Abstract

Next-generation sequencing techniques have emerged as powerful tools for the understanding of cancer genomes. In recent years, whole-exome and whole-genome sequencing strategies have enabled the annotation of a comprehensive mutation landscape of chronic lymphocytic leukemia (CLL), the most frequent leukemia in western countries. Several recurrently mutated genes have been identified, with a subset being validated as neoplastic drivers. Still, a main challenge remains for the differentiation between driver and passenger mutations among candidates as well as for the functional description of the newly discovered leukemogenic genes that could be utilized for personalized anti-tumor strategies. In this scenario, we have identified the metabolic enzyme sucrase–isomaltase (SI) as one of the most frequently mutated genes in a cohort of 105 CLL patients. Here, we demonstrate that these SI mutations result in loss of enzyme function by preventing the biosynthesis of catalytically competent SI at the cell surface. Transcriptome analyses of RNA from CLL patients with SI loss-of-function mutations have uncovered gene expression patterns that depict ample metabolic reprogramming, pinpointing SI as a putative player in the cancer-associated metabolic switch. These results highlight SI as a relevant target for clinical evaluation in future CLL studies.

INTRODUCTION

Chronic lymphocytic leukemia (CLL) is the most frequent leukemia in western countries (1–4). This disease can be classified under two major molecular subtypes, defined by either high or low number of somatic hypermutations in the variable region of the immunoglobulin genes. These molecular categories have been associated with clinical heterogeneity in CLL evolution, with some patients showing an indolent disease course, while others present an aggressive evolution and short survival times (5,6). The application of next-generation sequencing approaches has shed light on the underlying genetic defects and molecular mechanisms driving CLL pathogenesis. Thus, recent whole-genome sequencing analyses from CLL patients have uncovered NOTCH1, MYD88, XPO1 and KLHL6 as recurrently mutated driver genes in this disease (7,8). Further whole-exome sequencing analysis has significantly broadened the CLL mutational repertoire, identifying a subset of 78 genes showing recurrent somatic mutations (9). Importantly, the splicing factor SF3B1 was found to be somatically mutated in 10% of cases and correlated with poor clinical prognosis (10). This has been further supported by the identification of SF3B1 as a frequently mutated gene in two additional cohorts (11,12). Very recently, the shelterin complex component POT1 has been uncovered as a tumor suppressor associated with disease aggressiveness in CLL, due to mutations that drive to telomere fragility as well as to defects in telomere maintenance (13). All of these findings have facilitated a significant leap forward in the understanding of CLL pathogenesis. However, the comprehensive description of the mechanistic landscape in this complex disease is yet to be tackled through the functional analysis of the many candidates identified as recurrently mutated genes (14).

In the context of the whole-exome sequencing of matched tumor and normal samples from 105 CLL cases (9), significantly increased somatic mutation rates were identified not only for SF3B1, but also for additional putative cancer genes encoding, among others, chromodomain 2 (CHD2), a regulator of gene expression through chromatin structure modifications (15), low density lipoprotein receptor-related protein 1B (LRPB1), a recently described tumor suppressor (16) and the carbohydrate metabolism enzyme sucrase–isomaltase (SI) (17,18). Interestingly, mutations in the coding sequence of SI have also been found in 2 of the 91 CLL patients screened in an independent study (12) as well as in other different tumors, including head and neck, colorectal and ovarian carcinomas (COSMIC database, http://www.sanger.ac.uk/genetics/CGP/cosmic/). SI is a type II transmembrane glycoprotein with preferential expression in the apical membranes of the polarized enterocytes of the intestinal brush border membrane, where it is essential for the processing of dietary carbohydrates (18). This enzyme is composed of two highly similar sucrase and isomaltase subunits, originating from a single polypeptide precursor, pro-SI. The mature intestinal protein is a heterodimer, generated through proteolytic cleavage in the intestinal lumen, in which both sucrase and isomaltase domains remain associated by non-covalent interactions (19). The initial mannose-rich pro-SI form is heavily N- and O-glycosylated in the Golgi apparatus to render a complex glycosylated, mature and active protein that is finally sorted to the cell surface (20,21).

Due to the prominent expression of SI in intestinal brush-border membranes, the putative association of this enzyme with cancer has been almost exclusively investigated in colorectal malignancies. SI is not expressed in normal adult colon mucosa, but it is up-regulated in dysplastic adenomatous polyps, correlating with their progression to carcinoma (22), as well as in metastatic colon adenocarcinomas (23). The potential role of SI in cancer cells is yet unknown, but it is likely related to its metabolic activity. Indeed, altered cellular metabolism is considered one of the emerging hallmarks of cancer (24). Tumor growth and proliferation are promoted by the tendency of cancer cells to metabolize glutamine, to synthetize fatty acids and, more importantly, to outcompete surrounding tissue for glucose. The latter property relies on the catabolism of glucose through glycolysis even in the presence of oxygen, a phenomenon termed ‘the Warburg effect’ (25). In addition, some metabolic enzymes have been recently found to act as tumor suppressors, such as the succinate dehydrogenase complex (26,27) and the tricarboxylic acid specific enzyme fumarate hydratase (28,29), or as oncogenes such as the isocitrate dehydrogenases (30). Based on these previous studies, it is tempting to speculate a putative involvement of SI mutations in cancer development and tumor metabolism.

Here, we describe the functional and biochemical characterization of heterozygous somatic mutations found in the gene encoding SI in CLL patients. We demonstrate that these mutations induce loss of enzymatic function in cultured cells. Protein expression analysis and confocal imaging showed defective protein maturation patterns and significantly increased localization of mutant SI forms in the endoplasmic reticulum (ER), pointing to incomplete posttranslational modification of pro-SI, misfolding defects and defective sorting of glycosylated mature SI to the cell surface. Additionally, gene expression profiling of CLL patients with SI mutations showed remarkable metabolic reprogramming features, supporting the hypothesis of SI as a component of the tumor-specific metabolic switch.

RESULTS

SI mutations cause maturation defects and loss of protein function

The SI enzyme complex is a type-II transmembrane protein, with an N-terminal cytoplasmic tail, a transmembrane anchor domain and two highly similar isomaltase and sucrase domains that face the extracellular side. The N-terminal portion of the isomaltase domain harbors a rigid serine/threonine stalk, which is located proximal to the membrane and is heavily N- and O-glycosylated (18,31). Three of the four somatic mutations found in our series of CLL patients, p.D1193N, p.W1493C and p.T1680I, are located in the C-terminal sucrase domain, whereas the remaining mutation, p.R91T, is inserted in a trefoil motif situated N-terminal to the isomaltase domain (Fig. 1A). After assessing that SI is expressed in lymphocytes from SI-mutated and -unmutated CLL patients (Supplementary Material, Fig. S1), we aimed at evaluating the functional relevance of the CLL-associated SI mutations described above. For this purpose, we first generated p.R91T, p.D1193N and p.W1493C SI variants, and compared their biosynthetic features and enzymatic activities with those of wild-type SI. The catalytic competence of the different SI mutant forms was evaluated by analyzing their sucrase activity in cultured cells. Transfected cells were first incubated in phosphate buffered saline (PBS) containing 50 mm sucrose for 2 h and then the amounts of glucose generated by SI-driven sucrose breakdown were estimated in the supernatants by means of a glucose-based test. The obtained results reflected a remarkable loss of activity for all the SI mutants when compared with wild-type SI in living cells (Fig. 2A). The SID1193N mutant shows a 25% decrease in activity, which drops 4-fold for SIR91T and is almost completely abolished in the SIW1493C mutant form.

