Open Access

Assessment of a targeted resequencing assay as a support tool in the diagnosis of lysosomal storage disorders

  • Ana Fernández-Marmiesse1Email author,
  • Marcos Morey1,
  • Merce Pineda2,
  • Jesús Eiris3,
  • Maria Luz Couce1,
  • Manuel Castro-Gago3,
  • Jose Maria Fraga1,
  • Lucia Lacerda4,
  • Sofia Gouveia1,
  • Maria Socorro Pérez-Poyato5,
  • Judith Armstrong6,
  • Daisy Castiñeiras1 and
  • Jose A Cocho1
Orphanet Journal of Rare Diseases20149:59

DOI: 10.1186/1750-1172-9-59

Received: 16 October 2013

Accepted: 7 April 2014

Published: 25 April 2014

Abstract

Background

With over 50 different disorders and a combined incidence of up to 1/3000 births, lysosomal storage diseases (LSDs) constitute a major public health problem and place an enormous burden on affected individuals and their families. Many factors make LSD diagnosis difficult, including phenotype and penetrance variability, shared signs and symptoms, and problems inherent to biochemical diagnosis. Developing a powerful diagnostic tool could mitigate the protracted diagnostic process for these families, lead to better outcomes for current and proposed therapies, and provide the basis for more appropriate genetic counseling.

Methods

We have designed a targeted resequencing assay for the simultaneous testing of 57 lysosomal genes, using in-solution capture as the enrichment method and two different sequencing platforms. A total of 84 patients with high to moderate-or low suspicion index for LSD were enrolled in different centers in Spain and Portugal, including 18 positive controls.

Results

We correctly diagnosed 18 positive blinded controls, provided genetic diagnosis to 25 potential LSD patients, and ended with 18 diagnostic odysseys.

Conclusion

We report the assessment of a next–generation-sequencing-based approach as an accessory tool in the diagnosis of LSDs, a group of disorders which have overlapping clinical profiles and genetic heterogeneity. We have also identified and quantified the strengths and limitations of next generation sequencing (NGS) technology applied to diagnosis.

Keywords

In-solution enrichment Targeted resequencing Lysosomal storage disorders Diagnostic odysseys

Introduction

Lysosomal storage disorders (LSDs) are rare diseases with a combined incidence of ~1 in 1500 to 7000 live births [1, 2]. This group of inborn errors of metabolism encompasses >50 different diseases, each characterized by the accumulation of specific substrates [36]. Generally, newborns with LSDs appear normal at birth and symptoms develop progressively over the first few months of life. Late-onset juvenile and adult forms of LSDs, resulting from chronic substrate accumulation, also occur, but due to their varied signs and symptoms can have a delayed diagnosis. The recent development and availability of enzyme-replacement therapy (ERT) for several LSDs means that diagnosis early in the clinical process is of particular relevance [7]. Most importantly, early genetic diagnosis can provide parents with realistic information about their child’s prognosis, enable appropriate genetic counseling about future pregnancies, and prevent ‘diagnostic odysseys’ for families [8].

Because the clinical features of many LSDs overlap, establishing a diagnosis solely on the basis of clinical presentation is difficult. Until recently, clinicians have had different ways of approaching LSD diagnosis. The first option has been laboratory assays based on detection of the storage product. Although many of the clinical symptoms of different LSDs result primarily from substrate storage anomalies, presentation varies widely, depending on type, quantity, location, and time of extraction of the accumulated storage material, thus frequently giving rise to false negatives in biopsy analysis. Tests for elevated levels of secreted substrate material are routinely used to examine the pattern of glycosaminoglycans and oligosaccharides in patients suspected of having mucopolysaccharidoses (MPS) or disorders that present with oligosacchariduria. Although urine screens are very sensitive, affected individuals with normal urine screens have been reported mainly in young and adults; thus, when there is a strong index of suspicion, normal urine screening results should still be followed by enzyme analysis [911]. Enzyme activity detected in blood spots, either individually or simultaneously, is useful in the diagnosis of a small number of LSDs, but needs verification with a second type of assay; while measurement of enzyme activity in leukocytes and plasma serves this purpose for most LSDs, a proportion of cases may also not be detected using this method. Other limitations with enzyme activity tests is that they cannot detect heterozygous carriers of a disease, and are not suitable for potentially oligogenic LSDs (not described so far for LSD but recognized for retinitis pigmentosa, deaffness or ciliopathies) [12]. All of the above methods are laborious, time-consuming, and require accurate clinical diagnosis to reduce the number of enzymatic assays used for each patient. Moreover, all these techniques are semi-quantitative and subject to high variability, leading to false positives and negatives.

In summary, diagnosis of LSDs represents a challenge for clinicians and can take several years. Even reaching a diagnosis with traditional techniques, a genetic diagnosis also has to be made in order to provide the family with appropriate genetic counseling, itself arguably as important as the diagnosis. Genetic analysis is usually not performed as the primary screening tool in the diagnosis of LSD due to the cost and delay incurred by the sequential genetic tests necessary to diagnose any particular disorders. However, with the availability of next-generation sequencing (NGS) technologies, a genetically based diagnosis can be completed in 4 or 6 weeks, while reducing the cost to that of Sanger sequencing a single gene [1315]. Here, we present the results of a pilot project to evaluate the application of NGS to mutation screening in a diagnostic context. We show the strengths and limitations of this approach and, although this assay would never suffice as the sole diagnostic tool, we propose it as a useful adjunct to diagnosis for specialists in everyday clinical management who might suspect an LSD, given its ability to provide accurate information in a short time.

Methods

Ethics statement

The study protocol adhered to the tenets of the Declaration of Helsinki and was approved by the local Ethics Committee (Comité Ético de Investigación Clínica de Galicia - CEIC). Informed written consent was obtained from each study participant. Index patients underwent a full neurological examination in each source hospital. Whenever available, blood samples from affected and unaffected family members were collected for co-segregation analysis.

Probands

A total of 84 probands were collected from different institutions in Spain and Portugal, including 18 positive controls, and 66 patients with a suspected LSD. Positive controls underwent biochemical test and Sanger sequencing. Analyses of 13 controls and 33 patients were performed with SOLiD and 5 controls and 33 patients with Illumina platform.

LSD diagnostic suspicion index

Subjective parameter chosen by the clinical specialists who managed each case. We asked them to choose between three degree of suspicion: high (you believe your patient has a lysosomal disease with a high probability due to biochemical or clinical data), low (your main suspicion is another condition but there is a low probability that even if it is a lysosomal disease and it is important to discard it), medium (in the differential diagnosis are lysosomal diseases).

