all researchers who made the data used in this work publicly available, without their data this study would have not been possible.

Author contributions

C.M. and S.Gio. conceived the study. C.M., A.R. S.Gio., and T.C. contributed to sample preparation and metadata retrieval. T.C. and S.Gio. developed the theory and performed the computations. C.M., S.Gio, S.Ga., A.R., T.C. verified the analytical methods. C.M., S.Gio. and A.R. wrote the manuscript, S.Gio. and S.Ga. designed the figures. All authors discussed the results, provided critical feedback, and contributed to the final version of the manuscript.

Funding

This work was supported by the Italian Telethon Foundation (GJC22071 to C.M) and PNRR-CN3 (National Center for Gene Therapy and Drugs Based on RNA Technology-Spoke3 to C.P.).

Availability of data and materials

The analysis was performed on publicly available raw data from SRA experiments of bioprojects indicated in Table 1. All analyzed data in this study are provided in the supplementary information files (Additional File 2- Supplementary Tables 1-5). Normalized expression count tables, differential expression and alternative splicing tables for each configuration, and relevant sample metadata are available from the corresponding author on request.

Ethics approval and consent to partecipate

Not applicable

Consent for publication

Not applicable

Competing interests

The authors declare no competing interests

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Table 2 is available in the Supplementary Files section.

No competing interests reported.

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Integrated gene expression and alternative splicing analysis in human and mouse models of Rett Syndrome

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Version 1

Abstract

Background

Methods

Results

Limitations

Conclusions

Figures

Background

Methods

Data download

RNA-seq data processing

Quality control and trimming

TACACTCTTTCCCTACACGACGCTCTTCCGATCT

GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCT

>PE1

TACACTCTTTCCCTACACGACGCTCTTCCGATCT

AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTA

>PE2

AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC

AGATCGGAAGAGCACACGTCTGAACTCCAGTCAC

AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTA

Read alignment and annotation

Differential gene expression and GO enrichment analysis

Differential Alternative Splicing Analysis

Results

Dataset description and RNA-seq analysis

Gene expression analysis of human datasets

Gene expression analysis of mouse datasets

Comparative functional analysis of human and mouse DEGs

Alternative splicing analysis of human Rett datasets

Alternative splicing analysis of mutant MeCP2 mouse datasets

Discussion

Limitations

Conclusions

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Version 1