# Systematic screening of autosomal dominant tubulointerstitial kidney disease-MUC1 27dupC pathogenic variant through exome sequencing

Ilias Bensouna, Thomas Robert, Xavier Vanhoye, Marine Dancer, Laure Raymond, Pierre Delaugère, Pascale Hilbert, Hugues Richard, Laurent Mesnard

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## To cite this version:

Ilias Bensouna, Thomas Robert, Xavier Vanhoye, Marine Dancer, Laure Raymond, et al. Systematic screening of autosomal dominant tubulointerstitial kidney disease-MUC1 27dupC pathogenic variant through exome sequencing. Journal of the American Society of Nephrology, 2025, 36 (2), pp.256-263. 10.1681/ASN.0000000503. hal-04781152

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# Systematic screening of Autosomal Dominant Tubulointerstitial Kidney Disease-*MUC1* 27dupC pathogenic variant through exome sequencing

Ilias Bensouna¹,² (MD), Thomas Robert³,⁴ (MD, PhD), Xavier Vanhoye⁵ (Pharm.D), Marine Dancer⁵ (Pharm.D, PhD), Laure Raymond⁵ (Pharm.D), Pierre Delaugère⁶, Pascale Hilbert⁷ (PhD), Hugues Richard⁸,⁹ (PhD), Laurent Mesnard¹,²,¹⁰,¹¹ (MD, PhD)

1 Soins Intensifs Néphrologiques et Rein Aigu (SINRA), Nephrology Department, Tenon Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France

2 Inserm UMR_S1155, Paris, France

3 Nephrology and Kidney Transplantation Center, Assistance publique-Hôpitaux de Marseille, La Conception Hospital, Marseille, France

4 Aix Marseille Univ, INSERM, INRAE, C2VN, Marseille, France

5 Eurofins Biomis laboratory, Lyon, France

6 ISCD, Maison des Modélisations Ingénieries et Technologies, Sorbonne University

7 Center of Human Genetics, Institute of Pathology and Genetics, B-6041 Gosselies, Belgium

8 UMR_7238 - Biologie Computationnelle et Quantitative, Sorbonne University, Paris, France

9 Genome Competence Center (MF1) - Robert Koch Institute, Berlin, Germany

10 Centre Maladie Rare MAHREA, Tenon Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France

11 Medicine Faculty, Paris Sorbonne University, Paris, France

**Corresponding author:** laurent.mesnard@aphp.fr

Soins Intensifs Néphrologiques et Rein Aigu (SINRA), Nephrology Department, Tenon Hospital
4 rue de la Chine, 75020 Paris, France.

**Abstract word count:** 324

**Text word count:** 3563

**Number of figures:** 2 + 4 supplementary

**Number of tables:** 2 + 1 supplementary

**Key points:**

* *MUC1* is associated with autosomal dominant tubulointerstitial kidney disease, a genetic disorder progressing to kidney failure.

* Variations in this gene are not easily diagnosed by conventional sequencing methods due to the length of sequencing reads produced by current sequencers and the *MUC1* gene architecture, which contains GC-rich repeated sequences with a variable number of tandem repeats (VNTR).

* Using dedicated bioinformatics tools, we systematically detected the presence of 27dupC, the most common *MUC1* pathogenic variant from exome sequencing data.
**Abstract**

**Background**

The *MUC1* gene is associated with autosomal dominant tubulointerstitial kidney disease (ADTKD), leading to chronic kidney disease (CKD). Current methods of sequencing, like exome sequencing, rarely detect *MUC1* pathogenic variants because of the variable number of tandem repeats (VNTR) in *MUC1* exon2. We demonstrated that combining fast read filtering with a sensitive VNTR genotyping strategy enables systematic screening of 27dupC pathogenic *MUC1* variant from exome data.

**Methods**

We initially validated our bioinformatics pipeline in a proof-of-concept cohort incorporating exome data from 33 participants with a known *MUC1* pathogenic variant identified by Snapshot PCR and confirmed by 54 *MUC1*-negative individuals for negative control. We then retrospectively analyzed exome sequencing data from January 2019 to October 2023 from 3512 adult participants with nephropathy of unknown origin. Finally, we prospectively validated our pipeline in 825 additional participants enrolled from November 2023.

**Results**

SharkVNTyper accurately identified *MUC1* variants in 32 out of 33 participants and excluded its presence in all the 54 negative controls in the proof-of-concept cohort (sensitivity of 97%, specificity of 100%). Integration of Shark tool with VNTyper significantly reduced running time from 6-12 hours to 5-10 minutes per sample, allowing both retrospective and prospective analysis. In the retrospective cohort, SharkVNTyper identified 23 additional positive cases that were not suspected clinically and had been missed in the initial exome analysis; 18 of these cases were confirmed as carrying the *MUC1* 27dupC mutation by low-throughput Snapshot PCR. In the prospective cohort of 825 CKD cases, systematic screening discovered 13 positive cases, with 12 confirmed by PCR. Overall, of 63 participants (1.4% of 4653) with molecularly confirmed ADTKD-*MUC1*, comprehensive diagnoses and descriptions of the disease were available for 24 participants. Median age of kidney failure was 50 years, 38% exhibited bilateral multiple kidney cysts, 8% had early-onset gout, and 58% had arterial hypertension.