Capture array design, library construction, and NGS

A custom Sure Select oligonucleotide probe library was designed to capture the 551 exons and exon-intron-boundaries of 57 genes known to be associated with LSDs, according to GeneReviews (NCBI) (Table 1) [16]. Design includes all transcripts from each target gene. The eArray web-based probe design tool was used for this purpose [17]. The following parameters were chosen for probe design: 120 bp length for baits, 5X probe-tiling frequency, and 20 bp overlap in restricted regions identified by the implementation of eArray’s Repeat Masker program. A total of 5037 unique baits, covering 183,440 bp, were generated and synthesized by Agilent Technologies (Santa Clara, CA, USA). Sequence capture, enrichment, and elution were performed according to the manufacturer’s instructions.
Table 1

Genes included in the NGS-LSD assay and their associated disorders

Gene

Lysosomal storage dirorder

SMPD1

Niemann-Pick Disease, Type A and B

NPC1

Niemann-Pick Disease, Type C1

NPC2

Niemann-Pick Disease, Type C2

LIPA

Wolman Disease

GLA

Fabry Disease

GLB1

GM1-Gangliosidosis, Type I, II, III

GM2A

GM2-Gangliosidosis, AB Variant

HEXA

Tay-Sachs Disease

HEXB

Sandhoff Disease

GBA

Gaucher Disease

GAA

Pompe Disease

IDUA

MPS I: Hurler/Scheie

IDS

MPS II: Hunter Syndrome

SGSH

MPS IIIA: Sanfilippo Type A

NAGLU

MPS IIIB: Sanfilippo Type B

HGSNAT

MPS IIIC: Sanfilippo Type C

GNS

MPS IIID: Sanfilippo Type D

GALNS

MPS IVA: Morquio A

GLB1

MPS IVB: Morquio B

ARSB

MPS VI: Maroteaux-Lamy Syndrome

GUSB

MPS VII: Sly Syndrome

HYAL1

MPS IX

ASAH1

Farber Disease

ARSA

Metachromatic Leukodystrophy

GALC

Krabbe Disease

PSAP

Prosaposin deficiency

NEU1

Mucolipidosis I: Sialidosis

FUCA1

Fucosidosis

LAMP2

Danon Disease: Glycogen Storage Disease IIB

LAMP3

Candidate Gene For LSD

GNPTAB

Mucolipidosis II Alpha/Beta

GNPTG

Mucolipidosis III Gamma

MCOLN1

Mucolipidosis IV: Sialolipidosis

MAN2B1

Mannosidosis, Alpha B, Lysosomal

MANBA

Mannosidosis, Beta A, Lysosomal

PPT1

Ceroid Lipofuscinosis, Neuronal, 1

TPP1

Ceroid Lipofuscinosis, Neuronal, 2

CLN3

Ceroid Lipofuscinosis, Neuronal, 3 (Batten Disease)

CLN5

Ceroid Lipofuscinosis, Neuronal, 5

CLN6

Ceroid Lipofuscinosis, Neuronal, 6

CLN7

Ceroid Lipofuscinosis, Neuronal, 7

CLN8

Ceroid Lipofuscinosis, Neuronal, 8

CLN10

Ceroid Lipofuscinosis, Neuronal, 10

CTSA

Galactosialidosis

CTNS

Cystinosis

SLC17A5

Sialic Acid Storage Disease

CTSK

Pyknodysostosis

NAGA

Schindler Disease

SUMF1

Multiple Sulfatase Deficiency

HPS1

Hermansky-Pudlak Syndrome Type 1

AP3B1

Hermansky-Pudlak Syndrome Type 2

HPS3

Hermansky-Pudlak Syndrome Type 3

HPS4

Hermansky-Pudlak Syndrome Type 4

HPS5

Hermansky-Pudlak Syndrome Type 5

HPS6

Hermansky-Pudlak Syndrome Type 6

DTNBP1

Hermansky-Pudlak Syndrome Type 7

BLOC1S3

Hermansky-Pudlak Syndrome Type 8

SOLiD4 platform

Briefly, 3–4 μg of each genomic DNA was fragmented by sonication (Covaris S2, Massachusetts, USA), purified to yield 150–180 bp fragments and end-repaired. Adaptor oligonucleotides from Agilent technologies were ligated on repaired DNA fragments, which were then purified, size-selected by gel electrophoresis, nick-translated, and amplified by 12 PCR cycles. The libraries (500 ng) were then hybridized to the Sure Select biotinylated-RNA capture library for 24 h. After hybridization, washing, and elution, the captured fraction underwent 12 cycles of PCR amplification with barcoded primers followed by purification and quantification by qPCR. Forty-eight barcoded samples were then pooled in groups for sequencing on a SOLiD4 platform as single end 50 bp reads. 12 sample libraries were loaded per octet of SOLiD4 slide.

HiSeq2000 platform

The library preparation for capturing of selected DNA regions was performed according to the SureSelect XT Target Enrichment System protocol for Illumina paired-end sequencing (Agilent). In brief, 3 μg of genomic DNA was sheared on a Covaris™ E220 focused-ultrasonicator. Fragment size (150-200 bp) and quantity were confirmed with an Agilent 2100 Bioanalyzer 7500 chip. The fragmented DNA was end-repaired, adenylated and ligated to Agilent indexing-specific paired-end adaptors. The DNA with adaptor-modified ends was PCR amplified (6 cycles, Herculase II fusion DNA polymerase) with SureSelect primers, quality controlled using the DNA 7500 assay specific for a library size of 250–350 bp, and hybridized for 24 hr at 65°C. The hybridization mixture was washed in the presence of magnetic beads (Dynabeads MyOne Streptavidin T1, Life Technologies), and the eluate PCR amplified (16 cycles) to add index tags using SureSelectXT Indexes for Illumina. The final library size and concentration was determined using an Agilent 2100 Bioanalyzer 7500 chip and sequenced on an Illumina HiSeq 2000 platform with a paired-end run of 2 × 76 bp, following the manufacturer’s protocol. 36 sample libraries were loaded in three lanes of HiSeq 2000.

Data filtering and analysis pipeline

SOLiD4 platform

Image analysis and base calling was performed using the SETS (SOLiD experimental Tracking Software) pipeline to generate primary data. Sequence reads were aligned to the reference human genome UCSC hg19 using Life Technologies’ BioScope suite v1.3.1. Default parameters, recommended for targeted resequencing, were used. Variant calling was performed using two software programs in parallel: the diBayes alignment algorithm embeded in the Bioscope suite [18] and the Genome Analysis Toolkit (GATK) v1.5, a software package developed at the Broad Institute (Cambridge, MA) to analyze next-generation resequencing data [19]. The GATK Unified Genotyper is a state-of-the-art variant caller for NGS data and used extensively in human sequencing projects. The variant detection pipeline uses well-established statistical models for recalibration of the base quality score and variant calling. Low stringency parameters were selected to avoid false negatives although a high rate of false positives was expected.

HiSeq2000 platform

Base calling and quality control were performed on the Illumina Real Time Analysis (RTA) sequence analysis pipeline. Sequence reads were trimmed to keep only those bases with a quality index > 10 and then mapped to Human Genome build hg19 (GRCh37), using a Genome Multitool (GEM) [20] and allowing up to 4 mismatches. Reads not mapped by GEM were submitted to a last round of mapping with BLAT-like Fast Accurate Search Tool (BFAST) [21]. Uniquely mapping non-duplicate read pairs were locally realigned with GATK. Samtools suite [22] was used to call single nucleotide variants (SNVs) and short INDELs, taking into account all reads per position. Variants on regions with low mappability or variants in which there was not at least one sample with read depth ≥10 were filtered out.

