Quantitative Evaluation of Serum Proteins Uncovers a Protein

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Quantitative evaluation of serum proteins uncovers a protein signature related with maturity-onset diabetes of the young (MODY) Muhadasi Tuerxunyiming, Feng Xian, Jin Zi, Yilihamujiang Yimamu, Reshalaiti Abuduwayite, Yan Ren, Qidan Li, Abulizi Abudula, SiQi Liu, and Patamu Mohemaiti J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.7b00727 • Publication Date (Web): 28 Nov 2017 Downloaded from http://pubs.acs.org on November 29, 2017

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Journal of Proteome Research

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Quantitative evaluation of serum proteins uncovers a protein signature related with maturity-onset diabetes of the young (MODY) Muhadasi Tuerxunyiming1, Feng Xian2, Jin Zi3, Yilihamujiang Yimamu4, Reshalaiti Abuduwayite4, Yan Ren3, Qidan Li3, Abulizi Abudula1, SiQi Liu2,3, Patamu Mohemaiti1*

1 Xinjiang Medical University, Urumqi 830011, China,

2 University of Chinese Academy of Sciences, Beijing, 100049, China,

3 Proteomics Division, BGI-Shenzhen, Shenzhen, Guangdong, 518083, China.

4 First Affiliated Hospital, Xinjiang Medical University, Urumqi 830011, China

*To whom correspondence should be addressed:

Patamu Mohemaiti，School of Public Health, Xinjiang Medical University, Urumqi 830011, China. Tel:86-991-4362474; Email: [email protected]

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ABSTRACT Maturity-onset diabetes of the young (MODY) is an inherited monogenic type of diabetes. Genetic mutations in MODY often cause nonsynonymous changes that directly lead to the functional distortion of proteins and the pathological consequences. Herein, we proposed that the inherited mutations found in a MODY family could cause a disturbance of protein abundance, specifically in serum. The serum samples were collected from a Uyghur MODY family through three generations, and the serum proteins after depletion treatment were examined by quantitative proteomics to characterize the MODY-related serum proteins followed by verification using target quantification of proteomics. A total of 32 serum proteins were preliminarily identified as the MODY-related. Further verification test towards the individual samples demonstrated the 12 candidates with the significantly different abundance in the MODY patients. A comparison of the 12 proteins among the sera of type 1 diabetes, type 2 diabetes, MODY, healthy subjects was conducted, and revealed a protein signature related with MODY, comprising of the serum proteins such as SERPINA7, APOC4, LPA, C6, and F5.

KEYWORDS: Diabetes; Serum; Biomarkers; iTRAQ; MRM

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Introduction Diabetes mellitus (DM) is a group of metabolic diseases in which the patients suffer from high blood glucose levels over a prolonged period 1. DM is commonly divided into four categories, type 1 diabetes (T1D), type 2 diabetes (T2D), gestational diabetes (GD), and monogenic diabetes (MD). MD results from mutations in a single gene, and accounts for 1–2% of diabetes cases in young people. Maturity-onset diabetes of the young (MODY) is the most common form of MD mellitus, in which the patients present with diabetes symptoms before 25 years of age. A large number of MODY-related genetics variants have been identified, indicating that MODY propends a positive family history of hyperglycemia with an autosomal dominant mode of inheritance. Generally, MODY patients have similar symptoms to T1D or T2D patients such as pancreatic beta-cell dysfunction, increased thirst, and the presence of ketones in urine 2. There is currently no well-defined indicator that clearly distinguishes MODY from other forms of DM.

Disease biomarker in body fluids is an attractive issue to diagnose disease in biomedical research. Diabetes-related serum biomarkers have been reported by several research groups. Proteomics is a powerful method for globally detecting serum proteins and specifically identifying the disease biomarkers. For example, Zhi et al

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adopted a semi-quantitative approach to analyze serum samples from T1D

patients and controls, and identified the 21 T1D-related candidates, which were not clustered into a certain function, but instead had broad roles in inflammation, oxidation, metabolic regulation, and autoimmunity. Kim et al 4 assessed diagnostic biomarkers in T2D patients with nephropathy using two-dimensional electrophoresis (2-DE) and found two proteins, extracellular glutathione peroxidase and apolipoprotein E, as potential biomarkers for T2D with nephropathy. Through surveying the published documents that focused on discovery of diabetes-related 3



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biomarkers in serum, the result is not quite encouraging, because the reported candidates from different laboratories are so diverse that none has been generally accepted for clinical diagnosis so far, regardless of T1D or T2D. T1D and T2D are a group of metabolic disorders with many pathological traits that are likely resulted from the alterations of multiple genes and the interactions between genetic and environmental factors. The inconsistent results of study assessing diabetes biomarkers in serum hence are possibly attributed to the complicated disease pathogenesis. This leads to a hypothesis, whether diabetes-related biomarkers in serum might be relatively easy to be found if the pathological cause of diabetes is clearly defined.

