Improved characterization of the insulin secretory granule proteomes

Biomedical Proteomics Research Group, Department of Human Protein Sciences, University Medical Center, Geneva, Switzerland.
Journal of proteomics (Impact Factor: 3.89). 04/2012; 75(15):4620-31. DOI: 10.1016/j.jprot.2012.04.023
Source: PubMed
Insulin secretory granules (ISGs) are pivotal organelles of pancreatic ß-cells and represent a key participant to glucose homeostasis. Indeed, insulin is packed and processed within these vesicles before its release by exocytosis. It is therefore crucial to acquire qualitative and quantitative data on the ISG proteome, in order to increase our knowledge on ISG biogenesis, maturation and exocytosis. Despites efforts made in the past years, the coverage of the ISG proteome is still incomplete and comprises many potential protein contaminants most likely coming from suboptimal sample preparations. We developed here a 3-step gradient purification procedure combined to Stable Isotope Labeling with Amino acids in Cell culture (SILAC) to further characterize the ISG protein content. Our results allowed to build three complementary proteomes containing 1/ proteins which are enriched in mature ISGs, 2/ proteins sharing multiple localizations including ISGs, and finally 3/ proteins sorted out from immature ISGs and/or co-purifying contaminants. As a proof of concept, the ProSAAS, a neuronal protein found in ISGs was further characterized and its granular localization proved. ProSAAS might represent a novel potential target allowing to better understand the defaults in insulin processing and secretion observed during type 2 diabetes progression. This article is part of a special issue entitled: Translational Proteomics.


Available from: Domitille Schvartz, Dec 18, 2013
Improved characterization of the insulin secretory
granule proteomes
Domitille Schvartz
a, 1
, Yannick Brunner
a, 1, 2
, Yohann Couté
a, 3
, Michelangelo Foti
Claes B. Wollheim
, Jean-Charles Sanchez
Biomedical Proteomics Research Group, Department of Human Protein Sciences, University Medical Center, Geneva, Switzerland
Department of Cell Physiology and Metabolism, University Medical Center, Geneva, Switzerland
Article history:
Received 24 November 2011
Accepted 20 April 2012
Available online 27 April 2012
Insulin secretory granules (ISGs) are pivotal organelles of pancreatic ß-cells and represent a
key participant to glucose homeostasis. Indeed, insulin is packed and processed within
these vesicles before its release by exocytosis. It is therefore crucial to acquire qualitative
and quantitative data on the ISG proteome, in order to increase our knowledge on ISG
biogenesis, maturation and exocytosis. Despites efforts made in the past years, the
coverage of the ISG proteome is still incomplete and comprises many potential protein
contaminants most likely coming from suboptimal sample preparations. We developed
here a 3-step gradient purification procedure combined to Stable Isotope Labeling with
Amino acids in Cell culture (SILAC) to further characterize the ISG protein content. Our results
allowed to build three complementaryproteomes containing 1/ proteins which are enriched in
mature ISGs, 2/ proteins sharing multiple localizations including ISGs, and finally 3/ proteins
sorted out from immature ISGs and/or co-purifying contaminants. As a proof of concept, the
ProSAAS, a neuronal protein found in ISGs was further characterized and its granular
localization proved. ProSAAS might represent a novel potential target allowing to better
understand the defaults in insulin processing and secretion observed during type 2 diabetes
progression. This article is part of a special issue entitled: Translational Proteomics.
© 2012 Elsevier B.V. All rights reserved.
Subcellular fractionation
Insulin secretory granules
1. Introduction
Insulin is a key player in the adjustment of energy balance, as
it is involved in the regulation of glucose homeostasis. This
hormone is synthesized, stored and secreted by the ß-cells of
the pancreas. It is compacted into dense core secretory
vesicles, insulin secretory granules (ISGs), dedicated to its
regulated maturation and secretion. In rat ß-cells, there are
approximately 10,000 ISGs per ß-cell, and each ISG encloses
around 200,000 insulin molecules organized as insulin crys-
tals containing 6 molecules of insulin [1,2]. These vesicles
measure about 300350 nm. Their biogenesis starts with a
budding at the trans-Golgi network (TGN), where the proteins
are enveloped by TGN membranes. Two models of protein
JOURNAL OF PROTEOMICS 75 (2012) 4620 4631
Abbreviations: ISG, Insulin secretory granules; SILAC, Stable Isotope Labeling with Amino acids in Cell culture; TGN, Trans-Golgi
network; PC1/2, Prohormone convertase 1/2; SNAREs, Soluble NSF attachment protein receptors.