Sanger sequencing

To verify the DNA sequence variants detected by NGS, we amplified the target sites and flanking sequences of each variant with specific primers designed using the free software Primer3 v.0.4.0 [23]. Next, we sequenced the PCR products using the Sanger method to ascertain the precision of the variants identified by NGS. Sequencing reactions consisted of 1.0 μl of previously purified PCR products (ExoSAP-IT, USB, Cleveland, OH), 1 μl of each primer, and 1 μl of Big Dye Terminator v3.1 from the Cycle Sequencing kit (Applied Biosystems, Foster City, CA). The reactions were run in an ABI 3730 DNA Analyzer (Applied Biosystems, Foster City, CA). Analysis was performed with the Staden package free software.

Detection of gross indels

A simple homemade Excel table was designed for detection of gross deletions and duplications encompassing one or more exons. The table contained coverage peak areas for each exon and patient. Ratios of successive peak areas between different patients were compared to identify homozygous and heterozygous gross indels. To establish the extremes of deletion it is necessary to perform cDNA Sanger sequencing studies.

Filtering of annotated sequence data

We received the bam files and the annotated sequencing variants in Excel tables from the two platforms. Raw data were filtered with custom designed scripts using the free, open source statistical software R package [24]. We have submitted all novel variants included in the article to ClinVar database [25] from NCBI and to the Locus Specific Mutation Databases (LSMDs) from Human Genome Variation Society [26].

Assessment of the pathogenicity of variants

The following criteria were applied to evaluate the pathogenic nature of novel variations identified by NGS: 1) stopgain, frameshift and splicing variants, considered the most likely to cause disease; 2) the presence of a second mutant allele, taken to indicate recessive inheritance; 3) cosegregation; 4) the absence of the variant in other samples; 5) frequency <0.01 in the 1000 g2012 database; and 6) for missense mutations, amino acid conservation and prediction of pathogenicity. To evaluate criterion 6), we used the freely available bioinformatics web tools: SIFT (Sorting Intolerant From Tolerant) [27], Polyphen-2 [28] and Mutation Taster [29]. To evaluate the possible effect of synonymous variant in gene splicing we used the Human Splicer Finding web tool [30].

Results and discussion

Statistical data from two platforms

Total target sequence length, including coding exons and exon-intron junctions from 57 LSD genes, was 183,440 bp. Coverage distribution across the designed region was replicated among the samples for each platform. The overall sequence depth was 456X for SOLiD4 and 7,416X for HiSeq2000. SOLiD4 yielded a total of up to 158,863,367 raw 50-mer reads with 87.28% mapped reads overall. A total of 94.5% of bases were covered by at least 20 reads and the mean percentage of bases not covered per sample was 2.6%. The HiSeq2000 run resulted in (1292,371,000) QC-passed reads with 88.47% mapped reads overall. A total of 99.97% of bases were covered by at least 20 reads, involving a percentage of target bases with less than 20 reads of only 0.03%.

Limitations of the enrichment method

The most important challenge for this kind of enrichment is that local sequence architecture has a strong effect on the efficiency of DNA enrichment for individual exons [31]. Exons close to repetitive regions are not fully covered, because the eArray web tool does not design baits in repetitive regions. In our assay 5 exons were affected by this initial lack of baits. We tried to minimize this pitfall by using the 5X bait-tailing option, to reduce the gaps as much as possible. However, our results showed that such an approach overcame this problem only if coverage was increased maximally. Another significant limitation of the enrichment method is that coverage decreases dramatically, even to zero, for exons located in CpG islands. With 456X coverage, 22 of 57 genes showed gaps in coverage (<20X) in one exon, and 6 in more than one exon, with the most damaged genes being IDUA, GBA, and GAA. Thus, our NGS-LSD assay did not detect mutations in the IDUA gene in a patient with biochemically confirmed Hurler disease. With 7416X coverage no gaps were found in any of LSD genes. Therefore, to establish this assay as a diagnostic screening tool for routine use it will be essential to eliminate sequence gaps. Because enrichment failures with hybrid capture were reproducible, they may be amenable to rescue by individual PCRs or probe redesign.

Filtering of raw data

The main challenge of using NGS for diagnostic applications will be in interpreting the massive number of genomic variants detected by sequencing platforms. Fast and reliable identification of causative variants will be crucial to the implementation of this technology in diagnostics. In our study, simple filters based on variant function and frequency, and careful selection of index cases versus controls, was found to be a useful way of discriminating between pathogenic mutations and background polymorphisms. Priority was given to variants considered to be specific to individual patients, based on the assumption that a mutation underlying such a monogenic disorder is highly penetrant and rare. It was also assumed that these mutations are likely to be coding and to have a major phenotypic effect.

From the 12,179 and 52,303 variants detected by the SOLiD4 and HiSeq platforms, respectively, successive filters were applied to reduce these numbers to 77 and 68. SOLiD4 raw data provided by Bioscope SNP calling software were filtered twice, using specifically designed R scripts (Figure 1). Filter 1 selected variants with the following specific features: 1) non-reference allele (NRA) frequency ≤0.01 in the 1000 g2012 database; 2) location in exonic or splicing regions; 3) non-synonymous, indels, stopgain, or splicing; and 4) NRA frequency ≤4% in our study group. This filter reduced the initial number of variants to 219. Filter 2 then discriminated between real and false-positive changes, by fulfilling at least 3 of following conditions: 1) coverage of position ≥20; 2) percentage of NRA reads ≥30; 3) mean quality values (MQV) of reference and variant ≥15; 4) [reference-variant] MQV ≤5. The number of variants was reduced to 77, implying a false-positive rate of 65%. Careful observation of the false-positive variants showed that they appeared in characteristic areas which either had a low coverage or a sudden drop in coverage, or were considered coverage ‘valleys’. Therefore such false positives were the result of coverage irregularities arising from local sequence architecture, and were largely avoidable, either by improving enrichment efficiency or increased coverage, as shown by the results obtained with the Illumina NGS-LSD assay. Filter 2 was also adjusted by checking variants by Sanger sequencing (see next section) and comparing data from two different variant-calling software packages, Lifescope and GATK. Variants obtained from HiSeq2000 passed through the same Filter 1 as in SOLiD4, reducing the number of variants directly to 68. In this case, a second filter was not necessary as the high coverage achieved meant that the false-positive rate was insignificant using HiSeq2000.
https://static-content.springer.com/image/art%3A10.1186%2F1750-1172-9-59/MediaObjects/13023_2013_Article_758_Fig1_HTML.jpg
Figure 1

Global flowchart of the filtering pipeline used for selection of most likely pathogenic mutations, starting from SOLiD4 raw data. (NRA: non-reference allele).

