Comprehensive CCM3 Mutational Analysis in Patients with Syndromic Cerebral Cavernous Malformation

doi:10.21203/rs.3.rs-1991748/v1

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Research Article

Comprehensive CCM3 Mutational Analysis in Patients with Syndromic Cerebral Cavernous Malformation

https://doi.org/10.21203/rs.3.rs-1991748/v1

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published 01 Feb, 2023

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Cerebral Cavernous Malformation (CCM) is a vascular disease that affects the central nervous system, which familial form is due to autosomal dominant mutations in the genes KRIT1/CCM1, MGC4607/CCM2 and PDCD10/CCM3. Patients affected by the PDCD10 mutations usually have the onset of symptoms at an early age and a more aggressive phenotype. To contribute to knowledge about the disease, we performed clinical, functional, and neuroradiological analyses of the mutations in PDCD10/CCM3 in two patients comparing the findings with five patients with familial form from CCM1/KRIT1 or CCM2/MGC4607 mutations and six patients with sporadic form. In addition, we have evaluated the PDCD10/CCM3 gene expression by qPCR and developed a bioinformatic pipeline to assist in the possible clinical. The two CCM3 patients had an early onset of symptoms and a high lesion burden. Furthermore, the sequencing showed that P1 had a frameshift mutation (c.222delT;p.Asn75ThrfsTer14) and P2 a variant on the splicing region c.475-2A > G (p.A119Gfs*42). The mRNA expression was 4-fold lower in both patients with PDCD10/CCM3 mutation. In silico analysis, the prediction reveals that the frameshift mutation transcript lacks the C-terminal FAT-homology domain compared to the 212 aa-length wild-type PDCD10/CCM3 and preserves the N-terminal dimerization domain. We also demonstrated a related pathway that might explain the interplay between low-grade astrocytomas and PDCD10 CCM, a possible manifestation of the syndromic disease. The two mutations support the understanding of the protein-protein interaction between PDCD10 and several essential cellular proteins that might contribute to the mechanistic understanding of why some individuals with CCM3 have a syndromic phenotype.

Cerebral Cavernous Malformation

syndromic phenotype

PDCD10/CCM3

frameshift mutation

splicing mutation

gene expression

Cerebral cavernous malformation (CCM) is a major type of human genetic cerebrovascular disease. CCM can be manifested through the hole central nervous system (CNS) and magnetic resonance image (MRI) with a hemosiderin-sensitive technique as the susceptibility-weighted imaging (SWI) is the gold standard for diagnosis[1–3]. It is caused by loss of function autosomal dominant mutations on three non-homologous protein coding genes: KRIT1/CCM1, MGC4607/CCM2 and PDCD10/CCM3[4–8]. PDCD10/CCM3 mutations are the rarest cases, constituting less the 15% of proband genotypes by sequential mutation screenings and less than 2% of CCM cases at large[5, 9, 10]. Individuals that harbour PDCD10/CCM3 mutations had been identified as having a more aggressive clinical course with precocity of symptoms and a syndromic phenotype[11–13]. However, the biological mechanistic explanation of this unique form of CCM disease remains to be elucidated.

The functional roles of PDCD10 have been demonstrated using several cells, mice and zebrafish models[13–16]. PDCD10 is a protein that have been identified in a complex with MGC4607 that forms a trimer with KRIT1 and additional multiprotein complex that includes all three members of the germinal center kinase III (GCK-III)[17, 18]. PDCD10 exists as a homodimer in the cell due to presence of the N-terminal dimerization domain and is made up of four α-helices: α1, α2, α3, and small α4. These four α-helices interlocks with another set of four α-helices from a partner PDCD10 dimerization domain[19]. This dimerization domain is also used to bind to GCKIII kinases forming a heterodimer[20]. The GCKIII subfamily of proteins consists of STK24, STK25 and Mst4[21, 22]. The GCKIII kinases are involved in two important cellular processes: regulation of cell migration and modulation of cell death/proliferation[23–26]. Specifically, Mst4 and STK25 have been shown to associate with the cis-Golgi matrix protein GM130, in which they play a role in determining the proper localization and morphology of the Golgi complex[27]. Therefore, dysfunction of this complex interplay could result in the PDCD10 syndromic clinical and biological phenotype.