**Conclusions**

SharkVNTyper enables the analysis of highly repeated regions, such as the *MUC1* VNTR, and facilitates the systematic screening of ADTKD-*MUC1* from exome data, fostering 27dupC variation identification.
Introduction

The improvement of next-generation sequencing, mostly based on the high throughput sequencing of short reads, has revolutionized genomic analysis¹. Inherent to the length of reads produced, next-generation sequencing methods face limitations in detecting copy number variants (CNVs) or variants in regions with highly recurrent motifs or repeats.² This poses technical challenges for some clinical genetic diagnoses, particularly in diseases with genes involving variable number tandem repeat (VNTR) regions, such as the *MUC1* gene.

*MUC1*, encoding the transmembrane glycoprotein mucin-1, has been implicated in autosomal dominant tubulointerstitial kidney diseases (ADTKD), and is currently referred to as ADTKD-*MUC1*.³⁻⁵ The *MUC1* gene contains a distinctive region of 25 to 120 GC-rich VNTR repeats of 60 base pairs (20 amino acids). Notably, the number of VNTR repeats can significantly vary among individuals from 30 to 80. Additionally, VNTR repeat expansion and sequence may exhibit notable divergence across different participants. Therefore, significant differences may exist compared to a linear reference genome as healthy individuals with *MUC1* repeats in a family complicate the picture.⁶ Nevertheless, a cytosine insertion typically occurs in ADTKD-*MUC1* individuals, resulting in a 8C-homopolymer specifically located between positions 53 and 59 of the VNTR repeat unit. So far this variation – “27dupC” – has been found in more than 80% of ADTKD-*MUC1* participants.⁷ This insertion leads to a frameshift in the reading frame, generating a premature stop codon shortly after the VNTR domain, and consequently a truncated form of the mucin-1 protein.⁸ Kirby et al. and Wenzel and colleagues have studied several affected family members with the cytosine insertion within *MUC1* and revealed a common unit or patterns of sequences shared between all individuals, indicating that the structure of this VNTR is not completely random.⁹,¹⁰ Cases of *MUC1* variant detection by exome sequencing have been described where the variation affects the very first VNTR, facilitating the alignment of sequencing reads to the reference genome, but such occurrences appear to be somewhat anecdotal.¹¹

Currently, available diagnostic methods for ADTKD-*MUC1* include Snapshot PCR or immunohistochemistry of truncated proteins on kidney biopsies. These methods are either expensive, impractical for routine clinical use, or not tailored for systematic molecular screening.¹²⁻¹⁴ Recently, the VNTyper and the HaloPlex pipelines have been developed for fine-calling variants from the *MUC1* coding VNTR region from targeted next-generation sequencing panels. However, their applicability to larger datasets such as exome sequencing or genome sequencing, both usually associated with lower read depth, is under debate, partly due to issues related to computing scalability.³ Authors have pointed out that, in addition to the quality of reads, the applicability of VNTyper to exome sequencing or genome sequencing data can be limited due to an average coverage level around 80x to 30x,
respectively, compared to the optimal 300-500x coverage for which VNTyper has been built. The same limitations apply to the HaloPlex, which requires even higher read depth (>1000x) with specific HaloPlex probes.¹⁵

Current next-generation sequencing “targeted” trends rely on *in silico* analysis, exemplified by the Genomic England project PanelApps. These *in silico* approaches are cost-effective, particularly when applied to exome or genome sequencing data. This strategy appears relevant for the first-hand management of adult participants with unknown nephropathies. To bridge the gap between exome and genome sequencing data and the detection of *MUC1* variations, we further adapted the open source VNTyper pipeline by adding a filtering step using the Shark software.¹⁶,¹⁷ As opposed to VNTyper, this novel pipeline that we called SharkVNTyper allows computing scalability, with similar sensitivity and specificity. We prospectively tested its reliability for systematic detection of *MUC1* variation from exome sequencing data.

## Materials and methods

### SharkVNTyper pipeline for detecting *MUC1* VNTR pathogenic variants

The VNTyper pipeline, developed by Saei *et al.* in 2023, can detect the *MUC1* VNTR pathogenic variant from targeted sequencing data when the average sequence coverage is above 500x.¹⁸ This pipeline was built based on the open-source Kestrel mapping-free variant caller for short-read sequencing data.¹⁹ We fine-tuned VNTyper to scale it up, from panel sequencing data files (a few Mb) to much larger sequencing files (up to 100 Gb) such as the one generated from exome sequencing (coverage of 80x) or genome sequencing (30x). To achieve this, we utilized Shark (Denti et al., 2021, available at: https://github.com/AlgoLab/shark), a tool that uses Bloom filters to efficiently retrieve short reads corresponding only to a given set of gene sequences (in our case the *MUC1* gene). This filtering step significantly reduces the processing time of the variant-calling steps.¹⁶,¹⁷ Given the variability of *MUC1* sequences among individuals, we curated a comprehensive set of high quality *MUC1* alleles from previous publications, and used it as a basis for filtering.⁹,¹⁰ This approach ensured that only reads relevant to *MUC1* were retained for analysis. To validate the effectiveness of this workflow, combining Shark and VNTyper (called SharkVNType), we compared results obtained from both unfiltered fastq files and ‘sharded’ files, in terms of both accuracy and running time. The workflow of SharkVNTyper is illustrated in **Supplemental Figure S1**.

