# SRSF1 haploinsufficiency is responsible for a syndromic developmental disorder associated with intellectual disability

Elke Bogaert, Aurore Garde, Thierry Gautier, Kathleen Rooney, Yannis

Duffourd, Pontus Leblanc, Emma van Reempts, Frédéric Tran Mau-Them,

Ingrid M. Wentzensen, Kit Sing Au, et al.

► To cite this version:

Elke Bogaert, Aurore Garde, Thierry Gautier, Kathleen Rooney, Yannis Duffourd, et al.. SRSF1 haploinsufficiency is responsible for a syndromic developmental disorder associated with intellectual disability. American Journal of Human Genetics, 2023, 110 (5), pp.790-808. 10.1016/j.ajhg.2023.03.016. hal-04965122

HAL Id: hal-04965122

https://hal.science/hal-04965122v1

Submitted on 25 Feb 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
SRSF1 haploinsufficiency is responsible for a syndromic developmental disorder associated with intellectual disability

Authors

Elke Bogaert, Aurore Garde, Thierry Gautier, ..., Jérôme Govin, Bart Dermaut, Antonio Vitobello

Correspondence

bart.dermaut@ugent.be (B.D.),
antonio.vitobello@u-bourgogne.fr (A.V.)

Thanks to a large international data-sharing
effort, *in silico* structural protein modeling,
DNA methylation episignature analyses, and
*in vivo* splicing assays in *Drosophila*, Bogaert
et al. demonstrate that haploinsufficiency of
*SRSF1*, which encodes a pre-mRNA splicing
factor, causes a syndromic
neurodevelopmental disorder with mild to
moderate intellectual disability.

<figure><img src="image_2.png" /></figure>
# SRSF1 haploinsufficiency is responsible for a syndromic developmental disorder associated with intellectual disability

Elke Bogaert,<fnref n="1" />,²,⁴⁶ Aurore Garde,³,⁴,⁴⁶ Thierry Gautier,⁵,⁴⁶ Kathleen Rooney,⁶,⁷,⁴⁶ Yannis Duffourd,³,⁸ Pontus LeBlanc,<fnref n="1" />,² Emma van Reempts,<fnref n="1" />,² Frederic Tran Mau-Them,³,⁸ Ingrid M. Wentzensen,⁹ Kit Sing Au,¹⁰,¹¹ Kate Richardson,¹⁰,¹¹ Hope Northrup,¹⁰,¹¹ Vincent Gatinois,¹² David Geneviève,¹³,¹⁴ Raymond J. Louie,¹⁵ Michael J. Lyons,¹⁵ Lone Walentin Laulund,¹⁶ Charlotte Brasch-Andersen,¹⁷,¹⁸ Trine Maxel Juul,¹⁷ Fatima El It,³ Nathalie Marle,¹⁹ Patrick Callier,³,¹⁹ Raissa Relator,⁷ Sadegheh Haghshenas,⁷ Haley McConkey,⁶,⁷ Jennifer Kerkhof,⁷ Claudia Cesario,²⁰ Antonio Novelli,²⁰

*(Author list continued on next page)*

## Summary

SRSF1 (also known as ASF/SF2) is a non-small nuclear ribonucleoprotein (non-snRNP) that belongs to the arginine/serine (R/S) domain family. It recognizes and binds to mRNA, regulating both constitutive and alternative splicing. The complete loss of this proto-oncogene in mice is embryonically lethal. Through international data sharing, we identified 17 individuals (10 females and 7 males) with a neuro-developmental disorder (NDD) with heterozygous germline SRSF1 variants, mostly *de novo*, including three frameshift variants, three nonsense variants, seven missense variants, and two microdeletions within region 17q22 encompassing SRSF1. Only in one family, the *de novo* origin could not be established. All individuals featured a recurrent phenotype including developmental delay and intellectual disability (DD/ID), hypotonia, neurobehavioral problems, with variable skeletal (66.7%) and cardiac (46%) anomalies. To investigate the functional consequences of SRSF1 variants, we performed *in silico* structural modeling, developed an *in vivo* splicing assay in *Drosophila*, and carried out episignature analysis in blood-derived DNA from affected individuals. We found that all loss-of-function and 5 out of 7 missense variants were pathogenic, leading to a loss of SRSF1 splicing activity in *Drosophila*, correlating with a detectable and specific DNA methylation episignature. In addition, our orthogonal *in silico*, *in vivo*, and epigenetics analyses enabled the separation of clearly pathogenic missense variants from those with uncertain significance. Overall, these results indicated that haploinsufficiency of SRSF1 is responsible for a syndromic NDD with ID due to a partial loss of SRSF1-mediated splicing activity.

## Introduction

Neurodevelopmental disorders (NDDs) affect around 3% of individuals worldwide and have an incidence of approximately 2%–5% of births.<fnref n="1" /> They are characterized by abnormal cognitive functioning, which may affect

behavior, learning, thinking, reasoning, remembering, problem-solving, decision-making, and attention. Intellectual disability (ID), autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), schizophrenia, and bipolar disorders lie on a neurodevelopmental continuum.² NDDs are caused by numerous etiologies including

*(Affiliations continued on next page)*

<footnote n="1">¹Center for Medical Genetics, Ghent University Hospital, 9000 Ghent, Belgium; ²Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium; ³UMR1231 GAD, Inserm - Université de Bourgogne, Dijon, France; ⁴Centre de Référence Maladies Rares "Anomalies du Développement et Syndromes Malformatifs", Centre de Génétique, FHU-TRANSLAD, CHU Dijon Bourgogne, 21000 Dijon, France; ⁵Université Grenoble Alpes, Inserm U1209, CNRS UMR 5309, Institute for Advanced Biosciences (IAB), 38000 Grenoble, France; ⁶Department of Pathology and Laboratory Medicine, Western University, London, ON N5A 3K7, Canada; ⁷Verspeeten Clinical Genome Centre, London Health Science Centre, London, ON N6A 5W9, Canada; ⁸Unité Fonctionnelle Innovation en Diagnostic génomique des maladies rares, FHU-TRANSLAD, CHU Dijon Bourgogne, 21000 Dijon, France; ⁹GeneDx, Gaithersburg, MD, USA; ¹⁰Division of Medical Genetics, Department of Pediatrics, McGovern Medical School at the University of Texas Health Science Center at Houston (UTHealth Houston), Houston, TX, USA; ¹¹Children's Memorial Hermann Hospital, Houston, TX, USA; ¹²Unité de Génétique Chromosomique, CHU Montpellier, Montpellier, France; ¹³Montpellier University, Inserm U1183, Montpellier, France; ¹⁴Reference center for rare disease developmental anomaly malformative syndrome, Department of Medical Genetics, Montpellier Hospital, Montpellier, France; ¹⁵Greenwood Genetic Center, Greenwood, SC, USA; ¹⁶Department of Paediatrics, Odense University Hospital, Odense, Denmark; ¹⁷Department of Clinical Genetics, Odense University Hospital, 5000 Odense, Denmark; ¹⁸Human Genetics, Department of Clinical Research, Health Faculty, University of Southern Denmark, 5000 Odense, Denmark; ¹⁹Laboratoire de Génétique Chromosomique et Moléculaire, Pôle de Biologie, CHU de Dijon, Dijon, France; ²⁰Translational Cytogenomics Research Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy; ²¹Telethon Institute of Genetics and Medicine, Pozzuoli, Italy; ²²Department of Translational Medicine, University of Naples Federico II, Naples, Italy; ²³Medical Genetics Department, CHU Bordeaux, Bordeaux, France; ²⁴INSERM U1211, Laboratoire MRGM, Bordeaux University, Bordeaux, France; ²⁵Pathology Department, CHU Bordeaux, Bordeaux, France; ²⁶Service de génétique, CHU de Reims, Reims, France; ²⁷Service de génétique médicale, CHU de Nantes, Nantes, France; ²⁸L'institut du thorax, INSERM, CNRS, UNIV Nantes, CHU de Nantes, Nantes, France; ²⁹Broad Center for Mendelian Genomics, Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; ³⁰Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Parkville, VIC, Australia; ³¹Department of Pediatrics, Baylor College of Medicine, San Antonio, TX, USA; ³²Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA; ³³Section of Pediatric Neurology and Developmental Neuroscience, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA;</footnote>
Nicola Brunetti-Pierri,²¹,²² Michele Pinelli,²¹,²² Perrine Pennamen,²³ Sophie Naudion,²³ Marine Legendre,²³
Cécile Courdier,²³ Aurelien Trimouille,²⁴,²⁵ Martine Doco Fenzy,²⁶,²⁷,²⁸ Lynn Pais,²⁹ Alison Yeung,³⁰
Kimberly Nugent,³¹,³² Elizabeth R. Roeder,³¹,³² Tadahiro Mitani,³² Jennifer E. Posey,³² Daniel Calame,³²,³³,<fnref n="34" />
Hagith Yonath,³⁵,³⁶ Jill A. Rosenfeld,³²,³⁷ Luciana Musante,³⁸ Flavio Faletra,³⁸ Francesca Montanari,³⁹
Giovanna Sartor,³⁹ Alessandra Vancini,⁴⁰ Marco Seri,³⁹,⁴¹ Claude Besmond,⁴² Karine Poirier,⁴²
Laurence Hubert,⁴² Dimitri Hemelsoet,⁴³ Arnold Munnich,⁴² James R. Lupski,³¹,³²,<fnref n="34" />,⁴⁴
Christophe Philippe,³,⁸ Christel Thauvin-Robinet,³,⁸,⁴⁵ Laurence Faivre,³,⁴ Bekim Sadikovic,⁶,⁷,<fnref n="46" />
Jérôme Govin,⁵,<fnref n="46" /> Bart Dermaut,¹,²,<fnref n="46" />,* and Antonio Vitobello³,⁸,<fnref n="46" />,*

environmental and genetic factors, resulting in a heteroge-
neous group of diseases with possible clinical overlap.
Genetic causes, and in particular *de novo* mutations, likely
account for more than 80% of individuals affected by
NDDs, with an increasing number of contributing genes
being recognized worldwide.<sup>3-5</sup>

The implementation of next-generation sequencing (NGS) in clinical practice has allowed the identification of variants in “novel” genes or the implication of known disease-associated genes in new phenotypes. Clinical exome sequencing (ES) targeting genes involved in human genetic disorders (OMIM-morbid genes) has become a first-tier genetic tool to identify the potential genetic contributory cause of NDD and to reduce the diagnostic odyssey. ES can also be extended to non-OMIM-morbid and non-OMIM genes that, after recurrence by data sharing and genotype-phenotype correlation studies, can be identified as a genetic cause of newly described human disorders.⁶,⁷ However, this “conventional” manner of testing still leaves a substantial portion of affected individuals genetically undiagnosed or with genetic variants of uncertain clinical significance (VUSs). This is especially the case in diseases with high phenotypic and genetic heterogeneity, such as NDDs. Recently, advances in epigenetics have provided a complementary approach for VUS assessment and reclassification, through the analysis of genome-wide DNA methylation patterns associated with a single gene or a group of genes belonging to the same pathway.⁸⁻¹⁵

SRSF1 (MIM: 600812) is located in the 17q22 region and
encodes a polyfunctional protein regulating a diverse set of
cellular processes all related to the information flow from
DNA to RNA to protein. Maintenance of genomic stability,
transcriptional regulation, mRNA nuclear export, mRNA
stability and quality control, nonsense-mediated mRNA
decay (NMD), and translation are all processes in which
SRSF1 plays a role.<sup>16&20</sup> However, SRSF1 is best known for
its role in constitutive and alternative pre-mRNA

splicing.²¹ All three protein domains, the two RNA recognition motifs (RRMs) and the R/S domain, cooperate to regulate the splicing actions of SRSF1. The C-terminally located R/S domain promotes splicing by attracting limiting splicing factors to the pre-mRNA. This domain interacts with 5'- and 3'-splicing components and the branchpoint sequence to bridge 5'-and 3'-splice sites (SS).¹⁷,¹⁹,²²,²³ Phosphorylation of serine residues in this domain acts as a molecular switch to regulate and coordinate the actions of the R/S domain with those of the RRM5. The hypo-phosphorylated R/S domain interacts preferentially intramolecularly with its own RRM domains, whereas the hyper-phosphorylated R/S domain facilitates the recruitment of SRSF1 to active sites of transcription, where the RRM5 can bind preferentially to exonic splicing enhancer (ESE) sequences.²⁴ Upon ESE binding, the RRM5 consolidate U1 snRNP, binding to a 5'-SS-containing pre-mRNA by interacting with U1 70K and/or with U1 snRNA, both specific components of U1 snRNP.²³,²⁴ The interaction with U1 70K is mediated by the first RRM domain (RRM1) simultaneously binding the ESE site and U1 70K.²⁴ For the interaction with the U1 snRNA, both RRM5 are involved and bind stem loop 3 of the U1 snRNA.²³ Both SRSF1 and U1 snRNP components stimulate exon inclusion and affect 5'-SS selection.¹⁹,²³ Besides splicing enhancing, SRSF1 promotes exon skipping events as well. This function is thought to reside within the second RRM (RRM2, also referred to as pseudo RRM). The pseudo RRM regulates splicing by competing with the binding of other splicing factors to a GGA motif in the pre-mRNA rather than by recruiting them to the cassette exon.²⁵ SRSF1 is known to activate or repress the inclusion of hundreds of exons, and this activity is thought to be the main reason why it is an essential protein.²⁶⁻²⁸ In mice, the complete loss of SRSF1 is embryonically lethal, highlighting an important role for SRSF1 during development.²⁹ Not surprisingly, a tight control on SRSF1

<footnote n="34">³⁴Texas Children's Hospital, Houston, TX, USA; ³⁵Internal Medicine A, Danek Gertner Institute of Human Genetics, Sheba Medical Center, Ramat Gan, Israel; ³⁶Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel; ³⁷Baylor Genetics Laboratories, Houston, TX, USA; ³⁸Institute for Maternal and Child Health, IRCCS Burlo Garofolo, Trieste, Italy; ³⁹UO Genetica Medica, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy; ⁴⁰Neonatal Intensive Care Unit, Maggiore Hospital, Bologna, Italy; ⁴¹Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy; ⁴²Université Paris Cité, Imagine Institute, INSERM UMR1163, Paris 75015, France; ⁴³Department of Neurology, Ghent University Hospital, 9000 Ghent, Belgium; ⁴⁴Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA; ⁴⁵Centre de Référence Maladies Rares « Déficiences intellectuelles de causes rares », Centre de Génétique, FHU-TRANSLAD, CHU Dijon Bourgogne, Dijon, France</footnote>

<footnote n="46">⁴⁶These authors contributed equally</footnote>

<footnote>*Correspondence: bart.dermaut@ugent.be (B.D.), antonio.vitobello@u-bourgogne.fr (A.V.)</footnote>

<footnote>https://doi.org/10.1016/j.ajhg.2023.03.016.</footnote>
expression levels is crucial for cellular survival, and several feedback loops are at play to monitor its levels. A first mechanism is exerted by alternative splicing of its own pre-mRNA. These alternative transcripts contain premature termination codons (PTCs), which are targets for NMD. Shifting the translation mode from polysomes to monosomes, hence reducing translation efficiency for its own mRNA, is a second mechanism of autoregulation. Finally, SRSF1 modulates the expression of regulatory miRNAs.<sup>19</sup> Misregulation of SRSF1 levels is linked to diseases. Its overexpression, leading to dysregulation of alternative splicing (AS), has been reported in several human tumors including lung, colon, pancreas, and breast tumors.<sup>30–32</sup> Somatic variants in *SRSF1* had also been described in hematologic malignancies to be critical for the regulation of gene expression leading to leukemogenesis.<sup>33</sup> However, the effects of *SRSF1* pathogenic germline variants have not been reported.

In this work, through international data sharing, we describe clinical and molecular data from 17 individuals with heterozygous germline variations in *SRSF1* and a syndromic form of developmental delay. We provide *in silico*, *in vivo*, and *in vitro* functional evidence in support of the pathogenicity of *SRSF1* haploinsufficiency.

# Material and methods

## Research subjects

All affected individuals or their legal representative gave their informed consent for the sequencing procedures and the publication of their results along with clinical and molecular data. Special consent forms were signed authorizing publication of pictures when relevant. The study was performed within the framework of the GAD ("Génétique des Anomalies du Développement") collection and approved by the appropriate institutional review board of Dijon University Hospital (DC2011-1332).

## Genetic analyses

Solo or trio ES variant filtering and analysis were performed in individuals 1–5 and 7–16 at the respective institutions (supplementary methods). Alignment was made on the reference human genome GRCh37/hg19. Array-comparative genomic hybridization analysis (aCGH) was performed for individual 6 with a 44K whole-genome microarray (Agilent Technologies, Santa Clara, CA, USA) according to the manufacturer's instructions. Confirmation and segregation analysis of single-nucleotide or indel variants as well as the 17q22 microdeletion were performed by Sanger sequencing or quantitative polymerase chain reaction (PCR), respectively. We used the RefSeq (GenBank: NM_006924.5) transcript as reference. In individual 17, the deletion was confirmed by FISH (RP11-102J6); parents were negative by FISH. The array data came from Baylor College of Medicine Medical Genetics Laboratory (v.8.1 oligo microarray, 180K custom array from Agilent). The v.8.1 oligonucleotide microarray contains 180,000 oligonucleotides with exon-level coverage for over 1,700 genes (average of 4.2 probes/exon). The manufacturing of these arrays and microarray procedures has been previously described.<sup>34</sup>

## Protein structural analysis

*Drosophila* and human SRSF1 predicted structures were obtained from the AlphaFold Protein Structure Database. Files were loaded and aligned in PyMol (v.2.52). Flexible portions of the human sequence were trimmed to restrict information around RRM1 and RRM2. Data were validated by comparison with structures of both RRMs stored in the Protein Data Bank (PDB). In PyMol, both RRMs were rendered as a cartoon, and amino acids involved in the different alterations were added as spheres along the cartoon structure. When required, surface rendering was used with a transparency set to 1.0 to render a solid appearance or with a transparency set to 0.3 to allow for transparency through the structure. Involved amino acids were modeled as a colored side chain on the corresponding RRM and illustrated as spheres. A simulated model of the mutated amino acid was generated with the Wizard mutagenesis module in PyMol and rendered as described above. The wild-type (WT) and mutated situations were oriented and rendered to exhibit the surface modifications generated by the exchange of amino acid. The RRM sequence carrying the alteration was rendered as a semi-transparent surface set to 0.3, while the other one was rendered as a less semi-transparent surface (0.6). All chains were colored with a 70% gray level in PyMol.