Both clinical and preclinical studies have helped in understanding the molecular basis of CCMs, hence, identification and validation of pathogenic variants are mandatory to assist in the early diagnosis. Here, we identified a novel PDCD10/CCM3 frameshift mutation in a pediatric patient. Next Generation Sequencing revealed a deletion mutation (c.222delT/p.Asn75ThrfsTer14) within exon 4 of PDCD10/CCM3 gene in the proband patient. Although this mutation is not yet clinically classified, different pathogenicity prediction software considered the mutation as pathogenic. We have developed a pipeline to assist in the possible clinical validation of this variant. This frameshift mutation led to a premature termination codon (PTC) and we detected that the CCM3 mRNA levels in the blood sample of the proband were reduced compared to patients without any mutation in the CCM3 gene. Interestingly, the transcript profile observed in c.222delT/p.Asn75ThrfsTer14 mutation similar to that found in another patient with a c.475-2 A > G (p.A119Gfs*42) CCM3 mutation in a splice site previously described[13]. Furthermore, an extensive in silico analysis of a frameshift mutation in PDCD10/CCM3 that lead to an aberrant protein-protein interaction (PPI) in a syndromic cerebral cavernous malformation patient enhances the understanding of the PDCD10/CCM3 phenotype.

Clinical Feature

We presented two cases of familiar cerebral cavernous malformation with a syndromic clinical phenotype with their clinical and neuroradiological features and compared these two patients to other 5 patients with CCM1/KRIT1/CCM2/MGC4607 forms of familiar cerebral cavernous malformation and other 6 patients with sporadic form. Magnetic resonance imaging (MRI) was performed at least once in each subject, including susceptibility weighted imaging (SWI) at 1.5 Tesla. Lesion burden was assessed by cataloging every CCM lesion on conventional MRI T2-weighted sequences, as well as the most sensitive SWI sequences as validated previously when possible. For each of the others familiar and sporadic subjects we reviewed the past history (including previous medical records provided by the patient or family) and we noted the age at first symptom onset, age at diagnosis, age at last MRI and presenting symptom.

Genetic analyses

Blood sample was collected and processed by centrifugation to separate the plasma. CCM1/KRIT1, CCM2/MGC4607 and CCM3/PDCD10 gene sequencing was performed. Genomic DNA (gDNA) was extracted using PureLink Genomic DNA Mini Kit (Thermo Fisher Scientific). Quality of gDNA was determined by NanoDrop. 2000 (Thermo Fisher Scientific) followed by quantification using Qubit™ dsDNA HS Assay Kit (Thermo Fisher Scientific, Waltham) and Qubit. Fluorometer3.0 (Thermo Fisher Scientific, Waltham, MA, USA). CCM1/KRIT1, CCM2/MGC4607 and CCM3/PDCD10 gene sequencing were performed (Target panel sequencing was performed using Ion AmpliSeq Library Kit 2.0–96 LV (Life Technologies) for library preparation. 65 pM of library was used to generate template positive Ion SphereTM Particles (ISP) performed on Ion Chef™ Instrument (Thermo Fisher Scientific) using Ion Ion 510™ & Ion 520™ & Ion 530™ Chef Kit (Thermo Fisher Scientific). Next-generation sequencing (NGS) was carried on Ion S5TM Instrument (Thermo Fisher Scientific) using a 530 Chip. Reads were aligned using Torrent Mapping Alignment Program (TMAP) to the human reference genome build 19 (hg19-Genome Reference Consortium GRCh37) using Ion Torrent Suite Server (v4.0.2). Variant Caller (v4.0-r76860) and Coverage Analysis (v4.0-r77897) plugins were adopted for data analysis. Average depth was set at minimum 1000Å~ for total coverage and at minimum of 100Å~ coverage and 30 Phred QUAL Score for variant calls. Found variants were classified referring to the following databases: HGMDR, Clinical Variants (https://www.ncbi.nlm.nih.gov/clinvar), Exome Aggregation Consortium (ExAC) (http://exac.broadinstitute.org), 1000G (https://www.internationalgenome.org/), dbSNP (https://www.ncbi.nlm.nih.gov/projects/SNP/), MARRVEL (http://marrvel.org/) and PharmGKB (https://www.pharmgkb.org/).