This article is part of a Special Issue entitled: Translational Proteomics.
Corresponding author at: Biomedical Proteomics Research Group, Department of Human Protein Sciences, Rue Michel Servet 1, CH-1211 Genève 4,
Switzerland. Tel.: +41 22 379 54 86; fax: +41 22 379 55 05.
E-mail address: (J-C. Sanchez).
Both authors contributed equally to this work.
Present address: Swiss Center for Applied Human Toxicology, HEPIA, Geneva, Switzerland.
Present address: LEDyP (iRTSV / BGE), INSERM/CEA/UJF U1038, F-38054 Grenoble, France.
1874-3919/$ see front matter © 2012 Elsevier B.V. All rights reserved.
Available online at
Page 1
sorting at the TGN are debated: the sorting by entry or
sorting by retention hypotheses [35]. The first one relies on
the fact that proteins are sorted in different specific vesicles
directly at the TGN. According to the second hypothesis,
proteins at the TGN are incorporated into a unique immature
dense core vesicle, from which they are sorted along the
maturation processes. In ß-cells, several studies tend to prove
that the sorting by retention hypothesis might be more
plausible [4,6].
Immature ISGs undergo maturations during their progression
from the TGN to the plasma membrane. Notably , an acidif ication
of the lumen occurs, which is esse ntial for the vesicle composi-
tion. Actually, this acidification allows aggregation of soluble
proteins creating the dense core of the vesicles. This aggregation
prevents the sorting of the proteins out of the vesicles [7].
Acidification of granular medium leads also to the activation of
prohormone convertases (PC), giving rise to the conversion of
pro-insulin into insulin by PC1/2 and carboxipeptidase E [8,9].
This maturation process also comprises the removal of the
clathrin coat from the surface of the vesicles [10,11].
Once mature, ISGs can release their content by exocytosis
under a specific stimulus such as the increase of blood glucose
levels. The release of insulin at the plasma membrane is
performed through the fusion of the vesicles with the
membrane, and exocytosis of the ISG content. The priming,
docking and fusion mechanisms rely on the formation of
SNARE complexes [12,13], and their regulation by synaptotag-
min [14]. Several Rab GTPases and Rab-related proteins also
play an important role in vesicle exocytosis, being involved in
the targeting and docking of the secretory vesicles to the
plasma membrane, as well as the regulation of these steps [15].
ß-cell function and proper insulin secretion are essential
for glucose homeostasis. The characterization of ISG compo-
sition and content, as well as the understanding of their
biogenesis mechanisms, are indispensable information to
understand ß-cell function and its defaults in pathologies
such as type 2 diabetes. Thanks to the development of
proteomics tools, two independent groups already published
data on the ISG proteome using different approaches. Brunner
et al. [6] published a list of 130 ISG proteins, resulting of a 2-
step gradient purification of ISGs from INS-1E rat ß-cells in
culture. Mass spectrometry (MS) data were generated using a
MALDI-TOF/TOF instrument. Apart of granular proteins, some
proteins known to be localized in mitochondria or lysosomes
were also identified. In another study, Hickey and co-workers
[16] enriched ISGs from the same cell line but using affinity
purification. This allowed the identification of 51 proteins
related to ISGs. The set of proteins identified in these 2
independent and complementary works seem to be far from
the final figure of ISG related proteins. Indeed these studies
notably suffered from the weak sensitivity of the MS analyzers
employed. In addition, in a recent review, Suckale highlighted
the limitations of the strategies employed for ISG purifications
[17]. We propose here an improvement in ISG preparation and
present the results obtained when we combined it with
modern proteomics tools. For this, we add a third density
gradient to our previous 2-step granule preparation method
[6]. While associating it with the SILAC strategy [18], this
procedure expanded our knowledge on ISG biogenesis,
through the segregation of mature granules from immature
granules and potential contaminants. Finally, this work
pointed out a neuronal enzyme highly enriched in mature
ISGs, the ProSAAS. ProSAAS has been used to exemplify the
pertinence of the present enrichment procedure to further
detect granular proteins associated to insulin processing and
ß-cell dysfunction.