Figure 1.

SI mutations in CLL. Schematic representation of SI structure. The location of SI mutations annotated in COSMIC is shown in gray. The somatic mutations identified by whole-exome analysis of 105 CLL patients are shown in black. Amino acid sequence alignments of SI proteins from different species are shown below. GH31, glycosyl hydrolase family 31.

Figure 1.

SI mutations in CLL. Schematic representation of SI structure. The location of SI mutations annotated in COSMIC is shown in gray. The somatic mutations identified by whole-exome analysis of 105 CLL patients are shown in black. Amino acid sequence alignments of SI proteins from different species are shown below. GH31, glycosyl hydrolase family 31.

Figure 2.

Loss of function and defective protein maturation in SI mutants. (A) Sucrase activity assays in cultured COS-7 cells showed significant loss of function for the SI mutants under study, with activity declines ranging from 25% to residual activities similar to that of the control harboring the empty vector. (B) SI mutants present altered ratios of complex glycosylated mature protein (Sc, Mr = 245 kDa) versus immature high mannose pro-SI (Sh, Mr = 210 kDa). Quantification data were calculated from three independent replicates. β-Actin immunoblot for sample load normalization was performed in a parallel SDS–PAGE gel. (C) Endo H-treated samples present a cleavage-resistant and a cleavage-sensitive form corresponding to the presence of a complex glycosylated mature form and a high-mannose precursor in the untreated samples, respectively. This is also confirmed by the conservation of ratios between forms before and after treatment. No traces of complex glycosylated and Endo H-resistant forms are found for SIW1493C.

Figure 2.

Loss of function and defective protein maturation in SI mutants. (A) Sucrase activity assays in cultured COS-7 cells showed significant loss of function for the SI mutants under study, with activity declines ranging from 25% to residual activities similar to that of the control harboring the empty vector. (B) SI mutants present altered ratios of complex glycosylated mature protein (Sc, Mr = 245 kDa) versus immature high mannose pro-SI (Sh, Mr = 210 kDa). Quantification data were calculated from three independent replicates. β-Actin immunoblot for sample load normalization was performed in a parallel SDS–PAGE gel. (C) Endo H-treated samples present a cleavage-resistant and a cleavage-sensitive form corresponding to the presence of a complex glycosylated mature form and a high-mannose precursor in the untreated samples, respectively. This is also confirmed by the conservation of ratios between forms before and after treatment. No traces of complex glycosylated and Endo H-resistant forms are found for SIW1493C.

To delve into the biological reasons for the lower activity of the mutant proteins, we performed an immunoblotting analysis using a specific anti-SI antibody. This analysis showed two immunoreactive bands with clearly differentiated, mutant-specific intensity patterns (Fig. 2B). These bands, which migrate at molecular masses ∼245 and 210 kDa, are in agreement with those expected for the complex glycosylated mature SI (SIc) and the high-mannose immature SI form (SIh), respectively (18,19). The abundance of the high molecular mature form reaches over 60% of the total expressed SI in the wild-type cell lysates, whereas the SIc:SIh ratio appears to be significantly altered in the mutants. The proportion of the complex glycosylated form is ∼20% lower for SID1193N, but this maturation defect is even more prominent in SIR91T expressing cells where SIc is largely absent (19%). Finally, the SIW1493C mutant shows the most striking biosynthetic defect, with a 100% of the protein expressed as the high-mannose form. Importantly, the maturation states of the different SI forms are in agreement with the data derived from the sucrase functional assays, with the decrease in complex glycosylated species correlating to specific losses observed in enzymatic activities.

To verify the maturation-related glycosylation states of the different SI mutants, we subjected all samples to endo-β-N-acetylglucosaminidase H (Endo H) digestion. Endo H removes N-linked high-mannose as well as some hybrid-type oligosaccharides from glycoproteins, but it is unable to cleave high complex oligosaccharides, such as those present in mature SI. The effects of Endo H treatment in our samples are presented in Figure 2C. Wild-type SI shows a characteristic Endo H-resistant band corresponding to the complex glycosylated protein, while the removal of the high-mannose oligosaccharides from the immature form is confirmed by the presence of a new band migrating at Mr = 185 kDa. The SID1193N and SIR91T mutants showed a similar banding pattern to wild-type SI for their Endo H-treated proteins, displaying both a resistant and a sensitive form. In all cases, the SIc:SIh ratios from the untreated samples are similar to those observed for the Endo H-resistant and sensitive forms generated after treatment, confirming that SIc is a truly mature SI form, whereas SIh is a fully mannosylated precursor susceptible to Endo H deglycosylation. The treated SIW1493C mutant shows a unique band corresponding to the 185 kDa cleavage product, validating that the only SI form accumulated in this mutant corresponds to a mannosylated precursor.

Protein trafficking and subcellular localization are altered in SI mutants

The observed decrease or lack of complex glycosylated forms in the SI mutants would point to intracellular accumulation or retention of the protein in CLL cells. This has been reported for different SI mutations causing the congenital sucrase–isomaltase deficiency (CSID), in which altered SI maturation leads to increased accumulation of the protein in the ER and/or Golgi apparatus and, in some cases, altered turnover and reduced trafficking rates of the complex glycosylated protein at the cell surface (21,32). We therefore examined the subcellular localization of the SI mutant forms from CLL patients in transfected COS-7 cells by confocal immunofluorescence imaging and quantitative analysis of the resulting co-localization signals. The results obtained in these immunofluorescence experiments are summarized in Figure 3. Following confocal imaging, quantification of co-localization was performed following the Coastes' method for Pearson's coefficient minimization in order to finally estimate the background-thresholded Manders' overlap coefficient (33). Manders' coefficient (M) ranges between 0 and 1, with M = 0.5 meaning a 50% of above-background pixels from one channel overlapping with above-background pixels from the other (33). The results obtained from the analysis of 15 different images per transfected SI form confirmed a significant increment in the accumulation of the protein in the ER for all the mutants, as shown by the increase in intracellular co-localization signals with the ER marker calreticulin (Fig. 3A). The quantitative data for ER-co-localization were also in concordance with the sucrase activity rates and the biosynthetic features previously measured for each mutant. The M value for ER-co-localization increased from M = 0.57 ± 0.03, obtained for the wild-type control, to M = 0.7 ± 0.03 for the SID1193N mutant. Protein accumulation in the ER was even higher in the SIR91T-transfected cells (M = 0.76 ± 0.01), but the maximum ER-co-localization levels were observed in the cells expressing the catalytically inactive mutant SIW1493C (M = 0.84 ± 0.02). Moreover, no signal of cell-surface localization was detected for SIW1493C in any of the cells analyzed.

Figure 3.

Altered subcellular localization and protein trafficking in SI mutants. (A) SI mutants showed increased co-localization with the ER marker calreticulin as a consequence of ER retention of aberrant precursor polypeptides. No traces of cell surface staining could be detected for the SIW1493C mutant in any of the images acquired. (B) SI co-localization with Golgi marker 58K Golgi protein, showing partial association of SI and SID1193N with Golgi and no co-localization for SIR91T and SIW1493C. Co-localization is shown in gray-to-white shading.

Figure 3.