### Study population

We initially established a proof-of-concept cohort by screening 333 individuals with a clinical suspicion of ADTKD-*MUC1* between January 2019 and April 2024. All participants included in this cohort had
been previously analyzed negative by exome sequencing. Following Snapshot PCR detecting the 27dupC insertion, 33 individuals out of the 333 tested were positive, confirming that they were ADTKD-MUC1 cases belonging to 19 families, and serving as positive controls (Supplemental Figure S2 and Figure 1). The negative control group included 54 participants with a negative 27dupC result using Snapshot PCR and a classical exome sequencing pipeline.

To further validate our methodology, we conducted a retrospective analysis from any exome sequencing data generated from adult participants (n=3512) with nephropathy of unknown origin, sequenced from January 2019 to October 2023, excluding participants who already had a positive Snapshot PCR for suspected ADTKD-MUC1 during the initial clinical screening described above in the proof-of-concept cohort.²⁰ Indeed, the integration of SharkVNTyper reduced the processing time per sample by more than 100-fold, enabling the complete retrospective analysis of a large cohort of exomes. We then initiated a prospective use of our pipeline from October 2023 to December 2023 (n = 825). Samples exhibiting insertions were further confirmed for positivity through Snapshot PCR, verifying the presence of the 27dupC pathogenic variant. The participants' flowchart is described in Figure 1.

Among the participants diagnosed with ADTKD-MUC1, those followed at Sorbonne University and La Conception Hospital constituted the cohort for phenotypic description.

The study was performed in accordance with the ethical standards of the Declaration of Helsinki. Written informed consent was obtained from all participants regarding de-identified clinical and personal patient data collection (including demographic data: age, sex, self-declared ethnicity), analysis, and publication. The study was approved by an Institutional Review Board (Direction de la Recherche Clinique et de l'Innovation (APHP220461)) and the Ethics board of Sorbonne Université (CER-2022-009). Phenotype data were prospectively collected from the clinician when exome sequencing had been prescribed for unknown nephropathy with suspicion of a genetic origin.

## Exome sequencing and variant analysis

DNA was extracted from peripheral blood using the QIAsymphony® DSP DNA Mini Kit on a QIAsymphony instrument following the manufacturer's (QIAGEN) guidelines. Library preparation and capture was performed with Twist reagents (Human Comprehensive Exome or Human Exome 2.0 Plus Comprehensive Exome Spike-in). Sequencing was performed on the Illumina NovaSeq6000 in paired-end mode (2x150 bp reads). Raw data (bcl format) were converted to fastq format using the BCL Convert. Reads were aligned to the human reference genome (UCSC Genome Browser build hg38) with BWA-MEM aligner.

## Statistical analysis
All tests were two-sided, and a P-value < 0.05 was considered statistically significant. Analyses were
performed using R statistical software version 4.3.0. Non-normally distributed continuous variables
are expressed as median (interquartile range [IQR]) and are compared using 2-tailed paired t tests.
To assess Positive Predictive Value (PPV), a Snapshot PCR was performed using an orthogonal method
for the participants with positive SharkVNTyper.

Results

Comparison of VNTyper performance with and without Shark software for short-read exome or
genome sequencing data

From the proof-of-concept cohort (n=333), 33 participants were ultimately confirmed positive for the 27dupC variant by Snapshot PCR, accounting for 10% of the cohort. With VNTyper alone, all 33 participants with known 27dupC insertion in the tested cohort were also positive, demonstrating 100% sensitivity. All 54 negative control participants yielded negative results, indicating 100% specificity. This demonstrates the validity of using exome sequencing data instead of panel sequencing for *MUC1* typing. However, the running time ranged from 6 to 12 hours for each sample, impeding systematic large cohort screening. Integration of the Shark tool with VNTyper reduced the running time per sample to an average of 7.5 minutes (+/- 2.5 minutes standard deviation). The use of VNTyper from those filtered files appeared to be much faster, requiring less than a minute per sample. Using SharkVNTyper only one patient out of 33 positive controls was missed (sensitivity of 97%). Nevertheless, all 54 negative controls remained negative, with 100% specificity. Thus, there is an element of tradeoff for systematic *MUC1* exome sequencing detection with SharkVNTyper (Table 1). To note, six out of the 33 positive control participants had 27insCCCC instead of the classical 27dupC. This atypical insertion was also found with SharkVNTyper, confirming that the pipeline can identify both variants. Performance was also tested on genome sequencing to assess its feasibility across different datasets: we tested 10 genome sequencing samples with one positive control. The computing time is described in Table 2. SharkVNTyper was negative for the positive control on genome sequencing and this is likely due to the reduced sequence coverage (~30x for genome sequencing against ~80x for exome sequencing). We did not perform extensive testing in a large genome sequencing dataset on the capability of our pipeline to detect *MUC1* with positive controls.