Assessment of the novel genetic tool for LSD diagnosis

All putatively pathogenic variants detected by either of the two platforms were subsequently tested by Sanger sequencing of both the index case and parents, and all were confirmed. A more thorough analysis of the variants detected by SOLiD4 was carried out, to test the reliability of Filter 2 in discrimination of real from false variants. Of the 71 missense variants which passed Filter 2 and were confirmed with Sanger sequencing (see previous section), 83% were found to be real and 17% false-positive. Of 48 variants which did not pass through Filter 2, 14 were additionally selected for having values near the limit for one or more conditions; all of these were then verified as false-positive. Therefore, while overall we had a false-positive rate of 17%, we conclude that the second filter detected real variants with a high degree of confidence. If, on the other hand, we had only allowed variants fulfilling all 4 conditions of Filter 2 to pass through, we would not have had any false positives but instead we would have had a rate of 9% of false negatives. A comparison of the two variant calling software packages was also found to be useful in discriminating between real and false variants, as none of the false positives seen using Lifescope appeared when using GATK and vice versa.

Detection of gross deletions

One advantage of NGS-based strategies, as opposed to Sanger sequencing, is that, in addition to SNVs and small indels, they can also detect gross deletions and insertions which affect one or more exons. Thus, we detected two heterozygous macrodeletions, one an exon 5 deletion in GLB1 (P20), and another an exon 7 + 8 deletion in CLN3 (C18) (Figure 2). We also detected the same CLN3 macrodeletion in a homozygous state (C2 and P3; not shown).
https://static-content.springer.com/image/art%3A10.1186%2F1750-1172-9-59/MediaObjects/13023_2013_Article_758_Fig2_HTML.jpg
Figure 2

Heterozygous macrodeletions detected by LSD-NGS. A) GLB1 exon 5 deletion; B) CLN3 exon 7 + 8 deletion. The area of the coverage peak for each deleted exon is half that seen in the corresponding control sample.

Limitations of SNP calling software

Using Bioscope software, heterozygous variants were found with a percentage of NRA reads between 30 and 50%, while homozygous variants had percentages of between 70 and 100%. Lower percentages were seen for small deletions and insertions, comprising a potential problem with this technology. On the other hand, a comparison of the two SNP calling software packages showed there to be a significant proportion of false negatives with each package. Thus, of 105 variants confirmed by Sanger sequencing, Bioscope had failed to detect 3 (2.8%) and GATK failed to detect 9 (8.5%). This illustrates the importance of using more than one type of variant calling software in parallel, to discriminate between real and false variants as well as to reduce the false-negative rate as much as possible.

Diagnosis achieved with the NGS-LSD screening tool

We applied the NGS-LSD method to 84 probands with a spectrum of early-onset neurodegenerative disorders that were potentially caused by a deficiency in one of the 57 LSD proteins (Figure 3). From these 84, we selected 18 control samples with mutations previously identified by Sanger sequencing, three of which had only a single mutation in the affected gene (C14, 15, 18). Of the 33 mutations expected, all but one was detected using the NGS-LSD assay (Table 2). The undetected mutant was located on exon 1 of the NAGLU gene (C8), which was not covered by hybridization baits, due to its being located close to repetitive regions. It is important to highlight that this mutation would have been detected with Illumina platform. For the 66 unclassified patients, an LSD suspicion index was assigned, as low, moderate, or high in each case. For each case, clinical data and tests carried out for diagnostic purposes, including genetic, were collected (Table 3). Based on the presence of one or two mutations, we were able to achieve genetic diagnosis in 26 patients. Twenty-two of these were found to carry two mutations in the same gene, consistent with clinical and/or biochemical features and establishing a genetic diagnosis (Table 4). A further 26 patients were found to be carriers of mutations (Tables 4 and 5). In all cases, the gene harboring a mutation was carefully examined to find any other variant that might have been initially undetected due to its location in an intronic region, synonymity, or being a gross indel. Thus, a second mutation was found in 4 patients (P11, 20, 21, 23). Of the remaining carriers, we carried out biochemical assays, to confirm or exclude disease, in four patients (P12, 27, 28, 29) chosen for the severity of the variant found and correlation between genotype and phenotype. Biochemical tests on P27, P28 and P29 gave negative results. In P12 the diagnosis of NPC2 could not be confirmed but it could not be discarded neither (filipin staining was unconclusive but this is not unusual in a percentage of NPC2 patients and only one heterozygous pathogenic mutation was found). It is important to emphasize that in the current study we were readily able to confirm biochemically mutation pathogenicity in the majority of genes analyzed, but this is not possible for pathologies that have no biochemical markers and in which confirmation will need to be obtained by other methods. No mutations were found in 19 probands (p49-p65), but the LSD index of suspicion was low for all but one of these.
https://static-content.springer.com/image/art%3A10.1186%2F1750-1172-9-59/MediaObjects/13023_2013_Article_758_Fig3_HTML.jpg
Figure 3

Diagnosis achieved using NGS-LSD. (1x: one mutation found; 2x: two mutations found in the same gene; VCS: variant calling software; ??: not confirmed.)

Table 2

Results obtained for positive controls included in NGS-LSD assay

CC

GENE

REF_SEC

NT CHANGE

AA CHANGE

ZIG

MD

DIAGNOSIS

OMIM

C1

ARSA

NM_000487

c.1046delC

p.P349fs

HO

+

Metachromatic leukodystrophy

250100

C2

CLN3

NM_000086

c.461-280_677 + 382del966

p.[Gly154Alafs*29, Val155_Gly264del]

HO

+

Ceroid lipofuscinosis, neuronal, 3

204200

C3

FUCA1

NM_000147

c.464C > T

p.S155F

HT

-

Fucosidosis

230000

 

FUCA1

NM_000147

c.790C > T

p.R264X

HT

-

  

C4

GALNS

NM_000512

c.281G > T

p.R94L

HO

+

Mucopolysaccharidosis type IVA

253000

C5

GLB1

NM_000404

c.1581G > A

p.W527X

HO

+

GM1 gangliosidosis

230500

C6

GNPTAB

NM_024312

c.1208 T > C

p.I403T

HT

+

Mucolipidosis III alpha/beta

252600

 

GNPTAB

NM_024312

c.1999G > T

p.E667X

HT

+

  

C7

GUSB

NM_000181

c.526C > T

p.L176F

HT

+

Mucopolysaccharidosis VII

253220

 

GUSB

NM_000181

c.530C > T

p.T177I

HT

-

  

C8

NAGLU

NM_000263

c.900C > T

p.R234C

HT

+

Sanfilippo B

252920

C9

NEU1

NM_000434

c.700G > A

p.D234N

HT

+

Sialidosis

256550

 

NEU1

NM_000434

c.1021C > T

p.R341X

HT

+

  

C10

SGSH

NM_000199

c.120C > G

p.Y40X

HO

+

Sanfilippo A

252900

C11

SMPD1

NM_000543

c.739G > A

p.G247S

HO

+

Niemann-Pick disease, type A

257200

C12

TPP1

NM_000391

c. 622C > T

p.R208X

HO

+

Ceroid lipofuscinosis, neuronal, 1

256730

C13

CTSA

NM_000308

c.448G > A

p.V150M

HT

-

Galactosialidosis

256540

 