Altered subcellular localization and protein trafficking in SI mutants. (A) SI mutants showed increased co-localization with the ER marker calreticulin as a consequence of ER retention of aberrant precursor polypeptides. No traces of cell surface staining could be detected for the SIW1493C mutant in any of the images acquired. (B) SI co-localization with Golgi marker 58K Golgi protein, showing partial association of SI and SID1193N with Golgi and no co-localization for SIR91T and SIW1493C. Co-localization is shown in gray-to-white shading.

Co-localization of SI with the Golgi apparatus has been reported elsewhere (21,32). In our experiments, SID1193N presents Golgi co-localization levels with the 58K Golgi protein that are similar to those observed for wild-type SI (M = 0.57 ± 0.11 and M = 0.54 ± 0.05, respectively). This suggests that, despite misfolding or structural aberrations, a certain percentage of mutant protein is capable of evading ER quality control mechanisms to be fully modified at the Golgi apparatus, thus contributing the 40% of complex glycosylated form observed by immunoblotting. On the other hand, no significant Golgi-associated signals were detected for SIR91T and SIW1493C as a result of almost complete arrest of the mutants within the ER (Fig. 3B). These results point to the occurrence of SI misfolding events which block or retain the SI mutant forms in the ER, causing defective N- and O-glycosylation and preventing biosynthesis of the mature form. Glycosylation patterns, mainly those associated with the O-linked glycan structures, have been proved to be key functional regulators of SI protein expression at the cell surface, polarized membrane sorting and enzymatic activity (34–36).

Altogether, our findings indicate that the CLL mutations presented here impair SI function by altering the trafficking of this enzyme along the secretory pathway, causing intracellular accumulation of the high-mannose mutant precursors and eventually reducing or completely abrogating SI sorting to the cell surface.

Metabolic reprogramming in CLL cells with SI mutations

To evaluate the biological consequences of SI loss of function in tumor cells, we analyzed whole-genome expression arrays to compare three SI-mutated cases versus seven matched samples from CLL patients harboring no mutations in SI, neither in the known CLL driver genes SF3B1 and NOTCH1 (7–12) (Supplementary Material, Table S1). First, we performed an unsupervised hierarchical clustering analysis, which showed that the samples from patients harboring SI mutations clustered together (Supplementary Material, Fig. S2). To obtain deeper genetic information that could shed light into the specific functional properties of the SI-mutated samples, we subjected the expression array data to gene set enrichment analyses (Fig. 4). By these means, we found significant up-regulation (FDR q < 0.05) in gene sets uncovering core enrichment for specific biological pathways (Table 1). Importantly, in an initial analysis using the biological processes gene set, seven out of the nine significantly enriched Gene Ontology biological processes were metabolic, including carbohydrate metabolism, heterocycle metabolic process and cofactor biosynthesis. An extended analysis using the KEGG pathways gene set demonstrated a similar predominance of metabolic pathway changes in SI-mutated CLL patients, with 15 out of the 29 significantly up-regulated functional pathways being strictly metabolic (Table 1). These pathways comprise an outstanding representation of core cellular processes such as oxidative phosphorylation (Fig. 4A), glycolysis/gluconeogenesis (Fig. 4B) and the pentose phosphate pathway. Also of particular relevance to CLL is the observation of a significant enrichment of genes belonging to the pro-survival B-cell receptor signaling pathway in SI-mutated patients (Fig. 4C). The robustness of these results is reflected by further analyses aiming to compare SI-mutated patients with other patients harboring CLL driver mutations in either SF3B1 or NOTCH1. Again, SI-mutated CLL cases remain mostly enriched for metabolic pathways in both cases (FDR q < 0.25), in contrast with the other two patient sets (Supplementary Material, Tables S2 and S3). Collectively, these transcriptional changes reflect a substantial remodeling of metabolic fluxes coinciding with SI mutations in CLL. Preliminary experiments performed with COS-7 cells transiently expressing either SIWT or the most inactivating mutant SIW1493C in either high or low glucose medium suggest a trend towards slightly higher rates of lactic acid production in SIW1493C cells, although the differences were not significant (Supplementary Material, Fig. S3A). Similar experiments showed no differences in ATP synthesis (Supplementary Material, Fig. S3B). In the light of the aforementioned results, further studies involving a CLL or CLL-like context are warranted in order to unravel the suggested involvement and consequences of SI mutations in the metabolic switch of CLL cells.

Table 1.

Enriched pathways (FDR q < 0.05) in GSEA analysis of expression in SI-mutated CLL patients (n = 3) versus CLL patients harboring no mutations in SI, SF3B1 and NOTCH1 (n = 7)