Unlike T1D and T2D, the genetic causes of MODY are extensively studied. In European, most common causes are the mutations on coding genes of GCK and HNF-1α5-7. In Asia, the MODY-related mutations seem different from Europeans, only less 10% of mutations on GCK and HNF-1α 8. MODY-related mutations reported so far are of 13 genes

9-21

, HNF-4α, GCK, HNF-1α, IPF1, HNF1β, NEUROD1,

KLF11, CEL, PAX4, INS, BLK, ABCC8, and KCNJ11. Further functional analysis indicates that most MODY-related genes are involved in glucose homeostasis in pancreatic β-cells. A logical deduction is that these mutations in encoding regions are able to change amino acid residues and to bring perturbations to metabolic homeostasis, consequently resulting in the abundance response of proteins in body fluids. For instance, GCK is a glucokinase in glycolytic pathway22and the mutated GCK could disturb generation of the metabolic signal for insulin secretion and integration of hepatic glucose uptake. In addition, as MODY is a monogenic type of diabetes with strong family inheritance, the genetic mutations in a MODY family as well as the correspondent functional changes are likely presented in all the family members with diabetic symptoms.

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The abnormal changes of serum proteins in the MODY patients have been observed by several groups. For example, low levels of serum hs-CRP

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was detected in the

MODY patients in Japan with R229X mutation at HNF-1α, while decreased abundance of serum CD36

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was found in an Irish MODY patient with HNF-1α

mutation. The studies to explore the responses of serum proteins related with MODY are at an initial stage with traditional approaches and limited information obtained. Therefore, we proposed that the MODY patients from the same family might share similar responses of serum protein abundance, and a global survey of serum protein abundance could be efficient to screen and find these candidates as typical indicators of MODY.

Herein, we present a series of evidence from discovery to verification of the MODY-related serum proteins from a MODY family. We collected the serum samples from the members of a Uyghur MODY family, in which some members were diagnosed by clinical approaches. The MODY-related serum proteins were first screened in the pooled serum samples derived from MODY patients and controls using an iTRAQ-based method. Then the selected candidates of serum proteins were further verified in the individual serum samples of MODY patients or controls using MRM-based approach. MRM was also employed to quantify the MODY-related proteins in individual serum samples collected from T1D, T2D, and MODY patients. Taken all the evidence together, we come to a conclusion that the five serum proteins, SERPINA7, APOC4, LPA, C6, and F5, make up a protein signature that serves an indicator of MODY.

Methods The overall workflow in this study is presented in Figure 1. 5



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1. Blood sample collection and preparation Thirty-eight human serum samples were used for the iTRAQ and MRM verification methods. For iTRAQ identification, eight people from a family were selected, four MODY patients whose diabetic symptoms were first diagnosed at ~23 years old and four healthy family members as controls. The pedigree map of this family is illustrated in Fig. 2. The other 30 serum samples consisting of T1D patients (n = 10), T2D patients (n = 10), and healthy subjects (n = 10) were collected from the First Affiliated Hospital of Xinjiang Medical University. All participants underwent a thorough physiological examination and signed informed consent forms. The protocol for sample collection and data usage was submitted to and approved by The Ethics Committee in the First Affiliated Hospital of Xinjiang Medical University. All the clinical information and the responses to a detailed questionnaire regarding demographics and the family history of MODY are provided in Supplementary Tables S1 and 2. After a 12-h fasting, blood was drawn from the vein and centrifuged at 3000 × g for 20 min at 4°C. The clear serum fraction was aliquoted into cryotubes (Nunc GmbH & Co KG., Thermo Fisher Scientific, Langenselbold, Germany) and stored at −80°C.

2. Depletion of the serum proteins with high abundance The serum proteins with high abundance were depleted using a commercial kit, Proteominer (Bio-Rad, Hercules, CA, USA)

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. Following the manufacturer’s

instructions, 20 mg serum proteins were mixed with the conditioned Proteominer resin, and were incubated for 2 hours at room temperature with rotation. After removing the nonspecific unbound proteins, the bound proteins were eluted with the elution buffer containing 8 M urea and 2% CHAPS.

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3. Protein digestion and peptide labeling with 4-plex iTRAQ Fifty micrograms of depleted serum proteins were pooled from the four MODY samples and from the four controls. The proteins were reduced and alkylated with 10 mM dithiothreitol (DTT) and 55 mM iodoacetamide (IAM) followed by precipitation with 80% cold acetone at −20°C overnight. The treated proteins were digested using the filter-aided sample preparation (FASP) method

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in Vivacon 500 ultrafiltration

spin columns (Sartorius, Goettingen, Germany). The tryptic peptides for each pooled sample were labeled using one of 4-plex iTRAQ reagents (Applied Biosystems, Foster City,CA, USA) following the manufacturer's protocol. In the 4-plex labeling, reporters 113 and 118 were used for duplicates of the pooled MODY samples, and reporters 119 and 121 were used for duplicates of the pooled control samples. The four labelled samples were mixed, lyophilized, and resuspended in solution A (2% acetonitrile [ACN] and 20 mM ammonium formate [NH4FA], pH 10) for subsequent experiments.