## Fly stocks and maintenance

*Drosophila* strains were maintained on standard Nutrify formula food, yellow cornmeal, agar (type II), corn syrup solids, inactive nutritional yeast, and soy flour (Genesee Scientific) in a 12 h light/dark rhythm temperature-controlled environment. The w<sup>1118</sup> (Canton-S10) line and luciferase line (Bloomington Drosophila Stock Center, y[1]v[1]; P[y[+t7.7] v[+t1.8] = UAS-LUC.VALIUM10]attP2, BDSC#35788) were used as control, and the UAS-RFP line (w[*]; P[w[+mC] = UAS-mCD8.ChRFP]3, BDSC#27392), UAS-RFP-NLS line (w[*]; P[w[+mC] = UAS-mCherry.NLS]3, BDSC#38424), GMR-GAL4 line (Flystock, P. Callaerts, KU Leuven), CCAP-GAL4 line (y[1] w[*]; P[w[+mC] = CCAP-GAL4.P]16/CyO, BDSC#25685), and Nsyb-GAL4 line (y[1] w[*]; P[w[+m*] = nSyb-GAL4.S]3, BDSC#51635) were used to drive expression in the fly eye or pan-neuronally. To generate WT and mutant UAS-SRSF1 and UAS-SF2 fly lines, the coding region of those genes was subcloned in the pUAST-attB backbone (GenScript Biotech, the Netherlands), allowing the generation of transgenic fly lines by targeted insertion into the 68A4 attP locus on the third chromosome (GenetiVision, USA). We generated these UAS lines with and without the N-terminal GFP tag. Crosses for adult offspring frequencies and phenotypic data were performed at 25° for fly proteins and 29°C for human proteins.

## *Drosophila* AS analysis

Thirty brains from third-instar larvae expressing SRSF1 and SF2 under control of the GMR enhancer were dissected, and RNA was isolated using a Quick-RNA Tissue/Insect kit (Zymo Research, CA, USA). The RNA samples were sent for RNA sequencing (Macrogen Europe). The library preparation was done with an Illumina TruSeq Stranded Total RNA Ribo-Zero Gold kit, and the sequencing was done on a NovaSeq 6000 sequencer. Samples were run on an S4 flow cell at 50M reads/sample. The resulting Fasta files were aligned using TopHat v.2.2.1. Cufflinks v.2.2.1 was used to generate QC data and count files for the downstream analysis. For analyzing AS patterns, the rMATS turbo v.4.1.1 computational tool was used.<sup>35</sup> rMATS detects the five primary types of splicing
events: alternative 3'-splice sites (A3SSs), alternative 5' splice sites (A5SSs), skipped exons (SEs), retained introns (RIs), and mutually exclusive exons (MXEs). Additionally, it computes the p value and false discovery rate (FDR) of the ratio of isoforms between the two study conditions filtered by a user-defined difference threshold. For our analysis the threshold was left at the default setting of 0.0001 (0.01% splicing difference). AS events were selected as significant when the conditions FDR ≤ 0.01 and |Ψ| ≥ 0.1 were met.

## Drosophila misexpression studies: Offspring quantification and external eye phenotype

### Offspring assay

For each *Drosophila* cross, the collected offspring were divided by sex, and the genotypes were counted according to the balancers. The offspring ratio was determined by dividing counted offspring by expected offspring.

### External eye phenotype quantifications

Adult flies were anesthetized with CO₂, and images were taken with a zoom stereo microscope (Leica Z16 APO). The irregularity score was calculated by the Fiji plugin FLEYE, designed by Diez-Hermano and collaborators.³⁶ The pigmentation score was calculated using the color histogram tool in ImageJ. The percentage of red pixels in the fly eye was measured. In each condition a minimum of 10 female and 10 male flies were analyzed. A Kruskal-Wallis test with multiple comparison analysis was used to process the data statistically.

## Drosophila immunohistochemistry

Third-instar larval brains were dissected, fixed for 20 min in 4% PFA, and mounted using Fluoromount-G Mounting Medium. UAS lines expressing GFP-tagged SRSF1 variants alongside RFP or RFP-NLS were expressed in bursicon neurons using a Crustacean cardioactive peptide CCAP-GAL4 driver line.

## DNA methylation episignature analysis

The DNA methylation study was approved by the Western University Research Ethics Board (REB 106302, 10 August 2020). The analysis was performed with 500 ng of bisulfite-converted DNA as the input, using the Illumina Infinium MethylationEPIC Bead-Chip arrays (EPIC array) according to the manufacturer's protocols (Illumina, San Diego, CA, USA). Analysis and discovery of episignatures were carried out based on our laboratory's previously published protocols.³⁷ In brief, intensity data files (IDATs) containing the methylated and unmethylated signal intensities were analyzed in R v.4.1.1. Methylation data normalization was performed using the Illumina method, with background correction using the R minfi package v.1.40.0.³⁸ Probes with detection p value > 0.01, probes located on chromosomes X and Y, probes containing single-nucleotide polymorphisms (SNPs) at or near the CpG interrogation site or single-nucleotide extension site, and probes that cross-react with other genomic regions were eliminated. Samples containing failed probes of >5% (p value >0.1, calculated by the R minfi package v.1.40.0) were removed. Principal-component analysis (PCA) was performed to examine batch structure and identify outliers. Matched controls, at a ratio of 1:5, were randomly selected from the EpiSign Knowledge Database (EKD)⁹ and matched by array type, sex, and age using the R MatchIt package v.4.3.4.³⁹ Methylation levels for each probe (beta values) were converted to M values by logit transformation, and linear regression was applied to identify differentially methylated probes (DMPs) using the R limma package v.3.50.0.⁴⁰ Estimated

blood cell proportions were incorporated into the model matrix as confounding variables.⁴¹ p values were moderated using the eBayes function in the R limma package v.3.50.0.⁴⁰

Episignature probe selection was performed in three stages. First, 800 probes were retained with the highest product of methylation differences between *SRSF1* samples and controls and the negative of the logarithm of p values. Next, a receiver's operating characteristic (ROC) curve analysis was performed, and 267 probes were retained with the highest area under the curve. Lastly, probes with pairwise correlation greater than 0.6 measured using Pearson's correlation coefficients for all probes were eliminated. Unsupervised clustering models were applied, using the remaining 107 episignature probes, including hierarchical clustering using Ward's method on Euclidean distance in the R gplots package v.3.1.1 (https://CRAN.R-project.org/package=gplots) and multidimensional scaling (MDS) by scaling of the pairwise Euclidean distances between samples. The robustness of the episignature was assessed using multiple rounds of "leave one out" cross-validation: in each round, one sample was used for testing, and the remaining samples were used for probe selection. The corresponding unsupervised clustering plots were visualized. A support vector machine classifier (SVM) was trained using the R e1071 package v.1.7-9 and to construct a multi-class prediction model as previously described.⁹

## Functional correlation of the genome-wide methylation profiles of *SRSF1* and EpiSign disorders

Functional annotation and EpiSign cohort comparisons were performed according to our previously published methods.³⁷ In brief, to establish the genome-wide methylation profile of the *SRSF1* cohort, we used the same nine *SRSF1* samples as training and matched to array-, age-, and sex-matched controls at the same 1:5 ratio. Controls for correlation analysis consisted of samples from unaffected individuals as well as individuals negative for other episignatures in the EKD. In order to perform comparison with the previously published EpiSign disorders, only probes present in both the EPIC array and its predecessor, the Illumina 450K array (Illumina, San Diego, CA, USA), were considered for selection. Only probes with a methylation difference >5% and adjusted p value <0.01 were retained. To assess the percentage of DMPs shared between the *SRSF1* episignature and the 56 other neurodevelopmental conditions on the EpiSign v.3 clinical classifier, heatmaps and circos plots were produced. Heatmaps were generated using the R package plothmap (v.1.0.12) and circos plots using the R package circlize (v.0.4.15).⁴² In order to assess the relationship between the *SRSF1* cohort and the 56 other EpiSign disorders, the distance and similarities between cohorts were analyzed using clustering methods and visualized on a tree-and-leaf plot. This assessed the top 500 DMPs for each cohort, ranked by p value. For cohorts with less than 500 DMPs, all DMPs were used. Tree-and-leaf plots were generated using the R package TreeAndLeaf (v.1.6.1) (https://www.bioconductor.org, accessed October 2022), showing additional information including global mean methylation difference and total number of DMPs identified for each cohort.

## Identification of differentially methylated regions (DMRs)

DMRs were then detected, based on the list of DMPs produced for functional correlation above, using the DMRcate package in R (v.2.8.3),⁴³ and regions containing at least five significantly
different CpGs within 1,000 bp, with a minimum absolute mean methylation difference between *SRSF1* samples and controls of 0.05 and significant results were chosen using a Fisher's multiple comparison p value cut-off of <0.01. DMRs were annotated using the UCSC Genome Browser Data Integrator with GENCODE v.3lift37 comprehensive annotations and further characterized using UCSC Genome Browser tools (https://genome.ucsc.edu).

## Annotation of DMPs and DMRs

To determine the genomic location of the DMPs and DMRs, probes were annotated in relation to CpG islands (CGIs) and genes using the R package annotatr (v.1.20.0)⁴⁴ with AnnotationHub (v.3.2.2) and annotations hg19_cpgs, hg19_basicsgenes, hg19_genes_intergenic, and hg19_genes_intronexonboundaries. CGI annotations included CGI shores from 0 to 2 kb on either side of CGIs, CGI shelves from 2 to 4 kb on either side of CGIs, and inter-CGI regions encompassing all remaining regions. For gene annotations, “promoter” included up to 1 kb upstream of the transcription start site (TSS) and “promoter+” included the region 1–5 kb upstream of the TSS. Annotations to untranslated regions (5' UTR and 3' UTR), exons, introns, and exon/intron boundaries were combined into the “gene body” category.

## Results

### SRSF1 variants are common genetic defects in a cohort of 17 individuals with neurodevelopmental disorders

Singleton ES was performed in individual 1 (I1 [F1-II-1], Figure 1) referred for NDD associated with facial dysmorphism and skeletal features. The aCGH and ES analysis failed to detect pathogenic variants in OMIM-morbid genes. Research reanalysis identified a heterozygous frameshift variant, c.377_378del (GenBank: NM_006924.5) (p.Ser126Trpfs*17) (Table 1) in SRSF1, absent in the gnomAD database. This gene is intolerant to loss-of-function (LoF) variant alleles with an associated probability of LoF intolerance (pLI) score of 0.98 (gnomAD v.2.1.1, https://gnomad.broadinstitute.org), a LoF observed/expected upper bound fraction (LOEUF) score of 0.24, and a haploinsufficiency (HI) index score of 1.47 by DECIPHER (https://www.deciphergenomics.org).⁴⁵ SRSF1 is also intolerant to missense variants (Z score = 3.96 according to gnomAD v.2.1.1).

We performed a submission in the Matchmaker exchange tool GeneMatcher and the Undiagnosed Diseases Network International (UDNI) and contacted the referring physicians of 16 other individuals with rare SRSF1 heterozygous variants (Figure 1A).⁴⁶ A standardized questionnaire was sent to the referring physicians to collect molecular and clinical data including growth, neurodevelopment, congenital malformations, skeletal abnormalities, and facial features. The cohort was composed of 17 individuals, 10 females and 7 males, with DD, ID, hypotonia, behavioral disorders, and skeletal and cardiac anomalies as main features (Tables 2 and S1, see supplemental note), from 16 unrelated families (Figure 1B). Non-recurrent

facial dysmorphic features were observed in many individuals (Figure 1C).

In total, we observed 15 different SRSF1 variants in 17 NDD individuals, including two microdeletions of less than one megabase and three frameshift, three nonsense, and seven missense variants (Table 1). All LoF variants were predicted to induce NMD, except variants c.579dup (p.Val194Serfs*2) and c.601del (p.Ser201Valfs*87) (Table 1), both located in the last exon (exon 4).⁴⁷ Among the missense variants, 5 affected the RRM1 domain and 2 the RRM2 domain (Figure 1A). Individual 6 (F5-II-1) and individual 17 (F16-II-1) presented *de novo* heterozygous deletions of 734 kb and 866 kb, respectively, in the 17q22 region encompassing SRSF1 (Table 1, Figure S1). The 15 SNV/indel alleles and the CNV deletions were absent from the gnomAD population database (gnomAD v.2.1.1, https://gnomad.broadinstitute.org) and confirmed to be *de novo* (Figure 1B) except for individual 1 (F1-II-1), for whom the variant was found not to be inherited from her mother, but the paternal sample was unavailable for testing. Individual 2 (F2-II-2) and individual 3 (F2-II-3) are siblings (Figure 1B), and they both have the same SRSF1 variant (Table 1), suggesting germinal mosaicism in one parent. In three individuals, 10 (F9-II-3), 12 (F11-II-2), and 17 (F16-II-1), genetic analyses led to the identification of multilocus disease-causing or candidate genomic variations, i.e., genomic variations at more than one genetic locus accounting for distinct or blending phenotypes.⁴⁸,⁴⁹ Individual 10 (F9-II-3) also presented Hermansky-Pudlak syndrome 1 (MIM: 203300) due to a homozygous pathogenic variant in *HPS1* (MIM: 604982, c.973_974insC [GenBank: NM_000195.3] [p.Met325Thrfs*128]). Individual 12 (F11-II-2) also harbors a *de novo* frameshift VUS in *MBD6* (MIM: 619458, c.2337dup [GenBank: NM_052897.4] [p.Gly780Trpfs*13]); this gene is suspected to be associated with autism and language delay and could contribute to her phenotype.⁵⁰ Individual 17 (F16-II-1) also has a 15q11.2 BP1-BP2 microdeletion, which can be associated with developmental and language delay, neurobehavioral disturbances, and psychiatric problems.⁵¹

### *In silico* functional prediction and structural modeling of SRSF1 missense variants

The identification of nonsense, frameshift, and deletion variants suggested that haploinsufficiency of SRSF1 is the most likely common pathogenic mechanism in SRSF1-related NDD; therefore, we hypothesized that pathogenic missense variants likely also behave as LoF alleles. Functional *in silico* and *in vivo* evidence supporting LoF was thus essential to classify these missense variants as being (likely) pathogenic. We therefore first used eight *in silico* meta-predictors (BayesDel with AF, BayesDel without AF, MetaLR, MetaRNN, MetaSVM, REVEL, Eigen, CADD) embedded in the human genomic search engine VarSome to obtain functional prediction scores of the seven missense variants (Figure 2A).⁵² Recent studies indicate that BayesDel outperforms most other meta-predictors
<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /><figcaption>**Figure 1.** Clinical variants in *SRSF1* cause syndromic developmental disorder associated with intellectual disability</figcaption></figure>

(A) Gene, transcript, and protein structure of SRSF1. Clinical variants were shown on the protein level. Evolutionary conservation of the RRM domains is shown with bold amino acids showing evolutionarily conserved residues. Missense variants are indicated by green arrows, nonsense variants are indicated in red, and frameshift variants in orange.

(B) Pedigrees of the 16 families reported in this cohort.

(C) Photographs of individuals with *SRSF1* variants. Nonspecific facial features were observed in the individuals. Individuals 4 and 15 were referred for marfanoid features: they presented dolichostenomelia, arachnodactyly, and pectus deformity.
<table><caption>**Table 1.** Molecular data of individuals with SRSF1 variants</caption><thead><tr><th>Subjects</th><th>Genomic change<sup>g</sup></th><th>Coding change<sup>h</sup></th><th>Protein change</th><th>Variant type</th><th>Inheritance</th><th>Polyphen-2 score</th><th>Varsome prediction<sup>a,b,c,d,e,f</sup></th></tr></thead><tbody><tr><td>I1</td><td>g.56083708_56083709del</td><td>c.377_378del</td><td>p.Ser126Trpfs*17</td><td>Frameshift</td><td>Not inherited from the mother</td><td>–</td><td>Pathogenic: PVS1 PM2 PS2</td></tr><tr><td>I2, I3, I4</td><td>g.56083236C>T</td><td>c.478G>A</td><td>p.Val160Met</td><td>Missense</td><td>De novo</td><td>Probably damaging 0.999</td><td>Likely pathogenic: PS2 PM2 PP3</td></tr><tr><td>I5</td><td>g.56082937dup</td><td>c.579dup</td><td>p.Val194Serfs*2</td><td>Frameshift</td><td>De novo</td><td>–</td><td>Pathogenic: PVS1 PM2 PS2</td></tr><tr><td>I6</td><td>g.55806534_56540597del</td><td>Not applicable</td><td>Not applicable</td><td>Deletion</td><td>De novo</td><td>–</td><td>–</td></tr><tr><td>I7</td><td>g.56084417G>A</td><td>c.82C>T</td><td>p.Arg28*</td><td>Nonsense</td><td>De novo</td><td>–</td><td>Pathogenic: PVS1 PM2 PS2</td></tr><tr><td>I8</td><td>g.56083166T>C</td><td>c.548A>G</td><td>p.His183Arg</td><td>Missense</td><td>De novo</td><td>Probably damaging 1</td><td>Likely pathogenic: PS2 PM2 PP3</td></tr><tr><td>I9</td><td>g.56084380C>A</td><td>c.119G>T</td><td>p.Gly40Val</td><td>Missense</td><td>De novo</td><td>Probably damaging 1</td><td>Likely pathogenic: PS2 PM2 PP3</td></tr><tr><td>I10</td><td>g.56084402C>A</td><td>c.97G>T</td><td>p.Glu33*</td><td>Nonsense</td><td>De novo</td><td>–</td><td>Pathogenic: PVS1 PM2 PS2</td></tr><tr><td>I11</td><td>g.56082914del</td><td>c.601del</td><td>p.Ser201Valfs*87</td><td>Frameshift</td><td>De novo</td><td>–</td><td>Pathogenic: PVS1 PM2 PS2</td></tr><tr><td>I12</td><td>g.56083875C>T</td><td>c.208G>A</td><td>p.Ala70Thr</td><td>Missense</td><td>De novo</td><td>Probably damaging 1</td><td>Likely pathogenic: PS2 PM2 PP3</td></tr><tr><td>I13</td><td>g.56084428G>A</td><td>c.71C>T</td><td>p.Pro24Leu</td><td>Missense</td><td>De novo</td><td>Probably damaging 1</td><td>Likely pathogenic: PP3 PM2 PP2 PS2</td></tr><tr><td>I14</td><td>g.56083852A>C</td><td>c.231T>G</td><td>p.Tyr77*</td><td>Nonsense</td><td>De novo</td><td>–</td><td>Pathogenic: PVS1 PM2 PS2</td></tr><tr><td>I15</td><td>g.56083832A>C</td><td>c.251T>G</td><td>p.Leu84Arg</td><td>Missense</td><td>De novo</td><td>Probably damaging 1</td><td>Likely pathogenic: PS2 PM2 PP3</td></tr><tr><td>I16</td><td>g.56084369C>T</td><td>c.130G>A</td><td>p.Asp44Asn</td><td>Missense</td><td>De novo</td><td>Benign 0.029</td><td>Uncertain significance: PP2 PM2 PS2 BP4</td></tr><tr><td>I17</td><td>g.55442363_56309063del</td><td>Not applicable</td><td>Not applicable</td><td>Deletion</td><td>De novo</td><td>–</td><td>–</td></tr></tbody></table>

for clinical missense variant classification.<sup>53,54</sup> Although the eight tools were not unanimous for any of the seven missense variants, c.548A>G (p.His183Arg) and c.130G>A (p.Asp44Asn) (Table 1) obtained lower prediction scores with most meta-predictors, suggesting a reduced pathogenic potential and perhaps hypomorphic nature for these two variants. Higher scores were obtained for the five other missense variants, c.119G>T (p.Gly40Val), c.251T>G (p.Leu84Arg), c.208G>A (p.Ala70Thr), c.71C>T (p.Pro24Leu), and c.478G>A (p.Val160Met) (Table 1), with BayesDel supporting potential pathogenicity for all five.