Real-Time Quantitative Polymerase Chain Reaction (qPCR)

The qPCR assay was performed as described previously[28]. Total RNA was isolated from the blood samples (2 ml) collected from the c.222delT(p.Asn75ThrfsTer14) and c.475-2 A > G (p.A119Gfs*42) CCM3 patients, and three other patients who do not have any mutation in the CCM3 gene using PureLink™ RNA Mini Kit (Thermo Fisher). The quality and quantity of mRNA were determined by NanoDrop. 2000 (Thermo Fisher Scientific). One hundred nanograms of total RNA per sample was used for cDNA synthesis using High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher) according to the manufacturer’s protocol. The qPCR reaction volumes were 15µL containing 10ng of cDNA, 1µM of primer mix and 1 × Quantifast SYBR Green PCR master mix (Thermo Fisher). Reaction mixtures were initially denatured at 95°C for 5 min, followed by 40 cycles of 95◦C for 15 s, 60◦C for 1 min and 72◦C for 20 s in QuantStudio™ 3 Real-Time PCR System (Thermo Fisher). Each assay was replicated using three independent biological samples. Cycle threshold (Ct) values were averaged for target genes and normalized against β-actin (endogenous reference gene), and gene expression was quantified using the 2–^ΔΔCT method. All primers were quality controlled to ensure that each set (forward and reverse) generated a specific PCR product. We using primer’s sequences of previously published: β-actin F 5´-ACCAACTGGGACGACATGGA-3´and R 5´- CCAGAGGCGTACAGGGATAG-3´[29]; CCM3 F 5´- TGGCAGCTGATGATGTAGAAG − 3´and R 5´- TCGTGCCTTTTCGTTTAGGT − 3´[30].

In silico Structural Prediction of Mutated PDCD10 Proteins and Phylogenetic Analysis

Prediction the pathogenicity of unclassified variants was performed using PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2/), SIFT (http://sift.jcvi.org/www/SIFT_enst_submit.html) and the Human Genomics Community (https://varsome.com/), which predicts the clinical importance of the variant using the ACMG (American College of Medical Genetics) interpretation guides.

Mutated PDCD10/CCM3 transcript structure was performed by directly modifications of the wild-type PDCD10 (Wt PDCD10) (NP_009148.2) FASTA sequence obtained on NCBI database. The new FASTA file was submitted to the RaptorX and the PyMOL software (Molecular Graphics System, Version 2.0 Schrodinger, LLC) for overall structural analysis. RaptorX was used to develop the structural alignment of bot wild-type and mutated protein[5, 10, 31, 32].

The evolutionary history was inferred using the MEGA-X software using the maximum likelihood method and Jones-Taylor-Thornton (JTT) matrix-based model. We first gathered the PDCD10 wt sequence from Homo sapiens and 20 other vertebrates to study the evolutionary conservation of this protein. Initial tree for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the JTT model, and then selecting the topology with superior log likelihood value

Docking simulation

Structure-based docking approaches were used to evaluate the binding affinity of Wt PDCD10 homodiemrization; mutated PDCD10/CCM3 transcript homodimerization; Wt PDCD10 with MGC4607; mutated PDCD10/CCM3 transcript with MGC4607; Wt PDCD10 with GCK-III family and mutated PDCD10/CCM3 transcript with GCK-III family. The various tools used to perform docking were ISLAND to predict the ΔG (binding free energy) and Kd (dissociation constant) respectively; HDOCK to calculate negative docking score and ligand rmsd[33–36].

GEPIA2 Database

Gene Expression Profiling Interactive Analysis 2 (http://gepia2.cancer-pku.cn/, GEPIA2) is a web-based tool that can be used for comprehensive analyses of the different TCGA RNA-seq and Genotype-Tissue Expression (GTEx) project data GEPIA2 can provide a series of customized analysis[37]. Subsequently, we used the GEPIA2 to analyze correlative analysis of PDCD10 and other related genetic pathways in a cohort of low-grade astrocytomas patients. These tumors were selected base on the literature accordance of higher prevalence of intracranial tumors, specially astrocytomas, in PDCD10 patients. The correlation of non-normally distributed variables was determined using the Spearman correlation test. A p < 0.05 was considered statistically significant.