4. Separation of labeled peptides using reverse phase chromatography at high-pH The mixture of labeled peptides was fractionated using reverse phase chromatography at high-pH with a column (Luna C18, 4.6 mm inner diameter × 250 mm length, Phenomenex, Torrance, CA, USA), which was linked to a Prominence HPLC system (Shimadzu, Nakagyo-ku, Kyoto, Japan) at a flow rate of 1.0 mL/min, and at elution gradient, 0–10 min equilibration in 100% solution A, 10–15 min fast elution from 0–12% of solution B (80% ACN and 20 mM NH4FA, pH 10), 15–50 min linear elution from 12–56% of solution B, and 50–55 min washing elution from 56–80% of solution B. The eluted peptides were collected at 1 ml/tube and combined to 10 fractions in a concatenation pattern 27.

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5. LC-MS/MS identification and quantification of the labeled peptides The fractionated peptides were delivered onto a nano RP column (3 µm Hypersil C18, 75 µm × 150 mm, Thermo Fisher Scientific) mounted in a Prominence Nano HPLC system (Shimadzu, Nakagyo-ku, Kyoto, Japan), and were eluted with an ACN gradient of 5–40% containing 0.1% formic acid for 65 min at a flow rate of 300 nl/min. The elutes were directly delivered into a Q Exactive™ Hybrid Quadrupole-Orbitrap LC-MS (Thermo Fisher Scientific, Waltham, MA, USA), which was set in positive ion mode in a data-dependent manner. A full MS scan was performed from 350–1,500 m/z with resolution at 70,000. The MS/MS scan was performed with top 20 at 28% normalized collision energy (NCE) with a dynamic exclusion time at 30 sec, a minimum signal threshold at 5000, a resolution at 17,500, and an isolation width at 2 Da.

6. MRM assay To validate the results elicited from the iTRAQ quantification in the pooled serum samples, the original un-depleted sera were individually taken for further verification experiments with MRM approach. The serum was denatured with 8 M urea, treated with reduction and alkylation, and were further digested with trypsin according to the FASP protocol. To develop the MRM method, the target peptides and transitions were selected from a spectrum library constructed in our laboratory based on the triplicate profiling of a pooled sample of all MODY cases and controls on QTOF 5600 (AB SCIEX, Framingham, MA, USA). The transition list was revised on a QTRAP 5500 mass spectrometer to identify the top five or six transitions for each peptide with a satisfied signal. A schedule MRM method containing 352 transitions was developed. As shown in Table S5, the transition list contained 23 proteins, of which 17 proteins 8


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had two to four peptides. The BSA (Thermo Scientific Pierce, Waltham, USA) was spiked into the individual serum samples (20 fmol per 1 µg sample) as an internal reference for normalization. The MS parameters for QTRAP 5500 were set as follows, ion spray voltage (IS) at 2400 V, curtain gas (CUR) at 35.00, ion source gas 1 (GS1) at 23.00, collision-activated dissociation (CAD) at high, interface heater temperature (IHT) at 150°C, entrance potential (EP) at 10.00. For Q1 and Q3, the unit resolution and collision energy (CE) were calculated using the formula, CE = a × m/z + b, in which the parameter pairs a and b were set as the unknown ion at 0.044, 6, double charged ion at 0.036, 8.857 and triple charged ion at 0.0544, -2.4099. The m/z and RT information from higher quality transitions was adopted for additional MRM detection and quantification, in which the MRM detection window was 120 secs and the target scanning time was 1 sec. To confirm the MRM signals, 22 peptides were selected and were chemically synthesized (GL Biochem, Shanghai, China). The synthesized peptides were taken into MRM to confirm the retention time and the transition patterns.

7. Analysis of the MS Data For iTRAQ data analysis, the raw MS/MS data were converted into MGF format by Proteome Discoverer 1.3 (Thermo Fisher Scientific), and the exported MGF files were searched using Mascot 2.3 (Matrix Science, Boston, MA) against the Uniprot human database (http://www.uniprot.org). An automatic decoy database search was performed. Several parameters in Mascot were set for peptide searching, including iTRAQ 4-plex for quantification, a tolerance of one missed trypsin cleavage, iTRAQ 4-plex (N-term), iTRAQ 4-plex (K), Carbamidomethyl (C) as a fixed modification, oxidation (M), deamidatioin (N, Q), and iTRAQ 4-plex (Y) as a variable modification. The precursor mass tolerance was 15 ppm, and the production tolerance was 0.08 Da. For MRM data analysis, the raw MS data were processed using Skyline 3.5.0. The 9



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peaks with a q-value < 0.05 were selected following the Skyline tutorial using the reverse peptide sequences as a decoy library. The MRM results were exported and processed with Skyline tutorials as well. The three digested peptides from BSA (LVNELTEFAK, HLVDEPQNLIK, and LGEYGFQNALIVR) were used as the internal controls for normalizing the MRM signals.