CTSA

NM_000308

c.284delC

p.P95Lfs

HT

-

  

C14

NPC1

NM_000271

c.1552C > T

p.R518W

HT

+

Niemann-Pick disease, type C

257220

C15

NPC1

NM_000271

c.2594C > T

p.S865L

HT

+

Niemann-Pick disease, type C

257220

C16

SLC17A5

NM_012434

c.918 T > G

p.Y306X

HT

+

Sialic acid storage disorder,infantile

269920

 

SLC17A5

NM_012434

c.500 T > C

p.L167P

HT

-

  

C17

ARSB

NM_000046

c.427delG

p.V143Sfs

HO

+

Mucopolysaccharidosis VI

253200

C18

CLN3

NM_000086

c.461-280_677 + 382del966

p.[Gly154Alafs*29, Val155_Gly264del]

HT

+

Ceroid lipofuscinosis, neuronal, 3

204200

CC: Control Code (C1-C12 processed with SOLiD4 platform; C13-C18 processed with HiSeq2000); REF SEQ: reference sequence for which mutations are annotated; NT and AA CHANGE: nucleotide and amino acid change; ZIG: zigosity; HO: homozygotes; HT: heterozygotes; MD: Mutation description in Human Gene Mutation Database (+: previously associated with pathology; −: not previously associated).

Table 3

Diagnostic suspicions and biochemical/histopathological tests for patients diagnosed using the NGS-LSD tool

PC

AO

AD

GD

NGS-LSD

Initial signs

Diagnostic suspicions

Biochemical-histopathology studies*

Other genetic studies*

P1

2Y

-

12Y

CLN5

Clumsiness, frequent falls, apraxic gait

CLN

VL; SBIOP + .

PPT1, TTP1

P2

4Y

-

7Y

CLN6

Clumsiness, myoclonic movements

GM2, GCL, MLD, Fabry, Schindler, MANSA, MANBA, MPS, CLN

EA: HEXA, HEXB, GLA, NAGA, MANBA, MAN2B1, MPS, GALC, ARSA, PPT1, TTP1

MFSD8

P3

3Y

-

16Y

CLN3

Language regression, school inattention, social isolation

CDG, GM1, GM2, GSL, Schindler, GSD II, III, CLN

VL; FC; SPA ; EA: GLB1, HEXB, CTSA, NAGA, GAA, AGL; SBIOP + .

 

P4

2Y

-

23Y

CLN7

Language delay, clumsiness

CLN

SBIOP +; EA: CLN2.

CLN1

P5

4Y

-

16Y

CLN3

Conduct disorder, attention deficit disorder

CLN

VL +; SBIOP, MBIOP, NBIOP + .

PPT1, TTP1, CLN3, CLN5, CLN8

P6

3Y

4Y

7Y

MLD

Leukodystrophy

Spastic paraparesia

EA: ARSA +

 

P7

1Y

-

9Y

GM1

Global developmental delay, gait instability

CDG; RCCD; INAD

MBI; NBIOP +; MBS; SPA; EA: HEXA, HEXB

 

P8

2Y

7Y

4Y

GM2

Language delay, conduct disorder

Unspecific global developmental delay, Neurodegenerative disease; CLN, GM2

SBIOP; MBS; EA: HEXA + .

Caryotype; Fragile X; Smith-Magenis

P9

2Y

-

14Y

GM2

Clumsiness, frequent falls

Ataxia, Attention deficit disorder

MBI

Caryotype; Fragile X; CGH-60 k array.

P10

3Y

9Y

12Y

GM1

Clumsiness, frequent falls, language delay

RS, MPSIII, GM1

MBS, U-GAGs; EA: GLB1+

Caryotype, Fragile-X, MECP2

P11

5Y

-

22Y

GM2

Learning problems, language difficulties

CLN

SBIOP

CLN3, CLN8

P12

13Y

-

18Y

NPC2#

Clumsiness, short stature

MTDPS; RCCD; SCA; DRPLA

MBIOP; RCCFS; EA: muscular Coenzima Q10

mtADN(muscle); MTATP6; tRNALeu(UUR); SCA1,2,3,6,7,12,17; ATN1

P13

18 M

-

4Y

ML II,III

Growth delay, macrocephaly

MPS

U-GAGs

SHOX

P14

12 M

3Y

3Y

GM2

Psycomotor regression, language regression

RS, GM2

EA: HEXA +, HEXB

MECP2

P15

0 M

3 M

1Y

GM1

Hypotonia, hepatomegaly, cardiomyopathy, bony abnormalities

GSD II

EA: GAA

 

P16

9 M

2Y

2Y

MPSVI

Bilateral costal deformity, macrocephaly

AA, MPS

U-GAGs, Dermatan sulfate in urine, EA: ARSB

 

P17

0 M

-

2Y

ML II,III

Hypotonia, microcephaly, jaundice

MPS

MBS, U-OLG, U-GAGs

Caryotype; CGH array

P18

2 M

3Y

6Y

MPSII

Bilateral inguinal hernia

MPS

U-GAGs; EA: IDS +

 

P19

1Y

4Y

5Y

MPSI

Clubfoot, arthrogryposis, clawed hands

MPS

U-GAGs; EA: IDUA +, IDS, ARSB

 

P20

0 M

1Y

1Y

GM1

Hypotonia, severe psycomotor delay

GM1, GM2, GSL, ML I

MBS; VL; U-GAGs; EA: GBL1 +, HEXA, HEXB, CTSA, NEU1

 

P21

18 M

3Y

3Y

GM2

Unsteady gait, frequent falls

TORCH, Leukodystrophy

EA:

 

P22

2Y

-

6Y

CLN8

Cognitive regression, epilepsy

Lennox syndrome, CLN

PBIOP +

 

P23

3Y

-

9Y

CLN10

Psycomotor regression, dystonia, epilepsy

Neuroaxonal dystrophy, sphingolipidosis

MBIO

mtDNA

P24

3Y

5Y

23Y

MPSIIIB

Language delay, conduct disorder, learning disabilities, hypoacusia

MPS

Heparan sulfate +

 

P25

1Y

-

6Y

GM2AB

Developmental delay, regression

GM2, GSD II

EA: HEXA, HEXB, GAA (+); HPS GM2 +

GAA

P26

4Y

-

25Y

CLN8

Cognitive regression, epilepsy

CLN

 