Statistical Analysis

Statistical analyzes of the study were performed with STATA 13 version (StataCorp LP, TX, USA). ANOVA and two-tailed 2-sample t-test was used to examine Lesion burden on SWI, T2 and age at first symptom between the PDCD10 patients and two independent cohort of solely KRIT1/MGC4607 individuals and sporadic cases. Kaplan-Meyer curve was created and log-rank test was used to access the age at first symptomatic bleeding between all groups. Negative binomial regression was used to detect the independent effect of PDCD10 against both KRIT1/MGC4607 individuals and sporadic cases on lesion burden (SWI and T2) controlling for age.

Clinical Feature

We presented two cases of familiar cerebral cavernous malformation with syndromic clinical phenotype. The first patient (P1) is a 9 years-old male patient first presented at age of 1 year old with symptoms of irritability and a computed tomography had depicted signs of bleeding in the cerebellar vermis. Further MRI evidence a CCM located in the cerebellum and many other lesions throughout the CNS (Fig. 1A). This bleeding led him to develop hydrocephalus necessitating the installation of a ventricular-peritoneal shunt. He had hospital discharge and remained in close follow-up. At age of 2, he developed a new symptomatic hemorrhage in a CCM located in the left parietal lobe and one in the right frontal lobe with no major sequelae. As time gone by, the patient started to develop cognitive impairment and hypothyroidism, requiring the use of 75 mcg of levothyroxine. Nowadays, he has intense imbalance not being able to walk alone. He also does not have signs of scoliosis or intracranial benign tumors. There is no report of the disease in other family members (Fig. 1B).

The second patient (P2) is a 32 years-old female that first present at age of 8 years-old. She was admitted with acute ataxia and diplopia, which improved in a few days. SWI MRI demonstrated that she had numerous lesions (Fig. 1C). The patient has MRI for all the evolution of her case and control of the progression of lesions. She had three recent cerebral hemorrhages (out brainstem), with seizures, associated with the use of oral contraceptives for the treatment of ovarian cysts disease. Since then, she remains asymptomatic, using two anticonvulsants. Genetic and MRI screening suggests that she has a de novo case of cerebral cavernous malformation (Fig. 1D) The genetic analysis of this patient was previously published in Shenkar et al, and here we performed an extensive evaluation of her clinical and radiological data, as well as the evaluation of PDCD10 expression[13].

Precocity of Symptomatic Bleeding and Higher Lesion Burden

The mean age ate first symptom of the PDCD10 patients was 6 years. When comparing to both the KRIT1/MGC4607 and sporadic group the PDCD10 patients presented with precocity of symptomatic bleeding (p = 0.0008 and p = 0.0011, respectively) (Fig. 1E). The Kaplan-Meyer curve along with log-rank test also demonstrated that PDCD10 patients tends to have the first symptoms early in their life-time (p = 0.0004) (Fig. 1F). Lesion burden on T2 was exceptionally high. The mean number of lesions per patient on T2-weighted magnetic resonance imaging scan was 18 in PDCD10 cases, significantly greater than the mean lesion count of 2.2 in familial KRIT1/MGC4607 cases (P = 0.043) (Fig. 1G). When adjusted for age, both the T2-weighted and SWI lesion burden was also significantly greater in the PDCD10 cohort than in control familial cases with KRIT1/MGC4607 mutations (p = 0.0002 and p = 0.004, respectively)

Genetic Sequencing and functional analyses

For P1, direct sequencing allowed the detection of some benign variants in KRIT1/CCM1 and MGC4607/CCM2, several unclassified variants in both three genes and one variant in PDCD10/PDCD10 previously reported in the literature[38]. The variant was not listed in the ClinVar, human gene mutation database (HGMD), or ExAC database (GnomAD database). The P1 variant is a PDCD10/CCM3 frameshift mutation (PDCD10^FS) (c.222delT; p.Asn75ThrfsTer14) that leads to an 88aa-length protein lacking the last 124 C-terminal amino acids on its primary structure. Besides adding a premature stop codon, this PDCD10/CCM3 mutation also promotes an amino acid change at residue 75, substituting Threonine by an Asparagine. The P2 variant is a splicing region variant c.475-2A > G (p.A119Gfs*42) not directly influencing the primary structure previously described in Shenkar et al[13]. Real time qPCR results showed that the PDCD10 mRNA expression was decreased by 4 fold in the blood samples of the patients that had mutations in PDCD10 gene compared with patients without any mutation in PDCD10 gene (Fig. 2A). Both variants were predicted to be potentially pathogenic by in silico variant interpretation analysis (Supplementary data).

