Comprehensive Map and Functional Annotation of Human Pituitary

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A comprehensive map and functional annotation of human pituitary and thyroid proteome Xiaoyan Liu, Zhengguang Guo, Haidan Sun, wenting li, and Wei Sun J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00914 • Publication Date (Web): 05 Jul 2017 Downloaded from http://pubs.acs.org on July 6, 2017

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Journal of Proteome Research

A comprehensive map and functional annotation of human pituitary and thyroid proteome Xiaoyan Liu1, Zhengguang Guo1, Haidan Sun1, Wenting Li1*,Wei Sun1 *

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Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical

College, Beijing, China *Corresponding author: Prof. Wei Sun, E-mail: [email protected]; Tel: 0086-010-69156995 Prof. Wenting Li, E-mail: [email protected]; Tel: 0086-010-69156995

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ACS Paragon Plus Environment


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Abstract Knowledge about human tissue proteome will provide insights into health organ physiology. To construct a comprehensive dataset of human pituitary and thyroid proteins, post-mortem pituitaries and thyroids from 10 normal individuals were used. The pooled samples were prepared using two methods, one part of sample was processed using 14 high-abundance proteins immunoaffinity column. The other part was directly subjected to digestion. Finally, a total of 7596 proteins in pituitary and 5602 proteins in thyroid with high confidence were identified, with 6623 and 4368 quantified respectively. A total of 5781of pituitary and 3178 of thyroid proteins have not been previously reported in the normal pituitary and thyroid proteome. Comparison of pituitary and thyroid proteome indicated that thyroid prefers to be involved in nerve system regeneration and metabolic regulation, while pituitary mainly perform functions of signal transduction and cancer modulation. Our results, for the first time, comprehensively profiled and functionally annotated the largest high-confidence dataset of proteome of two important endocrine glands, pituitary and thyroid, which is important for further studies on biomarker identification and molecular mechanisms of pituitary and thyroid disorders. The mapping results can be freely downloaded at

http://www.urimarker.com/pituitary/ and http://www.urimarker.com/thyroid/.

And the raw data are available via ProteomeXchange with identifier PXD006471.

Keywords: Pituitary Thyroid Proteome Functional annotation

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Significance Knowledge about human tissue proteome will provide insights into health organ physiology and related disease study. Normal proteome of pituitary and thyroid is far from enough, and need to be further explored through technology improvements. The importance of this study is that we constructed a comprehensive dataset of human pituitary and thyroid proteins from low sample (immunoaffinity depletion of high abundance proteins) and raw sample (without immunoaffinity depletion) using proteomics techniques with advanced instrumentation, high pH-RPLC integrated with a TripleTOF 5600. Such a large number of proteins, 7596 in pituitary and 5602 in thyroid were identified with high confidence. A total of 5781 of pituitary and 3178 of thyroid proteins have not been previously reported in the normal pituitary and thyroid proteome. We further compared the function annotation of pituitary and thyroid by proteome differential analysis and systematically expounded the common and different functions of pituitary and thyroid tissues. This investigation will pave the way for our further comparative proteomics to clarify the molecular mechanisms involved in pituitary or thyroid disorders, and fill gaps in human proteomics study.

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Introduction In the last decades, there is much interest in annotating human genes at the level of proteins with the goal of defining function, localization, expression, and interactions of all proteins.

1,2

And the

advance of protein identification mass spectrometry instrument with high sensitivity and separation ability facilitated detailed analysis of the protein profiling in given tissue or cell.

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Mapping proteome profiling in different tissues or organs of human would greatly increase our knowledge of human biology and is important for further studies on biomarker discovery and molecular mechanisms of related tumor genesis. Pituitary and thyroid are two important endocrine glands. The pituitary is a pea-sized gland that sits in a protective bony enclosure called the sella turcica. It is covered by the dura mater and is supported by the sella turcica of the sphenoid bone. The stalk of the pituitary gland passes through an opening in the sellar diaphragm. There are two features of the pituitary-- Endocrine cells and magnocellular neurosecretory cells. Endocrine cells of anterior release hormones controlled by regulatory hormones in the hypothalamic. And the magnocellular neurosecretory cells of the posterior possess cell bodies located in the hypothalamus that project axons down the infundibulum to terminals in the posterior pituitary. Thyroid is a butterfly-shaped organ that sits at the front of the neck. It is composed of two lobes, left and right, connected by a narrow isthmus. There are two main features of the thyroid—follicular cells and parafollicular cells.