2. Experimental procedures
2.1. Cell culture
Rat insulinoma INS-1E cells were grown in RPMI1640 media
supplemented with 10% fetal bovine serum. Normal culture
conditions include 37 °C and 5% CO
in humidified atmo-
sphere. SILAC media were prepared from RPMI-1640 media
depleted in Arginine, Leucine and Lysine (Sigma-Aldrich), and
supplemented with 10% of dialyzed bovine serum. Cells were
grown 4 weeks (approximately 8 doublings) in either light or
heavy media for complete amino-acid incorporation (>98%).
Light medium was supplemented with Leucine (25 mg/L,
Sigma-Aldrich), Lysine (25 mg/L, Sigma-Aldrich), and Arginine
(100 mg/L, Sigma-Aldrich). Heavy medium was supplemen-
ted with
-Lysine (Cambridge Isotope
laboratories), and Arginine in the same concentrations.
2.2. Three-step gradient purification procedure
INS-1E cells were grown in the conditions described above. ISGs
were enriched according to the protocol published in Brunner et
al. [6] before being placed on the top of a continuous sucrose
gradient (1 2 M), and ultra-centrifuge d 8 h at 110,000 g.Nine
1 mL fractions were recovered, and sucrose was removed by a
methanol/chloroform precipitatio n. ELISA assessed the insulin
content (Mercodia), and repartit ion of marker proteins was
assessed by Western blot using antibodies raised against
Vamp4 (rabbit polyclonal antibody, Sigma), Beta-granin (rabbit
polyclonal antibody, Eurogentec), GDH (rabbit polyclonal anti-
body, Rockland) and Cathepsin L (mouse monoclonal antibody,
Abcam). Western blot quantitation was performed with Image-
Quant (GE Health Care Life Scie nces). Fractions were pooled to
obtain a mix of immature/contaminant proteins (fractions D
andE)andmature proteins (fractions G and H). The amount of
proteins in each group was determined by ImageMaster 2D
Patiniu m v6.0, on a silver staining of a 1D-gel. For quantitative
experiments, light mature and heavy immature pools were
mixed in a 1:1 ratio. The reverse experiment was also performed.
2.3. Protein separation by SDS PAGE
Ten μg of proteins of either immaturesC
mixes were solubilized in Laemmli
buffer [19], and heated 5 min at 95 °C, before separation on a
12.5%T, 2.6%C polyacrylamide gel. Proteins were run for 1.5 cm,
and the gel was stained with Coomassie Blue R250 (Merck).
2.4. Mass spectrometry analysis
Each SDS-PAGE lane (n=2) was cut into 8 identical slices and
proteins contained in each slice were digested in-gel by
4621JOURNAL OF PROTEOMICS 75 (2012) 4620 4631
Page 2
trypsin. Briefly, samples were reduced in 10 nM dithioerythri-
tol (DTE), alkylated in 55 μM iodoacetamide (IAA), and gel
pieces were dried. Samples were then treated with 6.25 ng/μl
of trypsin (Promega) overnight at 37 °C. Tryptic peptides were
extracted from gel slices, dried and suspended in an appro-
priate volume of 5% ACN; 0.1% FA for LC-MS/MS analysis.