8. Statistical analysis The identification and verification results were based upon statistical evaluation and were expressed as means ± SDs. Paired t-tests were used to judge the different significance with p < 0.05 28.

Results 1. The clinical characterization of the MODY family All the MODY patients were collected from a Uyghur family in Xinjiang Uyghur Autonomous Region, China. As illustrated in Fig. 2, total of 40 family members are alive, including 20 males and 20 females aged 11–69 years in three generations. Six family members (five males and one female aged ~ 34–69 years) were diagnosed with the diabetic symptom of hyperglycemia, whereas the other family members were found in normal blood glucose level. Among the six diabetic patients, the values of fasting blood-glucose (FBG) were 10.30, 9.56, 8.11, 8.93, 9.27, and 17.1 mmol/l, while the data of 2-hour postprandial blood glucose (2h PG) were 17.8, 11.6, 11.02, 10.7, 12.2, and 18.8 mmol/l, respectively. Importantly, all the diabetes patients were insensitive to insulin treatment. The diagnostic criteria for MODY include a family history of hyperglycemia, the appearance of symptoms at young age, no requirement for insulin, and a nonketotic status. Hence, the clinical features of the six diabetes patients were well matched with the characteristics, and were diagnosed as the 10


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MODY diabetes. Since an attempt to collect blood samples from a male and a female MODY patient was failed, only four MODY serum samples were used in the current study. To use an appropriate control, the blood samples were collected from four males aged 34–52 years in the same family who had normal blood glucose levels. The clinical information of the eight study subjects is described in Table S1. Besides, genomic DNA was prepared from the leucocytes collected from the blood samples from the MODY patients and healthy controls. The genomic sequences were determined by the joint statistical analysis of deep-coverage whole-genome sequences, resulting in 293 genetic variants identified. Importantly, the preliminary analysis identified five possible missense mutations in the MODY patients of this family (data not shown).

2. Identification of the differential serum proteins between the MODY and healthy family members A depletion approach was undertaken using ProteinMiner to remove high-abundance proteins in serum. The abundance distribution of the serum proteins after depletion treatment is shown in Fig. 3A, in which the green curve represents the published data of the abundance distribution of serum proteins29and the red curve indicates the protein abundance distribution after depletion in this study. Comparison of the two distribution curves revealed that the ProteinMiner depletion significantly lowered the high-abundance serum proteins. For example, albumin, alpha-1-antitrypsin, and Ig gamma-1 chain C region were the most abundant proteins in the untreated serum, whereas their abundance dropped to medium in the treated serum. The depletion treatment appeared effective to increase the detection sensitivity for the proteins at relatively lower abundance.

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The four MODY or four control serum samples were pooled and the pooled sera were completely digested with trypsin. Then each digested mixture was evenly divided into two groups (M1 and M2, and C1 and C2, respectively). The digested serum peptides were labeled with iTRAQ tags as M1 with 113, M2 with 118, C1 with 119, and C2 with 121. The qualitative and quantitative evaluation towards the iTRAQ-based proteomics data is summarized in Fig.3. The overlap rates of the identified unique proteins and peptides in the parallel injections were approximately 80.5% and 75.6%, respectively (Fig.3B). Pearson’s correlation was used to compare the protein quantification results between M1/C1, M1/C2, M2/C1, and M2/C2. The correlation coefficients for these linear regressions were 0.93, 0.89, 0.93, and 0.89, respectively (Fig.3C). The two sets of quality control were acceptable for further analysis to explore the differential serum proteins between the MODY and healthy family members.

The differential proteins between the two serum samples were defined with three criteria, 1) each protein should contain at least two unique peptides, 2) tag intensity ratios with log 2 fold changes of ≥ 0.6 and p < 0.05, and 3) a protein with a significant change in abundance in all labeling and injection replicates. In total, 293 serum proteins with 2551 unique peptides were matched with the criteria (Table S3). The volcano plots shown in Fig. 4A show the distributions of the tag intensity ratios and the corresponding p-values for all the identified and quantified proteins in M1/C1, M1/C2, M2/C1, and M2/C2 across the isobaric tags. Based on the plot, the differential proteins were 44, 44, 46, and 48 for M1/C1 and M1/C2, and 39, 38, 41, and 45 for M2/C1 and M2/C2, respectively. Finally, the differential proteins presented in all labeling and injection replicates were finalized, 32 differential proteins with 14 up-regulated and 18 down-regulated in MODY (Table S4). The biological pathways related to the differential proteins were briefly analyzed with PANTHER 12


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Classification System, suggesting that these proteins were mainly involved in metabolic processes, cellular processes, immune system, and regulation processes (Fig. 4B).