CLN6

Abbreviations: PC Patient Code, AO Age of Onset (Y: years; M: months; deceased), AD Age at Diagnosis, GD age at Genetic Diagnosis, NGS-LSD diagnosis reached with our NGS tool, CLN Neuronal Ceroid Lipofuscinosis, MLD metachromatic leukodystrophy, GM1 and 2 Gangliosidosis I and II, NPC2 Niemann-Pick disease, type C2, ML II III mucolipidosis III alpha/beta, MPS mucopolysaccharidosis, GM2AB Gangliosidosis type II, AB variant, MANSA Mannosidosis, alpha-, types I and II, MANBA mannosidosis beta, CDG congenital disorder of glycosylation, GSL galactosialidosis, GSD glycogen storage disease, GSD II Pompe disease, RCCD respiratory chain complex deficiency, INAD infantile neuroaxonal dystrophy, RS Rett syndrome, AA autoimmune arthritis, ML I sialidosis, TORCH Band-like calcification with simplified gyration and polymicrogyria, MBS metabolic study, SPA sialotransferrin profile analysis, VL vacuolated lymphocytes in peripheral blood, FC foam cells in bone marrow, RCCFS respiratory chain complex functional studies, GCL globoid cell leukodystrophy or Krabbe disease, SCA spinocerebellar ataxia, EA enzymatic assay, U-GAGs urinary glycosaminoglycan levels, U-OLG urinary oligosaccharides levels, SBIOP skin biopsy, MBIOP muscle biopsy, NBIOP nerve biopsy, HPS pathological study, mtDNA mithocondrial DNA deletions, depletion and punctual mutations, MTDPS mitochondrial depletion syndrome, CGH comparative genomic hybridization; #: not confirmed; *All results were negative unless indicated by a plus sign.

Table 4

Diagnosis achieved using the NGS-LSD tool

PC

AO

GDD

IS

GENE

OMIM

REF SEC

NT CHANGE

AA CH

MT

SS

PP

CS

ZIG

MD

BA

DIAGNOS

P1

2Y

10Y

Mod

CLN5

608102

NM_006493

c.335G > C/c.835G > A

p.R112P/p.D279N

099/0.99

0/0.16

1/1

+

HO

+

NA

Finnish variant late infantile CLN

P2

4Y

4Y

Mod

CLN6

606725

NM_017882

c.794_796del

p.S265del

-

-

-

 

HO

+

NA

Early juvenile late infantile CLN

P3

3Y

12Y

High

CLN3

607042

NM_000086

c.461-280_677 + 382del966

p.[Gly154Alafs*29, Val155_Gly264del]

-

-

-

 

HO

+

NA

Juvenile CLN

P4

2Y

21Y

Mod

MFSD8

611124

NM_152778

c.881C > A

p. T294K

0.99

0

0.99

+

HO

+

NA

Turkish variant late infantile CLN

P5

4Y

12Y

High

CLN3

607042

NM_000086

c.371_372insT

p.Y124fs

-

-

-

 

HO

-

NA

Juvenile CLN

P6

3Y

4.5Y

High

ARSA

607574

NM_000487

c.465 + 1G > A

----------

-

-

-

+

HO

+

+

Metachromatic leukodystrophy

P7

1Y

8Y

Low

GLB1

611458

NM_000404

c.922 T > C

p.F308L

0.99

0

1

+

HO

-

+

GM1 gangliosidosis

P8

3Y

1Y

High

HEXA

606869

NM_000520

c.533G > A

p.R178H

0.99

0

1

+

HO

+

+

GM2 gangliosidosis, B1variant

P9

4Y

10Y

Low

HEXA

606869

NM_000520

c.1496G > A

p.R499H

0.99

0

1

 

HT

+

+

GM2 gangliosidosis, juvenil (TS)

    

HEXA

 

NM_000520

c.1003A > T

p.I335F

0.99

0

0.97

 

HT

+

  

P10

3Y

7Y

High

GLB1

611458

NM_000404

c.602G > A

p.R201H

0.99

0.02

1

 

HT

+

+

GM1 gangliosidosis

    

GLB1

 

NM_000404

c.1188_1188dupG

p.P397fs

-

-

-

 

HT

-

  

P11

5Y

18Y

High

HEXA

606869

NM_000520

c.155C > A

p.S52X

-

-

-

+

HT

+

+

GM2 gangliosidosis juvenile (TS)

    

HEXA

 

NM_000520

c.1305C > T

p.Y435Y

-

-

-

 

HT

-

  

P12

13Y

6Y

Low

NPC2

 

NM_006432

c.441 + 1G > A

-----------

-

-

-

 

HT

-

-

Niemann-Pick disease, type C2?

P13

18 M

3Y

High

GNPTAB

607840

NM_024312

c.2354 T > G

p.L785W

0.71

0.09

0.022

+

HT

-

+

Mucolipidosis II/III

    

GNPTAB

 

NM_024312

c.1774G > A

p.A592T

0.98

0.01

0.955

 

HT

-

  

P14

12 M

4Y

High

HEXA

606869

NM_000520

c.718_719insT

p.K240fs

    

HT

-

+

GM2 gangliosidosis (TS)

    

HEXA

 

NM_000520

c.1003A > T

p.I335F

0.99

0.00

0.467

 

HT

+

  

P15

0 M

1Y

High

GLB1

611458

NM_000404

c.671_672delAT

p.H224Qfs

   

+

HO

-

+*

GM1 gangliosidosis

P16

9 M

1Y

High

ARSB

611542

NM_000046

c.382_384delCTC

p.L128del

    

HO

-

+*

Mucopolysaccharidosis VI

P17

0 M

3Y

High

GNPTAB

607840

NM_024312

c.3739_3742delCTTT

p.E1248fs

   

+

HO

-

+

Mucolipidosis II/III

P18

2 M

6Y

High

IDS

300823

NM_000202

c.425C > T

p.S142F

0.98

0.00

0.998

+

HE

+

+*

Hunter Syndrome

P19

3Y

5Y

High

IDUA

252800

NM_000203

c.1205G > A

p.W402X

   

+

HT

+

+*

Hurler Syndrome

    

IDUA

 

NM_000203

c.1874A > G

p.Y625C

0.98

0.00

0.99

 

HT

-

  

P20

10 M

2Y

High

GLB1

611458

NM_000404

c.947A > G

p.Y316C

0.999

0

0.79

 

HT

+

+*

GM1 gangliosidosis

    

GLB1

 

NM_000404

c.458-401_552 + 1033del1529

-----------

    

HT

+

  

P21

20 M

4Y

High

HEXA

606869

NM_000520

c.459 + 5G > A

-----------

    

HT

+

+*

GM2 gangliosidosis (TS)

    

HEXA

 

NM_000520

c.533G > A

p.R178H

0.99

0

1

 

HT

+

  

P22

2Y

4Y

High

CLN8

607837

NM_018941

c.509C > G

p.T170R

0.999

0.00

0.999

+

HO

+

NA

Ceroid lipofuscinosis, neuronal, 8

P23

3Y

9Y

High

CTSD

116840

NM_001909

c.470C > T

p.S157L

0.98

0.03

0.005

+

HT

-

NA

Ceroid lipofuscinosis, neuronal, 10

    

CTSD

 

NM_001909

c.353-12G > A

-----------

-

-

-

 

HT

-

pendε

 

P24

3Y

23Y

High

HGSNAT

610453

NM_152419

c.1250 + 1G > A

-----------

-

-

-

 

HT

+

+*

Sanfilippo C

    

HGSNAT

 

NM_152419

c.1270G > A

p. G424S

0.999

0.59

1

 

HT

+

  

P25

1Y

6Y

High

GM2A

613109

NM_000405

c.333delC

p. C112Vfs

-

-

-

 