ESI LTQ-OT MS was performed on a LTQ Orbitrap velos from
Thermo Electron (San Jose, CA, USA) equipped with a NanoAc-
quity system from Waters. Peptides were trapped on a home-
made 5 μm 200 Å Magic C18 AQ (Michrom) 0.1×20 mm pre-
column and separated on a home-made 5 μm 100 Å Magic C18
AQ (Michrom) 0.75×150 mm column with a gravity-pulled
emitter. The analytical separation was run for 65 min using a
gradient of H
O/FA 99.9%/0.1% (solvent A) and CH
CN/FA 99.9%/
0.1% (solvent B). The gradient was run as follows: 01 min 95%
A and 5% B, then to 65% A and 35% B at 55 min, and 20% A and
80% B at 65 min at a flow rate of 220 nL/min. For MS survey
scans, the OT resolution was set to 60,000 and the ion
population was set to 5 E+5 with a m/z window from 400 to
2000. Five precursor ions were selected for collision-induced
dissociation (CID) in the LTQ. For this, the ion population was
set to 7 E+3 (isolation width of 2 m/z). The normalized collision
energies were set to 35% for CID. Three injections were
performed for each band in order to obtain technical replicates.
2.5. Data analysis
Acquired raw files were processed with MaxQuant v1.0.13.13 as
described by Cox et al. [20]. Briefly, Quant module of MaxQuant
was used to generate peaklists, with the following parameters:
Orbitrap/FT Ultra; SILAC doublets with heavy labels Leu6 and
Lys8 and a maximum of three labeled amino acids. Generated
results were then submitted to Mascot search engine v2.2
(Matrix Science, London, UK) using parameters defined in
Quant: variable modifications were set as oxidation (Met) and
acetylation (protein N terminus) and carbamidomethyl (Cys)
was chosen as fixed modification. Rat International Protein
Index (IPI) v3.52 (40,168 entries, comprising 262 contaminant
entries) was selected as database, and enzyme was set on
trypsin with no proline restriction, and two missed cleavages;
MS/MS tolerance was selected to be 0.5 Da. The acquired .dat
files were then submitted to the Identify module in MaxQuant
for the quantification step, using the following parameters:
peptide and protein false discovery rates (FDR) were set to 1%;
maximum posterior error probability to 1; minimum of
peptides and minimum of unique peptides to 2; and minimum
of peptide length to 6 amino acids. For quantification, default
parameters were used. An experimental design template was
created in order to invert ratios of the second set of
experiments, and to separate the different biological (n=2)
and technical (n=3) replicates.
2.6. Immunofluorescence
INS-1E cells cultured on coverslips were fixed for 20 min in 3%
(w/v) paraformaldehyde in PBS, washed 3 times with PBS and
exposed for 20 min in 50 mM NH4Cl to avoid auto-fluorescence.
Cells were then washed 3 times with PBS, blocked 15 min in
10% FCS and exposed for 45 min in PBS containing primary
insulin (Sigma) or ProSAAS (Proteogenix) antibodies, 0.1%
saponin and 10% FCS. Cells were then exposed to fluorescent-
dye-conjugated secondary antibodies (1/100) for 30 min at
room temperature. Samples were analyzed using a Zeiss laser
confocal microscope (LSM 510 Meta). Images were taken with a
60× objective. For competition experiments, ProSAAS peptides
(Proteogenix) used for rabbit immunization and ProSAAS
GALLRVKRLE) were mixed with ProSAAS antibody (polyclonal
rabbit antibody from Proteogenix) 100 times in excess com-
pared to ProSAAS antibody concentration. PEN and LEN
peptides were synthesized by the peptides synthesis core
facility (Geneva University).
2.7. Transmission electron microscopy
For immunogold labeling of ultrathin cryosections, cells were
detached and fixed for 1 h in phosphate buffer (100 mM NaPO4,
pH 7.4) containing 4% paraformaldehyde (EMS) and 0.1%
glutaraldehyde (EMS). Thereafter, the fixative was rinsed out
three times with phosphate buffer and the cells were processed
for cryosectioning as described in [21]. Briefly, the cell pellet
was infiltrated with sucrose and frozen in liquid nitrogen.