SharkVNTyper validation on exome cohort

We screened 3512 chronic kidney disease (CKD) participants mainly affected by unknown
nephropathies. Processing every sample from the retrospective cohort took 11 days of computing in
total. In comparison, using the original version of VNTyper would have taken between 2.5 years and 4
years for the whole cohort. SharkVNTyper identified 23 participants considered positive for the
presence of *MUC1* variants. To assess PPV, we conducted orthogonal Snapshot PCR tests on these 23 participants, out of whom 18 were eventually confirmed as having the well-known deleterious *MUC1* 27dupC variant, while 5 were tested negatives, meaning that the PPV was 78%. Even though negative Snapshot PCR results could potentially indicate other or yet unknown minor *MUC1* genetic variants, for the sake of our analysis we considered them false positives. In the prospective element of the study, we implemented systematic SharkVNTyper for any exome performed for CKD and identified the presence of a relevant variant in 13 prospective participants. Among these, 12 were confirmed as having a *MUC1* 27dupC variant using the orthogonal method, yielding a PPV of 92%. This allowed us to establish a minimum estimate of specificity within our cohort. In total, considering that any negative Snapshot PCR results with positive SharkVNTyper were true false positives, a worst-case scenario of six false positives out of 4301 negative samples led to a specificity of 99.9%.

**Description of ADTKD-MUC1 participants from exome sequencing for unknown kidney diseases cohort followed at Sorbonne University Hospitals and La Conception Hospital**

Of the 63 ADTKD-MUC1 participants, 35 cases from 24 families were followed at Tenon Hospital and La Conception Hospital, with descriptions of the disease available for the 24 index cases. Of these, 14 were initially clinically suspected while 10 participants in whom there was no clinical suspicion were retrospectively identified by SharkVNTyper test screening and confirmed by Snapshot PCR (Table 2). Of the 24 index participants, there was an equal male-to-female ratio, with the majority being Caucasians (75%). The median age for kidney failure onset was 51 years [40 – 68], consistent with previous studies.⁹,¹⁰ Phenotypically, 38% of participants exhibited bilateral multiple kidney cysts with only one exception. Additionally, 8% had a history of early gout onset (before the age of 45), and 58% had associated arterial hypertension. Family history of nephropathy was reported for 88% of the participants, meaning that some of the participants had ADTKD-MUC1 even without any known familial nephropathy. The main suspected mode of inheritance was dominant, although a significant proportion of participants without suspected ADTKD-MUC1 had a family history suggestive of an undetermined or recessive mode of inheritance (Table 1). There was significant variability in disease severity among participants, even within the same family.

Despite a large proportion of participants presenting with tubulointerstitial nephropathy (10/24 participants, 42%), the initial presentation was nevertheless heterogeneous. Nine out of 24 participants presented with undetermined nephropathy (38%), two with glomerular nephropathy (8%), and two with vascular nephropathy (8%), whilst one patient presented with cystic nephropathy (4%) (Supplemental Figure S3). Of these participants, 10 (42%) underwent kidney biopsy, revealing tubular lesions in most cases (6/10 participants). A diagnosis workflow is presented in Figure 2.
Atypical ADTKD-*MUC1* presentation

In our ADTKD-*MUC1* cohort, some participants exhibited atypical clinical presentation. A 21-year-old female patient was initially referred for familial cystic kidney disease with kidney failure. Her mother, maternal aunt and maternal grandfather all had multi-cystic kidney disease. Her mother experienced early-onset kidney failure at the age of 27. The patient had no associated early-onset gout, but a cleft palate was reported. Despite negative results from both exome and genome sequencing, a targeted search for a pathogenic variant in *MUC1* led to a molecular diagnosis. Similarly, a 51-year-old male patient was referred for a nephrogenomics consultation to confirm a suspicion of Alport syndrome. He presented glomerular proteinuria, stage 4 CKD, bilateral cysts, hearing impairment and kidney biopsy consistent with rare mesangial IgA deposits, focal segmental glomerulosclerosis lesions and nephroangiosclerosis. Electron microscopy analysis did not reveal any lesions suggestive of Alport syndrome. Exome sequencing was finally found to be negative, and the retrospective use of our pipeline enabled the identification of a pathogenic variant in *MUC1*. This case underscores the value of systematic research in addressing challenging kidney phenotypes.