Next, we obtained the human SRSF1 protein structure from the AlphaFold Protein Structure Database. This showed that both RRMs of SRSF1 are brought in close vicinity by the tertiary structure (Figure 2B). We then

modeled the seven missense variants, which were all located in the RRM domains: five in the RRM1 and the last two in the RRM2 domain. A surface rendering of both RRMs showed that residues affected by the missense variants are in close interaction with each other, with positions Pro24 and Leu84 possibly involved in establishing or maintaining the interaction between the two RRMs. In addition, the surface view showed that most of the mutated amino acids are located inside the protein structure, which is more in favor of internal misfolding than with altered interactions with partners or other proteins (Figures 2C and 2D). Interestingly, the only positions that are pointed slightly out of the surface of the RRMs are Asp44 and His183, possibly explaining the reduced predicted pathogenic potential of p.Asp44Asn and p.His183Arg (Figure 2D).

<footnote><sup>a</sup>PVS1, predicted loss-of-function variant.</footnote>

<footnote><sup>b</sup>PS2, *de novo* (both maternity and paternity confirmed) in an individual with the disease and no family history.</footnote>

<footnote><sup>c</sup>PM2, absent from controls (or at extremely low frequency if recessive) in Exome Sequencing Project, 1000 Genomes Project, or Exome Aggregation Consortium.</footnote>

<footnote><sup>d</sup>PP2, missense variant in a gene that has a low rate of benign missense variation and where missense variants are a common mechanism of disease.</footnote>

<footnote><sup>e</sup>PP3, multiple lines of computational evidence support a deleterious effect on the gene or gene product.</footnote>

<footnote><sup>f</sup>BP4, multiple lines of computational evidence suggest no impact on gene or gene product.</footnote>

<footnote><sup>g</sup>GenBank: NC_000017.10</footnote>

<footnote><sup>h</sup>GenBank: NM_006924.5</footnote>
<table><caption>Table 2. Clinical characterization of individuals with heterozygous *SRSF1* variants</caption><thead><tr><th>Subjects</th><th>I1</th><th>I2</th><th>I3</th><th>I4</th><th>I5</th><th>I6</th><th>I7</th><th>I8</th><th>I9</th><th>I10</th><th>I11</th><th>I12</th><th>I13</th><th>I14</th><th>I15</th><th>I16</th><th>I17</th></tr><tr><th>Sex</th><th>F</th><th>F</th><th>M</th><th>M</th><th>F</th><th>F</th><th>M</th><th>F</th><th>M</th><th>M</th><th>F</th><th>F</th><th>F</th><th>M</th><th>M</th><th>F</th><th>F</th></tr></thead><tbody><tr><th colspan="18">Pregnancy/Birth</th></tr><tr><td>Pregnancy complications</td><td>+</td><td>+</td><td>-</td><td>-</td><td>-</td><td>-</td><td>+</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>+</td><td>+</td><td>-</td><td>+</td></tr><tr><td>Gestational weeks</td><td>36</td><td>40</td><td>38</td><td>Full term</td><td>39</td><td>37</td><td>31</td><td>38</td><td>Full term</td><td>ND</td><td>Full term</td><td>41</td><td>39</td><td>41</td><td>39</td><td>38</td><td>ND</td></tr><tr><td>Birth weight (g) (SD)</td><td>1,700 (-2.6)</td><td>3,300 (-0.5)</td><td>3,180 (0)</td><td>2,920 (-1.5)</td><td>3,100 (-0.5)</td><td>2,550 (-0.6)</td><td>1,765 (+0.6)</td><td>2,790 (-0.6)</td><td>3,827 (+0.5)</td><td>ND</td><td>3,430 (0)</td><td>3,700 (+0.6)</td><td>3,997 (+1.7)</td><td>ND</td><td>2,980 (-1)</td><td>3,065 (+0.1)</td><td>ND</td></tr><tr><td>Birth length (cm) (SD)</td><td>39 (-3.6)</td><td>49.5 (-0.4)</td><td>51 (+1)</td><td>45.5 (-2.8)</td><td>63 (-0.2)</td><td>46 (-0.7)</td><td>40.5 (-0.2)</td><td>45 (-1.8)</td><td>53.5 (+1.3)</td><td>ND</td><td>ND</td><td>51 (+0.4)</td><td>50.8 (+0.9)</td><td>ND</td><td>50 (0)</td><td>51 (+1.3)</td><td>ND</td></tr><tr><td>Birth OFC (cm) (SD)</td><td>30.5 (-1.5)</td><td>35 (+0.4)</td><td>35 (+0.4)</td><td>35 (-0.1)</td><td>34 (-0.3)</td><td>32 (-1)</td><td>30 (+0.4)</td><td>33.5 (-0.2)</td><td>35 (-0.2)</td><td>ND</td><td>ND</td><td>34 (-0.5)</td><td>ND</td><td>ND</td><td>35 (+0.2)</td><td>34 (+0.2)</td><td>ND</td></tr><tr><td>Neonatal complications</td><td>+</td><td>-</td><td>+</td><td>+</td><td>-</td><td>+</td><td>-</td><td>-</td><td>+</td><td>-</td><td>+</td><td>-</td><td>+</td><td>+</td><td>-</td><td>+</td><td>+</td></tr><tr><th colspan="18">Growth</th></tr><tr><td>Age at last visit</td><td>18 years</td><td>4 years 9 months</td><td>2 years</td><td>34 years</td><td>6 years 8 months</td><td>4 years</td><td>5 years 2 months</td><td>3 years 1 month</td><td>8 years 5 months</td><td>23 years 9 months</td><td>5 years</td><td>3 years 8 months</td><td>2 years 2 months</td><td>28 years</td><td>18 years</td><td>1 year 2 months</td><td>13 years 6 months</td></tr><tr><td>Weight last visit (kg) (SD)</td><td>54.2 (-0.3)</td><td>17.5 (+0.2)</td><td>11.6 (-0.8)</td><td>65.6 (-0.4)</td><td>20 (-0.5)</td><td>14.5 (-0.8)</td><td>19 (+0.1)</td><td>10 (-2.8)</td><td>128 (+0.1)</td><td>41 (-3.3)</td><td>19.7 (+0.5)</td><td>17.8 (+1)</td><td>12.7 (+0.2)</td><td>57.7 (-1.4)</td><td>57.6 (-1)</td><td>8.9 (-1.2)</td><td>ND</td></tr><tr><td>Height last visit (cm) (SD)</td><td>152 (-1.8)</td><td>103 (-0.5)</td><td>86 (-0.4)</td><td>189.5 (+2)</td><td>119.5 (+0.1)</td><td>98.3 (-0.6)</td><td>112.8 (+0.7)</td><td>84 (-2.7)</td><td>28 (+0.4)</td><td>156 (-2.9)</td><td>109 (+0.3)</td><td>99 (+0.7)</td><td>84.2 (+0.9)</td><td>174 (-0.4)</td><td>180.5 (+0.7)</td><td>77.4 (+0.5)</td><td>144 (-2)</td></tr><tr><td>BMI last visit (kg/m²)</td><td>23.5 (+0.6)</td><td>16.5 (+0.8)</td><td>15.7 (-0.2)</td><td>18.3 (-1.6)</td><td>14 (-0.9)</td><td>15 (-0.2)</td><td>14.9 (-0.3)</td><td>14.2 (-1)</td><td>17.1 (+0.7)</td><td>16.8 (-2.4)</td><td>16.6 (+0.8)</td><td>18.2 (+1.8)</td><td>17.9 (+1.6)</td><td>19.1 (-0.9)</td><td>17.7 (-1.4)</td><td>14.9 (-0.9)</td><td>ND</td></tr><tr><td>OFC last visit (cm) (SD)</td><td>55 (+0.6)</td><td>51 (+0.6)</td><td>50 (+1)</td><td>59 (+2.7)</td><td>52.5 (+0.6)</td><td>48.5 (-0.7)</td><td>51 (-0.1)</td><td>45.5 (-2.9)</td><td>51.8 (-0.4)</td><td>54.5 (-0.4)</td><td>48.7 (-1.1)</td><td>49 (-1)</td><td>47.8 (+0.1)</td><td>56 (+0.6)</td><td>55 (-0.1)</td><td>47 (+1.1)</td><td>50 (-2.6)</td></tr><tr><td>Failure to thrive</td><td>+</td><td>-</td><td>-</td><td>-</td><td>-</td><td>ND</td><td>-</td><td>+</td><td>-</td><td>+</td><td>-</td><td>+</td><td>-</td><td>+</td><td>-</td><td>-</td><td>+</td></tr><tr><td>Truncal overweight</td><td>+</td><td>-</td><td>Trunk adiposity</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>+</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><th colspan="18">Neurological abnormalities</th></tr><tr><td>ID/DD</td><td>Mild to moderate</td><td>Mild to moderate</td><td>Mild to moderate</td><td>LD</td><td>Mild</td><td>Yes</td><td>Severe</td><td>Severe</td><td>Borderline</td><td>Severe</td><td>Moderate</td><td>Moderate</td><td>Yes</td><td>Mild</td><td>Mild</td><td>Yes</td><td>Yes</td></tr><tr><td>Motor delay</td><td>+</td><td>+</td><td>+</td><td>-</td><td>+</td><td>+</td><td>+</td><td>+</td><td>-</td><td>+</td><td>-</td><td>+</td><td>+</td><td>+</td><td>-</td><td>+</td><td>+</td></tr><tr><td>Speech delay</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>-</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td></tr><tr><td>Behavioral disorders</td><td>+</td><td>+</td><td>-</td><td>+</td><td>+</td><td>-</td><td>+</td><td>-</td><td>+</td><td>+</td><td>+</td><td>+</td><td>-</td><td>+</td><td>+</td><td>+</td><td>+</td></tr></tbody></table>

(Continued on next page)
<table><caption>**Table 2. Continued**</caption><thead><tr><th>Subjects</th><th>I1</th><th>I2</th><th>I3</th><th>I4</th><th>I5</th><th>I6</th><th>I7</th><th>I8</th><th>I9</th><th>I10</th><th>I11</th><th>I12</th><th>I13</th><th>I14</th><th>I15</th><th>I16</th><th>I17</th></tr><tr><th>Sex</th><th>F</th><th>F</th><th>M</th><th>M</th><th>F</th><th>F</th><th>M</th><th>F</th><th>M</th><th>M</th><th>F</th><th>F</th><th>F</th><th>M</th><th>M</th><th>F</th><th>F</th></tr></thead><tbody><tr><td>Hypotonia</td><td>+</td><td>+</td><td>+</td><td>+</td><td>Buccal hypotonia</td><td>-</td><td>-</td><td>+</td><td>-</td><td>+</td><td>-</td><td>-</td><td>+</td><td>+</td><td>+</td><td>+</td><td>ND</td></tr><tr><td>Seizures</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>+</td><td>-</td><td>-</td><td>-</td><td>+</td><td>-</td><td>+</td><td>-</td></tr><tr><td>Brain abnormalities MRI</td><td>+</td><td>+</td><td>-</td><td>-</td><td>-</td><td>ND</td><td>+</td><td>+</td><td>-</td><td>-</td><td>-</td><td>-</td><td>+</td><td>ND</td><td>ND</td><td>+</td><td>ND</td></tr><tr><td colspan="19"><strong>Neurosensory abnormalities</strong></td></tr><tr><td>Hearing loss</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>ND</td><td>-</td><td>-</td><td>+</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Vision problems</td><td>+</td><td>+</td><td>-</td><td>ND</td><td>-</td><td>+</td><td>-</td><td>+</td><td>-</td><td>+</td><td>-</td><td>ND</td><td>+</td><td>-</td><td>+</td><td>-</td><td>+</td></tr><tr><td colspan="19"><strong>Congenital Malformations</strong></td></tr><tr><td>Cardiac</td><td>+</td><td>+</td><td>-</td><td>-</td><td>-</td><td>+</td><td>-</td><td>ND</td><td>+</td><td>+</td><td>-</td><td>-</td><td>-</td><td>+</td><td>-</td><td>-</td><td>ND</td></tr><tr><td>Urogenital/ kidney</td><td>-</td><td>-</td><td>+</td><td>+</td><td>-</td><td>ND</td><td>-</td><td>-</td><td>+</td><td>+</td><td>-</td><td>ND</td><td>-</td><td>-</td><td>+</td><td>-</td><td>ND</td></tr><tr><td>Others</td><td>Polycystic spleen</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Hernia, diastasis</td><td>-</td><td>-</td><td>-</td></tr><tr><td colspan="19"><strong>Skeletal abnormalities</strong></td></tr><tr><td>Scoliosis</td><td>+</td><td>-</td><td>+</td><td>+</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>+</td><td>+</td><td>-</td><td>-</td></tr><tr><td>Pectus deformity</td><td>-</td><td>-</td><td>-</td><td>+</td><td>-</td><td>+</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>+</td><td>+</td><td>-</td><td>-</td></tr><tr><td>Other</td><td>Genu varum, Equinova-rus</td><td>-</td><td>-</td><td>-</td><td>Arachno-dactyly</td><td>Brachymetatarsia - Fallen arches</td><td>-</td><td>-</td><td>L1 vertebral hypoplasia Kyphosis</td><td>Asymmetric chest</td><td>-</td><td>-</td><td>-</td><td>Dolichostenomelia Equinova-rus Postaxial hexadactyly</td><td>Metatarsus varus Arachnodactyly</td><td>-</td><td>Genu valgum</td></tr><tr><td colspan="19"><strong>Others</strong></td></tr><tr><td>Facial features</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>-</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td><td>+</td></tr><tr><td>Cutaneous abnormalities</td><td>+</td><td>-</td><td>-</td><td>-</td><td>+</td><td>+</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>+</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Other diagnosis</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Diagnosed Hermansky Pudlak Syndrome</td><td>-</td><td>MBD6 variant</td><td>-</td><td>-</td><td>-</td><td>-</td><td>15q11.2 BP1-BP2 microdeletion</td></tr></tbody></table>

ND, no data; OFC, occipito-frontal circumference; BMI, body mass index.
<figure><img src="image_6.png" /><figcaption>**Figure 2.** Bio-informatic pathogenicity predictions and structural modeling of missense variants</figcaption></figure>

(A) Pathogenicity prediction scores for the seven SRSF1 missense variants generated by eight meta-prediction tools (BayesDel with AF, BayesDel without AF, MetaLR, MetaRNN, MetaSVM, REVEL, CADD, Eigen) as implemented in the VarSome human genomic search engine.⁵² Colored bars on the right represent the number of meta-tools supporting pathogenic (red), uncertain (brown), and benign (green) predictions.

(B) Structural prediction of SRSF1 using AlphaFold and PyMol.

(C) Left: missense variants superimposed on the structural prediction. Right: Surface rendering of the SRSF1 protein structure. Arrows indicate p.Asp44Asn and p.His183Arg, two residues closer to the protein surface.

(D) Structural prediction of the seven missense variants. The underlined variants, p.Asp44Asn and p.His183Arg, are located at the protein surface. The other variants are more oriented toward the internal structure of the protein. Carbons are represented in yellow, nitrogens in blue, and oxygens in red for the wild-type amino acids; carbons are represented in purple in the modelized alterations. Asterisks indicate potential steric clashes in the mutated structures.

**In vivo modeling in *Drosophila* identifies SRSF1 splicing-defective clinical variants**

Since the *in silico* predictions of the seven suspected disease-causing *de novo* missense variants displayed diverging levels of support for pathogenicity (Figure 2), and two LoF variants (p.Val194Serfs*2 and p.Ser201Valfr*87) were predicted to escape NMD, we decided to use a quantitative *Drosophila* SRSF1 splicing model to further address the pathogenicity of missense and truncating variants (see supplemental information for additional details, Figures S2–S4).

The visual system of flies is studied extensively, and many of the signaling pathways involved in its development have been identified.⁵⁵,⁵⁶ The compound eye of *Drosophila* consists of more than 700 hexagonal ommatidia. Each ommatidium contains eight light-sensing

neuronal photoreceptor cells and 12 supporting non-neuronal cells (cone and pigment cells). The eye therefore serves as a powerful genetic model system for studying nervous system development.⁵⁶

All identified SRSF1 residues affected by missense variants (except for p.Asp44Asn) are conserved in the *Drosophila* ortholog SF2 (FlyBase Gene Report: Dmel\SF2) (Figure 1A). In the literature, overexpression of WT SF2 in flies leads to phenotypic alterations in eye organogenesis, including quantitative changes such as depigmentation and loss of eye regularity, due to alternative splicing of key genes involved in eye development.⁵⁷ We replicated these results and found that eye-specific overexpression of splicing-active versions of SF2 and SRSF1 indeed led to an eye phenotype, whereas a previously described splicing-inactive version of SRSF1 (referred to here as SRSF1
<figure><img src="image_7.png" /><figcaption>Figure 3. Eye and neuronal splicing read-outs of clinical variants</figcaption></figure>

(A) Representative eye picture of flies expressing luciferase, SF2, SRSF1, and SRSF1 clinical variants in the fly eye under the control of the GMR-GAL4 enhancer.

(B) The irregularity score or regularity index of flies expressing luciferase (negative control), SRSF1 (positive control), a SRSF1 splicing-deficient protein (F56D/F58D/K138A) (negative control), and SRSF1 clinical variants. n > 10, *p < 0.01, data are represented as mean ± SEM. p.Asp44Asn and p.His183Arg display a lower IREG score similar to the SRSF1-overexpressing flies.

(C) Pigmentation score measuring the depigmentation in the different controls and the clinical variants. n > 10, *p < 0.01, data are represented as mean ± SEM.

(D) Offspring frequencies were measured in flies expressing luciferase, SRSF1, a splicing-deficient SRSF1 protein, and the clinical variants pan-neuronally. n > 10, *p < 0.01.