3. Verification of the differential serum proteins related with MODY Although the 32 differential proteins were acquired using stringent criteria, a quantitative evaluation using an alternative approach was necessary to verify these results. MRM is a well-accepted approach to accurately quantify target peptides/proteins. To expand the verification targets that are reported diabetes-related, in addition to the 32 differential proteins identified in iTRAQ, we selected the nine proteins in the MRM experiments, CATG, LG3BP, POSIN, FIBG, CO6, FA5, MASP2, CD5L, and KV110. Total of 41 targets thus were included for the verification. Considering fractionation approach to lead to poorer reproducibility in MRM assay, the tryptic peptides derived from the undepleted serum were directly loaded onto LC MS/MS for peptide detection. To ensure that the target peptides were measurable in the un-fractionated samples, all the peptides obtained from individual MODY and healthy cases were first pooled and made as a peptide mixture, and the mixture was delivered to QTOF 5600 MS to acquire all possibly detectable peptides . Total of 45 peptides from 23 target proteins were identified successfully in the mixture. After peptide sequences being imported to Skyline (version 3.5.0.), the peptide evaluation in MRM mode suggested all the peptides measurable and quantitative (Table S5).

A scheduled MRM method was established in the QTRAP 5500 MS with a 2-min window, in which 352 transitions were derived from the target peptides, β-Gal and BSA. The peptide samples were prepared individually from the serum sample, the sample of MODY and healthy case by case. The abundance ratios of peptides with 13



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p-value between the individual MODY and control serum samples were estimated with volcano plot (Fig. 5A). The proteins that were different in abundance between MODY and healthy cases based upon the MRM signals were defined as fold-changes of the accumulated area of the MRM signal ≥ 1.5 with a p-value ≤ 0.05. The quantitative evaluation of the MRM signals revealed that six proteins were down regulated, SERPINA7, CD14, CETP, PZP, SERPIND1, and SHBG, and six were upregulated, APOC4, SERPINA1, CRP, LPA, C6 and F5 in MODY sera. The other 11 proteins were found insignificant abundance change. Moreover, the serum abundance of these peptides in the MRM signals was evaluated statistically, indicating that the levels of the 22 unique peptides from 12 proteins in MODY were significantly different from the healthy samples (p ≤ 0.05). The abundance distribution of the 12 proteins related to MODY is shown in Fig. 5B. More importantly, the directions of abundance changes for the 12 proteins were similar to the data acquired from iTRAQ, and almost identical among the individual MODY serum samples. Hence, the MODY-related serum proteins discovered by labeling quantification proteomics were successfully verified by the target proteomics.

4. Comparison of the MODY-related serum proteins among MODY and other diabetes The proteomic evidence described above supports the hypothesis that the MODY caused by the mutation(s) in encoding region of a gene may lead to a wide response in the serum protein abundance. Even though the 12 proteins are common components of serum, they have been reported to be partially involved in diseases. However, these proteins are not typical indicators of diabetes based upon the document reported so far. This raises a question whether the 12 MODY-related serum proteins are specific for MODY or globally for diabetes. Therefore, we further quantitatively evaluated the abundance of the 12 serum proteins among the samples of T1D, T2D, MODY, and 14


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healthy subjects. Total of 30 individual serum samples were collected and treated with similar approaches previously described, specifically monitoring the abundance of the 12 proteins using MRM with the healthy samples as the references. To assess the specificity of the 12 MODY-related proteins, the abundance of these serum proteins was first compared among the T1D, T2D, and healthy group to remove the proteins with significant difference among the non-MODY samples. The significant differences in protein abundance were statistically compared as T1D vs healthy and T2D vs healthy. The comparison showed that the abundance of SHPG and PZP in T1D was different from the healthy (Fig. 6A), while of CRP, CETP, and SERPIND1 in T2D was different from healthy one (Fig. 6B). These five proteins were removed from the 12 MODY-related proteins, and only the seven proteins were assessed for analysis of MODY-specificity. As illustrated in Fig.6, the abundance of all seven proteins in MODY, SERPINA1, SERPINA7, APOC4, LPA, CD14, C6, and F5, was significantly different from either T2D or healthy samples (Fig. 6C), and of the five proteins in MODY, SERPINA7, APOC4, LPA, C6, and F5, were different from T1D (Fig.6D and E). Taking all the analysis together, we proposed that a protein signature consisting of SERPINA7, APOC4, LPA, C6, and F5 may be an indicator of MODY, at least for the MODY family.

Discussion There is strong evidence suggesting that most MODY cases result from mutations in a single gene. The current genomic sequencing data further support this hypothesis by revealing that MODY patients from a Uyghur family in Xinjiang had several shared mutations. As the mutations related to MODY reported to date are missense mutations located in coding regions, the corresponding protein products are changed accordingly. If the mutated proteins have functions in metabolic or signaling pathways, such 15



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influence is somehow to reflect to the abundance responses of serum protein in MODY patients. In the current study, we designed a proteomic strategy based upon quantifying serum proteins and attempted to testify the hypothesis rationality. Regarding the specific samples from a MODY family, there was a tough challenge of which as total of four patients and four healthy samples were collected from the family, how the statistical evaluation towards the serum protein changes was conducted as to achieve a significant result.