Follicular

cells secrete the thyroid hormones T3 and T4 when stimulated by thyroid stimulating hormone (TSH). Parafollicular cells secrete calcitonin and so are also called C cells. Pituitary and thyroid perform multiple hc hormones under the control of the hypothalamus, which in turn regulate the secretion of hormones by a number of target endocrine glands. Thyroid-stimulating hormone 4


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(TSH) secreted by pituitary drives the thyroid to secrete thyroid hormones and promotes metabolism and development later. Pituitary and thyroid diseases are the general popular malignancy of the endocrine system.

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Proteomics technology has proved to be a useful

strategy for biomarker discovery and function annotation. And several studies have carried out on proteome changes of pituitary and thyroid on their pathological state.

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However, it is also

important to characterize the normal tissue proteome, for knowledge of the protein composition and expression levels will provide a basis for comparison researches and also give the molecular insight into health pituitary and thyroid function. The earliest study of normal pituitary proteome, to the best of our knowledge, was conducted by Sarka Beranova-Giorgianni in 2000.

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This study combined 2-DE and MALDI-TOFMS to

study the pituitary proteome. However, nine proteins of interest were identified in this study and could not represent the truly pituitary proteome. In 2002, Sarka Beranova-Giorgianni et al improved the analysis method and only 38 proteins were identified from 62 prominent protein 2-DE spots. 10 In recent years, LC-MS technology has been commonly used for proteome analysis. 11 ， 12 ， 13

In 2011, Krishnamurthy, D et al., analyzed human pituitary proteome using data

independent label-free nLC-MSE technology and identified 1007 proteins. normal pituitary proteome was led by Liu, Yingchao 2011.

14

12

The latest study of

A total of 1660 proteins in normal

pituitary were identified using 2D nanoLC-MS. The earliest research of thyroid proteome was led in 2002. 15 However, this study focused on comparison analysis of proteome in normal thyroid, multinodular goiter, diffuse hyperplasia, follicular adenoma, follicular carcinoma and papillary carcinoma, and could not represent the normal thyroid proteome. Though there were a few studies of thyroid diseases, 5


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a large-scale


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proteome profile of normal thyroid was not available until 2014. Wilhelm et al.

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2

drafted mass

spectrometry-based human proteome based on re-analyzing previous raw data. They presented a proteome profile in thyroid with 2778 protein identifications. Compared with the human genome, the number of proteins verified by experiments in pituitary and thyroid is far from enough, and need to be further explored through technology improvements. Blood contamination of tissue samples is commonly occurred, especially for tissues that surrounded by large numbers of blood vessels such as thyroid. And a few high-abundance proteins in blood constitute a large fraction of the total protein amounts, which will seriously decrease the sensitivity of mass spectrometry and mask lower abundance proteins of biological/clinical interest.

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Therefore, it might be helpful to increase the proteome coverage

to deplete the high-abundance proteins before drafting of pituitary and thyroid proteome. Immune affinity capture has been proved the efficient method of high abundant proteins depletion.

19, 20

Therefore, in this study, a comprehensive profiling proteome analysis of normal pituitary and thyroid were carried out. The pooled samples were analyzed using two methods. One part sample was processed using 14 high-abundance proteins immunoaffinity column (referred as low sample), and another was not subjected to immunoaffinity depletion (referred as raw sample). Resulting proteomics data from low samples and raw samples were used to produce a comprehensive map of the human pituitary and thyroid proteome. Such a large number of proteins were identified in pituitary and thyroid with high confidence, 7596 and 5602, respectively. And a total of 5483 and 3429 proteins were quantified in pituitary and thyroid samples. Function annotation of pituitary and thyroid was further conducted. The proteome dataset offers a useful reference for biomarker discovery of pituitary and thyroid disease and provide insights into the physiological function of 6


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pituitary and thyroid.

Materials and methods Reagents and instruments HPLC-grade acetonitrile (ACN), formic acid, trifluoroacetic acid, ammonium bicarbonate, iodoacetamide (IAA), and dithiothreitol (DTT) were purchased from Sigma (St. Louis, MO, USA). Sequencing-grade trypsin was purchased from Promega. A TripleTOF 5600 mass spectrometer from ABsciex (Framingham, MA, USA) and an ACQUITY UPLC system from Waters (Milford, MA, USA) were used. Pituitaries and thyroids specimens All pituitary and thyroid tissues were obtained from the Bank of Chinese Academy of Medical Sciences & Peking Union Medical College, which collected tissues from donors through a whole-body donation program. All donors had given informed consent for using the donated body tissue for medical research. After death, bodies were rapidly transferred to a designated autopsy facility. The pituitary gland is located on the inferior aspect of the brain in the region of the diencephalon and is attached to the brain by pituitary stalk. It is covered by the dura mater and is supported by the sella turcica of the sphenoid bone. The stalk of the pituitary gland passes through an opening in the sellar diaphragm,