In Silico Structural Analysis of frameshift PDCD10/CCM3 Transcript

In silico structural analysis prediction reveals that the PDCD10^FS transcript lacks the C-terminal FAT-homology domain (FAT) compared to the 212 aa-length wild-type PDCD10/CCM3 and preserves the N-terminal dimerization domain, responsible to interact during the formation of the PDCD10 homodimer (Fig. 2B). The structural alignment (Supplementary data) of both wild-type and mutated protein predicted that they share a similar fold (Lali: 78; RMSD: 1.34; uGDT(GDT): 75(86); TMscore: 0.849)

The frameshift mutation (c.222delT; p.Asn75ThrfsTer14) occurred in highly conserved domain, within 75% of the species sharing this site (Fig. 2C). As the frameshift mutation (c.222delT; p.Asn75ThrfsTer14) resulted in a PTC, the mutated transcripts of PDCD10 were predicted to undergo degradation rapidly, a process named non-sense mediated mRNA decay (NMD). Alternatively, the mutated transcript might be stabilized by polyadenylation, which might produce a truncated protein.

Docking analysis

The docking analysis demonstrates that although preserving the N-terminal dimerization domain (Fig. 2D), the PDCD10^FS had lower affinity during homodimerization docking than the PDCD10^wt (Fig. 2E). HDOCK demonstrates that PDCD10^wt had a better homodimerization docking performance than the PDCD10^FS, however ISLAND had a great ΔΔG for the PDCD10^FS homodimer. In contrast, PDCD10^wt had a worse binding affinity during interaction with MGC407 (Fig. 2F) than PDCD10^FS (Fig. 2G) and ISLAND had the same ΔΔG for the interaction of both PDCD10^wt and PDCD10^FS with MGC407 (Table 1).

Analyzing the docking affinity with the GCK-III proteins, HDOCK predicted that the interaction of PDCD10^FS with STK24 and STK26 had lower affinity than the PDCD10^wt and the interaction of PDCD10^FS with STK25 had higher affinity than PDCD10^wt; ISLAND predicted that the ΔΔG energy was higher in the interaction of PDCD10^wtwith all the 3 GCK-III proteins (table 1).

Table 1

Wild-Type and Frameshift Mutation PDCD10 Protein-Protein Interaction

	Docking Score	Ligand RMSD (Å)	ΔΔG (kcal/mol)	Kd (M)
Wt homodimer	-624,26	1.03	-9.616	8,8420477548E + 03
FS homodimer	-558,5	0,69	-10.088	3,9837496981E + 03
Wt x MGC407	-258,83	83,6	-10,870	1,0640947950E + 01
FS x MGC407	-264,32	126,67	-10,870	1,0640947893E + 03
Wt x STK24	-275,15	125,83	-9.473	1.12672357017e-07
FS x STK24	-228,07	50,18	-9.579	9,4223424542E + 03
Wt x STK25	-256,66	70,46	-9,51	1,0587602091E + 03
FS x STK25	-272,62	50,02	-9.619	8,7999288401E + 03
Wt x STK26	-257,05	142,34	-9.232	1,6919347273E + 04
FS x STK26	-232,07	146,75	-9.303	1,5017470363E + 04

We also performed docking analysis of different PDCD10 mutations presented in the literature to access the impact of alternative variants of the PDCD10 gene (supplementary table). In matter of the capability of homodimerization and interaction with the MGC407 and the GCK-III proteins, the others mutations had the same behavior of the PDCD10^FS, with low scores on HDOCK. Of note, the c.131C p.Leu44Pro mutation performed similar on homodimerization.