A three-phase experiment was designed to identify and verify MODY-related serum proteins. In the first phase, to reduce interference from high abundance proteins in individual serum samples, iTRAQ-based quantification was used in pooled serum samples from which high abundance proteins had been depleted. The comparison revealed that 32 serum proteins were present at different levels in the four MODY and healthy samples. In the second phase, targeted proteomics was used to monitor the abundance of the potentially MODY-related serum proteins in individual serum samples. During these experiments, the serum samples were not depleted and the protein targets were only the candidates derived from the iTRAQ experiment. Both technique advantages from un-depletion and multiple transition MS/MS signals could ensure that the quantitative results were convincing and significant. The MRM verification resulted in the identification of 12 MODY-related serum proteins. In the final phase, non-family serum samples from healthy individuals and T1D and T2D patients were used in targeted quantitation proteomic experiments. The experiments excluded serum proteins dependent upon non-family or other diabetic serum. The results led to the identification of a MODY-specific serum protein signature consisting of SERPINA7, APOC4, LPA, C6, and F5. To the best of our knowledge, this is the first time an integrated quantification proteomics-based approach was implemented to explore MODY-related serum proteins. Although the mechanism by which the 16


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abundance of proteins is altered in the serum of MODY patients remains unclear, the results obtained from the current quantitative analysis demonstrated that missense mutations in MODY patients could lead to global changes in the abundance of tissue or cellular proteins, including serum proteins. In addition, the abundance of the protein signature derived from the MODY family was distinct from healthy controls as well as T1D and T2D patients. Thus, the combined quantitative proteomics approach

provides

an

alternative

method

of

identifying

MODY-specific

macromolecules.

Numerous studies identifying defects in insulin action, insulin secretion, hepatic glucose release, or adipocyte function have shed considerable light on the pathophysiology of diabetes. Recently, omics has become a powerful tool for identifying novel pathways that contribute to diabetes. It is possible that proteomics holds more promise than genetic analyses because proteins play a functional role in regulating metabolic pathways. Moreover, the evidence obtained from omics revealed that the pathological causes of most diabetes cases are very complex and involve many genes. In addition, some diabetes cases are likely driven by a single gene mutation that causes global changes in gene expression within cells 30.

There is currently no diabetic indicator at either the genomic or proteomic level that is widely

accepted.

However,

several

serum

proteins

were

reported

as

diabetes-dependent, regardless of T1D, T2D, GD, or MD. In spite of the absence diabetes-specific biomarkers, several serum proteins have a close association with diabetes. In the current study, a protein signature consisting of five serum proteins was identified as MODY-related. It is noteworthy that all the MODY-related candidate proteins have been reported as diabetes-dependent. For example, F5 is a critical cofactor for the prothrombinase activity of factor Xa. It was up regulated in the serum 17



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of MODY patients in all the three quantitative measurements in the current study. A similar phenomenon was observed by Tsutsui et al31, in which increased F5 levels were detected in the serum of diabetic mice. In human serum, Ahluwalia et al32 demonstrated that F5 was significantly associated with fasting serum levels of T2D. APOC4 is predominantly associated with VLDL, and it plays an important role in triglyceride metabolism. The current proteomic data indicated that this was upregulated in MODY serum. Consistent with this, Allan et al

33

reported that APOC4

was over expressed in transgenic diabetic mice, whereas Resal et al

34

revealed that

increased APOC4 levels in serum were associated with insulin resistance before the onset of T2D. SERPINA7 that plays a role in thyroid hormone transportation in serum was perceived an abundance augment in the MODY serum, whereas Connors et al 35

reported the opposite changes in serum since SERPINA7 levels were decreased in

60 diabetic patients.

Two MODY-related candidates attracted our attention in the current study, LPA and C6, because these were identified by other group as MODY indicator s. LPA is a main constituent of lipoprotein, and the current quantitative analyses revealed that its levels were increased in serum samples from MODY patients. Iwasaki et al 36examined LPA levels in three different MODY groups with heterozygous mutations in HNF-1a, HNF-1b, and HNF-4a, and demonstrated that LPA levels were significantly elevated in all MODY patients. C6 is a constituent of MAC and plays a key role in the innate and adaptive immune response by forming pores in the plasma membrane of target cells. Elevated C6 levels were detected in the MODY serum samples in the current study, which is consistent with the study by Karlsson et al 37, who reported that elevated serum levels of C5, C8, and TTR were related to MODY patients with mutations in HNF-1a and HNF-4a. They also revealed that the interaction between C5 and C6 to form a lytic complex could be used to form a risk stratification for MODY 18


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patients. Although, the quantitative proteomics in the current study did not reveal any novel serum proteins as diabetic indicators, the identification of a protein signature that indicates MODY in this Uyghur MODY family opens a new path toward the develop of serum indicators related to diabetes and to take an overview to the responses of serum components in diabetes.