F56D/F58D/K138A) lost this capacity (Figure S2, supplemental information).²⁴,²⁵ Both eye roughness (IREG score, Figures 3A and 3B) and depigmentation (Figures 3A–3C) were quantified and used to estimate the phenotype-inducing capacity, and hence the splicing activity, of the clinical variants. Variants p.Pro24Leu, p.Gly40Val, p.Ala70Thr, p.Leu84Arg, p.Val160Met, and p.Val194-Serfs*2 resulted in the loss of the phenotype induced by SRSF1 overexpression. The IREG and pigmentation scores were comparable to WT and splicing-deficient SRSF1 F56D/F58D/K138A eyes, hence different from the active protein. These data indicate that these variants behaved as “loss of splicing activity” variants. Variants p.Asp44Asn

and the p.His183Arg were as potent as the WT protein to induce the phenotype and thus did not show a loss of the splicing activity. We excluded that tissue-specific splicing alterations might explain these findings (Figure 3D). All variants that were unable to induce an eye phenotype also failed to induce pharate adult lethality upon overexpression in the nervous system, whereas p.Asp44Asn and p.His183Arg were lethal and not different from WT. Furthermore, WT SRSF1, as well as p.His183Arg and p.Val160Met, all localized to the nuclear compartment in CCAP neurons as expected, suggesting that altered subcellular localization is likely not responsible for the varying results in the *in vivo* splicing assay among missense
<figure><img src="image_8.png" /><figcaption>Figure 4. Episignature assessment of SRSF1 VUSs p.Asp44Asn and p.His183Arg</figcaption></figure>

(A) Heatmap indicates that the two VUS samples (orange) are clustering with controls (blue) and away from the SRSF1 samples with confirmed pathogenic variants (individuals I1–I6, I11, I14, and I15 used for episignature discovery) (red). Each row represents one of the 107 probes selected as the episignature, and each column represents an individual with either an SRSF1 variant (red or orange) or a control (blue).

(B) Multidimensional scaling plot (MDS) also shows clustering of the SRSF1 VUS samples with controls.

(C) Support vector machine classifier model (SVM) shows that the VUSs have a probability score (methylation variant pathogenicity score, MVP) of close to 0 compared with the SRSF1 samples carrying confirmed pathogenic variants with MVP scores of close to 1. The model is trained using the 107 selected SRSF1 episignature probes and 75% of controls and other neurodevelopmental disorder samples on EpiSign (blue circles). The 25% remaining are used as testing samples (gray circles).

(D) Circos plot representing the differentially methylated probes (DMPs) shared between each pair of cohorts. The thickness of the connecting lines indicates the number of probes shared between the paired cohorts. SRSF1 cohort is indicated by the green arrow.

(E) Tree-and-leaf visualization of Euclidean clustering of the SRSF1 cohort alongside the 56 other EpiSign disorders using the top n DMPs for each cohort, where n = 500 or the max number of DMPs available if <500. Cohort samples are aggregated using the median value of each probe within a group. Each leaf (node) represents a cohort, with node sizes illustrating relative scales of the number of selected DMPs for the corresponding cohort, and node colors indicative of the global mean methylation difference where blue is more

*(legend continued on next page)*
variants (Figure S4). Taken together, our results show that LoF, truncating variants abolishing the R/S domain, and 5 out of 7 missense variants display strongly reduced splicing activity, in line with haploinsufficiency as the underlying genetic mechanism in SRSF1-mediated NDD. The analysis is in accordance with the *in silico* predictions of a reduced pathogenic potential for p.Asp44Asn and p.His183Arg.

## Episignature analysis

To determine whether SRSF1 variants would cause a detectable change in DNA methylation, we compared methylation beta values between nine samples with confirmed SRSF1 splicing-defective pathogenic variants (i.e., individuals I1–I6, I11, I14, and I15) against matched controls. We identified 107 differentially methylated CpG probes for the SRSF1 episignature (Table S2). Unsupervised clustering methods, including hierarchical ( heatmap) and MDS, demonstrated that the CpG probes selected as a clinical biomarker were capable of segregating the SRSF1 samples with confirmed pathogenic variants from controls (Figures S5A and S5B). “Leave one out” cross-validation was performed, and the results were visualized using unsupervised heatmap and MDS clustering methods, which confirmed the robustness and sensitivity of the episignature (Figure S6). All testing samples were correctly clustered with the discovery training samples (Figure S5). A SVM model was constructed using the 107 selected episignature probes. All SRSF1 samples with confirmed pathogenic variants showed a methylation variant pathogenicity (MVP) score close to 1, indicating the similarity of the observed methylation pattern to the SRSF1 episignature (Figure S5C).

Previous studies have shown that episignatures are clinical biomarkers that can be used to aid in the classification of VUSs and screening of individuals with suspected genetic disorders.⁵⁸,⁵⁹ Using the 107 selected episignature probes, we assessed the two samples with normal splicing activity in our *Drosophila* assay, p.Asp44Asn and p.His183Arg, and classified these samples using unsupervised (hierarchical and MDS) clustering as well as supervised SVM methods. Both samples clustered with controls in

heatmap and MDS (Figures 4A and 4B) and had an MVP prediction score of close to 0 (Figure 4C). These results show that p.Asp44Asn and p.His183Arg did not exhibit an aberrant DNA methylation pattern in common with the mapped SRSF1 episignature, confirming the *in silico* predictions and *in vivo* results obtained in *Drosophila*.

## Functional correlation of the SRSF1 genome-wide methylation profile to other EpiSign V3 classifier disorders

To perform functional correlation analyses, we compared the SRSF1 cohort to episignature-negative age- and sex-matched controls using probes present on both the Illumina EPIC and 450K arrays. Probes with a methylation difference >5% and adjusted p value <0.01 were retained and resulted in a list of 1,485 DMPs (Table S3). The SRSF1 DMPs were compared to the DMPs of 56 other EpiSign disorders previously described.³⁷ Heatmap showed the percentage of the DMPs shared between cohorts; the highest overlaps for the SRSF1 DMPs were with autosomal dominant cerebellar ataxia, deafness, and narcolepsy (ADCADN; MIM: 604121) (~49%), Hunter McAlpine syndrome (HMA; MIM: 601379) (~9%), Tatton-Brown-Rahman syndrome (TBRS; MIM: 615879) (~10%), Sotos syndrome (Sotos; MIM: 117550) (~11%), and Rahman syndrome (RMNS; MIM: 617537) (~11%) (Figure S5D). The overlap with ADCADN is likely the result of the sheer number of DMPs contained within the ADCADN methylation profile (n = 151,848). These overlaps were further visualized in a circos plot (Figure 4D). These overlaps in DMPs may indicate a common underlying biological process shared between these disorders and may provide insights into the molecular pathways of these conditions.

Using the DMRcate algorithm with p-cutoff set to default (FDR) and beta-cutoff input of 0.05 mean methylation difference and 5 CpGs within 1,000 bp, we identified 34 DMRs (Table S4).⁴³ Thirteen DMRs were hypomethylation events and 21 hypermethylation. Next, we annotated the genomic locations of the DMPs and the DMRs in relation to CpG islands and genes. This showed that the DMPs are predominantly found in the CpG shores and in

hypomethylated and red hypermethylated. The SRSF1 cohort with confirmed pathogenic variants is highlighted in green. ADCADN, cerebellar ataxia deafness and narcolepsy syndrome; AUTS18, susceptibility to autism 18; BEFAHRS, Beck-Fahrner syndrome; BFLS, Borjeson-Forssman-Lehmann syndrome; BISS, blepharophimosis intellectual disability SMARCA2 syndrome; CdLS, Cornelia de Lange syndrome; CHARGE, CHARGE syndrome; Chr16p11.2del, chromosome 16p11.2 deletion syndrome; CSS, Coffin-Siris syndrome; CSS4, Coffin-Siris syndrome 4; CSS9, Coffin-Siris syndrome 9; Down, Down syndrome; Dup7, 7q11.23 duplication syndrome; DYT28, dystonia 28; EEOC, epileptic encephalopathy-childhood onset; FLHS, Floating-Harbor syndrome; GTPTS, genitopatellar syndrome; HMA, Hunter McAlpine craniosynostosis syndrome; HVDAS, Helsmoortel-van der Aa syndrome; ICF, immunodeficiency-centromeric instability-facial anomalies syndrome; IDDSELD, intellectual developmental disorder with seizures and language delay; Kabuki, Kabuki syndrome; KDVS, Koolen-De Vries syndrome; Kleefstra, Kleefstra syndrome; LLS, Luscan-Lumish syndrome; MKHK, Menke-Hennekam syndrome; MLASA2, myopathy lactic acidosis and sideroblastic anemia 2; MRD23, intellectual developmental disorder 23; MRD51, intellectual developmental disorder 51; MRX93, intellectual developmental disorder X-linked 93; MRX97, intellectual developmental disorder X-linked 97; MRXSA, intellectual developmental disorder X-linked syndromic Armfield type; MRXSCH, intellectual developmental disorder X-linked syndromic Christianson type; MRXSCJ, intellectual developmental disorder X-linked syndromic Claes-Jensen type; MRXSN, intellectual developmental disorder X-linked syndromic Nascimento type; MRXSSR, intellectual developmental disorder X-linked syndromic Snyder-Robinson type; PHMDS, Phelan-McDermid syndrome; PRC2, PRC2 complex (Weaver and Cohen-Gibson) syndrome; RENS1, Renpenning syndrome; RMNS, Rahman syndrome; RSTS, Rubinstein-Taybi syndrome; SBBYSS, Ohdo syndrome; Sotos, Sotos syndrome; TBRS, Tatton-Brown-Rahman syndrome; WDSTS, Wiedemann-Steiner syndrome; WHS, Wolf-Hirschhorn syndrome; Williams, Williams syndrome.
<table><caption>Table 3. ACMG classification of SRSF1 clinical variants before and after functional studies</caption><thead><tr><th>SRSF1 variant</th><th>Splicing deficient (Drosophila assay)</th><th>SRSF1 episignature</th><th>ACMG classification (before functional studies)</th><th>Total score</th><th>ACMG classification (after functional studies)</th><th>Total score</th></tr></thead><tbody><tr><td>p.Ser126Trpfs*17</td><td>NA</td><td>Yes</td><td>Likely Pathogenic: PVS1, PM2</td><td>9</td><td>Pathogenic: PVS1, PM2, PS3</td><td>13</td></tr><tr><td>p.Val160Met</td><td>Yes</td><td>Yes</td><td>Likely Pathogenic: PP3, PM2, PP2, PS2</td><td>8</td><td>Pathogenic: PP3, PM2, PP2, PS2, PS3</td><td>12</td></tr><tr><td>p.Val194Serfs*2</td><td>Yes</td><td>Yes</td><td>Pathogenic: PVS1, PM2, PS2</td><td>13</td><td>Pathogenic: PVS1, PM2, PS2, PS3</td><td>17</td></tr><tr><td>p.Arg28*</td><td>NA</td><td>NA</td><td>Pathogenic: PVS1, PM2, PS2</td><td>13</td><td>NA</td><td>NA</td></tr><tr><td>p.His183Arg</td><td>No</td><td>No</td><td>Likely Pathogenic: PM2, PP2, PS2</td><td>6</td><td>Uncertain significance: PM2, PP2, PS2, BS3</td><td>2</td></tr><tr><td>p.Gly40Val</td><td>Yes</td><td>NA</td><td>Likely Pathogenic: PP3, PM2, PP2, PS2</td><td>8</td><td>Pathogenic: PP3, PM2, PP2, PS2, PS3</td><td>12</td></tr><tr><td>p.Glu33*</td><td>NA</td><td>NA</td><td>Pathogenic: PVS1, PM2, PS2</td><td>13</td><td>NA</td><td>NA</td></tr><tr><td>p.Ser201Valfs*87</td><td>Yes</td><td>Yes</td><td>Pathogenic: PVS1, PM2, PS2</td><td>13</td><td>Pathogenic: PVS1, PM2, PS2, PS3</td><td>17</td></tr><tr><td>p.Ala70Thr</td><td>Yes</td><td>NA</td><td>Likely Pathogenic: PP3, PM2, PP2, PS2</td><td>8</td><td>Pathogenic: PP3, PM2, PP2, PS2, PS3</td><td>12</td></tr><tr><td>p.Pro24Leu</td><td>Yes</td><td>NA</td><td>Likely Pathogenic: PP3, PM2, PP2, PS2</td><td>8</td><td>Pathogenic: PP3, PM2, PP2, PS2, PS3</td><td>12</td></tr><tr><td>p.Tyr77*</td><td>NA</td><td>Yes</td><td>Pathogenic: PVS1, PM2, PS2</td><td>13</td><td>Pathogenic: PVS1, PM2, PS2, PS3</td><td>17</td></tr><tr><td>p.Leu84Arg</td><td>Yes</td><td>Yes</td><td>Likely Pathogenic: PP3, PM2, PP2, PS2</td><td>8</td><td>Pathogenic: PP3, PM2, PP2, PS2, PS3</td><td>12</td></tr><tr><td>p.Asp44Asn</td><td>No</td><td>No</td><td>Uncertain significance: PM2, PP2, PS2, BP4</td><td>4</td><td>Uncertain significance: PM2, PP2, PS2, BP4, BS3</td><td>0</td></tr></tbody></table>

ACMG variant classification using Varsome: PVS1 (very strong, +8 points): null variant (nonsense, frameshift, canonical +/-1 or 2 splice sites, initiation codon, single or multiexon deletion) in a gene where LoF is a known mechanism. PM2 (supporting, +1 point): absent from controls (or at extremely low frequency if recessive) (based on gnomAD frequencies). PP2 (supporting, +1 point): missense variant in a gene that has a low rate of benign missense variation and in which missense variants are a common mechanism of disease (based on gnomAD missense Z score). PP3 (moderate, +2 points): multiple lines of computational evidence support a deleterious effect on the gene or gene product (based on BayesDel_addAF score). PS2 (strong, +4 points): *de novo* (both maternity and paternity confirmed) in an individual with the disease and no family history. PS3 (strong, +4 points): well-established *in vitro* or *in vivo* functional studies show no damaging effect on protein function or splicing. BS3 (strong, -4 points): well-established *in vitro* or *in vivo* functional studies show no damaging effect on protein function or splicing. BP4 (moderate, -2 points): multiple lines of computational evidence suggest no impact on gene or gene product (based on BayesDel_addAF score).

promoter regions (Figure S7). Annotation was also performed for DMRs in relation to CpG islands and genes and showed the DMPs predominantly in CpG islands and a pronounced association with promoter regions when annotated in relation to genes. Next, all DMPs were used to calculate the mean beta values for each cohort and determine the overall methylation trend, i.e., hypo- or hypermethylation (Figure S8). Genome-wide methylation profile of the SRSF1 cohort showed an overall hypermethylation trend, in line with the majority of hypermethylation DMRs identified. Lastly, we aimed to analyze the relatedness of genome-wide methylation profiles by comparing the SRSF1 cohort and all 56 other disorders. To assess this relationship, clustering analysis was performed using up to the top 500 DMPs for each cohort. For cohorts with less than 500 DMPs, the total DMPs for those cohorts were used in the analysis. Results were visualized using a binary tree with each node representative of a cohort (Figure 4E). SRSF1 is shown to cluster closest to the Renpenning syndrome 1 (RENS1; MIM: 309500) in the tree-and-leaf plot, and both show a global hypermethylation profile.

## ACMG classification of the identified clinical SRSF1 variants

Our functional studies were important to establish SRSF1 haploinsufficiency as the common genetic mechanism in SRSF1-related NDD, also in individuals with missense variants. To examine the exact impact of functional studies on the ACMG variant classification, we compared the ACMG scores and classifications before and after functional analysis (including variant modeling in *Drosophila* and/or epigenetic analysis). For 9 out of 11 variants for which functional data were available (Table 3), the total scores increased in such a way that it resulted in a reclassification from “likely pathogenic” to “pathogenic” in six (6/9, 55%). For one variant the total score further decreased, resulting in a reclassification from “likely pathogenic” to “uncertain significance” for p.His183Arg, while p.Asp44Asn remained a variant of “uncertain significance” after functional analysis. In total, from the 13 intragenic SRSF1 variants, ACMG criteria classified 11 as “pathogenic” (85%) and 2 as “uncertain” (15%). The frequencies of the main clinical features described in individuals harboring SRSF1 variants before and after reclassification are summarized in Table 4. These
<table><caption>**Table 4.** Summary of main clinical features described in individuals harboring *SRSF1* variants</caption><thead><tr><td></td><td>Number of individuals with pathogenic <i>SRSF1</i> variant</td><td>Total number of individuals with <i>SRSF1</i> variant including VUS variants</td></tr></thead><tbody><tr><td>ID or DD</td><td>15/15</td><td>17/17</td></tr><tr><td>Speech delay</td><td>14/15</td><td>16/17</td></tr><tr><td>Motor delay</td><td>11/15</td><td>13/17</td></tr><tr><td>Hypotonia</td><td>9/14</td><td>11/16</td></tr><tr><td>Behavior disorders</td><td>12/15</td><td>13/17</td></tr><tr><td>Abnormal brain MRI</td><td>4/10</td><td>6/12</td></tr><tr><td>Cardiac malformation</td><td>6/13</td><td>6/14</td></tr><tr><td>Urogenital malformation</td><td>6/11</td><td>6/13</td></tr><tr><td>Skeletal abnormalities</td><td>10/15</td><td>10/17</td></tr><tr><td>Marfanoid features</td><td>3/15</td><td>3/17</td></tr></tbody></table>

DD, developmental delay; ID, intellectual disability; VUS, variant of uncertain significance.

results highlight the clinical importance of functional studies for variant interpretation.