HO

-

+#

GM2 gangliosidosis, AB variant

P26

4Y

>20Y

High

CLN8

607837

NM_018941

c.792C > G

p. N264K

0.97

0

0.99

+

HO

-

NA

Ceroid lipofuscinosis, neuronal, 8

Notes and abbreviations: Samples from Patients P1-P12 were processed using SOLiD4 (Life Technologies), but those from P13-P26 using HiSeq2000 (Illumina); PC: Patient Code; AO: Age of Onset (Y: years; M: months); GDD: Genetic Diagnosis Delay (Y: years; M: months: DEC: deceased); IS: index of suspicion of LSD (Mod: moderate); REF SEC: reference sequence for which mutations are annotated; Nt and Aa Change: nucleotide and amino acid change (population frequency ≤0.01 according to 1000G2012 database); MT: Mutation Taster, SS: Sift Score and PP: Polyphen 2 score (in silico values to assess pathogenicity of missense variants); CS: Cosegregation; ZIG: zigosity; HO: homozygote; HT: heterozygote; HE: hemizygous; MD: Mutation description in Human Gene Mutation Database (+: previously associated with pathology; −: not previously associated); BA: Biochemical assay (+: positive; NA: test not available; −: negative); CLN: Neuronal Ceroid Lipofuscinosis; TS: Tay-Sachs; *Levels detected before NGS-LSD assay; εmRNA splicing analysis; #histopathology congruent with GM2 diagnosis.

Table 5

Results found for patients remaining un-diagnosed

PC

IS

Gene

Ref sec

Coding variants*

dbSNP ID

MAF

Zig

MD

BA

P27

High

MANBA

NM_005908

c.1922G > A

p.R641H

--

 

HT

+

NegΨ

P28

High

HYAL1

NM_033159

c.676C > T

p.R226C

--

 

HT

-

NCI

  

HEXB

NM_000521

c.383 T > G

p.L128R

--

 

HT

-

NegΨ

P29

High

SMPD1

NM_000543

c.1460C > T

p.A487V

--

 

HT

+

NegΨ

P30

Low

NEU1

NM_000434

c.1070G > A

p.R357Q

--

 

HT

-

NCI

P31

Mod

MFSD8

NM_152778

c.50C > G

p.T17R

--

 

HT

-

NCI

P32

Low

NAGA

NM_000262

c.697G > A

p.V233M

--

 

HT

-

NCI

  

NPC1

NM_000271

c.665A > G

p.N222S

rs1805081

0.001

HT

+

NCI

P33

Low

SMPD1

NM_000543

c.1550A > T

p.E517V

--

 

HT

+

NCI

P34

High

SGSH

NM_000199

c.308A > G

p.K103R

--

 

HT

-

NCI

  

CLN6

NM_017882

c.755G > C

p.R252P

--

 

HT

-

NCI

P35

Low

TPP1

NM_000391

c.1117C > G

p.Q373E

--

 

HT

-

NCI

  

GAA

NM_000152

c.1367G > T

p.R456M

--

 

HT

-

NCI

P36

High

CLN5

NM_006493

c.606G > A

p.M202I

--

 

HT

-

NCI

P37

High

SMPD1

NM_000543

c.8G > A

p.R3H

--

 

HT

-

NCI

P38

Low

IDUA

NM_000203

c.251G > C

p.G84A

--

 

HT

-

NCI

P39

Low

IDS

NM_000202

c.754G > A

p.D252N

--

 

HO

+

Neg*

P40

Low

CLN3

NM_000086

c.995C > T

p.A332V

--

 

HT

-

NA

P41

High

CLN5

NM_006493

c.726C > A

p.N242K

--

 

HT

-

NA

P42

High

IDUA

NM_000203

c.650G > A

p.R217Q

--

 

HT

-

NCI

  

NPC1

NM_000271

c.2257G > A

p.V753M

--

 

HT

-

NCI

P43

Mod

ASAH1

NM_177924

c.2 T > C

p.M1T

--

 

HT

-

NCI

P44

Low

SMPD1

NM_000543

c.1460C > T

p.A487V

--

 

HT

+

NCI

P45

Low

CLN3

NM_000086

c.995C > T

p.A332V

--

 

HT

-

NA

P46

Low

CLN5

NM_006493

c.606G > A

p.M202I

--

 

HT

-

NA

  

GALNS

NM_000512

c.1127G > A

p.R376Q

--

 

HT

+

NCI

P47

Low

GNPTG

NM_032520

c.857C > T

p.T286M

--

 

HT

+

NCI

  

NPC1

NM_000271

c.3535A > G

p.M1179V

rs61731969

0.002

HT

-

NCI

P48

Low

MAN2B1

NM_000528

c.844C > T

p.P282S

rs45576136

0.003

HT

-

NCI

Notes and abbreviations: Samples from P27-39 were processed using SOLiD4; those from P40-P48 using HiSeq2000; P49-P66 no mutation was found. PC: Patient Code; IS: Index of LSD Suspicion (Mod: moderate); *detected with frequency ≤0.01 in 1000 g2012 database; MAF: minor allele frequency; Zig: zygosity; HO: homozygote; HT: heterozygote; MD: Mutation description in Human Gene Mutation Database (+: previously associated with pathology; −: not previously associated); BA: Biochemical assay (+: positive; NA: test not available; Neg: negative; NCI: not clinically indicated; Ψ Enzimatic assay; *GAGs).

Unexpected diagnoses

In cases P7, P9 and P11, the diagnoses were unexpected, adding significant value to our method. In case P7, the patient’s history did not initially suggest type 1 gangliosidosis (GM1), due to presence of cerebellar atrophy on magnetic resonance imaging (MRI) at first consultation (age 2 years) and a consistently raised lactic acid level in both blood and cerebrospinal fluid (CSF). Together with updated clinical data, therefore, a mitochondrial cytopathy seemed likely and a muscle biopsy was performed, which showed a slight deficiency in complex I of the mitochondrial respiratory chain. Subsequent electroneurography (at 4.7 years) showed results consistent with a mixed polyneuropathy. This finding, along with the clinical data, MRI, and minimal deficiency in complex I, suggests a probable neuroaxonal dystrophy. With enzymatic and genetic data from the NGS-LSD assay, we concluded that this child suffered from a late infantile GM1 that could be categorized as atypical due to the presence of cerebellar atrophy, mixed polyneuropathy, and increased lactate in the blood and CSF. In classical GM1, MRI is usually normal or shows some late brain atrophy, while polyneuropathy and high lactate levels are not usually present.