Name Size NES NOM p-val FDR q-val FWER p-val 
GO biological processes gene set 
 Heterocycle metabolic process 25.00 1.99 0.00 0.02 0.02 
 Cofactor metabolic process 49.00 1.99 0.00 0.01 0.03 
 Cellular carbohydrate catabolic process 22.00 1.94 0.00 0.02 0.05 
 Cofactor biosynthetic process 21.00 1.93 0.00 0.02 0.07 
 Secondary metabolic process 25.00 1.89 0.00 0.03 0.12 
 Carbohydrate catabolic process 23.00 1.88 0.00 0.02 0.14 
 Regulation of cytoskeleton organization and biogenesis 28.00 1.87 0.00 0.03 0.18 
 Pigment metabolic process 18.00 1.82 0.00 0.05 0.33 
 Actin filament-based process 108.00 1.81 0.00 0.05 0.36 
KEGG gene set 
 Oxidative phosphorylation 110.00 2.43 0.00 0.00 0.00 
 Parkinson's disease 106.00 2.29 0.00 0.00 0.00 
 Pentose phosphate pathway 26.00 2.07 0.00 0.00 0.00 
 Lysosome 115.00 2.05 0.00 0.00 0.00 
 Huntington's disease 164.00 2.01 0.00 0.00 0.00 
 Alzheimer's disease 149.00 2.01 0.00 0.00 0.00 
 Glycolysis/gluconeogenesis 60.00 1.99 0.00 0.00 0.01 
 Fructose and mannose metabolism 34.00 1.96 0.00 0.00 0.01 
 B-cell receptor signaling pathway 71.00 1.94 0.00 0.00 0.02 
 Propanoate metabolism 31.00 1.93 0.00 0.00 0.03 
 Valine, leucine and isoleucine degradation 43.00 1.89 0.00 0.00 0.04 
 Porphyrin and chlorophyll metabolism 31.00 1.84 0.00 0.01 0.06 
 Base excision repair 33.00 1.83 0.00 0.01 0.07 
 Glyoxylate and dicarboxylate metabolism 15.00 1.81 0.00 0.01 0.10 
 Galactose metabolism 25.00 1.80 0.00 0.01 0.11 
 Beta-alanine metabolism 22.00 1.78 0.00 0.01 0.13 
 Proteasome 42.00 1.78 0.00 0.01 0.13 
 Pathogenic Escherichia coli infection 43.00 1.77 0.00 0.01 0.16 
 Glycerophospholipid metabolism 64.00 1.76 0.00 0.01 0.16 
 Purine metabolism 149.00 1.75 0.00 0.01 0.18 
 Pyruvate metabolism 40.00 1.73 0.00 0.01 0.24 
 DNA replication 34.00 1.73 0.00 0.01 0.24 
 Asthma 27.00 1.72 0.00 0.01 0.25 
 Pyrimidine metabolism 88.00 1.72 0.00 0.01 0.25 
 Leishmania infection 62.00 1.69 0.00 0.02 0.34 
 Viral myocarditis 67.00 1.68 0.00 0.02 0.39 
 Butanoate metabolism 31.00 1.67 0.00 0.02 0.43 
 Primary immunodeficiency 35.00 1.66 0.00 0.02 0.46 
 Graft versus host disease 37.00 1.61 0.00 0.04 0.65 
Name Size NES NOM p-val FDR q-val FWER p-val 
GO biological processes gene set 
 Heterocycle metabolic process 25.00 1.99 0.00 0.02 0.02 
 Cofactor metabolic process 49.00 1.99 0.00 0.01 0.03 
 Cellular carbohydrate catabolic process 22.00 1.94 0.00 0.02 0.05 
 Cofactor biosynthetic process 21.00 1.93 0.00 0.02 0.07 
 Secondary metabolic process 25.00 1.89 0.00 0.03 0.12 
 Carbohydrate catabolic process 23.00 1.88 0.00 0.02 0.14 
 Regulation of cytoskeleton organization and biogenesis 28.00 1.87 0.00 0.03 0.18 
 Pigment metabolic process 18.00 1.82 0.00 0.05 0.33 
 Actin filament-based process 108.00 1.81 0.00 0.05 0.36 
KEGG gene set 
 Oxidative phosphorylation 110.00 2.43 0.00 0.00 0.00 
 Parkinson's disease 106.00 2.29 0.00 0.00 0.00 
 Pentose phosphate pathway 26.00 2.07 0.00 0.00 0.00 
 Lysosome 115.00 2.05 0.00 0.00 0.00 
 Huntington's disease 164.00 2.01 0.00 0.00 0.00 
 Alzheimer's disease 149.00 2.01 0.00 0.00 0.00 
 Glycolysis/gluconeogenesis 60.00 1.99 0.00 0.00 0.01 
 Fructose and mannose metabolism 34.00 1.96 0.00 0.00 0.01 
 B-cell receptor signaling pathway 71.00 1.94 0.00 0.00 0.02 
 Propanoate metabolism 31.00 1.93 0.00 0.00 0.03 
 Valine, leucine and isoleucine degradation 43.00 1.89 0.00 0.00 0.04 
 Porphyrin and chlorophyll metabolism 31.00 1.84 0.00 0.01 0.06 
 Base excision repair 33.00 1.83 0.00 0.01 0.07 
 Glyoxylate and dicarboxylate metabolism 15.00 1.81 0.00 0.01 0.10 
 Galactose metabolism 25.00 1.80 0.00 0.01 0.11 
 Beta-alanine metabolism 22.00 1.78 0.00 0.01 0.13 
 Proteasome 42.00 1.78 0.00 0.01 0.13 
 Pathogenic Escherichia coli infection 43.00 1.77 0.00 0.01 0.16 
 Glycerophospholipid metabolism 64.00 1.76 0.00 0.01 0.16 
 Purine metabolism 149.00 1.75 0.00 0.01 0.18 
 Pyruvate metabolism 40.00 1.73 0.00 0.01 0.24 
 DNA replication 34.00 1.73 0.00 0.01 0.24 
 Asthma 27.00 1.72 0.00 0.01 0.25 
 Pyrimidine metabolism 88.00 1.72 0.00 0.01 0.25 
 Leishmania infection 62.00 1.69 0.00 0.02 0.34 
 Viral myocarditis 67.00 1.68 0.00 0.02 0.39 
 Butanoate metabolism 31.00 1.67 0.00 0.02 0.43 
 Primary immunodeficiency 35.00 1.66 0.00 0.02 0.46 
 Graft versus host disease 37.00 1.61 0.00 0.04 0.65 

NES, normalized enrichment score; NOM, nominal; FWER, family-wise error rate.

Figure 4.

Transcriptome changes in CLL patients related to SI mutations. Transcriptome microarrays of CLL patient samples with identified mutations in sucrase isomaltase (SI) (labeled SI; n = 3 patients) and randomly selected CLL patient samples with no SI mutations (labeled unmutated; n = 7). Concordant differences in biological pathways between the two sample groups were determined through analysis of the data with Gene Set Enrichment Analysis (GSEA) v2.07 utilizing the gene set for KEGG pathways. Heat maps display the expression values, where the range of colors (red, pink, light blue and dark blue) represents the range of expression values (high, moderate, low and lowest, respectively). From the GSEA analysis, three of the KEGG pathways demonstrating significant gene enrichment in CLL samples with SI mutations were (A) oxidative phosphorylation, (B) glycolysis/gluconeogenesis and (C) B-cell receptor signaling.

Figure 4.

Transcriptome changes in CLL patients related to SI mutations. Transcriptome microarrays of CLL patient samples with identified mutations in sucrase isomaltase (SI) (labeled SI; n = 3 patients) and randomly selected CLL patient samples with no SI mutations (labeled unmutated; n = 7). Concordant differences in biological pathways between the two sample groups were determined through analysis of the data with Gene Set Enrichment Analysis (GSEA) v2.07 utilizing the gene set for KEGG pathways. Heat maps display the expression values, where the range of colors (red, pink, light blue and dark blue) represents the range of expression values (high, moderate, low and lowest, respectively). From the GSEA analysis, three of the KEGG pathways demonstrating significant gene enrichment in CLL samples with SI mutations were (A) oxidative phosphorylation, (B) glycolysis/gluconeogenesis and (C) B-cell receptor signaling.

DISCUSSION

The unmatched capacity of next-generation sequencing techniques to provide fast, massive and reliable whole-genome information sets the founding bases for the International Cancer Genome Consortium (37). This collaborative project aims to the complete understanding of the molecular mechanisms of the disease, covering most cancer genetic subtypes, in order to maximize therapeutic efficiency on the basis of personalized treatments. In this scenario, SI was classified among the most promising candidates identified by whole-exome sequencing analysis of 105 CLL patients. SI was found mutated in almost 4% of the cases (9), which represents a highly mutated gene in the context of CLL. In fact, if gene size and codon composition are taken into account, SI is the fifth most frequently mutated gene in this study, after SF3B1, POT1, CHD2 and NXF1. Moreover, no CLL-related mutations were found for maltase-glucoamylase, a highly similar brush border disaccharidase that shares a 58% identity and some overlapping enzymatic activities with SI (38). Taking into account the high mutation rates, the number of SI mutations found in different cancers (as annotated in COSMIC) and the putative functional association of the enzyme with cancer metabolism, SI could be an overlooked cancer gene, especially in CLL.

Despite the fact that none of the mutated residues under study is predicted to be catalytic, sucrase activity assays in transfected cells showed loss of function for all the mutants, with activity decreases ranging from 25% to almost complete ablation. This suggests a similar disrupting mechanism for all mutations without directly acting on catalysis. This is reminiscent of CSID, a metabolic syndrome where the inactive SI forms fail to properly mature after being synthesized, causing defective sugar absorption and osmotic diarrhea due to SI malfunction (39). In most CSID cases, SI precursors are arrested in ER and/or Golgi apparatus, thus reducing or completely preventing the expression of cell surface mature SI (20,21,32,39). Consistent with many CSID mutations, immunoblotting and Endo H-specific cleavage assays showed that all the defective SI forms studied here present significantly decreased levels of SIc when compared with wild-type SI, with a proportional increase in the putative mannosylated form SIh. We also observed that SIc:SIh ratios are altered to different degrees for each mutant, presenting a robust quantitative correlation between mature protein levels and activity. Indeed, the inactive mutant SIW1493C shows almost undetectable SIc levels. It has been shown that the mannose-rich SI form is characterized by very low activity levels, and that terminal glycosylation is a key step for the acquisition of full enzymatic capabilities (40). This is consistent with our protein expression results—showing SIc loss and SIh accumulation—to support the hypothesis of SI loss of function resulting from the deficiency of mature SI form rather than from catalytic ablation of the sucrase active site.