## Discussion

In this study, we clinically and molecularly described a cohort of 17 individuals from 16 families, with 15 different heterozygous germline variants in *SRSF1*. The main clinical features were DD or ID. Other features were variably present and included skeletal anomalies, behavioral disorders, congenital heart defects, and urogenital malformation. Among the five adult individuals, three presented marfanoid features with long and thin habitus, pectus excavatum or carinatum, dolichostenomelia, arachnodactyly, scoliosis, and highly arched palate. In the literature, phenotypes linked with *SRSF1* overexpression causing dysregulation of alternative splicing have been associated with cancer.30–32 Here, we provide genetic, epigenetic, and structural arguments, in combination with evidence gathered from *in vivo* functional modeling, for a LoF as a pathogenic mechanism. The identification of microdeletions encompassing *SRSF1*, nonsense, and frameshift variants points toward haploinsufficiency. This is further supported by gnomAD data showing that *SRSF1* is highly intolerant to LoF, according to the pLI = 0.98 and LOEUF = 0.24 scores. For five of the seven missense variants, p.Gly40Val, p.Leu84Arg, p.Ala70Thr, p.Pro24Leu, and p.Val160Met, we obtained combined *in silico* evidence, *in vivo* modeling arguments, and supportive epigenetic data to classify them as pathogenic LoF variants. In contrast, for the two missense variants, p.Asp44Asn and p.His183Arg, the data did not support their pathogenic role. The functional and structural prediction tools that were used point toward a lower pathogenicity for the latter two variants. Structurally,

these variants were located at the protein surface, whereas the other five missense variants are predicted to cause internal misfolding. These *in silico* data were consistent with the data obtained in two independent functional splicing read-outs in our *Drosophila* model system. Both in the fly eye and in the nervous system, overexpression of *SRSF1* and its *Drosophila* ortholog, SF2, leads to severe phenotypes due to splicing alterations. All five missense variants lost their potency to induce eye and brain phenotypes, pointing toward LoF mutations, whereas p.Asp44Asn and p.His183Arg retained this ability. Interestingly, p.Asp44Asn was the only missense variant located in a less conserved region of the RRM1 domain and not conserved in the fruit fly. We validated the use of *Drosophila* to model *SRSF1* function by providing evidence for structural and functional conservation even at the molecular level. Our data are in line with previously reported experimental studies in *Drosophila* showing that the splicing activity of *SRSF1* is evolutionarily conserved.25,57 A combination of biochemically characterized splicing variants and transcriptome analysis make us hypothesize that we are mainly modeling splicing alteration involving U1 snRNP activity. As *SRSF1* is shown to have other splicing functions apart from U1 snRNP activity, this might be a second explanation for the absence of phenotype-inducing capacity of both modeled variants. Thirdly, both variants might exert their toxicity through a different pathogenic mechanism. Both variants are indeed located at the protein surface and therefore more likely to intervene with protein interaction and might hamper other functions of *SRSF1*. To gain insight into the possible existence of a common disease pathway, we investigated the epigenetic signature associated with *SRSF1* variants in blood obtained from the affected individuals. DNA methylation data also corroborated the data obtained in *Drosophila*. Therefore, we argue that these variants be classified as being of uncertain significance (i.e., VUS). However, it is important to highlight that they should not be classified as benign because the functional models used in this study do not address all of the functions of this protein. Further allelic series studies and genome analyses in affected cohorts may clarify whether these variants are pathogenic or benign.

Besides the identification of *SRSF1* haploinsufficiency as a cause of ID/DD, we possibly identified *SRSF1* as being an important gene responsible for the neurodevelopmental features associated with 17q22 microdeletions as well. Individuals with 1.8–2.5 Mb microdeletions of the 17q22 region have been reported in the literature.60–64 An important causal gene related to the clinical manifestations of 17q22 microdeletion is *NOG* (MIM: 602991). When it is included in the microdeletion, *NOG*-related bone and joint features such as symphalangism, conductive hearing loss, and joint contractures are present, as are visual impairment and facial dysmorphic features.60 However, additional features not related to *NOG* haploinsufficiency may also be present such as ID and ADHD.60 Among the
reported individuals with 17q22 contiguous microdeletions, six had loss of SRSF1 and presented with syndromic ID.⁶⁰⁻⁶³ More recently, Pang et al. reported a family with 1.6 Mb microdeletion in chromosome 17q22 with NOG-related symphalangism spectrum disorder including conductive hearing loss, proximal symphalangism of the fifth fingers, small palpebral fissures, broadened hemicylindrical nose with a bulbous tip, amblyopia, and strabismus without ID or any other neurodevelopmental abnormalities.⁶⁴ In comparison with the genes included in the microdeletion of their family and the genomic intervals deleted by other microdeletions in chromosome 17q22, the authors suggested two candidate genomic intervals for ID. Among the distal candidate genomic intervals, SRSF1 was included.

In our cohort, we reported two microdeletions of the 17q22 region with sizes of 734 kb and 866 kb, including SRSF1 but excluding NOG. Therefore, our study further supports the role of SRSF1 as one of the critical “driver genes” for the 17q22 contiguous microdeletion-related syndrome, accounting for at least part of the neurodevelopmental features associated with it. Interestingly, the DNA methylation analysis showed that the sample with the CNV variant from individual 6 (F5-II-1) clustered with the other samples with pathogenic missense, frameshift, or nonsense variants in SRSF1, supporting the growing evidence that intragenic variants within critical genes and microdeletions encompassing them share similar DNA methylation profiles.⁶⁵⁻⁶⁷

In conclusion, we described a cohort of individuals with heterozygous variants in SRSF1, responsible for a syndromic form of DD characterized by learning disabilities with mild to severe ID and, to a variable extent, associated with skeletal anomalies and with cardiac or urogenital malformations. Additional functional studies are needed to fully understand the pathogenic mechanisms at play in the SRSF1-related NDD.

## Data and code availability

The published article includes all datasets generated or analyzed during this study. SRSF1 genetic variants identified in our study were submitted to ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/) under the accession IDs ClinVar: SCV003803742-SCV003803756.

## Supplemental information

Supplemental information can be found online at https://doi.org/10.1016/j.ajhg.2023.03.016.

## Acknowledgments

This work was supported by grants from Dijon University Hospital, the ISITE-BFC (PIA ANR), the European Union through the FEDER programs, an Odysseus type 1 grant of the Research Foundation Flanders (3G0H8318), a starting grant from the Ghent University Special Research Fund (01N10319), the U.S. NIH BHCMG

and the GREGoR grants (HG006542 and HG011758), and U.S. NIH NINDS R35 (NS105078). Individual 4 was part of PHRC national 2008-A00515-50, funded by the French Ministry of Health.

Sequencing and analysis of individual 8 was provided by the Broad Institute of MIT and Harvard Center for Mendelian Genomics (Broad CMG) and was funded by the National Human Genome Research Institute, the National Eye Institute, and the National Heart, Lung, and Blood Institute grant UM1 HG008900 and in part by National Human Genome Research Institute grant R01 HG009141.

The results reported here were generated using funding received from the Solve-RD project within the European Rare Disease Models & Mechanisms Network (RDMM-Europe). The Solve-RD project (https://solve-rd.eu/) has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement no. 779257. Several authors of this publication are members of the European Reference Network (ERN) for Developmental Anomalies and Intellectual Disability (ERN-ITHACA) and Rare Neurological Diseases (ERN-RND). DNA methylation work was funded by the government of Canada through Genome Canada and the Ontario Genomics Institute (OGI-188).

The authors thank the families for participating and supporting this study. We thank the “Centre de Ressources Biologiques Ferdi-nand Cabanne” (CHU Dijon) for sample biobanking.

## Declaration of interests

I.M.W. is an employee of GeneDx, LLC. J.R.L. has stock ownership in 23andMe, is a paid consultant for the Regeneron Genetics Center, and is a co-inventor on multiple United States and European patents related to molecular diagnostics for inherited neuropathies, eye diseases, and bacterial genomic fingerprinting. The Department of Molecular and Human Genetics at Baylor College of Medicine receives revenue from clinical genetic testing conducted at Baylor Genetics (BG) Laboratories. J.R.L. serves on the Scientific Advisory Board of BG.

Received: December 14, 2022

Accepted: March 23, 2023

Published: April 17, 2023

## Web resources

ClinVar, https://www.ncbi.nlm.nih.gov/clinvar/

Decipher, https://www.deciphergenomics.org

GeneMatcher, https://www.genematcher.org

gnomAD, https://gnomad.broadinstitute.org

gplots, https://CRAN.R-project.org/package=gplots

PolyPhen-2, http://genetics.bwh.harvard.edu/pph2/

Varsome, https://varsome.com/

## References

1. Anderson, L.L., Larson, S.A., MapelLentz, S., and Hall-Lande, J. (2019). A systematic review of U.S. studies on the prevalence of intellectual or developmental disabilities since 2000. Intellect. Dev. Disabil. 57, 421-438. https://doi.org/10.1352/1934-9556-57.5.421

2. Morris-Rosendahl, D.J., and Crocq, M.-A. (2020). Neurodevelopmental disorders—the history and future of a diagnostic
concept. Dialogues Clin. Neurosci. 22, 65-72. https://doi.org/10.31887/DCNS.2020.22.1/macrocq.

3. Wright, C.F., McRae, J.F., Clayton, S., Gallone, G., Aitken, S., FitzGerald, T.W., Jones, P., Prigmore, E., Rajan, D., Lord, J., et al. (2018). Making new genetic diagnoses with old data: iterative reanalysis and reporting from genome-wide data in 1,133 families with developmental disorders. Genet. Med. 20, 1216-1223. https://doi.org/10.1038/gim.2017.246.

4. Mitani, T., Isikay, S., Gezdirici, A., Gulec, E.Y., Punetha, J., Fatih, J.M., Herman, I., Akay, G., Du, H., Calame, D.G., et al. (2021). High prevalence of multilocus pathogenic variation in neurodevelopmental disorders in the Turkish population. Am. J. Hum. Genet. 108, 1981-2005. https://doi.org/10.1016/j.ajhg.2021.08.009.

5. Wright, C.F., FitzPatrick, D.R., and Firth, H.V. (2018). Paediatric genomics: diagnosing rare disease in children. Nat. Rev. Genet. 19, 253-268. https://doi.org/10.1038/nrg.2017.116.

6. Nambot, S., Thevenon, J., Kuentz, P., Duffourd, Y., Tisserant, E., Bruel, A.-L., Mosca-Boidron, A.-L., Masurel-Paulet, A., Lelhalle, D., Jean-Marçais, N., et al. (2018). Clinical whole-exome sequencing for the diagnosis of rare disorders with congenital anomalies and/or intellectual disability: substantial interest of prospective annual reanalysis. Genet. Med. 20, 645-654. https://doi.org/10.1038/gim.2017.162.

7. Karaca, E., Harel, T., Pehlivan, D., Jhangiani, S.N., Gambin, T., Coban Akdemir, Z., Gonzaga-Jauregui, C., Erdin, S., Bayram, Y., Campbell, I.M., et al. (2015). Genes that affect brain structure and function identified by rare variant analyses of mendelian neurologic disease. Neuron 88, 499-513. https://doi.org/10.1016/j.neuron.2015.09.048.

8. Haghshenas, S., Levy, M.A., Kerkhof, J., Aref-Eshghi, E., McConkey, H., Balci, T., Siu, V.M., Skinner, C.D., Stevenson, R.E., Sadikovic, B., and Schwartz, C. (2021). Detection of a DNA methylation signature for the intellectual developmental disorder, X-linked, syndromic, armfield type. Int. J. Mol. Sci. 22, 1111. https://doi.org/10.3390/ijms22031111.

9. Aref-Eshghi, E., Kerkhof, J., Pedro, V.P., Groupe DI France, Barat-Houari, M., Ruiz-Pallares, N., Andrau, J.C., Lacombe, D., Van-Gils, J., Fergelot, P., et al. (2020). Evaluation of DNA methylation episignatures for diagnosis and phenotype correlations in 42 mendelian neurodevelopmental disorders. Am. J. Hum. Genet. 106, 356-370. https://doi.org/10.1016/j.ajhg.2020.01.019.

10. Aref-Eshghi, E., Bend, E.G., Colaiacovo, S., Caudle, M., Chakrabarti, R., Napier, M., Brick, L., Brady, L., Carere, D.A., Levy, M.A., et al. (2019). Diagnostic utility of genome-wide DNA methylation testing in genetically unsolved individuals with suspected hereditary conditions. Am. J. Hum. Genet. 104, 685-700. https://doi.org/10.1016/j.ajhg.2019.03.008.

11. Fahrner, J.A., and Bjornsson, H.T. (2019). Mendelian disorders of the epigenetic machinery: postnatal malleability and therapeutic prospects. Hum. Mol. Genet. 28, R254-R264. https://doi.org/10.1093/hmg/ddz174.

12. Choufani, S., Cytrynbaum, C., Chung, B.H.Y., Turinsky, A.L., Grafodatskaya, D., Chen, Y.A., Cohen, A.S.A., Dupuis, L., Butcher, D.T., Siu, M.T., et al. (2015). *NSD1* mutations generate a genome-wide DNA methylation signature. Nat. Commun. 6, 10207. https://doi.org/10.1038/ncomms10207.

13. Choufani, S., Gibson, W.T., Turinsky, A.L., Chung, B.H.Y., Wang, T., Garg, K., Vitriolo, A., Cohen, A.S.A., Cyrus, S., Goodman, S., et al. (2020). DNA methylation signature for EZH2 functionally classifies sequence variants in three PRC2

complex genes. Am. J. Hum. Genet. 106, 596-610. https://doi.org/10.1016/j.ajhg.2020.03.008.

14. Mirza-Schreiber, N., Zech, M., Wilson, R., Brunet, T., Wagner, M., Jech, R., Boesch, S., Škorvánek, M., Necpál, J., Weise, D., et al. (2022). Blood DNA methylation provides an accurate biomarker of KMT2B-related dystonia and predicts onset. Brain 145, 644-654. https://doi.org/10.1093/brain/awab360.

15. Ferilli, M., Ciolfi, A., Pedace, L., Niceta, M., Radio, F.C., Pizzi, S., Miele, E., Cappelletti, C., Mancini, C., Galluccio, T., et al. (2022). Genome-wide DNA methylation profiling solves uncertainty in classifying *NSD1* variants. Genes 13, 2163. https://doi.org/10.3390/genes13112163.

16. Huang, Y., and Steitz, J.A. (2005). SRprises along a messenger's journey. Mol. Cell 17, 613-615. https://doi.org/10.1016/j.molcel.2005.02.020.

17. Long, J.C., and Caceres, J.F. (2009). The SR protein family of splicing factors: master regulators of gene expression. Biochem. J. 417, 15-27. https://doi.org/10.1042/BJ20081501.

18. Zhong, X.-Y., Wang, P., Han, J., Rosenfeld, M.G., and Fu, X.-D. (2009). SR proteins in vertical integration of gene expression from transcription to RNA processing to translation. Mol. Cell 35, 1-10. https://doi.org/10.1016/j.molcel.2009.06.016.

19. Paz, S., Ritchie, A., Mauer, C., and Caputi, M. (2021). The RNA binding protein SRSF1 is a master switch of gene expression and regulation in the immune system. Cytokine Growth Factor Rev. 57, 19-26. https://doi.org/10.1016/j.cytogfr.2020.10.008.

20. Shepard, P.J., and Hertel, K.J. (2009). The SR protein family. Genome Biol. 10, 242. https://doi.org/10.1186/gb-2009-10-10-242.

21. Krainer, A.R., Conway, G.C., and Kozak, D. (1990). Purification and characterization of pre-mRNA splicing factor SF2 from HeLa cells. Genes Dev. 4, 1158-1171. https://doi.org/10.1101/gad.4.7.1158.

22. Chen, M., and Manley, J.L. (2009). Mechanisms of alternative splicing regulation: insights from molecular and genomics approaches. Nat. Rev. Mol. Cell Biol. 10, 741-754. https://doi.org/10.1038/nrm2777.

23. Jobbins, A.M., Campagne, S., Weinmeister, R., Lucas, C.M., Gosliga, A.R., Clery, A., Chen, L., Eperon, L.P., Hodson, M.J., Hudson, A.J., et al. (2022). Exon-independent recruitment of SRSF1 is mediated by U1 snRNP stem-loop 3. EMBO J. 41, e107640. https://doi.org/10.15252/embj.2021107640.

24. Cho, S., Hoang, A., Chakrabarti, S., Huynh, N., Huang, D.-B., and Ghosh, G. (2011). The SRSF1 linker induces semi-conservative ESE binding by cooperating with the RRMs. Nucleic Acids Res. 39, 9413-9421. https://doi.org/10.1093/nar/gkr663.

25. Cléry, A., Sinha, R., Anczuków, O., Corriponero, A., Moursy, A., Daubner, G.M., Valcárcel, J., Krainer, A.R., and Allain, F.H.-T. (2013). Isolated pseudo-RNA-recognition motifs of SR proteins can regulate splicing using a noncanonical mode of RNA recognition. Proc. Natl. Acad. Sci. USA 110, E2802-E2811. https://doi.org/10.1073/pnas.1303445110.

26. Wang, J., Takagaki, Y., and Manley, J.L. (1996). Targeted disruption of an essential vertebrate gene: ASF/SF2 is required for cell viability. Genes Dev. 10, 2588-2599. https://doi.org/10.1101/gad.10.20.2588.

27. Lin, S., Xiao, R., Sun, P., Xu, X., and Fu, X.-D. (2005). Dephosphorylation-dependent sorting of SR splicing Factors during mRNP maturation. Mol. Cell 20, 413-425. https://doi.org/10.1016/j.molcel.2005.09.015.
28. Longman, D., Johnstone, I.L., and Cáceres, J.F. (2000). Functional characterization of SR and SR-related genes in Caenorhabditis elegans. EMBO J. 19, 1625-1637. https://doi.org/10.1093/emboj/19.7.1625.

29. Xu, X., Yang, D., Ding, J.-H., Wang, W., Chu, P.-H., Dalton, N.D., Wang, H.-Y., Bermingham, J.R., Ye, Z., Liu, F., et al. (2005). ASF/SF2-regulated CaMKIIδ alternative splicing temporally reprograms excitation-contraction coupling in cardiac muscle. Cell 120, 59-72. https://doi.org/10.1016/j.cell.2004.11.036.

30. Karni, R., de Stanchina, E., Lowe, S.W., Sinha, R., Mu, D., and Krainer, A.R. (2007). The gene encoding the splicing factor SF2/ASF is a proto-oncogene. Nat. Struct. Mol. Biol. 14, 185-193. https://doi.org/10.1038/nsmb1209.

31. Das, S., and Krainer, A.R. (2014). Emerging functions of SRSF1, splicing factor and oncoprotein, in RNA metabolism and cancer. Mol. Cancer Res. 12, 1195-1204. https://doi.org/10.1158/1541-7786.MCR-14-0131.

32. Jiang, L., Huang, J., Higgs, B.W., Hu, Z., Xiao, Z., Yao, X., Conley, S., Zhong, H., Liu, Z., Brohawn, P., et al. (2016). Genomic landscape survey identifies SRSF1 as a key oncodriver in small cell lung cancer. PLoS Genet. 12, e1005895. https://doi.org/10.1371/journal.pgen.2005895.

33. Min, J.-W., Koh, Y., Kim, D.-Y., Kim, H.-L., Han, J.A., Jung, Y.-J., Yoon, S.-S., and Choi, S.S. (2018). Identification of novel functional variants of SIN3A and SRSF1 among somatic variants in acute myeloid leukemia patients. Mol. Cells 41, 465-475. https://doi.org/10.14348/molcells.2018.0051.

34. Boone, P.M., Bacino, C.A., Shaw, C.A., Eng, P.A., Hixson, P.M., Pursley, A.N., Kang, S.-H.L., Yang, Y., Wiszniewska, J., Nowakowska, B.A., et al. (2010). Detection of clinically relevant exonic copy-number changes by array CGH. Hum. Mutat. 31, 1326-1342. https://doi.org/10.1002/humu.21360.

35. Shen, S., Park, J.W., Lu, Z.x., Lin, L., Henry, M.D., Wu, Y.N., Zhou, Q., and Xing, Y. (2014). rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data. Proc. Natl. Acad. Sci. USA 111, E5593-E5601. https://doi.org/10.1073/pnas.1419161111.