Low-Grade Astrocytoma GEPIA2 genetic analysis

Since the PDCD10 genotype confers a syndromic phenotype to the affected individuals, we hypothesized that the PDCD10 protein or any of the GCK-III complex might be alternatively expressed in low-grade astrocytomas independent tissue, a hallmark tumor of the syndromic type of cerebral cavernous malformation. However, the overall expression of PDCD10 and GCK-III protein complex was not statistically different between low-grade astrocytomas and normal brain tissue (Fig. 3A). The literature also argues that PDCD10 may play a hole in a permissive microbiome, regulating the TLR4-MEKK3-KLF2/4 and PIK3CA signaling pathways. We identified that TLR4, CD14, ERK2 and PIK3CA (Fig. 3B) were all statistically over expressed in low-grade astrocytomas tissue when compared to normal brains. Figure 3C resumes the hypothesized related pathway that might explain this interplay between low-grade astrocytomas and PDCD10 CCM genotype.

PDCD10 mutation carriers have a greater chance of spontaneous mutation, an increased CCM burden, and a younger age of presentation, which is often associated with clinical hemorrhage[13]. There is also a significant association with other manifestations including skin CCMs, scoliosis, spinal cord cavernous malformations, cognitive disability, and benign brain tumor including meningioma, vestibular schwannoma, and astrocytoma[11, 38–40]. Indeed, there is a lack of knowledge of why some patients that harbor PDCD10/CCM3 mutations might develop this syndromic clinical phenotype. Here we provided two important information to the clinical phenotype of the syndromic type of cerebral cavernous malformation. First, our PDCD10 mutation carriers have a greater CCM burden and a younger age of presentation, which is often associated with clinical hemorrhage. Besides that, we performed an extensive in silico structural analysis with a profound docking site study of a frameshift and a splicing site mutation with the major proteins knowing to interact with PDCD10 that might enhances the comprehension of this rare disease.

Similar to the known CCM mutations, the PDCD10/CCM3 c.222delT (p.Asn75ThrfsTer14) mutation identified can result in a PTC[41]. Abnormal mRNAs containing PTCs are normally degraded by the nonsense-mediated mRNA decay (NMD) system, but can escape from the NMD system[42, 43]. Our qPCR results showed a significant reduction of mRNA in the blood samples of the patient that had this mutation. In this line, reduction of mRNA in the blood sample suggested that mutated transcript of PDCD10 be degraded by the quality control NMD-system and thus, truncated protein might not exist or be present at a very low level. Specifically, in relation to P2 patient the mutation c.475-2 A > G (p.A119Gfs*42) in the splicing site also reduced the expression of PDCD10, for the first time demonstrated here. The consequences of splice mutations on the expression of the corresponding transcript variants are diverse, thus, the functional evaluation of this type of variant is increasingly necessary[44].

Protein structure prediction revealed that the frameshift (c.222delT; p.Asn75ThrfsTer14) mutation lead to a substantial loss of αG-helix of the focal adhesion targeting (FAT)-homology domain, which is responsible for direct interaction with MGC4607 and with paxillin. This interaction is essential for cell-cell junctions regulation, cell-extracellular matrix adhesion, cell cycle regulation, proliferation and migration of cells and cell cycle regulation[45].

The surface of this domain contains a hydrophobic patch, which is found between α7 and α8 helix being the site for MGC4607 binding. This interaction is mostly made up of hydrophobic residues from MGC4607 (T225, I226, F228, L229, A232, I233, G236, and A237) binding to hydrophobic residues from PDCD10 (I131, I134, A135, I138, L142, V168, F174, L178, S171, K132, and K139)[17, 46]. Interestingly, the interaction of MGC4607 with PDCD10 reciprocally protects the proteins from degradation[47]. Thus, it is reasonable to hypothesize that this the mutations studied here may influence the formation of the MGC4607-PDCD10 complex.

Once the in silico structural analysis prediction reveals that PDCD10^FS only disrupted the FAT domain one could conclude that the formation of the PDCD10-GCKIII complex was intact. Nonetheless, docking analyses revealed that this frameshift mutation implied in a more severe disruption. We demonstrated that both docking scores predicted that PDCD10^FS had reduced affinity with the three STK proteins. Since previous studies had demonstrated that the interplay between PDCD10 and GCKIII family is essential to maintain proper levels of the GCKIII proteins and that PDCD10-GCKIII complex seems crucial for the proper position of the Golgi apparatus and cell polarity this disturbance might influence the correct function of several cellular mechanisms in different organs of syndromic cerebral cavernous malformation patients[48, 49]. You et al (2013)[30] previously reported that PDCD10 deficiency also is able to downregulation of DLL4-Notch signalling, an important regulator for hyper-angiogenetic phenotype, in surgical specimens of CCMs[50]. Accumulating evidence indicates a crucial role of PDCD10 also in Erk1/2 activation and consequently VEGF pathway[51, 52]. In the context of pathological relevance between PDCD10 mutations, this data reveals the importance of the interaction of PDCD10 with other signaling pathways[53].