To evaluate the predicted alterations caused by the SI mutations in intracellular protein trafficking, we studied the subcellular localization of the SI mutants by immunofluorescence-based confocal microscopy. Both imaging and quantitative co-localization analyses showed a significant increase in the accumulation of SI in the ER of cells expressing the mutant forms. Importantly, the extent of ER-protein co-localization inversely correlates with the amount of mature protein detected by immunoblotting and with sucrase activity data. Moreover, no SI-associated cell surface signal could be detected in any case for the almost null mutant SIW1493C, which in turn showed the highest levels of intracellular SI staining. This result suggests similar cellular phenotypes for the three SI mutations under study, despite the fact that they are located in different structural and functional domains. The p.R91T mutation is located in a trefoil motif or P-domain—probably involved in protein–protein or lectin-like interactions (41)—and very close to a serine/threonine-rich stalk, which is decorated with O-glycan structures reportedly regulating cell surface expression of SI (36). The CLL-associated p.R91T mutation is likely responsible for a P-domain misfolding, very much similar in fashion to another known SI mutation (p.Q117R), responsible for the phenotype IV of CSID (42). This alteration would result in an aberrant protein that does not fulfill the requirements for the quality control mechanisms in ER. Also, the proximity of the mutation to the O-glycosylation target stalk could be directly hampering this posttranslational modification, essential for cell surface sorting of SI.

The remaining SI mutations under study, SID1193N and SIW1493C, are located in the sucrase domain, but none of them is predicted to be key catalytic residues, despite the proximity of W1493C to the predicted proton donor D1500 in the active site. In fact, SIW1493C is the mutant that displays the most dramatic phenotype in terms of lack of mature protein expression, complete loss of function and massive intracellular protein accumulation. Such remarkable functional and cellular defects point to highly aberrant folding of the vast majority of the mannosylated polypeptide, which would eventually cause blocking or retention of the SI precursor in the ER. Both mutations could be involved in a common mechanism that can prevent the synthesis of physiological SI levels. The sucrase domain has been described as an SI intramolecular chaperone, participating in the correct folding and processing of its isomaltase counterpart (43). The mutated sucrase domains are very likely affected in their chaperone capabilities, or can be misfolded themselves, thereby blocking the correct SI maturation process along the secretory pathway. Therefore, the phenotypes observed for the three CLL-associated mutants seem to be the consequence of mutated pro-SI precursors undergoing intracellular arrest in ER and triggering protein quality control mechanisms. As a consequence, little or no mannosylated precursors undergo final posttranslational modifications in the Golgi apparatus to follow normal trafficking and cell surface sorting, resulting in delayed or null maturation (21,32,44).

To gain insight into the pathological consequences of SI loss of function in the CLL context, we performed expression arrays employing lymphocytic RNA from three patients presenting mutated SI. Interestingly, unsupervised clustering analysis of the expression data showed that the SI-mutated samples were arranged together in a separate cluster, apart from the rest of CLL patients used as controls, strongly suggesting that these samples could be representing a distinct CLL subset. In fact, gene set enrichment analysis showed significant up-regulation of several processes known to participate in cancer metabolic reprogramming (25,45), such as glycolysis/gluconeogenesis, the pentose phosphate pathway or oxidative phosphorylation. The wide deregulation of carbohydrate metabolism is also noteworthy and in good agreement with the loss of SI function. On the other hand, the disturbance of normal B-cell receptor signaling appears to be of special interest for CLL pathogenesis. Nevertheless, the pleiotropic effect observed in many interconnected anabolic and catabolic pathways suggests a wide shift in the metabolic state of SI-mutated CLL cells, supporting the hypothesis of loss of SI activity as part of a cancer metabolic switch. The regulation of SI is tightly dependent on glucose levels, as shown in Caco-2 colon cancer cells, where prolonged growth of confluent cells in low glucose levels increased SI expression (46). The relevance of this finding comes from cancer cells requiring a substantial increase in glucose intake processes (25,45). The negative feedback mechanism linking SI expression to glucose use remains so far unknown, but SI-mediated signal transduction cannot be ruled out. The cytoplasmic tail of SI can be phosphorylated at Ser6 by the cAMP-dependent protein kinase A (47), and in this scenario SI could participate in intracellular signaling events that regulate cell metabolism. The alteration of these signaling processes by loss of functional SI at the cell surface could be part of the switch that toggles oncogenic reprogramming of core metabolic pathways. Further efforts are needed to unravel the hitherto unknown regulatory process that links SI integrity with metabolic homeostasis. This work should bring new pieces of information that will help fulfill the final goals of the CLL-ICGC project, the dissection of CLL oncogenesis into its true driver genes for the design of a new generation of targeted and personalized anti-cancer therapies.

MATERIALS AND METHODS

Patients and clinical information

Information about the clinical characteristics of patients, ethical procedures and sample collection has been published elsewhere (9). Tumor samples were always collected before the administration of any treatment. All patients gave informed consent for their participation in the study following the International Cancer Genome Consortium (ICGC) guidelines (37).

Cell culture and transfection

COS-7 cells were maintained in Dulbecco's modified Eagle's medium (DMEM, Gibco) supplemented with 10% fetal bovine serum and 1% penicillin–streptomycin–glutamine (Gibco). Cells were seeded in 6- or 12-well plates and transfected using lipofectamine plus or lipofectamine 2000 (Invitrogen) following manufacturer's instructions. For selection, cells grown in six-well plates were trypsinized 48 h after transfection, and cultured in 150 mm dishes with the growth medium containing 500 μg/ml G-418 (Gibco).

DNA constructs and site-directed mutagenesis

The C-terminal Myc-DDK tagged full-length SI, cloned into the pCMV6-Entry vector, was purchased from OriGene technologies. The R91T SI variant was generated by site-directed mutagenesis of the wild-type cDNA using the QuikChange XL site-directed mutagenesis kit (Stratagene) and the oligonucleotides 5′-CAGAGGGAATTTGTGCACAGACGGGCTGCTGCTG-3′ and 5′-CAGGCAGCAGCCCGTCTGTGCACAAATTCCCTCTG-3′, the D1193N SI variant using oligonucleotides 5′-CGTACAGTTGGAGGGATCTTGAATTTTTATATGTTTTTGGGCC-3′ and 5′-GGCCCAAAAACATATAAAAATTCAAGATCCCTCCAACTGTACG-3′ and the W1493C SI variant using oligonucleotides 5′-CTAGTGGACGATGCGGAGGACACTGGC-3′ and 5′-GCCAG TGTCCTCCGCATCGTCCACTAG-3′. The nucleotide sequence of all variants was verified before experimental use.