Case P9 was shown to have juvenile type 2 gangliosidosis (GM2), following a delayed suspicion of a storage disorder due to the late onset and low specificity of symptoms, and slow progression of the disease. In case P11, ceroid lipofuscinosis neuronal (CLN) was initially suspected due to epilepsy, cognitive regression, and a loss of memory and expressive language. Progressive multifocal epilepsy was refractory to combination therapy, the patient’s vision worsened, and he appeared ataxic and lost bowel control. There was also a reduced retinal vascular tree with papillary pallor, thought to signify the onset of retinopathy; however the detection of a severe mutation in the GM2-associated gene HEXA altered the diagnosis. P11 currently has progressive optic atrophy. Whilst infantile GM2 is easy to recognize due to a cherry-red spot in the fundus, juvenile GM2 is difficult without the identification of genetic variants, as discussed above. As it is shown in the Table 4, second mutation in HEXA for P11 was synonymous. Familial co segregation was demonstrated and the program Human splicing finder predicted the disruption of an enhancer motif for SRp40 protein and the simultaneous creation of a new silencer motif.

A finding of interest was the presence of a hemizygous variant of the Hunter-associated gene IDS in patient P25. While this mutation is already registered in Human Gene Mutation Database (HGMD) as associated with the Hunter phenotype, the patient showed none of the clinical features usually found in Hunter patients; he did not showed increased levels of urine GAGs and no skeletal deformities were shown in radiological test, excluding completely a Hunter diagnosis. This lends further weight to what has long been suspected, that we have only seen the ‘tip of the iceberg’ of genotype-phenotype associations in clinical genetics and that NGS will change some universally accepted paradigms and lead to new genetic prescripts, such as the implication of more than one gene in classically monogenic disorders [32].

Time-to-diagnosis and cost: comparisons with Sanger sequencing

The period between onset of disease and genetic diagnosis (GDD column, Table 4) also enhances the value of the NGS-LSD tool. Diagnostic delay in our study was on average 7 (range 1–23) years. Using NGS-LSD ended 14–15 diagnostic odysseys, thus opening the way for genetic counseling and carrier tests. In terms of cost, the difference between classical and next-generation sequencing technology is $2400 and $0.1 per million bases, respectively. Two examples from the current study serve to illustrate the difference between two technologies. Although a CLN was strongly suspected, patient P5 (with 13 years of diagnostic delay) underwent five classical genetic analyses without a positive result and at a total cost of about 4000€. With an early NGS-LSD assay, the diagnosis could have been made in 8 weeks at a cost of about 900€. In case P3 several diseases were suspected over a period of 12 years, including GM1, Sandhoff, galactosialidosis, Schindler disease and CLN. If the NGS-LSD tool had been used on first suspicion, the time-to-diagnosis would have been reduced to 1–2 months, and the cost to that of a single genetic analysis.

Future approaches to reduce non-diagnosis

The current study ended with 39–40 patients still undiagnosed, although 64% of these had a low or moderate index of suspicion of LSD. Clearly, these undiagnosed cases indicate a significant problem with our proposed tool, which is due to the considerable phenotypic overlap between many LSD and non-LSD neurodevelopmental conditions for which the genetic variants are not included in the NGS-LSD technique. To address this problem, we are currently developing a broad-range genetic panel that encompasses most known neurometabolic disorders, the NGS-NMD1 tool, which will be updated as new genes come to light with continued research. Such an approach will form an important next step in optimizing the neurometabolic diagnostic process. Six of our undiagnosed patients are currently undergoing whole-exome resequencing.

Application of NGS-LSD to clinical diagnosis

NGS technologies have significantly improved sequencing capacity in the past 5 years. These technologies are now widely used for research purposes and are starting to find their way into clinical applications [3335]. Whole-genome and exome sequencing approaches are being successfully implemented in research projects [31, 3642], but are not yet routine strategies in diagnosis due to high costs, long turnover times (run and analysis time) and ethical issues. Targeted resequencing, on the other hand, is appealing in a clinical setting due to lower sequencing costs, shorter sequencing time, simpler data analysis, and greater sensitivity per gene due to the greater coverage achieved [43, 44]. The results of the current study also illustrate the importance of good coverage to reliability in detection of mutations in NGS in clinical settings.

Due to the nature of the technology, NGS also brings with it uncertainties and limitations of a higher order of magnitude than previously seen in genetic diagnosis. However, uncertainty in genetic testing is nothing new, and we also have the foresight and fortitude to develop tools to deal with these issues. Assays must also be carefully validated with pilot projects to assess sensibility and accuracy of these new technologies. Several groups are working in this direction, designing resequencing assays for a panel of genes related to groups of diseases that have similar clinical manifestations and difficult diagnoses [4554].

To the best of our knowledge, the current study is the first to use NGS in LSD screening. Overall, it shows that a high rate of detection of mutations is possible when sequence coverage is sufficient and gaps due to limitations in the enrichment method can be overcome. Our results show that the combination of in-solution based capture and NGS can be used for the parallel screening of multiple disease genes and can successfully identify disease-causing mutations. This assay, therefore, can be used as a support genetic tool, that always in combination with biochemical and clinical data could facilitate a diagnosis. Finally, we have shown the power of our approach as a tool for making diagnoses that are particularly challenging and for bringing diagnostic odysseys to a more rapid conclusion. It will be important, therefore, to continue to work with specialists to optimize this powerful and promising technology for the benefit of patients and their families”.

Abbreviations

LSDs: 

Lysosomal storage disorders

NGS: 

Next-generation sequencing

ERT: 

Enzyme-replacement therapy

MPS: 

Mucopolysaccharidoses

SETS: 

SOLiD experimental Tracking Software

GATK: 

Genome Analysis Toolkit

RTA: 

Illumina Real Time Analysis

GEM: 

Genome Multitool

BFAST: 

BLAT-like Fast Accurate Search Tool

SNVs: 

Single nucleotide variants

LSMDs: 

Locus Specific Mutation Databases

SIFT: 

Sorting Intolerant From Tolerant

NRA: 

Non-reference allele

MQV: 

Mean quality values

GM1: 

Type 1 gangliosidosis

GM2: 

Type 2 gangliosidosis

MRI: 

Magnetic resonance imaging

CSF: 

Cerebrospinal fluid

CLN: 

Ceroid lipofuscinosis neuronal

HGMD: 

Human gene mutation database.

Declarations

Acknowledgements

We are indebted to the families affected by LSDs who participated by allowing us to use genetic samples and clinical information. We also thank the clinicians who collected samples and data, including those not presented in the final manuscript. This study was financially supported by a grant from the Spanish Ministerio de Sanidad y Consumo (PI10/1193).

Authors’ Affiliations

(1)
Unidad Diagnóstico y Tratamiento de Errores Congénitos del Metabolismo (Servicio de Neonatología), Facultad de Medicina y Odontología de la Universidad de Santiago de Compostela
(2)
Neuropediatra Fundación Hospital San Juan de Dios, CIBERER
(3)
Servicio de Neuropediatría, ospital Clínico Universitario de Santiago de Compostela, Facultad de Medicina y Odontología de la Universidad de Santiago de Compostela
(4)
Unidade de Bioquímica Genética, Centro de Genética Médica Jacinto Magalhães, Centro Hospitalar do Porto
(5)
Unidad de Neuropediatría, Hospital Clínico Universitario Marqués de Valdecilla
(6)
Servicio de Genética Molecular, Hospital San Juan de Dios

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© Fernández-Marmiesse et al.; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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