Some reports had associated low-grade astrocytomas to the PDCD10/CCM3 genotype, however there is no pathogenesis model to this interplay[54]. Here we demonstrated that, low-grade astrocytomas do not have statistically difference in the expression of PDCD10 or any of the GSKIII proteins when comparing to normal brain tissue, but had statistically higher expression of TLR4, CD14, ERK2 and PIK3CA. There is some new evidence that the TLR4-MEKK3-KLF2/4 and PIK3CA pathway may be up regulated in PDCD10 individuals duo to a permissive microbiome since the PDCD10 protein have a distinct cellular role on the gut epithelium[53, 55]. These is a unique finding, opening a road to a mechanistic link between PDCD10 genotype and the occurrence of low-grade astrocytomas in the syndromic phenotype of cerebral cavernous malformations.

Differently from patients with other genotypes, PDCD10/CCM3 individuals tend to harbor higher bleeding risk associated with the precocity of symptoms and higher lesion burden. Although we had presented here some evidence that aberrant protein-protein interaction of PDCD10 may influence the proper cellular mechanism, further studies should be done with a larger cohort of patients assessing the genetic profile as a possible prognostic biomarker. This may be a new avenue to expand the spectrum of CCM mutations and assist to guide genetic counseling and early genetic diagnosis.

In summary, we added some relevant information about PDCD10/CCM3 mutations, transcript expression, and its interactions through in silico studies. A few studies have demonstrated the relationship between genotype–phenotype and it this difficult to address the detailed functions and signaling pathways underlying PDCD10/CCM3. Although only analyzing two mutations, our findings enhance the understanding of the complex protein-protein interaction of PDCD10 and some other crucial proteins in cellular functions that may help explain why some PDCD10/CCM3 patients present with a syndromic clinical phenotype.

Authors contributions

Material preparation and analysis were performed by Gustavo da Fontoura Galvão and Fabrícia Lima Fontes-Dantas. Data collection were performed by Gustavo da Fontoura Galvão and Jorge Marcondes de Souza. Sample processing and lab work was performed by Fabrícia Lima Fontes-Dantas, Elielson Veloso da Silva and Luisa Menezes Trefilio. The first draft of the manuscript was written by Gustavo da Fontoura Galvão and review by Fabrícia Lima Fontes-Dantas and Jorge Marcondes de Souza. Funding acquisition, resources and supervision were performed by Soniza Vieira Alves-Leon, Jorge Marcondes de Souza and Fabrícia Lima Fontes-Dantas. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Fundings

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Brazilian National Council for Scientific and Technological Development (CNPq Number 440779/2016-2), Senator Romario Faria parliamentary amendment (no. 37990007 EIND) for the financial support and the Coordination for the Improvement of Higher Education Personnel (CAPES Number 88887.130752/2016-00).

Ethics Approval

Due to the sensitive nature of the data collected, qualified researcher on human subjects conducted this study having been approved by the National Council for Ethics in Research (CAAE 69409617.9.0000.5258).

Competing Interests

The authors declare that they have no competing interests.

Availability of Data and Material

Not applicable

Code Availability

Not applicable

Competing Interests

The authors declare that they have no competing interests.

Acknowledgements

The authors thank the Capes, CNPq and Cavernoma Alliance do Brasil for the logistic assistance.

Competing interests

The authors declare that they have no competing interests.

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No competing interests reported.

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Comprehensive CCM3 Mutational Analysis in Patients with Syndromic Cerebral Cavernous Malformation

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Abstract

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Introduction

Methods

Clinical Feature

Genetic analyses

Real-Time Quantitative Polymerase Chain Reaction (qPCR)

Docking simulation

GEPIA2 Database

Statistical Analysis

Results

Clinical Feature

Precocity of Symptomatic Bleeding and Higher Lesion Burden

Genetic Sequencing and functional analyses

Docking analysis

Low-Grade Astrocytoma GEPIA2 genetic analysis

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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Journal Publication

Version 1