Atypical clinical presentations were also associated with a second pathogenic variant in a second gene for two families in our cohort. In the first family, a kidney biopsy of a 58-year-old female patient confirmed tubulointerstitial nephropathy of unknown origin, with a single kidney cyst, leading to kidney failure at the age of 56. This was even though her family had been initially labelled as ADPKD due to a brother having a pathogenic variant in *PKD1* and a typical ADPKD phenotype. The patient's tubulointerstitial nephropathy without associated enlarged multi-cystic kidney prompted further investigation. This led to the identification of a pathogenic variant in *MUC1*, explaining her nephropathy. Her ADPKD brother was negative for *MUC1* variation and a third brother with ADTKD phenotype had a variant in *MUC1* but not in *PKD1*. The last brother, who died, had ADTKD with a variant in *MUC1*. The patient's two sisters, who had multi-cystic kidney disease and kidney failure, both had the familial variant in *MUC1* and *PKD1* (**Supplemental Figure S4**). On the other hand, a 68-year-old patient presented with glomerular nephropathy characterized by microscopic hematuria, focal segmental glomerulosclerosis lesions on kidney biopsy, bilateral kidney cysts and hearing impairment, raising suspicion of Alport syndrome. Although the presence of a heterozygous pathogenic variant in the *COL4A3* gene confirmed Alport syndrome, the severity of the phenotype, with kidney failure onset at age 36 years, prompted further investigation. SharkVNTyper revealed the concomitant presence of a pathogenic variant in *MUC1*, confirmed by Snapshot PCR, explaining the early onset of kidney failure with both clinical features of Alport syndrome and ADTKD-*MUC1* syndrome. Two other participants in the cohort had a second variant of interest in a second gene which warranted genetic counseling. One patient had a pathogenic variant in the *BRCA1* gene, leading to oncogenetic follow-up and discussion
of preventive mastectomy. Another patient had a heterozygous variant in the *CFTR* gene. The second variants found in another gene are described in **Supplemental Table S1**.

Discussion

To our knowledge, we are the first to use exome sequencing data to systematically screen for *MUC1* variation. *MUC1* presents a challenge for sequencing due to its VNTR in exon 2, making conventional sequencing methods demanding and difficult outside of Snapshot PCR. Moreover, the duplicated VNTR sequence varies among individuals. Within this VNTR, the pathogenic insertion of a cytosine (27dupC) results in a frameshift and a truncated protein, observed in over 90% of cases. Olinger et al. reported a 94% 27dupC pathogenic variant prevalence in ADTKD-*MUC1* participants with a molecular diagnosis in a cohort of 93 families.²¹ This insertion can be detected by Snapshot PCR, but it is a costly and low throughput method that can detect *only* 27dupC variants. As we increasingly transition to *in silico* panels based on exome or genome sequencing data, developing a rapid tool to detect pathogenic variations in *MUC1* directly from exome data becomes crucial.

VNTyper was developed for gene panel analysis data, with greater sequencing depth on target regions compared to exome studies. This method appears sensitive but its performance was assessed with very high-depth targeted sequencing data. We therefore sought to validate this tool using files from exome sequencing. The reduction in sequencing depth did not appear to affect the tool's diagnostic performance. We observed a slight decrease in sensitivity for the SharkVNTyper tool but without any loss of specificity. One significant challenge faced with VNTyper alone was the extended running time per sample with exome sequencing data, averaging 10 hours per sample. To mitigate this, we adopted Shark software, which significantly reduced the running time per sample by focusing only on the *MUC1* region of interest. The running time per sample decreased from the previous 6 to 12 hours to less than 10 minutes on average. This computational improvement allows large patient cohort screening or even routine diagnosis in cases of undetermined nephropathy. The SharkVNTyper tool was also able to detect a variant other than 27dupC, namely 27insCCCC. This could be attributed to the fact that 27insCCCC shares the same sequence of 8 consecutive cytosines. However, the ability of SharkVNTyper to detect variants in *MUC1* beyond these two remains unknown and should be addressed as *MUC1* datasets become more comprehensive.

SharkVNTyper is an easy-to-use complementary tool, even with exome sequencing data. We propose a diagnostic algorithm integrated into the bioinformatics processing of exome sequencing data. Concurrent with basic analysis, SharkVNTyper could be systematically included in the post-sequencing pipeline as a screening test to detect the potential presence of a pathogenic variant in *MUC1*. If a variant is detected within the run, an alert could be raised, enabling the biologist in charge to validate
the presence of this variant directly using an orthogonal approach, even before interpreting the exome
data. This would therefore be complementary information obtained alongside classic exome
sequencing data.

SharkVNTyper is an effective systematic screening test for the 27dupC variation in patients with unknown kidney disease explored through exome sequencing. Indeed, among the 4337 participants of the prospective and retrospective cohort, 4301 were negative with our test, and of these there were no cases of known ADTKD-MUC1. For participants with clinical suspicion of ADTKD-MUC1 who tested negative with SharkVNTyper, Snapshot PCR was performed to ensure the absence of 27dupC; all results were negative. This screening test should always be confirmed by a diagnostic test, such as Snapshot PCR. In cases of suspected ADTKD-MUC1, the diagnostic test should always take precedence over a negative screening test. Currently, we cannot validate the use of this method for detecting variants other than 27dupC. Further investigation with participants known to have other types of variants, confirmed by a secondary method, would be required to establish this capability.

Adding Shark to VNTyper, detection of 27dupC variant resulted in a negative detection in one of the 33 positive controls of the proof-of-concept cohort, although it was positive using VNTyper alone. In our opinion, the most probable explanation is that this participant's variant is included in a pattern that is not yet described and therefore not yet included in our pattern dictionary used as a reference by Shark. To improve SharkVNTyper, we continue to add new patterns to this dictionary as we discover them.