Because we were limited by the sample source, we were unable to compare the protein signature in several MODY subgroups. Therefore, the application of this protein signature to MODY is questionable and it is unclear whether it is present in all MODY sera. If our hypothesis is correct and protein mutations result in global changes in the abundance of proteins in serum, it is likely that different missense mutations could bring different changes in serum proteins. The question remains whether the serum proteins sensitive to diabetic development could form different patterns that are potentially valuable in clinical practice. We are working toward further verifying the protein signature identified this study in additional diabetes serum samples.

ACKNOWLEDGMENTS This work was supported by the Science and Technology aid Xinjiang Foundation of Xinjiang Uyghur Autonomous Region, Xinjiang, China (201491183) ； National Natural Science Foundation of China (U1403121).

Conflict of Interest Statement 19



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The authors declare no competing financial interest.

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(31) Ahluwalia, T. S.; Allin, K. H.; Sandholt, C. H.; Sparsø, T. H.; Jørgensen, M. E.; Rowe, M.; Christensen, C.; Brandslund, I.; Lauritzen, T.; Linneberg, A. Discovery of coding genetic variants influencing diabetes-related serum biomarkers and their impact on risk of type 2 diabetes. The Journal of Clinical Endocrinology & Metabolism, 2015,100, E664-E671. (32) Allan, C.; Taylor, J. Expression of a novel human apolipoprotein (apoC-IV) causes hypertriglyceridemia in transgenic mice. Journal of lipid research, 1996, 37, 1510-1518. (33) Raj, R.; Bhatti, J. S.; Badada, S. K.; Ramteke, P. W. Genetic basis of dyslipidemia in disease precipitation of coronary artery disease (CAD) associated type 2 diabetes mellitus (T2DM). Diabetes/metabolism research and reviews, 2015,31, 663-671. (34) Connors, M. H.; Dunger, D. B.; Chapel, H.; Jefferson, I.; Jowett, T. P.; Edwards, P. R. Diminished thyroxine-binding globulin in pubertal diabetic children. Diabetes care, 1996,19, 246-248. (35) Iwasaki, N.; Ogata, M.; Tomonaga, O.; Kuroki, H.; Kasahara, T.; Yano, N.; Iwamoto, Y. Liver and kidney function in Japanese patients with maturity-onset diabetes of the young. Diabetes Care 1998, 21, 2144-2148. (36) Karlsson, E.; Shaat, N.; Groop, L., Can complement factors 5 and 8 and transthyretin be used as biomarkers for MODY 1 (HNF4A‐MODY) and MODY 3 (HNF1A‐MODY)? Diabetic Medicine, 2008, 25, 788-791.

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Figure legends

Figure 1.A strategic flowchart of the quantitative proteomics approaches used in this study.

Figure 2. Hierarchical diagram of the MODY probands in the family. Squares denote male family members and circles denote female family members. Solid symbols represent subjects with MODY and open symbols represent nondiabetic individuals.

Figure 3.Quality control for the proteomic study. (A) Comparison of the abundance of serum proteins in depleted and undepleted serum samples. The green curve shows the distribution in undepleted serum using data obtained from the Plasma Proteome Database 28 and the red curve stands shows the distribution in depleted serum samples using the data acquired in the current study. The x-axis shows the list of serum proteins and the y-axis shows the normalized abundance of the serum proteins.(B) A Venn diagram of the overlapped ratios of proteins and unique peptides between proteomics duplicates. (C) Correlation analysis of the signals in the biological replicates. The log2 values of the normalized tag intensities of the proteins in one sample are plotted against those in the parallel sample. The squares of the correlation coefficients (R2) and the slope of the regression curve are listed in the insert. The samples were labeled as follows: M1, 113; M2, 118; C1, 119; and C2, 121.

Figure 4. iTRAQ-based proteomics results. (A) Volcano plot showing the abundance ratios of serum proteins between MODY patients and healthy controls. The x-axis shows the logarithmic fold changes of proteins in the two groups and the y-axis shows 24


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the logarithmic p-values for the changes. (B) Analysis of the gene ontology of the differentially expressed serum proteins between MODY patients and healthy controls.

Figure 5. Verification of the MODY-related serum proteins using MRM. (A) Volcano plot comparing the abundance ratios of serum proteins between MODY patients and healthy controls. The x-axis represents the logarithmic fold-changes of proteins the two groups and the y-axis denotes the logarithmic p-values for the fold changes. (B) Paired comparison of the abundance of the MODY-related proteins between individual MODY and healthy control samples. The distribution plot was used to compare the differences in the abundance of 22 peptides from 12 serum proteins between the two groups.

Figure 6. Verification of the MODY-related proteins in serum samples from patients with other forms of diabetes and healthy controls. (A) Paired comparison of the abundance of MODY-related proteins between individual T1D (red) and healthy (green) samples. (B) Paired comparison of the abundance of the proteins between individual T2D (red) and healthy (green) samples. (C) Paired comparison of the abundance of the proteins between the individual MODY (green) and T2D (red) samples. (D) Paired comparison of the abundance of the proteins between individual MODY (green) and T1D (red) samples. (E) Paired comparison of the abundance of the proteins between individual MODY (red) and healthy (green) samples. The grey icons indicate a significant difference between two groups, and the yellow icons mean no difference. The distribution plot was used to compare the differences in the abundance of 22 peptides from 12 serum proteins between the two groups.