Immunoblotting assays

Cultured cells were washed with PBS and scraped in the presence of 50 μl of cold Tris buffer (100 mm) containing 150 mm NaCl, 10 mm EDTA pH 8, 1% deoxycholic acid, 1% Triton X-100, 0.1% SDS and Complete® protease inhibitor cocktail (Roche Applied Science). Cell extracts were then sonicated and centrifuged at 12,000g for the removal of cell debris. Protein concentration of the cell-free extracts was determined using the bicinchoninic acid assay (BCA protein assay kit, Pierce Biotechnology), and 30 μg of each cell extract was loaded onto 6% SDS–polyacrylamide gels. Following electrophoresis, proteins were electrotransferred onto PVDF membranes, and then the membranes were blocked with 3% non-fat dried milk in PBT (phosphate buffered saline with 0.05% Tween-20). The primary antibody, goat anti-sucrase–isomaltase (Santa Cruz Biotechnology), was incubated overnight in blocking buffer. After three washes with PBT, the membranes were incubated with horseradish peroxidase-conjugated rabbit anti-goat IgG (Thermo Scientific) at a 1:20 000 dilution in 2.5% milk in PBT and developed with the Luminata Forte Western HRP substrate (Millipore).

Endoglycosidase H digestion

SI glycosylation was assessed by treatment with endo-β-N-acetylglucosaminidase H (Endo H; New England Biolabs) following manufacturer instructions. Briefly, 60 μg of extracts obtained from transfected cells were denatured by incubation at 100°C for 10 min in glycoprotein denaturing buffer (10 μl final volume). Then, the samples were brought to 20 μl by addition of 10X G5 reaction buffer, 1 μl of Endo H and water. The reactions were allowed to proceed for 3 h at 37 °C and then stopped by addition of SDS–PAGE loading buffer. Twelve microliters of each sample were loaded per lane for further identification of the reaction products by immunoblotting.

Sucrase activity assays

COS-7 cells were cultured in 12-well plates and transfected in triplicate with 0.7 μg of each plasmid, as described above. Thirty-six hours after transfection, the cells were washed three times with PBS and incubated in 300 μl of PBS containing 50 mm sucrose. After 2 h, the concentration of glucose in the supernatants was assessed using the Glucose and Sucrose Assay Kit (Abcam) following manufacturer's instructions. In this assay, glucose oxidase reacts with free glucose to modify a specific probe and generate the fluorescent compound resorufin. To estimate the glucose generated by sucrase activity in the reactions, 50 μl of each supernatant was mixed with 46 μl glucose assay buffer, 2 μl glucose probe and 2 μl glucose enzyme mix, and analyzed in a Biotek Synergy H4 hybrid reader at 37°C. Fluorescence was monitored every 30 s for 45 min using excitation and emission wavelengths of 530 and 590 nm, respectively. The linear portion of the fluorescence curves was used to estimate the velocity of resorufin generation and thus, glucose concentrations. All assays were performed in triplicate and activities plotted as mean ± SEM values.

Confocal immunofluorescence microscopy

Transfected COS-7 cells were seeded onto gelatinized glass cover slips, allowed to recover for 24 h and either fixed for 10 min at −20°C in ice-cold methanol or for 10 min at room temperature in 4% p-formaldehyde-buffered solution followed by methanol permeabilization. Cell preparations were blocked with 15% goat serum in PBS for 1 h and then co-incubated for 1 h at room temperature with anti-DYKDDDDK (anti-FLAG) antibody (1:800, Cell Signaling) and anti-calreticulin antibody (1:250, Abcam) for ER co-localization studies. For Golgi co-localization analysis, the anti-FLAG antibody was co-incubated with anti-58K Golgi protein antibody (1:200, Abcam). The secondary antibodies used for immunofluorescence detection were 488 Alexa Fluor antibody for SI detection and 546 Alexa Fluor antibody for either ER or Golgi detection. The cover slips were mounted on slides using 4′,6-diamidino-2-phenylindole (DAPI)-containing Vectashield mounting medium (Vector Laboratories) and imaged in a Leica TCS-SP2-AOBS confocal microscope (Leica Microsystems). Images were processed using Image J software. Co-localization analyses and quantification were performed using the co-localization test and co-localization Threshold plug-ins from Dr T. Collins (Wright Cell Imaging Facility, Toronto, Canada).

Whole-transcriptome analysis

Total RNA from CLL samples were analyzed using GeneChip®Human Genome U133 plus 2.0 chips according to the manufacture's protocol. RNAs from three CLL patients presenting SI mutations were compared with samples from seven patients showing no mutations in any of the previously identified CLL drivers (Supplementary Material, Table S1). Raw array signals were processed using Expression Console software (Affymetrix) using the Robust Multi-Chip Average (RMA) method for normalization. Unsupervised hierarchical analysis was performed with R and the EMA package (48). Briefly, RMA-normalized microarray values were filtered with the standard options. Then, the 100 probesets with the highest interquartile range values were used to build a tree with the Ward method using the Pearson distance metric. Concordant differences in biological pathways between both groups were determined through analysis of the expression data with Gene Set Enrichment Analysis (GSEA) v2.07 (http://www.broadinstitute.org/gsea/) utilizing the complete gene sets of biological processes and KEGG pathways (c5.bp.v2.5.symbols.gmt and c2.cp.kegg.v3.1.symbols.gmt).

SUPPLEMENTARY MATERIAL

Supplementary Material is available at HMG online.

FUNDING

This work was funded by the Spanish Ministry of Economy and Competitiveness through the Instituto de Salud Carlos III (ISCIII) and Red Temática de Investigación del Cáncer (RTICC) del ISCIII. C.L-O. is an investigator of the Botín Foundation.

ACKNOWLEDGEMENTS

We are grateful to Diana A. Puente and Sonsoles Alvarez for excellent technical assistance, and Nathalie Villahoz and Carmen Muro for excellent work in the coordination of the CLL Spanish Consortium. We are also very grateful to all patients with CLL who have participated in this study.

Conflict of Interest statement. None declared.

REFERENCES

1
Chiorazzi
N.
Rai
K.R.
Ferrarini
M.
Chronic lymphocytic leukemia
N. Engl. J. Med.
 , 
2005
, vol. 
352
 (pg. 
804
-
815
)
2
Rozman
C.
Montserrat
E.
Chronic lymphocytic leukemia
N. Engl. J. Med.
 , 
1995
, vol. 
333
 (pg. 
1052
-
1057
)
3
Zenz
T.
Mertens
D.
Küppers
R.
Döhner
H.
Stilgenbauer
S.
From pathogenesis to treatment of chronic lymphocytic leukaemia
Nat. Rev. Cancer
 , 
2010
, vol. 
10
 (pg. 
37
-
50
)
4
Puente
X.S.
López-Otín
C.
The evolutionary biography of chronic lymphocytic leukemia
Nat. Genet
 , 
2013
, vol. 
45
 