Employing a tool tailored to exome sequencing data for identifying the 27dupC variant in *MUC1* demonstrates increased efficacy for systematic screening. This approach combines the advantages of exome sequencing, especially with families with complex phenotypes or multiple variants of interest, while simultaneously highlighting the presence of a pathogenic variant in *MUC1*. It also enables the detection of this specific variant in patients with atypical presentation or with another variant previously found with exome sequencing. Importantly, a significant proportion of our cohort of ADTKD-*MUC1* participants had undergone kidney biopsy. Efficient genetic research could potentially obviate the need for this invasive procedure, with its associated high risk of bleeding complications.

Conclusion

Despite the historical challenges of exome sequencing associated with the VNTR structure of the *MUC1* gene, we demonstrate for the first time the feasibility of identifying the 27dupC variation in routine analysis. We showed that SharkVNTyper is an effective systematic screening tool for detecting the 27dupC variation from short-read next-generation sequencing exome data. It shows promise for routine clinical molecular diagnosis, offering the potential for uncovering previously unidentified
molecular diagnoses in adult CKD cases. Our findings highlight the utility of bioinformatics tools in identifying pathogenic variants in challenging genetic contexts, bypassing alignment to a reference genome. However, the detection of minor *MUC1* variants may still require additional exploration, particularly in families with such suspected variants.

**Table of contents of Supplemental Materials**

**Supplemental Figure S1:** Schematic of VNTyper and SharkVNTyper Pipeline Integration Workflow

**Supplemental Figure S2:** Snapshot PCR for detection of 27dupC

**Supplemental Figure S3:** Initial presentation of participants with ADTKD-*MUC1*

**Supplemental Figure S4:** Pedigree of a family with combined variants in *MUC1* and *PKD1*, demonstrating the utility of systematic screening for *MUC1*

**Supplementary Table S1:** Second Hit or Variant Found in Another Gene for Participants with ADTKD-*MUC1* in the ES-UKD Cohort

**Disclosures**

The authors have no conflicts of interest to declare. In particular, the authors have no personal potential financial conflicts of interest regarding the use of the technology described in this study.

**Funding**

**Acknowledgements**

We would like to thank the Genetics Department of Pitie-Salpêtrière Hospital (Anne-Sophie Lebre, Jean-Madeleine De Sainte Agathe, Julie Bogoin) for their technical support. We would also like to thank Edouard Henrion, Boris Lipinski, Fanny Ponce, Vincent Gassend and Barbara Adde from the Bioinformatics Department of Biomis Laboratory for their technical support.

**Author contributions**

**Data sharing statement**
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<table><caption>Table 1: Performance of VNTyper and SharkVNTyper on ES and GS data in adult participants with suspected autosomal dominant tubulointerstitial kidney disease</caption><thead><tr><th></th><th>Running time</th><th>Detection of MUC1 variant</th><th>Rejection of MUC1 variant</th></tr></thead><tbody><tr><th>VNTyper on ES data</th><td>6 – 12 hours</td><td>33 out of 33 positive controls</td><td>54 out of 54 negative controls</td></tr><tr><th>SharkVNTyper on ES data</th><td>10 minutes</td><td>32 out of 33 positive controls</td><td>54 out of 54 negative controls</td></tr><tr><th>SharkVNTyper on GS data</th><td>55 minutes</td><td>0 out of 1 positive control</td><td>9 out of 9 negative controls</td></tr></tbody></table>

Running time is expressed in minutes, except for VNTyper whose running time shows huge variability, depending on the sample.

ES = exome sequencing; GS = genome sequencing
<table><caption>Table 2: Patient characteristics from ADTKD-MUC1 cohort followed at Sorbonne University Hospitals and La Conception Hospital with available clinical data</caption><thead><tr><th rowspan="2">Characteristic<sup>a</sup></th><th colspan="3">MUC1</th></tr><tr><th>Not suspected<br>(N = 10)</th><th>Suspected<br>(N = 14)</th><th>Total<br>(N = 24)</th></tr></thead><tbody><tr><td>Age at diagnosis (years)</td><td>51 [47 - 54]</td><td>43 [36 - 69]</td><td>51 [40 - 68]</td></tr><tr><td>Male, n (%)</td><td>8 (80)</td><td>4 (29)</td><td>12 (50)</td></tr><tr><td>Geographic origin, n (%)</td><td></td><td></td><td></td></tr><tr><td>African</td><td>2 (20)</td><td>0 (0)</td><td>2 (8)</td></tr><tr><td>European</td><td>5 (50)</td><td>13 (93)</td><td>18 (75)</td></tr><tr><td>North African</td><td>3 (30)</td><td>1 (7)</td><td>4 (17)</td></tr><tr><td>Severity of CKD, n (%)</td><td></td><td></td><td></td></tr><tr><td>Mild (stage 1 to 3)</td><td>3 (30)</td><td>4 (29)</td><td>7 (29)</td></tr><tr><td>Severe (stage 4 or 5)</td><td>7 (70)</td><td>10 (71)</td><td>17 (71)</td></tr><tr><td>Kidney failure age (years)</td><td>46 [38 - 50]</td><td>54 [37 - 65]</td><td>50 [36 - 59]</td></tr><tr><td>Kidney cyst, n (%)</td><td>3 (30)</td><td>6 (43)</td><td>9 (38)</td></tr><tr><td>Arterial hypertension, n (%)</td><td>8 (80)</td><td>6 (43)</td><td>14 (58)</td></tr><tr><td>Early gout onset, n (%)</td><td>0 (0)</td><td>2 (14)</td><td>2 (8)</td></tr><tr><td>Positive family history of nephropathy, n (%)</td><td>7 (70)</td><td>14 (100)</td><td>21 (88)</td></tr><tr><td>Suspected mode of transmission, n (%)</td><td></td><td></td><td></td></tr><tr><td>Dominant</td><td>5 (100)</td><td>14 (100)</td><td>19 (100)</td></tr><tr><td>Undetermined</td><td>0 (0)</td><td>0 (0)</td><td>0 (0)</td></tr><tr><td>Recessive</td><td>0 (0)</td><td>0 (0)</td><td>0 (0)</td></tr><tr><td>Kidney biopsy, n (%)</td><td>4 (40)</td><td>6 (43)</td><td>10 (42)</td></tr><tr><td>History of TMA, n (%)</td><td>1 (10)</td><td>0 (0)</td><td>1 (4)</td></tr></tbody></table>