36. Diez-Hermano, S., Valero, J., Rueda, C., Ganfornina, M.D., and Sanchez, D. (2015). An automated image analysis method to measure regularity in biological patterns: a case study in a *Drosophila* neurodegenerative model. Mol. Neurodegener. 10, 9. https://doi.org/10.1186/s13024-015-0005-z.

37. Levy, M.A., McConkey, H., Kerkhof, J., Barat-Houari, M., Bargiacchi, S., Biamino, E., Bralo, M.P., Cappuccio, G., Ciolfi, A., Clarke, A., et al. (2022). Novel diagnostic DNA methylation episignatures expand and refine the epigenetic landscapes of Mendelian disorders. HGG Adv. 3, 100075. https://doi.org/10.1016/j.xhgg.2021.100075.

38. Aryee, M.J., Jaffe, A.E., Corrada-Bravo, H., Ladd-Acosta, C., Feinberg, A.P., Hansen, K.D., and Irizarry, R.A. (2014). Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics 30, 1363-1369. https://doi.org/10.1093/bioinformatics/btu049.

39. Ho, D.E., Imai, K., King, G., and Stuart, E.A. (2011). MatchIt: nonparametric preprocessing for parametric causal inference. J. Stat. Softw. 42. https://doi.org/10.18637/jss.v042.i08.

40. Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., and Smyth, G.K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47. https://doi.org/10.1093/nar/gkv007.

41. Houseman, E.A., Accomando, W.P., Koestler, D.C., Christensen, B.C., Marsit, C.J., Nelson, H.H., Wiencke, J.K., and Kelsey, K.T. (2012). DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinf. 13, 86. https://doi.org/10.1186/1471-2105-13-86.

42. Gu, Z., Gu, L., Eils, R., Schlesner, M., and Brors, B. (2014). circle implements and enhances circular visualization in R. Bioinformatics 30, 2811-2812. https://doi.org/10.1093/bioinformatics/btu393.

43. Peters, T.J., Buckley, M.J., Statham, A.L., Pidsley, R., Samaras, K., V Lord, R., Clark, S.J., and Molloy, P.L. (2015). De novo identification of differentially methylated regions in the human genome. Epigenet. Chromatin 8, 6. https://doi.org/10.1186/1756-8935-8-6.

44. Cavalcante, R.G., and Sartor, M.A. (2017). annotatr: genomic regions in context. Bioinformatics 33, 2381-2383. https://doi.org/10.1093/bioinformatics/btx183.

45. Firth, H.V., Richards, S.M., Bevan, A.P., Clayton, S., Corpas, M., Rajan, D., Van Vooren, S., Moreau, Y., Pettett, R.M., and Carter, N.P. (2009). DECIPHER: database of chromosomal imbalance and phenotype in humans using ensemble resources. Am. J. Hum. Genet. 84, 524-533. https://doi.org/10.1016/j.ajhg.2009.03.010.

46. Sobreira, N., Schiettecatte, F., Valle, D., and Hamosh, A. (2015). GeneMatcher: a matching tool for connecting investigators with an interest in the same gene. Hum. Mutat. 36, 928-930. https://doi.org/10.1002/humu.22844.

47. Kuzmiak, H.A., and Maquat, L.E. (2006). Applying nonsense-mediated mRNA decay research to the clinic: progress and challenges. Trends Mol. Med. 12, 306-316. https://doi.org/10.1016/j.molmed.2006.05.005.

48. Posey, J.E., Harel, T., Liu, P., Rosenfeld, J.A., James, R.A., Coban Akdemir, Z.H., Walkiewicz, M., Bi, W., Xiao, R., Ding, Y., et al. (2017). Resolution of disease phenotypes resulting from multilocus genomic variation. N. Engl. J. Med. 376, 21-31. https://doi.org/10.1056/NEJMoa1516767.

49. Narayanan, D.L., Udyawar, D., Kaur, P., Sharma, S., Suresh, N., Nampoothiri, S., do Rosario, M.C., Somashekar, P.H., Rao, L.P., Kaustubhambam, N., et al. (2021). Multilocus disease-causing genomic variations for Mendelian disorders: role of systematic phenotyping and implications on genetic counselling. Eur. J. Hum. Genet. 29, 1774-1780. https://doi.org/10.1038/s41431-021-00933-7.

50. Cukier, H.N., Lee, J.M., Ma, D., Young, J.I., Mayo, V., Butler, B.L., Ramsook, S.S., Rantus, J.A., Abrams, A.J., Whitehead, P.L., et al. (2012). The Expanding Role of MBD Genes in Autism: Identification of a MECP2 Duplication and Novel Alterations in MBD5, MBD6, and SETDB1: ASD patients with novel variants in MBD genes. Autism Res. 5, 385-397. https://doi.org/10.1002/aur.1251.

51. Cox, D.M., and Butler, M.G. (2015). The 15q11.2 BP1-BP2 microdeletion syndrome: a review. Int. J. Mol. Sci. 16, 4068-4082. https://doi.org/10.3390/ijms16024068.

52. Kopanos, C., Tsiolkas, V., Kouris, A., Chapple, C.E., Albarca Aguilera, M., Meyer, R., and Massouras, A. (2019). VarSome: the human genomic variant search engine. Bioinformatics 35, 1978-1980. https://doi.org/10.1093/bioinformatics/bty897.

53. Tian, Y., Pesaran, T., Chamberlin, A., Fenwick, R.B., Li, S., Gau, C.-L., Chao, E.C., Lu, H.-M., Black, M.H., and Qian, D. (2019). REVEL and BayesDel outperform other in silico meta-predictors for clinical variant classification. Sci. Rep. 9, 12752. https://doi.org/10.1038/s41598-019-49224-8.
54. Anderson, D., and Lassmann, T. (2022). An expanded phenotype centric benchmark of variant prioritisation tools. Hum. Mutat. **43**, 539–546. https://doi.org/10.1002/humu.24362.

55. Şahin, H.B., and Çelik, A. (2013). *Drosophila* Eye Development and Photoreceptor Specification. In eLS, John Wiley & Sons, Ltd, ed. (Wiley). https://doi.org/10.1002/9780470015902.a0001147.pub2.

56. Straub, J., Gregor, A., Sauerer, T., Fliedner, A., Distel, L., Suchy, C., Ekici, A.B., Ferrazzi, F., and Zweier, C. (2020). Genetic interaction screen for severe neurodevelopmental disorders reveals a functional link between Ube3a and Mef2 in *Drosophila melanogaster*. *Sci. Rep.* **10**, 1204. https://doi.org/10.1038/s41598-020-58182-5.

57. Gabut, M., Dejardin, J., Tazi, J., and Soret, J. (2007). The SR family proteins B52 and dASF/SF2 modulate development of the *Drosophila* visual system by regulating specific RNA targets. Mol. Cell Biol. **27**, 3087–3097. https://doi.org/10.1128/MCB.01876-06.

58. Kerkhof, J., Squeo, G.M., McConkey, H., Levy, M.A., Piemontese, M.R., Castori, M., Accadia, M., Biamino, E., Della Monica, M., Di Giacomo, M.C., et al. (2022). DNA methylation episignature testing improves molecular diagnosis of Mendelian chromatinopathies. Genet. Med. **24**, 51–60. https://doi.org/10.1016/j.gim.2021.08.007.

59. Sadikovic, B., Levy, M.A., Kerkhof, J., Aref-Eshghi, E., Schenkel, L., Stuart, A., McConkey, H., Henneman, P., Venema, A., Schwartz, C.E., et al. (2021). Clinical epigenomics: genome-wide DNA methylation analysis for the diagnosis of Mendelian disorders. Genet. Med. **23**, 1065–1074. https://doi.org/10.1038/s41436-020-01096-4.

60. Laurell, T., Lundin, J., Anderlid, B.-M., Gorski, J.L., Grigelio niene, G., Knight, S.J.L., Krepischi, A.C.V., Nordenskjöld, A., Price, S.M., Rosenberg, C., et al. (2013). Molecular and clinical delineation of the 17q22 microdeletion phenotype. Eur. J. Hum. Genet. **21**, 1085–1092. https://doi.org/10.1038/ehjg.2012.306.

61. Puusepp, H., Zilina, O., Teek, R., Männik, K., Parkel, S., Kruustük, K., Kuuse, K., Kurg, A., and Õunap, K. (2009). 5.9Mb microdeletion in chromosome band 17q22-q23.2 associated with tracheo-esophageal fistula and conductive hearing loss. Eur. J. Med. Genet. **52**, 71–74. https://doi.org/10.1016/j.ejmg.2008.09.006.

62. Khattab, M., Xu, F., Li, P., and Bhandari, V. (2011). A de novo 3.54 Mb deletion of 17q22-q23.1 associated with hydrocephalus: a case report and review of literature. Am. J. Med. Genet. **155A**, 3082–3086. https://doi.org/10.1002/ajmg.a.34307.

63. Durmaz, C.D., Altiner, Ş., Taşdelen, E., Karabulut, H.G., and Ruhi, H.I. (2021). Extending phenotypic spectrum of 17q22 microdeletion: growth hormone deficiency. Fetal Pediatr. Pathol. **40**, 486–492. https://doi.org/10.1080/15513815.2019.1710789.

64. Pang, X., Luo, H., Chai, Y., Wang, X., Sun, L., He, L., Chen, P., Wu, H., and Yang, T. (2015). A 1.6-Mb microdeletion in chromosome 17q22 leads to NOG-related symphalangism spectrum disorder without intellectual disability. PLoS One **10**, e0120816. https://doi.org/10.1371/journal.pone.0120816.

65. Awamleh, Z., Choufani, S., Cytrynbaum, C., Alkuraya, F., Scherer, S., Fernandes, S., Rosas, C., Louro, P., Dias, P., Neves, M., et al. (2022). ANKRD11 pathogenic variants and 16q24.3 microdeletions share an altered DNA methylation signature in patients with KBG syndrome. Hum. Mol. Genet., ddac289. https://doi.org/10.1093/hmg/ddac289.

66. McConkey, H., White-Brown, A., Kerkhof, J., Dyment, D., and Sadikovic, B. (2022). Genetically unresolved case of Rauch-Steindl syndrome diagnosed by its Wolf-Hirschhorn associated DNA methylation episignature. Front. Cell Dev. Biol. **10**, 1022683. https://doi.org/10.3389/fcell.2022.1022683.

67. Krzyzewska, I.M., Maas, S.M., Henneman, P., Lip, K.V.D., Venema, A., Baranano, K., Chassevent, A., Aref-Eshghi, E., van Es sen, A.J., Fukuda, T., et al. (2019). A genome-wide DNA methyl- ation signature for SETD1B-related syndrome. Clin. Epigenetics **11**, 156. https://doi.org/10.1186/s13148-019-0749-3.
Supplemental information

**SRSF1 haploinsufficiency is responsible
for a syndromic developmental disorder
associated with intellectual disability**

Elke Bogaert, Aurore Garde, Thierry Gautier, Kathleen Rooney, Yannis Duffourd, Pontus LeBlanc, Emma van Reempts, Frederic Tran Mau-Them, Ingrid M. Wentzensen, Kit Sing Au, Kate Richardson, Hope Northrup, Vincent Gatinois, David Geneviève, Raymond J. Louie, Michael J. Lyons, Lone Walentin Laulund, Charlotte Brasch-Andersen, Trine Maxel Juul, Fatima El It, Nathalie Marle, Patrick Callier, Raissa Relator, Sadegheh Haghshenas, Haley McConkey, Jennifer Kerkhof, Claudia Cesario, Antonio Novelli, Nicola Brunetti-Pierri, Michele Pinelli, Perrine Pennamen, Sophie Naudion, Marine Legendre, Cécile Courtier, Aurelien Trimouille, Martine Doco Fenzy, Lynn Pais, Alison Yeung, Kimberly Nugent, Elizabeth R. Roeder, Tadahiro Mitani, Jennifer E. Posey, Daniel Calame, Hagith Yonath, Jill A. Rosenfeld, Luciana Musante, Flavio Faletra, Francesca Montanari, Giovanna Sartor, Alessandra Vancini, Marco Seri, Claude Besmond, Karine Poirier, Laurence Hubert, Dimitri Hemelsoet, Arnold Munnich, James R. Lupski, Christophe Philippe, Christel Thauvin-Robinet, Laurence Faivre, Bekim Sadikovic, Jérôme Govin, Bart Dermaut, and Antonio Vitobello
# Supplemental Note

## Case reports

The antenatal period was uneventful for 11/17 individuals. Pregnancy was marked by the detection of intrauterine growth restriction (IUGR) with normal prenatal karyotype in individual 1 (I1 - F1-II-1), poor active fetal movements in individual 2 (I2 - F2-II-2), pre-eclampsia in individual 14 (F13-II-3), premature rupture of membranes in individual 7 (F6-II-1), a positive non-invasive prenatal testing for trisomy 21 leading to amniocentesis with normal karyotype in individual 15 (46,XY, F14-II-1) and an increased nuchal translucency observed at the first trimester ultrasound in individual 17 (F16-II-1) (Table 3, Table S1). Neonatal growth parameters were in the normal range except for individual 1 (F1-II-1) and individual 4 (F3-II-2) who had presented IUGR during pregnancy. Neonatal complications were observed in 10/15 individuals: individual 1 (F1-II-1) required oxygen therapy for 3 weeks because of pulmonary hypoplasia; individual 3 (F2-II-3) presented congenital torticollis; individual 14 (F13-II-3) had neonatal hypotonia and sucking difficulties; individual 6 (F5-II1) had feeding difficulties and jaundice, also present in individual 13 (F12-II-1) and individual 16 (F15-II-3); individual 11 (F10-II-1) had transient tachypnea and increased muscle tone; individual 4 (F3-II-2) was monitored for intrauterine growth restriction and had neonatal hypocalcemia; individual 17 (F16-II-1) had transient tachypnea. Congenital heart defect, including ventricular septal defect (VSD), right aortic arch, and aberrant subclavian artery were detected in individual 9 (F8-II-1). The majority of individuals presented growth parameters in the normal range. However, individuals 8 (F7-II-1), 10 (F9-II-3) and 17 (F16-II-1) had short stature with low body weight for individuals 8 and 10. Individual 8 (F7-II1) also had postnatal microcephaly with OFC at -2.9 SD at the last evaluation at the age of 3.1 years. On the contrary, individual 4 (F3-II-2) had postnatal macrocephaly with OFC at +2.7 SD at the last evaluation at the age of 34 years. Failure to thrive was reported for 6/16 individuals. All of them presented ID or DD ranging from mild to severe. Individual 4 (F3-II-2) had learning disabilities with an intellectual quotient (IQ) of 76. Motor delay was observed in 13/17 individuals and speech delay in 16/17. Hypotonia was reported in ten individuals and oral motor hypotonia was observed in one individual. Behavioral disorders were described in 13/17 individuals, including outbursts of anger (8/13), social difficulties (5/13), stereotypies (4/13), hyperactivity (2/13), hetero-aggressive behavior (1/13), and anxiety (1/13). Three individuals had seizures. Among them, two individuals had a single episode of febrile seizure (I14 - F3-II-2; I10 - F9-II-3) and one individual had recurrent episodes of fixed and deviant gaze (I16 - F15-II-3). I7 (F-6-II-1) had some EEG anomalies while falling asleep without clinical signs of seizures.
Cerebral MRI was performed in 13 individuals. Among them, 6 were abnormal, including deficient or delayed myelination in three individuals. Additional MRI anomalies were observed in 6 individuals (Table S1, Table 3). No hearing loss was observed in the cohort. Vision problems were present in 8/15 individuals, including hypermetropia (2/8), myopia (4/8), astigmatism (3/8), strabismus (3/8) and nystagmus (1/8). Skeletal anomalies were observed in ten individuals: four had pectus deformity, pectus excavatum in 2 individuals and pectus carinatum in 2 individuals; five had scoliosis and individual 1 (FI-II-1) needed scoliosis brace and arthrodesis at the age of 13 years. Among the five adults, three of them were referred for marfanoid features with dolichostenomelia (I14 - F13-II-3), arachnodactyly, pectus deformity, scoliosis (I4 - F3-II-2; I14 - F13-II-3; I15 - F14-II-1), and highly arched palate (I4 - F3-II-2; I14 - F13-II-3). One individual with marfanoid features was part of PHRC national 2008-A00515-50, funded by the French Ministry of Health.¹ Congenital malformations were observed in nine individuals. Five individuals had urogenital malformations including cryptorchidism (3/5), hypospadias (2/5), hydrocele (1/5). Nonspecific facial features were observed in the cohort including downslanted palpebral fissures, low-set ears, smooth philtrum, thin upper lip and high arched palate were reported (Figure 1C, Table S1).
Supplemental Figures and legends
A

<figure><img src="image_9.png" /></figure>

B

<figure><img src="image_10.png" /></figure>
**Figure S1. 17q22 deletions observed in individuals 6 and individual 17. (A)** All genes present in the chromosomal deletion 17q22 (558065434-5654497)x1, and (B) (55442363-56309063)x1 are listed in the box.
<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>
**Figure S2: Functional splicing read-outs of SRSF1 in the eye and nervous system of Drosophila. (A)**

Schematic presentation of the sequence conservation of humans SRSF1 and SF2, the Drosophila orthologue. The sequence of the RRM1 and RRM2 domains is 81% and 76% identical respectively, the RS domain is less conserved with a sequence identity of 45%. (B) Structural conservation of SRSF1 and SF2 protein. (C) RNA splicing analysis indicates functional conservation of SRSF1 and SF2. Five different alternative splicing events were measured compared to luciferase control flies: Skipped Exon (SE), Retained Intron (RI), Alternative 5' Splice Site (A5SS), and Alternative 3' Splice Site. (D) Representative pictures of flies expressing Luciferase, SRSF1, SF2, D66A/D69A, K38A/Y39A, I32A/V35A, F56D/F58D, Y149A, K138A, F56D/F58D/K138A. Amino acid substitutions corresponding to previously reported biochemically characterized SRSF1 are abbreviated using one letter codes to distinguish them from clinical variants (in three letter codes). (E) The irregularity score or regularity index of flies expressing luciferase, and different splicing controls of RRM1 are quantified. (n >10 and * indicates p-value <0.01, data are represented as mean ± SEM). (F) Pigmentation score measuring the depigmentation in the flies expressing luciferase, SRSF1 and SRSF1 splicing controls. (n >10 and * indicates p-value <0.01, data are represented as mean ± SEM). (G) Offspring frequencies were measured in flies expressing Luciferase, SRSF1, and splicing controls (n >10 and * indicates p-value <0.01).
A