5
Damle
R.N.
Wasil
T.
Fais
F.
Ghiotto
F.
Valetto
A.
Allen
S.L.
Buchbinder
A.
Budman
D.
Dittmar
K.
Kolitz
J.
, et al.  . 
Ig V gene mutation status and CD38 expression as novel prognostic indicators in chronic lymphocytic leukemia
Blood
 , 
1999
, vol. 
94
 (pg. 
1840
-
1847
)
6
Hamblin
T.J.
Davis
Z.
Gardiner
A.
Oscier
D.G.
Stevenson
F.K.
Unmutated Ig V(H) genes are associated with a more aggressive form of chronic lymphocytic leukemia
Blood
 , 
1999
, vol. 
94
 (pg. 
1848
-
1854
)
7
Puente
X.S.
Pinyol
M.
Quesada
V.
Conde
L.
Ordóñez
G.R.
Villamor
N.
Escaramis
G.
Jares
P.
Beà
S.
González-Díaz
M.
, et al.  . 
Whole-genome sequencing identifies recurrent mutations in chronic lymphocytic leukaemia
Nature
 , 
2011
, vol. 
475
 (pg. 
101
-
105
)
8
Fabbri
G.
Rasi
S.
Rossi
D.
Trifonov
V.
Khiabanian
H.
Ma
J.
Grunn
A.
Fangazio
M.
Capello
D.
Monti
S.
, et al.  . 
Analysis of the chronic lymphocytic leukemia coding genome: role of NOTCH1 mutational activation
J. Exp. Med.
 , 
2011
, vol. 
208
 (pg. 
1389
-
1401
)
9
Quesada
V.
Conde
L.
Villamor
N.
Ordóñez
G.R.
Jares
P.
Bassaganyas
L.
Ramsay
A.J.
Beà
S.
Pinyol
M.
Martínez-Trillos
A.
, et al.  . 
Exome sequencing identifies recurrent mutations of the splicing factor SF3B1 gene in chronic lymphocytic leukemia
Nat. Genet.
 , 
2011
, vol. 
44
 (pg. 
47
-
52
)
10
Quesada
V.
Ramsay
A.J.
Lopez-Otin
C.
Chronic lymphocytic leukemia with SF3B1 mutation
N. Engl. J. Med.
 , 
2012
, vol. 
366
 pg. 
2530
 
11
Rossi
D.
Bruscaggin
A.
Spina
V.
Rasi
S.
Khiabanian
H.
Messina
M.
Fangazio
M.
Vaisitti
T.
Monti
S.
Chiaretti
S.
, et al.  . 
Mutations of the SF3B1 splicing factor in chronic lymphocytic leukemia: association with progression and fludarabine-refractoriness
Blood
 , 
2011
, vol. 
118
 (pg. 
6904
-
6908
)
12
Wang
L.
Lawrence
M.S.
Wan
Y.
Stojanov
P.
Sougnez
C.
Stevenson
K.
Werner
L.
Sivachenko
A.
DeLuca
D.S.
Zhang
L.
, et al.  . 
SF3B1 and other novel cancer genes in chronic lymphocytic leukemia
N. Engl. J. Med.
 , 
2011
, vol. 
365
 (pg. 
2497
-
2506
)
13
Ramsay
A.J.
Quesada
V.
Foronda
M.
Conde
L.
Martinez-Trillos
A.
Villamor
N.
Rodríguez
D.
Kwarciak
A.
Gallardo
M.
López-Guillermo
A.
, et al.  . 
POT1 mutations cause telomere dysfunction in chronic lymphocytic leukemia
Nat. Genet
 , 
2013
 