<footnote><sup>a</sup>For quantitative variables, values are expressed as median [interquartile ranges].</footnote>

<footnote>CKD = chronic kidney disease; TMA = thrombotic microangiopathy.</footnote>
FIGURES

**Figure 1: Workflow for SharkVNTyper development, the proof-of-concept cohort in 333 clinically suspected ADTKD participants, retrospective cohort of 3512 CKD participants, and prospective cohort of 825 participants**

(a) SharkVNTyper utilizes both Shark and VNTyper tools to detect the 27dupC pathogenic variant from fastq files of exome sequencing. (b) For the proof-of-concept cohort, we applied VNTyper alone (without Shark) and SharkVNTyper (the tool combining both Shark and VNTyper) to control participants to evaluate both methods. VNTyper alone was positive for all positive control participants, but when using Shark with VNTyper (SharkVNTyper), one of the participants tested negative. (c) After the proof-of-concept phase, SharkVNTyper was retrospectively applied to any exome sequencing case prescribed for unknown nephropathy from August 2021 to September 2023 (excluding 333 ADTKD participants allocated to a proof-of concept cohort from Sorbonne University). Confirmatory Snapshot PCR was performed to any SharkVNTyper-positive participants. (d) From October 2023 and onwards, SharkVNTyper was prospectively integrated with exome sequencing analysis to concurrently identify pathogenic 27dupC *MUC1* variant. Again, a confirmatory Snapshot PCR was performed for each positive sample.

Snapshot PCR was initially indicated based on clinical suspicion, yielding a diagnostic rate of 10% (33/333 positive Snapshot PCRs) between January 2019 and April 2024. In the retrospective cohort, despite being depleted in ADTKD patients, SharkVNTyper still achieved a diagnostic yield of 78% (18/23 positive Snapshot PCRs over 3512 participants) while in the prospective cohort, it reached 92% (12/13 positive Snapshot PCRs over 825 participants).

**Figure 2: Proposed diagnostic framework for ADTKD-*MUC1* with exome sequencing**

A) Initial two-stage decision tree, particularly for retrospective analysis in participants with negative exome sequencing. B) Preferred alternative approach allowing for simultaneous *MUC1* analysis alongside initial exome sequencing analysis, enabling dual diagnosis.

*ADTKD = autosomal dominant tubulointerstitial kidney disease*
<figure><img src="image_3.png" /><figcaption>**Figure 1:** Workflow for SharkVNTyper development, the proof-of-concept cohort in 333 clinically suspected ADTKD participants, retrospective cohort in 3512 CKD participants, and prospective cohort in 825 participants</figcaption></figure>

(a) SharkVNTyper utilizes both Shark and VNTyper tools to detect the 27dupC pathogenic variant from fastq files of exome sequencing. (b) For the proof-of-concept cohort, we applied VNTyper alone (without Shark) and SharkVNTyper (the tool combining both Shark and VNTyper) to control participants to evaluate both methods. VNTyper alone was positive for all positive control participants, but when using Shark with VNTyper (SharkVNTyper), one of the participants tested negative. (c) After the proof-of-concept phase, SharkVNTyper was retrospectively applied to any exome sequencing case prescribed for unknown nephropathy from August 2021 to September 2023 (excluding 333 ADTKD participants allocated to a proof-of-concept cohort from Sorbonne University). Confirmatory Snapshot PCR was performed to any SharkVNTyper-positive participants. (d) From October 2023 and onwards, SharkVNTyper was prospectively integrated with exome sequencing analysis to concurrently identify pathogenic 27dupC *MUC1* variant. Again, a confirmatory Snapshot PCR was performed for each positive sample.