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After we removed the whole brain, the pituitary could be

quickly isolated. The thyroid gland is located anteriorly in the lower neck and very superficial to the skin. It is also the largest of the endocrine glands. The boundary of thyroid is very clear

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and we could

isolate it quickly: be careful removing the thin skin on the neck and then the sternocleidomastoid 7



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and sternohyoid muscles appeared. Identify the right lobe and left lobe of the thyroid gland and quickly isolate. After isolating the tissues, the pituitary and thyroid glands were placed in phosphate buffered saline (PBS) solution. The tissues were rinsed repeatedly using PBS to remove the blood and dots. The blood vessels on tissues should be removed as more as possible to avoid blood commination and stored in 80◦C freezer until use. Protein extraction First, 80 mg samples from each of the 10 frozen tissue samples selected for the proteomics screening were rinsed with PBS, and each sample was then mixed with lysis buffer (50 mM Tris-HCl, 2.5 M thiourea, 8 M urea,4% CHAPS, 65 mM DTT) for total protein extraction. The total protein concentration of each sample was determined using the Bio-Rad RC DC Protein Assay. Immunoaffinity depletion of 14 high-abundance proteins The pooled tissue samples were depleted of 14 high-abundance proteins (albumin, IgG, antitrypsin, IgA, transferrin, haptoglobin, fibrinogen, alpha2-macroglobulin, alpha1-acid glycoprotein, IgM, apolipoprotein AI, apolipoprotein AII, complement C3 and transthyretin) using a 4.6×50 mm human 14 affinity LC column (Agilent, St. Louis, MO,USA). The depletion was performed according to the manufacturer's instructions. The flow-through proteins and the original proteins were subjected to further sample preparation and analysis described following. Protein digestion Each sample was digested using filter-aided sample preparation (FASP) method.

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The proteins

were reduced by 10 mM DTT at 37 °C for 1 h and were carboxyamidomethylated by 55mMIAM 8


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at room temperature in dark for 45 min. Then, the samples were loaded onto 10 kDa ultrafilter tube (Pall, Port Washington, NY, USA), and were washed three times by 8 M urea. Next, the protein samples were further washed three times by 25 mM NH4HCO3. Lastly, trypsin resolved in 25 mM NH4HCO3 were added in protein samples, and digested the protein samples at 37 °C overnight. The digested peptides were collected as a filtrate. High-pH HPLC separation The lyophilized peptide mixtures were redissolved in 0.1% formic acid and fractionated with a high-pH RPLC column from Waters (4.6 mm × 250 mm, Xbridge C18, 3µm). Each peptide mixture was loaded onto the column in buffer A2 (H2O, pH = 10). The elution gradient was 5%–30% buffer B2 (90% ACN, pH = 10; flow rate, 1 mL/min) for 60 min. The eluted peptides were collected as one fraction per minute. The dried 60 fractions were re-suspended by 0.1 % formic acid and pooled into 20 samples by combining fractions 1, 21 and 41; 2, 22 and 42; and so on. A total of 20 fractions from one sample were analyzed by LC–MS/MS. LC-MS/MS analysis Each sample was analyzed by LC-MS/MS using an a reverse-phase C18 self-packed capillary LC column (75 µm×100 mm, 3µm; Packing: Reprosil-PUR, C18-AQ, 1.9 µm, 120 Å, Dr. Maisch). An elution gradient of 5–30% buffer B1 (ACN, 0.1% formic acid; buffer A1: 98% H2O, 2% ACN, 0.1% formic acid; flow rate, 0.3µL/min) for 50 min was used for the analysis. A TripleTOF 5600 mass spectrometer was used to analyze eluted peptides from LC. A nano source was used. The MS data were acquired in high sensitivity mode with detailed parameters being set as following: ion spray voltage was 2200V, curtain gas was 25, gas 1 was 5, gas 2 was 0, temperature was 150, declustering potential was100, mass range was 350–1250 for MS and 250–1800 for MS/MS, 9