.
14
Ramsay
A.J.
Martínez-Trillos
A.
Jares
P.
Rodríguez
D.
Kwarciack
A.
Quesada
V.
Next generation sequencing reveals the secrets of the chronic lymphocytic laeukemia genome
Clin. Transl. Oncol.
 , 
2012
, vol. 
15
 (pg. 
3
-
8
)
15
Nagarajan
P.
Onami
T.M.
Rajagopalan
S.
Kania
S.
Donnell
R.
Venkatachalam
S.
Role of chromodomain helicase DNA-binding protein 2 in DNA damage response signaling and tumorigenesis
Oncogene
 , 
2009
, vol. 
28
 (pg. 
1053
-
1062
)
16
Prazeres
H.
Torres
J.
Rodrigues
F.
Pinto
M.
Pastoriza
M.C.
Gomes
D.
Cameselle-Teijeiro
J.
Vidal
A.
Martins
T.C.
Sobrinho-Simões
M.
, et al.  . 
Chromosomal, epigenetic and microRNA-mediated inactivation of LRP1B, a modulator of the extracellular environment of thyroid cancer cells
Oncogene
 , 
2011
, vol. 
30
 (pg. 
1302
-
1317
)
17
Hauser
H.
Semenza
G.
Sucrase-isomaltase: a stalked intrinsic protein of the brush border membrane
CRC Crit. Rev. Biochem.
 , 
1983
, vol. 
14
 (pg. 
319
-
345
)
18
Naim
H.Y.
Sterchi
E.E.
Lentze
M.J.
Biosynthesis of the human sucrase-isomaltase complex. Differential O-glycosylation of the sucrase subunit correlates with its position within the enzyme complex
J. Biol. Chem.
 , 
1988
, vol. 
263
 (pg. 
7242
-
7253
)
19
Hauri
H.-P.
Quaroni
A.
Isselbacher
K.J.
Biogenesis of intestinal plasma membrane: Posttranslational route and cleavage of sucrase-isomaltase
Proc. Natl Acad. Sci. USA
 , 
1979
, vol. 
76
 (pg. 
5183
-
5186
)
20
Pröpsting
M.J.
Kanapin
H.
Jacob
R.
Naim
H.Y.
A phenylalanine-based folding determinant in intestinal sucrase-isomaltase that functions in the context of a quality control mechanism beyond the endoplasmic reticulum
J. Cell Sci.
 , 
2005
, vol. 
118
 (pg. 
2775
-
2784
)
21
Alfalah
M.
Keiser
M.
Leeb
T.
Zimmer
K.-P.
Naim
H.Y.
Compound heterozygous mutations affect protein folding and function in patients with congenital sucrase-isomaltase deficiency
Gastroenterology
 , 
2009
, vol. 
136
 (pg. 
883
-
892
)
22
Wiltz
O.
O'Hara
C.J.
Steele
G.D.
Mercurio
A.M.
Sucrase-isomaltase: a marker associated with the progression of adenomatous polyps to adenocarcinomas
Surgery
 , 
1990
, vol. 
108
 (pg. 
269
-
276
)
23
Wiltz
O.
O'Hara
C.J.
Steele
G.D.J.
Mercurio
A.M.
Expression of enzymatically active sucrase-isomaltase is a ubiquitous property of colon adenocarcinomas
Gastroenterology
 , 
1991
, vol. 
100
 (pg. 
1266
-
1278
)
24
Hanahan
D.
Weinberg
R.A.
Hallmarks of cancer: the next generation
Cell
 , 
2011
, vol. 
144
 (pg. 
646
-
674
)
25
DeBerardinis
R.J.
Thompson
C.B.
Cellular metabolism and disease: what do metabolic outliers teach us?
Cell
 , 
2012
, vol. 
148
 (pg. 
1132
-
1144
)
26
Burnichon
N.
Brière
J.J.
Libé
R.
Vescovo
L.
Rivière
J.
Tissier
F.
Jouanno
E.
Jeunemaitre
X.
Bénit
P.
Tzagoloff
A.
, et al.  . 
SDHA is a tumor suppressor gene causing paraganglioma
Hum. Mol. Genet.
 , 
2010
, vol. 
19
 (pg. 
3011
-
3020
)
27
Ni
Y.
He
X.
Chen
J.
Moline
J.
Mester
J.
Orloff
M.S.
Ringel
M.D.
Eng
C.
Germline SDHx variants modify breast and thyroid cancer risks in Cowden and Cowden-like syndrome via FAD/NAD-dependant destabilization of p53
Hum. Mol. Genet.
 , 
2012
, vol. 
21
 (pg. 
656
-
663
)
28
Alam
N.A.
Rowan
A.J.
Wortham
N.C.
Pollard
P.J.
Mitchell
M.
Tyrer
J.P.
Barclay
E.
Calonje
E.
Manek
S.
Adams
S.J.
, et al.  . 
Genetic and functional analyses of FH mutations in multiple cutaneous and uterine leiomyomatosis, hereditary leiomyomatosis and renal cancer, and fumarate hydratase deficiency
Hum. Mol. Genet.
 , 
2003
, vol. 
12
 (pg. 
1241
-
1252
)
29
Tomlinson
I.P.
Alam
N.A.
Rowan
A.J.
Barclay
E.
Jaeger
E.E.
Kelsell
D.
Leigh
I.
Gorman
P.
Lamlum
H.
Rahman
S.
, et al.  . 
Germline mutations in FH predispose to dominantly inherited uterine fibroids, skin leiomyomata and papillary renal cell cancer
Nat. Genet.
 , 
2002
, vol. 
30
 (pg. 
406
-
410
)
30
Dang
L.
White
D.W.
Gross
S.
Bennett
B.D.
Bittinger
M.A.
Driggers
E.M.
Fantin
V.R.
Jang
H.G.
Jin
S.
Keenan
M.C.
, et al.  . 
Cancer-associated IDH1 mutations produce 2-hydroxyglutarate
Nature
 , 
2009
, vol. 
462
 (pg. 
739
-
744
)
31
Hunziker
W.
Spiess
M.
Semenza
G.
Lodish
H.F.
The sucrase-isomaltase complex: primary structure, membrane-orientation, and evolution of a stalked, intrinsic brush border protein
Cell
 , 
1986
, vol. 
46
 (pg. 
227
-
234
)
32
Keiser
M.
Alfalah
M.
Pröpsting
M.J.
Castelletti
D.
Naim
H.Y.
Altered folding, turnover, and polarized sorting act in concert to define a novel pathomechanism of congenital sucrase-isomaltase deficiency
J. Biol. Chem.
 , 
2006
, vol. 
281
 (pg. 
14393
-
14399
)
33
Manders
E.M.M.
Verbeek
F.J.
Aten
F.J.
Measurement of localization of objects in dual-color confocal images
J. Microsc.
 , 
1993
, vol. 
169
 (pg. 
375
-
382
)
34
Alfalah
M.
Jacob
R.
Preuss
U.
Zimmer
K.-P.
Naim
H.
Naim
H.Y.
O-linked glycans mediate apical sorting of human intestinal sucrase-isomaltase through association with lipid rafts
Curr. Biol.
 , 
1999
, vol. 
9
 (pg. 
593
-
596
)
35
Wetzel
G.
Heine
M.
Rohwedder
A.
Naim
H.Y.
Impact of glycosylation and detergent-resistant membranes on the function of intestinal sucrase-isomaltase
Biol. Chem.
 , 
2009
, vol. 
390
 (pg. 
545
-
549
)
36
Lee
S.H.
Yu
S.-Y.
Nakayama
J.
Khoo
K.-H.
Stone
E.L.
Fukuda
M.N.
Marth
J.D.
Fukuda
M.
Core2 O-Glycan structure is essential for the cell surface expression of sucrase isomaltase and dipeptidyl peptidase-IV during intestinal cell differentiation
J. Biol. Chem.
 , 
2010
, vol. 
285
 (pg. 
37683
-
37692
)
37
Hudson
T.J.
Anderson
W.
Artez
A.
Barker
A.D.
Bell
C.
Bernabé
R.R.
Bhan
M.K.
Calvo
F.
Eerola
I.
Gerhard
D.S.
, et al.  . 
International network of cancer genome projects
Nature
 , 
2010
, vol. 
464
 (pg. 
993
-
998
)
38
Nichols
B.L.
Avery
S.
Sen
P.
Swallow
D.M.
Hahn
D.
Sterchi
E.
The maltase-glucoamylase gene: common ancestry to sucrase-isomaltase with complementary starch digestion activities
Proc. Natl Acad. Sci. USA
 , 
2003
, vol. 
100
 (pg. 
1432
-
1437
)
39
Naim
H.Y.
Roth
J.
Sterchi
E.E.
Lentze
M.
Milla
P.
Schmitz
J.
Hauri
H.-P.
Sucrase-isomaltase deficiency in humans. Different mutations disrupt intracellular transport, processing, and function of an intestinal brush border enzyme
J. Clin. Invest.
 , 
1988
, vol. 
82
 (pg. 
667
-
679
)
40
Sjostrom
H.
Norén
O.
Danielsen
E.M.
Enzymatic activity of ‘high-mannose’ glycosylated forms of intestinal microvillar hydrolases
J. Pediatr. Gastroenterol. Nutr.
 , 
1985
, vol. 
4
 (pg. 
980
-
983
)
41
Hoffmann
W.
Hauser
F.
The P-domain or trefoil motif: a role in renewal and pathology of mucous epithelia?
Trends Biochem. Sci.
 , 
1993
, vol. 
18
 (pg. 
239
-
243
)
42
Spodsberg
N.
Jacob
R.
Alfalah
M.
Zimmer
K.-P.
Naim
H.Y.
Molecular basis of aberrant apical protein transport in an intestinal enzyme disorder
J. Biol. Chem.
 , 
2001
, vol. 
276
 (pg. 
23506
-
23510
)
43
Jacob
R.
Pürschel
B.
Naim
H.Y.
Sucrase is an intramolecular chaperone located at the C-terminal end of the sucrase-isomaltase enzyme complex
J. Biol. Chem.
 , 
2002
, vol. 
277
 (pg. 
32141
-
32148
)
44
Ritz
V.
Alfalah
M.
Zimmer
K.-P.
Schmitz
J.
Jacob
R.
Naim
H.Y.
Congenital sucrase-isomaltase deficiency because of an accumulation of the mutant enzyme in the endoplasmic reticulum
Gastroenterology
 , 
2003
, vol. 
125
 (pg. 
1678
-
1685
)
45
Hsu
P.P.
Sabatini
D.M.
Cancer cell metabolism: Warburg and beyond
Cell
 , 
2008
, vol. 
134
 (pg. 
703
-
707
)
46
Chantret
I.
Rodolosse
A.
Barbat
A.
Dussaulx
E.
Brot-Laroche
E.
Zweibaum
A.
Rousset
M.
Differential expression of sucrase-isomaltase in clones isolated from early and late passages of the cell line Caco-2: evidence for glucose-dependent negative regulation
J. Cell Sci.
 , 
1994
, vol. 
107
 (pg. 
213
-
225
)
47
Keller
P.
Semenza
G.
Shaltiel
S.
Phosphorylation of the N-terminal intracellular tail of sucrase-isomaltase by cAMP-dependent protein kinase
Eur. J. Biochem.
 , 
1995
, vol. 
233
 (pg. 
963
-
968
)
48
Servant
N.
Gravier
E.
Gestraud
P.
Laurent
C.
Paccard
C.
Biton
A.
Brito
I.
Mandel
J.
Asselain
B.
Barillot
E.
, et al.  . 
EMA—A R package for Easy Microarray data analysis
BMC Res. Notes
 , 
2010
, vol. 
3
 pg. 
277