<figure><img src="image_18.png" /></figure>

B

<figure><img src="image_19.png" /></figure>
**Figure S3: Splicing role of SRSF1 in *Drosophila*.** (A) Venn diagram of RNA-splicing analysis depicted in Figure S2C, representing the alternatively spliced genes due to overexpression of SRSF1 (yellow) or SF2 (orange). The numbers indicate the total number of alternatively spliced genes. (B) Metascape analysis for gene ontology on biological processes on the overlapping genes that were alternatively spliced by SF2 and SRSF1 overexpression.
A

<figure><img src="image_20.png" /></figure>

B

<figure><img src="image_21.png" /></figure>
**Figure S4. Subcellular localization of SRSF1 and V160M and H183R. (A)** Confocal image of a ventral nerve cord of third instar larvae expressing RFP in CCAP neurons. **(B)** Confocal image of CCAP-neurons in the ventral nerve cord of flies expressing RFP to mark the CCAP-neuron or RFP-NLS to mark the nucleus of CCAP-neurons. GFP-tagged SRSF1 protein variants were imaged in green. In the last lane, a zoomed image of representative CCAP-neurons is shown.
<figure><img src="image_22.png" /><figcaption>B</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>C</figcaption></figure>

<figure><img src="image_24.png" /><figcaption>D</figcaption></figure>

<figure><img src="image_25.png" /></figure>
**Figure S5. SRSF1 episignature.** (A) Heatmap indicates clear separation of the 9 SRSF1 samples (red) from age and sex-matched controls (blue). Each row represents one of the 107 probes selected as the episignature and each column represents either an SRSF1 individual or a control. (B) Multidimensional scaling plot (MDS) shows clustering of the SRSF1 samples together and away from controls. (C) Support vector machine classifier model (SVM). The model is trained using the 107 selected SRSF1 episignature probes and 75% of controls and other neurodevelopmental disorder samples on EpiSign™ (blue circles). The 25% remaining are used as testing samples (grey circles). The SRSF1 samples have a probability score (methylation variant pathogenicity score, MVP) of close to 1 compared with all other samples. The SVM shows the specificity of the SRSF1 episignature in classifying the SRSF1 samples. (D) Heatmap of differentially methylated probes (DMPs) shared between the SRSF1 cohort and 56 other EpiSign™ disorders with known genome-wide methylation profiles; The heatmap shows the percentage of probes shared between each paired cohort. Colours indicate the percent of the y-axis cohort's DMPs that are also found in the x-axis cohort's DMPs. The SRSF1 cohort is highlighted by the green arrows. SRSF1 DMPs show the largest overlap with ADCADN (~49%) and the next most overlapping are several cohorts with ~9-11% overlap (Hunter McAlpine syndrome, HMA; Tatton-Brown Rahman syndrome, TBRS; Sotos syndrome, Sotos; and Rahman syndrome, RMNS).
A

<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /></figure>

<figure><img src="image_30.png" /></figure>

<figure><img src="image_31.png" /></figure>

<figure><img src="image_32.png" /></figure>

<figure><img src="image_33.png" /></figure>

<figure><img src="image_34.png" /></figure>

<figure><img src="image_35.png" /></figure>

<figure><img src="image_36.png" /></figure>

<figure><img src="image_37.png" /></figure>

<figure><img src="image_38.png" /></figure>

<figure><img src="image_39.png" /></figure>

<figure><img src="image_40.png" /></figure>

<figure><img src="image_41.png" /></figure>

<figure><img src="image_42.png" /></figure>

<figure><img src="image_43.png" /></figure>

<figure><img src="image_44.png" /></figure>

<figure><img src="image_45.png" /></figure>

<figure><img src="image_46.png" /></figure>

<figure><img src="image_47.png" /></figure>

<figure><img src="image_48.png" /></figure>

<figure><img src="image_49.png" /></figure>

<figure><img src="image_50.png" /></figure>

<figure><img src="image_51.png" /></figure>

<figure><img src="image_52.png" /></figure>

● SRSF1 Test Case
● SRSF1 Training Case
● Control
**Figure S6. DNA methylation data leave-1-out cross-validation results.** In each round a single SRSF1 sample with confirmed pathogenic variant was used for testing (red) and plotted with the remaining eight SRSF1 samples (those used for episignature training) (blue) and controls (green). The heatmap and MDS clustering methods confirmed the robustness and sensitivity of the SRSF1 episignature and the high MVP scores for training and testing cases in the SVM confirm the specificity.
<figure><img src="image_53.png" /></figure>

<figure><img src="image_54.png" /></figure>

<figure><img src="image_55.png" /></figure>

<figure><img src="image_56.png" /></figure>
**Figure S7.** Differentially methylated probes (DMPs) and regions (DMRs) annotated in the context of CpG islands and genes. (A) DMPs annotated in relation to CpG islands. Island, CpG island; Shore, within 0-2kb of a CpG island; Shelf, within 2-4kb of a CpG island boundary; Inter_CGI, all other regions in the genome. (B) DMPs annotated in the context of genes. Promoter, 0-1kb upstream of the transcription start site (TSS); Promoter+, 1-5kb upstream of the TSS; CDS, coding sequence; Intergenic, all other regions of the genome. The probes column in A and B represent the background distribution of probes. (C) DMRs annotated in relation to CpG islands. (D) DMRs annotated in the context of genes. For DMR analysis, the numbers above indicate the number of DMRs identified in each cohort. The *SRSF1* cohort is outlined in green boxes in A-D.
A

<figure><img src="image_57.png" /></figure>
**Figure S8. Overall methylation trend of each cohort.** Methylation differences of all differentially methylated probes (DMPs) for each cohort, sorted by mean methylation. Each circle represents one probe. Red lines indicate the mean methylation values. SRSF1 cohort is outlined in the green box.
Supplemental tables

<table><caption>Table S1 Clinical features of the SRSF1 cohort.</caption><thead><tr><th>Subject</th><th>Individual 1</th><th>Individual 2</th><th>Individual 3</th><th>Individual 4</th><th>Individual 5</th><th>Individual 6</th><th>Individual 7</th><th>Individual 8</th><th>Individual 9</th><th>Individual 10</th><th>Individual 11</th><th>Individual 12</th><th>Individual 13</th><th>Individual 14</th><th>Individual 15</th><th>Individual 16</th><th>Individual 17</th></tr></thead><tbody><tr><td>Sex</td><td>Female</td><td>Female</td><td>Male</td><td>Male</td><td>Female</td><td>Female</td><td>Male</td><td>Female</td><td>Male</td><td>Male</td><td>Female</td><td>Female</td><td>Female</td><td>Male</td><td>Male</td><td>Female</td><td>Female</td></tr><tr><td>Molecular finding</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SRSF1 Genomic coordinates (hg19)</td><td>NC_000017.10:g.56083708_56083709del</td><td>NC_000017.10:g.56083236C>T</td><td>NC_000017.10:g.56083237C>T</td><td>NC_000017.10:g.56083236C>T</td><td>NC_000017.10:g.56082935dup</td><td>NC_000017.10:g.(55806534_56540597)del</td><td>NC_000017.10:g.56084417G>A</td><td>NC_000017.10:g.56083166T>C</td><td>NC_000017.10:g.56084380C>A</td><td>NC_000017.10:56084402C>A</td><td>NC_000017.10:g.56082914del</td><td>NC_000017.10:g.56083875C>T</td><td>NC_000017.10:g.56084428G>A</td><td>NC_000017.10:56083852A>C</td><td>NC_000017.10:56083832A>C</td><td>NC_000017.10:g.(55442363-56309063)del</td></tr><tr><td>Transcript (NM_006924.5)</td><td>c.377_378del</td><td>c.478G>A</td><td>c.478G>A</td><td>c.478G>A</td><td>c.579dup</td><td>-</td><td>c.82C>T</td><td>c.548A>G</td><td>c.119G>T</td><td>c.97G>T</td><td>c.601del</td><td>c.208G>A</td><td>c.71C>T</td><td>c.231T>G</td><td>c.251T>G</td><td>c.130G>A</td><td>-</td></tr><tr><td>Protein</td><td>p.(Ser126Trpfs*17)</td><td>p.(Val160Met)</td><td>p.(Val160Met)</td><td>p.(Val194Serfs*2)</td><td>-</td><td>p.(Arg28*)</td><td>p.(His183Arg)</td><td>p.(Gly40Val)</td><td>p.(Glu33*)</td><td>p.(Ser201Valfs*87)</td><td>p.(Ala70Thr)</td><td>p.(Pro24Leu)</td><td>p.(Tyr77*)</td><td>p.(Leu84Arg)</td><td>p.(Asp44Asn)</td><td>-</td><td>-</td></tr><tr><td>Mode of inheritance</td><td>Undetermined</td><td>De novo</td><td>De novo</td><td>De novo</td><td>De novo</td><td>De novo</td><td>De novo</td><td>De novo</td><td>De novo</td><td>De novo</td><td>De novo</td><td>De novo</td><td>De novo</td><td>De novo</td><td>De novo</td><td>De novo</td><td>De novo</td></tr><tr><td>Pregnancy / Birth</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Pregnancy complications</td><td>IUGR</td><td>Poor active fetal movements</td><td>C-section</td><td>No</td><td>Ovarian stimulation C-section for breech positioning</td><td>No</td><td>Premature rupture of membranes at 31w+2d</td><td>No</td><td>C-section for fetal distress</td><td>No follow up</td><td>No</td><td>No</td><td>No</td><td>Toxemia C-section</td><td>Screening test positive: amniocentesis with karyotype 46,XY</td><td>No</td><td>Increased nuchal translucency</td></tr><tr><td>Gestational weeks (weeks + days)</td><td>36</td><td>40</td><td>38+4</td><td>Full term</td><td>39</td><td>37+1</td><td>31+2</td><td>38</td><td>Full term</td><td>ND</td><td>Full Term</td><td>41+2</td><td>39+2</td><td>41</td><td>39</td><td>38+6</td><td>ND</td></tr><tr><td>Birth weight g (SD)</td><td>1700 (-2.6)</td><td>3300 (-0.5)</td><td>3180 (M)</td><td>2920 (-1.5)</td><td>3100 (-0.5)</td><td>2550 (-0.6)</td><td>1765 (+0.6)</td><td>2790 (-0.6)</td><td>3827 (+0.5)</td><td>ND</td><td>3430 (0)</td><td>3700 (+0.6)</td><td>3997 (+1.7)</td><td>ND</td><td>2980 (-1)</td><td>3065 (+0.1)</td><td>ND</td></tr><tr><td>Birth length cm (SD)</td><td>39 (-3.6)</td><td>49.5 (-0.4)</td><td>51 (+1)</td><td>45.5 (-2.8)</td><td>51 (0.6)</td><td>46 (-0.7)</td><td>40.5 (-0.2)</td><td>45 (-1.8)</td><td>53.5 (+1.3)</td><td>ND</td><td>50 (0)</td><td>34 (-0.5)</td><td>50.8 (+0.9)</td><td>ND</td><td>50 (0)</td><td>51 (+1.3)</td><td>ND</td></tr><tr><td>Birth OFC cm (SD)</td><td>30.5 (-1.5)</td><td>35 (+0.4)</td><td>35 (+0.4)</td><td>35 (-0.1)</td><td>34 (-0.3)</td><td>32 (-1)</td><td>30 (+0.4)</td><td>33.5 (-0.2)</td><td>35 (-0.2)</td><td>ND</td><td>34 (-0.1)</td><td>34 (-0.5)</td><td>ND</td><td>ND</td><td>35 (+0.2)</td><td>34 (+0.2)</td><td>ND</td></tr><tr><td>Neonatal complications</td><td>Oxygen requirement because of pulmonary hypoplasia</td><td>No</td><td>Congenital torticollis</td><td>RCIU, hypocalcemia</td><td>No</td><td>Feeding difficulties Icterus</td><td>No</td><td>No</td><td>Congenital heart defect</td><td>No</td><td>Transient tachypnea Increased muscle tone</td><td>No</td><td>Icterus</td><td>Neonatal hypotonia Sucking difficulties</td><td>No</td><td>Icterus</td><td>Transient tachypnea of the newborn</td></tr><tr><td>Growth</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Age at last examination (Year + month)</td><td>18 years</td><td>4 years and 9 months</td><td>2 years</td><td>34 years</td><td>6 years and 8 months</td><td>4 years</td><td>5 years and 2 months</td><td>3 years and 1 month</td><td>8 years and 5 months</td><td>23 years and 9 months</td><td>5 years</td><td>3 years and 8 months</td><td>2 years and 2 months</td><td>28 years</td><td>18years</td><td>14 months</td><td>13 years and 6 months</td></tr><tr><td>Height at last visit cm (SD)</td><td>152 (-1.8)</td><td>103 (-0.5)</td><td>86 (-0.4)</td><td>189.5 (+2)</td><td>119.5 (+0.1)</td><td>98.3 (-0.6)</td><td>112.8 (+0.7)</td><td>84 (-2.7)</td><td>128 (+0.1)</td><td>156 (-2.9)</td><td>109 (+0.3)</td><td>99 (-0.7)</td><td>84.2 (+0.9)</td><td>174 (-0.4)</td><td>180.5 (+0.7)</td><td>77.4 (+0.5)</td><td>144 (-2 SD)</td></tr><tr><td>Weight at last visit kg(SD)</td><td>54.2 (-0.3)</td><td>17.5 (+0.2)</td><td>11.6 (-0.8)</td><td>65.6 (-0.4)</td><td>20 (-0.5)</td><td>14.5 (-0.8)</td><td>19 (+0.1)</td><td>10 (-2.8)</td><td>28 (+0.4)</td><td>41 (-3.3)</td><td>19.7 (+0.5)</td><td>17.8 (+0.9)</td><td>12.7 (+0.2)</td><td>57.7 (-1.4)</td><td>57.6 (-1)</td><td>8.9 (-1.2)</td><td>ND</td></tr><tr><td>BMI at last visit kg/m2 (SD)</td><td>23.5 (+0.6)</td><td>16.5 (+0.8)</td><td>15.7 (-0.2)</td><td>18.3 (-1.6)</td><td>14 (-0.9)</td><td>15 (-0.2)</td><td>14.9 (-0.3)</td><td>14.2 (-1)</td><td>17.1 (+0.7)</td><td>16.8 (-2.4)</td><td>16.6 (+0.8)</td><td>18.2 (+1.8)</td><td>17.9 (+1.6)</td><td>19.1 (-0.9)</td><td>17.7 (-1.4)</td><td>14.9 (-0.9)</td><td>ND</td></tr><tr><td>OFC at last visit cm (SD)</td><td>55</td><td>51 (+0.6)</td><td>50 (+1)</td><td>59 (+2.7)</td><td>52.5 (+0.6)</td><td>48.5 (-0.7)</td><td>51 (-0.1)</td><td>45.5 (-2.9)</td><td>51.8 (-0.4)</td><td>54.5 (-0.4)</td><td>48.7 (-1.1)</td><td>49 (-1)</td><td>47.8 (+0.1)</td><td>56 (+0.6)</td><td>55 (-0.1)</td><td>47 (-1.1)</td><td>50 (-2.6 SD)</td></tr><tr><td>Failure to thrive</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>No</td><td>ND</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td><td>No</td><td>Infant reflux</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>Yes</td></tr><tr><td>Truncal overweight</td><td>Yes</td><td>No</td><td>Trunk adiposity</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Mildly overweight</td><td>No</td><td>No</td><td>No</td><td>No</td></tr></tbody></table>
<table><thead><tr><th>Seizures</th><th>No</th><th>No seizure Normal EEG</th><th>No</th><th>No</th><th>No</th><th>No</th><th>No clinical seizure reported EEG: some anomalies during the process of falling asleep, not associated with clinical signs</th><th>No</th><th>No seizure Normal EEG</th><th>Febrile seizure (one time in infancy)</th><th>No</th><th>No</th><th>No</th><th>Myoclonus with fever</th><th>No</th><th>Staring spells with deviant gaze Normal EEG</th><th>No</th></tr></thead><tbody><tr><td>Brain MRI</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Structural anomalies</td><td>Global widening of the ventricular system, cortical-subcortical atrophy, low amount of myelin</td><td>Myelinisation delay</td><td>Normal MRI</td><td>Normal MRI</td><td>Normal MRI</td><td>ND</td><td>Thin splenium of the corpus callosum Leptomeningeal cyst of the left temporal lobe</td><td>Microcephaly Small frontal lobes with abnormal white matter intensity Simplification of gyral pattern Delayed subcortical white matter myelination Small brain stem Atrophic lateral rectus muscles</td><td>Normal MRI</td><td>Normal MRI</td><td>Normal MRI</td><td>Normal MRI</td><td>Old bilateral caudothalamic groove (germinal matrix) hemorrhages and few punctate foci of old hemorrhage in the cerebellum. Few scattered foci of periventricular and subcortical T2 hyperintensity Nonspecific but may be sequelae of prior injury.</td><td>ND</td><td>ND</td><td>Midline pituitary cyst which may represent pars intermedia cyst or Rathe's cleft cyst Otherwise normal</td><td>ND</td></tr><tr><td>Congenital malformations</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Cardiac</td><td>ASD Pericardial effusion</td><td>Small ovale foramen</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Tricupsid valve leakage</td><td>ND</td><td>Ventricular septal defect (spontaneous closure) Right aortic arch Aberrant subclavian artery with subclavian steal</td><td>Minimal aortic regurgitation Mitral regurgitation</td><td>No</td><td>No</td><td>No</td><td>Myxoid dystrophy Cardiomegaly Mitral insufficiency</td><td>No</td><td>No</td><td>ND</td></tr><tr><td>Urogenital / kidney</td><td>No</td><td>No</td><td>Right hydrocele</td><td>Cryptorchidism, hypospadia</td><td>No</td><td>ND</td><td>No</td><td>No</td><td>Bilateral cryptorchidism</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Balanopreputial hypospadias</td><td>No</td><td>ND</td></tr><tr><td>Other</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>Hernia, diastasis</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Skeletal abnormalities</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Scoliosis</td><td>Yes</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>Yes</td><td>No</td><td>ND</td></tr><tr><td>Pectus deformity</td><td>No</td><td>No</td><td>No</td><td>Pectus excavatum</td><td>No</td><td>Pectus carinatum</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>Pectus excavatum</td><td>Pectus carinatum</td><td>No</td><td>ND</td></tr><tr><td>Other</td><td>Genu valum, Equinovarus</td><td>-</td><td>-</td><td>Marfanoid habitus with arachnodactyly and elbow flessum</td><td>Brachymetatarsi a, fallen arches</td><td>-</td><td>-</td><td>-</td><td>L1 vertebral hypoplasia with associated kyphosis, has improved with age</td><td>Asymmetric chest</td><td>-</td><td>-</td><td>-</td><td>Marfanoid habitus with dolichostenomel, arachnodactyly, flat feet and elbow flessum Equinovarus Postaxial hexadactyly on left foot Onydhodystrophy</td><td>Marfanoid habitus with arachnodactyly Genu valgum Metatarsus varus</td><td>-</td><td>Genu valgum</td></tr><tr><td>Facial features</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
<table><tr><td>Facial features</td><td>Arched eyebrow<br>High and prominent<br>forehead<br>Frontal bossing<br>Asymmetric<br>occipital<br>plagiocephal<br>Triangular face<br>Low anterior<br>hairline<br>Prominent eyes<br>Small, low-set<br>ears<br>Wide nasal<br>bridge<br>Short philtrum<br>High-arched<br>palate<br>Downturned of<br>corners of<br>mouth<br>Prominent chin</td><td>Thin upslanted<br>eyebrowes<br>Uplansted<br>palpebral<br>fissures<br>Deep eyes,<br>epicanthus<br>Low-set ears<br>Long philtrum<br>Downturned of<br>corners of<br>mouth<br>Chin crease</td><td>Upslanted<br>palpebral<br>fissures<br>Deep eyes,<br>epicanthus<br>Low-set ears<br>Long philtrum<br>Downturned of<br>corners of<br>mouth<br>Chin crease</td><td>Aquiline nose<br>Retrognathia<br>Protuding ears<br>High arched<br>palate<br>Malar hypoplasia</td><td>Long face<br>Horizontal<br>eyebrows<br>Bilateral<br>épićanthus<br>Posterior<br>rotating ears<br>Curved upper lip<br>Buccal hypotonia<br>High arched<br>palate</td><td>Downslanting<br>palpebral<br>fissures<br>Smooth philtrum<br>Low-set ears<br>Exaggerated<br>cupid's bow</td><td>Hypertelorism<br>Mild<br>micrognathia<br>Smooth<br>philtrum<br>Thin lips<br>Gengival<br>hyperplasia<br>Wide nasal<br>bridge<br>Low set ears</td><td>No dysmorphic -<br>appropriate for<br>family</td><td>Brachycephaly<br>Malar<br>hypoplasia<br>Left preauricular<br>pit<br>Narrow ear<br>canals<br>Smooth philtrum<br>Thin upper lip<br>vermilion<br>Mildly<br>broad/low<br>posterior<br>hairline</td><td>Downslanting<br>palpebral<br>fissures<br>Strabismus<br>Maxiller<br>hypoplasia</td><td>Synophris<br>Highly arched<br>eyebrow<br>Tubular nose<br>with bulbous tip<br>Thin upper lips<br>Clinical suspicion<br>of Comelia<br>deLange<br>syndrome</td><td>Thin upper lip<br>Hypertelorism<br>Short chin</td><td>Full cheeks<br>Epicthalial folds<br>Telecanthus<br>Thin upper lip</td><td>Oval face<br>Thick lips<br>Pointed chin<br>Long and thin<br>nose<br>Microretrognath<br>ism<br>Downslanted<br>palpebral<br>fissures</td><td>Triangular face<br>with facial<br>asymmetry<br>Upslanging<br>palpebral<br>fissures<br>Long and tubular<br>nose<br>Long and<br>smooth philtrum<br>Retrognathia</td><td>Downslanted<br>palpebral<br>fissures<br>Depressed nasal<br>bridge<br>Midly<br>downturned<br>corners of the<br>mouth</td><td>Elongated face<br>Low hair line<br>Ptosis</td></tr><tr><td>Other features</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Cutaneous<br>abnormalities</td><td>Stretch marks</td><td>ND</td><td>No</td><td>No</td><td>Truncal and<br>upper limb<br>hypertrichosis<br>Palmar<br>erythema</td><td>Hirsutism</td><td>No</td><td>No</td><td>No</td><td>Light skin color<br>Multiple freckles</td><td>No</td><td>No</td><td>No</td><td>-</td><td>No</td><td>No</td></tr><tr><td>Additional clinical<br>features</td><td>Missing teeth<br>Endometriosis<br>Mixed malignant<br>germ cell tumor<br>of the left ovary</td><td>Relative pain<br>insensitivity</td><td>Relative pain<br>insensitivity</td><td>No</td><td>Unexplained<br>episodes of<br>thrombocytosis</td><td>Sacro-coccygeal<br>dimple</td><td>No</td><td>Severe<br>constipation</td><td>No</td><td>No</td><td>No</td><td>Sleeping<br>problems</td><td>Sparse hair<br>2 hemangiomas</td><td>Moebius<br>syndrome</td><td>Bifid uvula</td><td>No</td></tr><tr><td>Additional<br>molecular features</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>NM_000195.3;<br>c.973_974insC,<br>p.Met325Thrfs*<br>128<br>Diagnosed with<br>Hermansky<br>Pudlak<br>Syndrome</td><td>-</td><td>MBD6 variant<br>NM_052897.3:c.<br>2337dupT</td><td>-</td><td>-</td><td>-</td><td>-</td><td>15q11.2 BP1-BP2<br>microdeletion</td></tr></table>