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collision energy was 35, and the resolution of MS and MS/MS was 40000 and 20000. Thirty data-dependent MS/MS scans were acquired for every full scan. The normalized collision energy used was 35%, and charge state screening (including precursors with +2 to +4 charge state) and dynamic exclusion (exclusion duration of 15 s) were performed. Analyst TF 1.6 was used to control the instruments. Data processing For database searching, all wiff. MS/MS data were imported into ProteinPilot (Version 4.5) to convert as mgf. format. And then analyzed using Mascot (Matrix Science, London, UK; version 2.3.02). Mascot was set up to search the SwissProt human database (SwissProt 2016_05, 20202 sequences) assuming the digestion enzyme trypsin. The parent and fragment ion mass tolerance was 0.05 Da. Carbamidomethyl of cysteine was specified as a fixed modification, and 2 mis-cleavage sites were allowed. Scaffold (version 4.4.6, Proteome Software Inc., Portland, OR) was used to validate MS/MS based peptide and protein identifications. Protein identification was accepted at false discovery rate (FDR) less than 1.0 % on protein level and with at least 2 unique peptides. Protein FDR is calculated as the number of decoy proteins divided by the number of target proteins in Scaffold, a widely used software for protein FDR estimation. It is a common practice to incorrectly conflate the protein FDR (the percent of identified proteins that are actually absent) with protein-level target-decoy, a particular method for estimating the protein-level FDR. It is unsurprising that each of methods for estimating protein FDR has its own set of assumptions; therefore, each has its own strengths and weaknesses. More accurate and innovative methods for false discovery rate estimation and multiple testing correction need to be created and evaluated in future. 23 Proteins that contained similar peptides and could not be differentiated based on MS/MS 10


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analysis alone were grouped to satisfy the principles of parsimony. Intensity-based absolute quantification (iBAQ) of proteins Protein abundances were estimated using the iBAQ algorithm.

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The detailed protocol is

provided below: (1) The protein intensities were first computed by Progenesis LC–MS (v4.1, Nonlinear Dynamics, UK) as the sum of all identified peptide intensities (maximum peak intensities of the peptide elution profile, including all peaks in the isotope cluster). (2) The protein intensities were then divided by the number of theoretically observable peptides (calculated by in silico protein digestion; all fully tryptic peptides between 6 and 30 amino acids were counted). (3) The resulting intensities were iBAQ values, which are shown in Table S2 (4) The relative iBAQ intensities were computed by dividing the absolute iBAQ intensities by the sum of all absolute iBAQ intensities. The relative iBAQ intensities were applied to estimate the relative protein abundances (the proportions of protein amounts to total protein amounts, which is the same for thyroid and pituitary sample). Function annotation All proteins identified by the two approaches were assigned a gene symbol using the Panther database (http://www.pantherdb.org/). Protein classification was performed based on the functional annotations of the GO project for cellular compartment, molecular functional and biological processed. When more than one assignment was available, all of the functional annotations were considered in the results. Moreover, all of the selected proteins with a significant fold changes were used for pathway analysis using the IPA software (Ingenuity Systems, 11



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Mountain View, CA) for network analysis.

Results and Discussions Workflow of proteome analysis To construct a comprehensive dataset of human pituitary and thyroid proteome, separate protein extracts were prepared from 10 post-mortem pituitaries and thyroids. Pooled samples were first depleted of 14 high-abundance proteins with an immunoaffinity column. The flow-through proteins and the original proteins were respectively digested using filter-aided sample preparation (FASP) method. The digested peptides were firstly separated into 20 fractions by high-pH RPLC. And each fraction was subjected to analysis by nanoRPLC–MS/MS. For each tissue, the results from two approaches were combined and used for proteome analysis and function annotation. Fig. 1 showed the general workflow of our research. High-confidence identification of pituitary and thyroid proteome The pituitary and thyroid proteome were produced by combination of the protein results from the two approaches. To ensure high confidence of results, strict thresholds were applied. First, mass error of 0.05 Da was allowed for both of the MS and MS/MS spectra during database search. Second, the proteins only with at least 2 unique peptide identifications with FDR 2) in thyroid. Proteins involved in these processes included CUX1, VPS4B, and TNPO2 and so on, and the activated up-regulator molecular was identified as mir-122, a circadian metabolic regulator (Fig. 4C). While in pituitary, the higher proteins mainly involved in pathways of protein ubiquitination pathway, gluconeogenesis I, oxidative phosphorylation and phagosome maturation, which reflected the important role of pituitary on protein metabolic regulation. The corresponding disease and function including microtubule dynamics, organization of cytoskeleton, cytoplasm and cancer were more activated (z-score

Comprehensive Map and Functional Annotation of Human Pituitary

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