Snapshot PCR was initially indicated based on clinical suspicion, yielding a diagnostic rate of 10% (33/333 positive Snapshot PCRs) between January 2019 and April 2024. In the retrospective cohort, despite being depleted in ADTKD patients, SharkVNTyper still achieved a diagnostic yield of 78% (18/23 positive Snapshot PCRs over 3512 participants) while in the prospective cohort, it reached 92% (12/13 positive Snapshot PCRs over 825 participants).
<figure><img src="image_4.png" /><figcaption>Figure 2: Proposed diagnostic framework for ADTKD-MUC1 with exome sequencing</figcaption></figure>

A) Initial two-stage decision tree, particularly for retrospective analysis in participants with negative exome sequencing. B) Preferred alternative approach allowing for simultaneous *MUC1* analysis alongside initial exome sequencing analysis, enabling dual diagnosis.

*ADTKD = autosomal dominant tubulointerstitial kidney disease*
**Table of contents:**

**Supplemental Figure S1:** Schematic of VNTyper and SharkVNTyper Pipeline Integration Workflow

**Supplemental Figure S2:** Snapshot PCR for detection of 27dupC

**Supplemental Figure S3:** Initial presentation of participants with ADTKD-*MUC1*

**Supplemental Figure S4:** Pedigree of a family with combined variants in *MUC1* and *PKD1*, demonstrating the utility of systematic screening for *MUC1*

**Supplementary Table S1:** Second Hit or Variant Found in Another Gene for Participants with ADTKD-*MUC1* in the ES-UKD Cohort
<caption>Supplemental Figure S1: Schematic of VNTyper and SharkVNTyper Pipeline Integration Workflow.</caption>

A

<figure><img src="image_5.png" /></figure>

B

<figure><img src="image_6.png" /></figure>

A) In the VNTyper pipeline, sequencing files (fastq) from exome sequencing (ES) are directly processed from BAM or fastq files, resulting in an output file listing the bioinformatically detected insertion. B) In the SharkVNTyper pipeline, sequencing files (fastq) from ES are first 'sharked' to enrich the sequences of interest (in this case, the *MUC1* gene). These 'sharked' files are then processed with VNTyper.
<figure><img src="image_7.png" /><figcaption>Supplemental Figure S2: Snapshot PCR for detection of 27dupC</figcaption></figure>

Top: positive result showing the presence of a cytosine instead of an adenine, revealing a 27dupC variant. Bottom: negative result clearly showing the presence of an adenine at this position.
<figure><img src="image_8.png" /><figcaption>Supplemental Figure S3: Initial presentation of participants with ADTKD-MUC1.</figcaption></figure>

A) Initial nephropathy category suspected in 24 participants with available clinical data. Nephropathy was classified as tubulointerstitial in the absence of microhematuria or glomerular albuminuria and was classified as cystic when multiple bilateral cysts were present (at least 5 cysts per kidney).

B) Dominant lesions identified on renal biopsy performed in 10 out of 24 index cases. Among the six participants with tubular lesions, five had interstitial fibrosis with tubular atrophy. Of the two participants with vascular lesions, one exhibited thrombotic microangiopathy lesions in addition to nephroangiosclerosis (in the context of severe hypertension).
<figure><img src="image_9.png" /><figcaption>Supplemental Figure S4: Pedigree of a family with combined variants in *MUC1* and *PKD1*, demonstrating the utility of systematic screening for *MUC1*.</figcaption></figure>

The pathogenic 27dupC variant in *MUC1* appears to have been inherited from the mother, while the variant in *PKD1* was likely inherited from the father. Patient II.5 passed away before the *MUC1* variant could be tested.
<table><caption>Supplementary Table S1: Second Hit or Variant Found in Another Gene for Participants with ADTKD-MUC1 in the ES-UKD Cohort.</caption><thead><tr><th>Gene</th><th>Name</th><th>ACMG-AMP class</th><th>Implication</th></tr></thead><tbody><tr><td>PKD1</td><td>NM_000296.4:c.12565dup, p.(Ser4189PhefsTer20)</td><td>4</td><td>Associated familial ADPKD, siblings with several bilateral renal cysts</td></tr><tr><td>COL4A3</td><td>NM_000091.5:c.725G&gt;A, p.(Gly242Glu)</td><td>4</td><td>Associated Alport syndrome (microscopic hematuria, focal segmental glomerulosclerosis lesions, hearing impairment)</td></tr><tr><td>BRCA1</td><td>NM_007294.3:c.5030_5033del, p.(Thr1677ilefsTer2)</td><td>5</td><td>Risk of early breast and ovarian cancer (asymptomatic)</td></tr><tr><td>CFTR</td><td>NM_000492.3: c.1521_1523del, p.(Phe508del)</td><td>5</td><td>Genetic counseling (heterozygote carrier)</td></tr></tbody></table>

ACMG = American College of Medical Genetics; ADPKD = Autosomal Dominant Polycystic Kidney Disease; AMP = Association for Molecular Pathology; BRCA = Breast Cancer; CFTR = Cystic Fibrosis Transmembrane Conductance Regulator; COL4A3 = Collagen Type 4 Alpha3; ES-UKD = Exome Sequencing for Unknown Kidney Disease; PKD1 = Polycystin 1.