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Supporting information available: The supporting information contains five supporting tables. Table S1. The basic information of the MODY family members. Table S2. The basic information of the T1D, T2D and normal subjects. Table S3. List of proteins in the iTRAQ study (≥2 unique peptides). Table S4. The basic information of 32 differentially expressed proteins identified in the iTRAQ quantification. Table S5. Transitions list of the final 23 MODY- related proteins identified in MRM.

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Fig.1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41


Discovery Case from MODY family (n=4)

Control from MODY family (n=4)

Serum depletion and digestion Peptide pooling Case (A)

MODY patients (n=4)

MODY normal (n=4)

MRM quantification(QTRAP-5500) iTRAQ labeling 113(M1) 118(M2) 119(C1) 121(C2)

Data analysis

Related proteins

Differential proteins

Control (B)

LC-MS/MS(Q-Exactive)

Comparison

Verification

Data analysis to define the verified MODY related protein in serum (SKYLINE) ACS Paragon Plus Environment

Verify the related biomarkers in other type diabetes and normal people

T1D patients (n=10)

T2D patients (n=10)

Data analysis

Normal people (n=10)

Journal of Proteome Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41

Fig.2

Ⅰ

Ⅱ

Ⅲ

Ⅳ


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Fig.3 C.

Normalized protein abundance

A.

R² = 0.9282

R² = 0.9347

M2/C1

R² = 0.8932

M2/C2

Type Public_log10_LFQ iBAQ

R² = 0.8933

B.

M1/C1

M1/C2

Serum Proteins

Tech-Replicates of protein

Tech-Replicates of peptides

X-axis: log2(Fold Change) Replication 1 ACS Paragon Plus Environment

Y-axis: log2(Fold Change) Replication 2


Fig.4 A.

-Log10-p value

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41

M1/C2

M1/C1

M2/C1

B.

M2/C2

Log2(Fold Change) ACS Paragon Plus Environment

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Fig.5 A.

Page 31 of 32

B.

Down-Regulated Peptides Normalized-area

Up-Regulated Down-Regulated None Regulated

-Log10-p value

Samples .L .I .G .G .F .N P.S 4.E D14 1.G D1.Q HBG ZP TP A7 IND1 1 PZ D P E N D N N C I S I I C C RP SERP RP SERP SE SE

Case Control

Normalized-area

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41


Log2(Fold Change)

Up-Regulated Peptides

A T .L E 5.E Y A.LLPA. INA1 NA1.S 4.D C6.V RP.E RP.G CRP. F5.A F5.A F P C L C O C RP ERPI AP SE S


SERPINA1.S

SERPINA7.N

SERPIND1.F

SERPIND1.G

SERPIND1.Q

SHBG.1

SERPINA1.S

SERPINA7.N

SERPIND1.F

SERPIND1.G

SERPIND1.Q

SHBG.1

PZP.S

PZP.S

PZP.S

PZP.G

PZP.G

PZP.G

SERPINA1.L

LPA.T

LPA.T PZP.G

LPA.T

LPA.T

PZP.S

LPA.L

LPA.L

LPA.L

LPA.L

SERPINA1.L

F5.E

F5.E

F5.E

SERPINA1.L SERPINA1.S SERPINA7.N SERPIND1.F SERPIND1.G SERPIND1.Q SHBG.1

SERPINA1.L SERPINA1.S SERPINA7.N SERPIND1.F SERPIND1.G SERPIND1.Q SHBG.1

F5.AE

F5.AA

CRP.Y

F5.E

CRP.Y

CRP.G

F5.AE

CRP.Y

CRP.Y

CRP.G

CRP.E

F5.AA

CRP.G

CRP.G

CRP.E

CETP.G

F5.AA

CRP.E

CRP.E

CD14.L CETP.G

CD14.L

F5.AE

CETP.G

CETP.G

CD14.E

C6.V

APOC4.D

F5.AA

CD14.L

CD14.L

CD14.E

C6.V

Log2(Area)

F5.AE

CD14.E

CD14.E

E.

C6.V

D.

C6.V

C. APOC4.D

B.

APOC4.D

Log2(Area) Log2(Area)



SERPIND1.G

SERPIND1.F

SERPINA7.N

SERPINA1.S

SERPINA1.L

PZP.S

PZP.G

LPA.T

LPA.L

F5.E

F5.AE

F5.AA

CRP.Y

CRP.G

CRP.E

CETP.G

CD14.L

CD14.E

C6.V

APOC4.D

A.

APOC4.D

Log2(Area)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39

Log2(Area)

Fig.6 SHBG.1

SERPIND1.Q

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Quantitative Evaluation of Serum Proteins Uncovers a Protein

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