a ND, no data;

b OFC, occipito-frontal circumference;

c SD, standard deviation;

d +, present;

e -, not present
# Supplemental methods

## Exome sequencing

Representative exome sequencing analyses are reported here below.

**Individual 8:** whole exome sequencing and data processing were performed by the Genomics Platform at the Broad Institute of MIT and Harvard with an Illumina Nextera or Twist exome capture (~38 Mb target) and sequenced (150 bp paired reads) to cover >80% of targets at 20x and a mean target coverage of >100x. Exome sequencing data was processed through a pipeline based on Picard and mapping done using the BWA aligner to the human genome build 38. Variants were called using Genome Analysis Toolkit (GATK) HaplotypeCaller package version 3.5.

**Individual 9:** Using genomic DNA from the proband and parents, the exonic regions and flanking splice junctions of the genome were captured using the IDT xGen Exome Research Panel v1.0 (Integrated DNA Technologies, Coralville, IA). Massively parallel (NextGen) sequencing was done on an Illumina system with 100bp or greater paired-end reads. Reads were aligned to human genome build GRCh37/UCSC hg19, and analyzed for sequence variants using a custom-developed analysis tool. Reported variants were confirmed, if necessary, by an appropriate orthogonal method in the proband and, if submitted, in selected relatives. Additional sequencing technology and variant interpretation protocol has been previously described.² The general assertion criteria for variant classification are publicly available on the GeneDx ClinVar submission page (http://www.ncbi.nlm.nih.gov/clinvar/submitters/26957/)

**Individual 15:** WES methods and data processing, including sequence alignment to GRCh37/hg19, variant filtering and prioritization, predicted functional impact, inheritance and validation, were performed as previously reported.³ In Brief: WES sequencing was performed on the trio (patient and both parents) was performed at the IRCCS Burlo Garofolo (Trieste, Italy). According to the manufacturer, DNA was processed for library enrichment with the Twist Human Core for Enrichment-Exome panel (Twist). Sequencing was performed on NextSeq 500 (Illumina, San Diego, USA) instrument. Sequencing reads were aligned to the human reference genome (GRCh37/hg19) employing the BWA-mem tool, and variant calling were performed with GATK v4.1.2 HaplotypeCaller (EnGenome, Pavia, Italy). Annotation and prioritization of the variants were conducted with eVAI-enGenome software based on the American College of Medical Genetics and Genomics (ACMG) guidelines.⁴ SNVs and INDELs were filtered referring to public databases (dbSNP build150, NHLBI Exome Sequencing Project (ESP), Exome Aggregation Consortium (ExAC) database, Genome Aggregation Database (gnomAD)) and led to ruling out those variants previously reported as polymorphism. In particular, a minor allele frequency (MAF) cut-off of ≤0.01% was utilized. The trio was examined for the following inheritance patterns: de novo, homozygous recessive, compound
heterozygous, and hemizygous. The pathogenicity of variants was assessed with the ClinVar, The Human Gene Mutation Database (HGMD), Online Mendelian Inheritance in Man (OMIM), and DECIPHER. Several in silico tools, such as PolyPhen-2,⁵ Sorting Intolerant from Tolerant (SIFT),⁶ PaPI,⁷ DANN,⁸ dbscSNV score,⁹ and Combined Annotation Dependent Depletion (CADD) score,¹⁰ were applied to establish the pathogenicity of novel variants. The diagnostic procedure adopted included discussion of WES data in the context of phenotypic data at interdisciplinary meetings; the interpretation of WES data was supported by systematic bibliographic review and public database consultation.¹¹ Variants' validation and segregation analyses were carried out by Sanger sequencing.

## A quantitative in vivo splicing assay in *Drosophila* to model human SRSF1 activity

To develop an *in vivo* readout for human SRSF1 function, we used *Drosophila* as a model system. Human SRSF1 and its fly ortholog, SF2 (DIOPT weighted score of 12.87, https://www.flyrnai.org/cgi-bin/DRSC_orthologs.pl), are evolutionarily highly conserved and show an overall identity of 63% increasing to 81% and 76% in RRM1 and RRM2 respectively (Figure S2A). In accordance, when we aligned the human and fly protein models from the AlphaFold Protein Structure Database, we observed that both RRM domains adopt a nearly identical folding. Even the prediction for the flexible parts outside the RRMs was very similar (Figure S2B). These data support structural and functional conservation of both proteins.

We therefore generated transgenic fly lines expressing wild-type (WT) and mutant human SRSF1 and fly SF2. We used PhiC31 integrase-mediated insertion to minimize the influence of genomic background on expression levels. We performed a transcriptome analysis in our overexpression lines (Figure S2C). The alternative splicing events induced by SRSF1 and SF2 proteins were very similar, confirming the functional conservation of the two proteins at the molecular level. Alternative 5' splicing and exon skipping events were the splicing events with the main alterations, in line with the reported splicing activity of SRSF1.¹²⁻¹⁴ Furthermore, more than 60% of the altered genes are shared between both proteins and pathway analysis on the differentially spliced genes shows enrichment for genes involved in morphogenesis and development (Figure S4).

We therefore ectopically expressed SRSF1 and SF2 in the visual system. Eye-specific expression of human SRSF1 and fly SF2 resulted in a strong rough eye phenotype characterized by disorganized ommatidia, misorientation of the interommatidial bristles, heterogenous depigmentation and necrotic spots (Figure S2D), supporting the functional homology of both proteins. To causally link the splicing alterations with the observed eye phenotype, we generated *SRSF1* constructs harboring biochemically characterized amino acid changes (Figure S2D).¹⁴⁻¹⁶
SRSF1 p.Ile32Ala/p.Val35Ala and p.Phe56Asp/p.Phe58Asp (hereafter indicated as I32A/V35A and F56D/F58D respectively) completely destabilize U1snRNP recruitment to the ESE binding side. Therefore, they can be considered splicing-defective variants. SRSF1 I32A/V35A specifically intervenes with the U1-70K binding sites of RRM1 and F56D/F58D abolishes the RNA binding capacity of RRM1.¹⁶ Both splicing deficient SRSF1 proteins lost their capacity to induce an eye phenotype showing that recruitment and stabilization of the U1 snRNP at the 5'SS is necessary to induce the phenotype. Furthermore, a control variant p.Asp66Ala/p.Asp69Ala (D66A/D69A) with preserved splicing activity retained this capacity.¹⁶ SRSF1 p.Lys38Ala/p.Tyr39Ala (K38A/Y39A) with limited splicing activity presented with an intermediate phenotype accordingly.¹⁶ The phenotype-inducing capacity of SRSF1 p.Lys138Ala (K138A), a variant abolishing specifically the functionality of the RRM2 or pseudo RRM, was not altered compared to a control variant p.Tyr149Ala (Y149A) with retained capacity.¹⁵ We thus quantified the severity of the eye phenotype based on the roughness (IREG-score, **Figure S2E**) and depigmentation level of all RRM1 variants (**Figure S2F**). We observed a clear difference in both readouts between splicing active (D66A/D69A) and inactive species (I32A/V35A; F56D/F58D and F56D/F58D/K138A). To confirm that these results were not tissue-dependent, we tested the effect of overexpression of SRSF1 active and splicing deficient species in the nervous system (**Figure S2G**). Misexpression of wild-type SRSF1 in the nervous tissue resulted in pharate adult lethality, which is absent in flies overexpressing a splicing-deficient SRSF1. Together these results indicate that the modeling mainly addresses the stabilization of the U1 snRNA on the pre-mRNA by SRSF1 and that this model can be used to study the splicing activity of human SRSF1 *in vivo* in a quantitative manner.

## Acknowledgements

We thank the University of Burgundy "Centre de Calcul" (CCuB) for technical support and management of the informatics platform. We also thank the Undiagnosed Diseases Network International (UDNI) for contributing to affected individual identification. This work was made possible only thanks to international data sharing through GeneMatcher.¹⁷ Our cohort is composed of 17 individuals from 13 different medical centers and 6 different countries. We submitted our first individual with a SRSF1 variant in 2019 and we have been able to gather 16 additional individuals in three years. GeneMatcher is a powerful tool to create interactions between researchers, and has been essential to accelerate the identification of novel disease genes in the last years.¹⁸,¹⁹
Supplemental references

1. Chevarin, M., Duffourd, Y., A. Barnard, R., Moutton, S., Lecoquierre, F., Daoud, F., Kuentz, P., Cabret, C., Thevenon, J., Gautier, E., et al. (2020). Excess of de novo variants in genes involved in chromatin remodelling in patients with marfanoid habitus and intellectual disability. J. Med. Genet. 57, 466-474. 10.1136/jmedgenet-2019-106425.

2. Retterer, K., Juusola, J., Cho, M.T., Vitazka, P., Millan, F., Gibellini, F., Vertino-Bell, A., Smaoui, N., Neidich, J., Monaghan, K.G., et al. (2016). Clinical application of whole-exome sequencing across clinical indications. Genet. Med. Off. J. Am. Coll. Med. Genet. 18, 696-704. 10.1038/gim.2015.148.

3. Musante, L., Faletra, F., Meier, K., Tomoum, H., Najarzadeh Torbati, P., Blair, E., North, S., Gärtner, J., Diegmann, S., Beiraghi Toosi, M., et al. (2022). TTC5 syndrome: Clinical and molecular spectrum of a severe and recognizable condition. Am. J. Med. Genet. A. 188, 2652-2665. 10.1002/ajmg.a.62852.

4. Richards, S., Aziz, N., Bale, S., Bick, D., Das, S., Gastier-Foster, J., Grody, W.W., Hegde, M., Lyon, E., Spector, E., et al. (2015). Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. Off. J. Am. Coll. Med. Genet. 17, 405-424. 10.1038/gim.2015.30.

5. Adzhubei, I., Jordan, D.M., and Sunyaev, S.R. (2013). Predicting Functional Effect of Human Missense Mutations Using PolyPhen-2. Curr. Protoc. Hum. Genet. 76. 10.1002/0471142905.hg0720s76.

6. Sim, N.-L., Kumar, P., Hu, J., Henikoff, S., Schneider, G., and Ng, P.C. (2012). SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Res. 40, W452-457. 10.1093/nar/gks539.

7. Limongelli, I., Marini, S., and Bellazzi, R. (2015). PaPI: pseudo amino acid composition to score human protein-coding variants. BMC Bioinformatics 16, 123. 10.1186/s12859-015-0554-8.

8. Quang, D., Chen, Y., and Xie, X. (2015). DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinforma. Oxf. Engl. 31, 761-763. 10.1093/bioinformatics/btu703.

9. Jian, X., Boerwinkle, E., and Liu, X. (2014). In silico prediction of splice-altering single nucleotide variants in the human genome. Nucleic Acids Res. 42, 13534-13544. 10.1093/nar/gku1206.

10. Kircher, M., Witten, D.M., Jain, P., O'Roak, B.J., Cooper, G.M., and Shendure, J. (2014). A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310-315. 10.1038/ng.2892.
11. Musante, L., Costa, P., Zanus, C., Faletra, F., Murru, F.M., Bianco, A.M., La Bianca, M., Ragusa, G., Athanasakis, E., d'Adamo, A.P., et al. (2022). The Genetic Diagnosis of Ultrarare DEEs: An Ongoing Challenge. *Genes* **13**, 500. 10.3390/genes13030500.

12. Paz, S., Ritchie, A., Mauer, C., and Caputi, M. (2021). The RNA binding protein SRSF1 is a master switch of gene expression and regulation in the immune system. *Cytokine Growth Factor Rev.* **57**, 19–26. 10.1016/j.cytogfr.2020.10.008.

13. Jobbins, A.M., Campagne, S., Weinmeister, R., Lucas, C.M., Gosliga, A.R., Clery, A., Chen, L., Eperon, L.P., Hodson, M.J., Hudson, A.J., et al. (2022). Exon-independent recruitment of SRSF1 is mediated by U1 snRNP stem-loop 3. *EMBO J.* **41**. 10.15252/embj.2021107640.

14. Cho, S., Hoang, A., Chakrabarti, S., Huynh, N., Huang, D.-B., and Ghosh, G. (2011). The SRSF1 linker induces semi-conservative ESE binding by cooperating with the RRMs. *Nucleic Acids Res.* **39**, 9413–9421. 10.1093/nar/gkr663.

15. Cléry, A., Sinha, R., Anczuków, O., Corrionero, A., Moursy, A., Daubner, G.M., Valcárcel, J., Krainer, A.R., and Allain, F.H.-T. (2013). Isolated pseudo-RNA-recognition motifs of SR proteins can regulate splicing using a noncanonical mode of RNA recognition. *Proc. Natl. Acad. Sci. U. S. A.* **110**, E2802-2811. 10.1073/pnas.1303445110.

16. Cho, S., Hoang, A., Sinha, R., Zhong, X.-Y., Fu, X.-D., Krainer, A.R., and Ghosh, G. (2011). Interaction between the RNA binding domains of Ser-Arg splicing factor 1 and U1-70K snRNP protein determines early spliceosome assembly. *Proc. Natl. Acad. Sci. U. S. A.* **108**, 8233–8238. 10.1073/pnas.1017700108.

17. Sobreira, N., Schiettecatte, F., Valle, D., and Hamosh, A. (2015). *GeneMatcher: A Matching Tool for Connecting Investigators with an Interest in the Same Gene*. *Hum. Mutat.* **36**, 928–930. 10.1002/humu.22844.

18. Bruel, A.-L., Vitobello, A., Mau-Them, F.T., Nambot, S., Duffourd, Y., Quéré, V., Kuentz, P., Garret, P., Thevenon, J., Moutton, S., et al. (2019). 2.5 years' experience of GeneMatcher data-sharing: a powerful tool for identifying new genes responsible for rare diseases. *Genet. Med.* **21**, 1657–1661. 10.1038/s41436-018-0383-z.

19. Julkowska, D., Austin, C.P., Cutillo, C.M., Gancberg, D., Hager, C., Halftermeyer, J., Jonker, A.H., Lau, L.P.L., Norstedt, I., Rath, A., et al. (2017). The importance of international collaboration for rare diseases research: a European perspective. *Gene Ther.* **24**, 562–571. 10.1038/gt.2017.29.