Frozen sections (45 nm thickness) were cut with a Leica FCS
cryotome, transferred to grids, and incubated with primary
antibodies against insulin (mouse monoclonal antibody from
Sigma) and ProSAAS (polyclonal rabbit antibody from Proteo-
genix) followed by incubation with anti mouse secondary
10 nm gold-conjugated and anti-rabbit secondary 15 nm gold-
conjugated antibodies (British Biocell). Grids were examined
with a Technai 20 electron microscope (FEI Company).
2.8. Quantitative RT-PCR
Total RNA extraction was performed using an RNeasy kit
(Qiagen) according to the instructions of the manufacturer.
Primers design for real-time RT-PCR was carried out with the
software PRIMER EXPRESS (Applied Biosystems). Primers used
were: 3GGATGCCGCTGACGAGACT5 (forward) and 3GAGGA-
TCCGCCCTAGCAAGT5 (reverse). First-str and cDNA synthesis
was carried out with 500 ng of RNA by usin g random hexamers
and Superscript II reverse transcriptase following the instruc-
tions of the manufacturer (Invitrogen). Real-time RT-PCR was
carried out in optical 384-well plates and labeled by using the
SYBR green master mix (Applied Biosystems), and the flu ores-
cence was quantif ied with a Prism 7900 HT sequence detection
system (Applied Biosystems). The results were normalized
against the rat ß-Tubulin; EEF1A1 and RPS9 genes.
3. Results
3.1. Combination of a triple density gradient and SILAC to
point out protein contaminants in the ISG purification
Insulin having an indispensable role in glucose homeostasis, it
is essential to increase our knowledge on ß-cells, and more
specifically on ISGs. These vesicles have a key role in insulin
secretion mechanisms. Efforts were undertaken last decades to
decipher ISG proteomes, and improve our understanding in
4622 JOURNAL OF PROTEOMICS 75 (2012) 4620 4631
Page 3
their biogenesis [6,16]. However, the recent publications on ISG
proteomes highlighted bona fide resident proteins of this
organelle. In addition, none of them precisely addressed the
question of the differences between the proteomes of mature
and immature ISGs. In a first effort, we purified ISGs using a
double density gradient as described by Brunn er et al., and
analyzed them with the Orbitrap Velos. This protocol enabled
the identification of more than 1200 proteins, with a major part
representing probable contaminants coming from lysosome s
and mitochondria (data not shown).
In order to improve our purification protocol, we loaded
our two-step gradient enriched ISG fraction on a third sucrose
density gradient predicted to segregate immature ISGs from
mature ISGs since mature granules are denser than immature
ones. Localization of insulin in these gradients was tested by
ELISA and distributions of Beta-granin, Vamp4, Cathepsin L,
and GDH, respectively markers of mature ISGs, immature
ISGs, lysosomes and mitochondria, were verified by Western
blot (Fig. 1). As expected, lysosomes, mitochondria and
immature ISGs were localized in the higher/lighter fractions
of the sucrose gradient, whereas mature ISGs were concen-
trated in the lower/heavier fractions.
To further characterize the enrichment of some proteins in
one or the other fraction, we combined this 3-step purification
procedure with the SILAC strategy. Practically, as described in
Fig. 2, we pooled light immature ISG-containing fraction with
heavy mature ISG-containing one and vice-versa for the
duplicate experiment. After data processing with MaxQuant
of the 2 biological replicates and the 3 technical replicates, we
selected proteins with significantly different ratios according
to the Significance A value, appearing in at least one
experiment and with a minimum of 2 quantified peptides.
This allowed to sort out 3 groups of proteins. The first group
(G1) contains proteins specifically enriched in the mature
granules, and that were not detected in other co-purifying
organelles (Table 1). The second group (G2) corresponds to
proteins with a ratio which is not significantly different
between fractions. This pointed out proteins that are localized
in several sub-cellular compartments, including ISGs. The
third group (G3) incorporates proteins, which are not detected
in mature ISGs. These could encompass proteins from
immature ISGs, which are sorted during the maturation of
the granules and contaminant proteins from lysosomes and
mitochondria (Supplemental data).
3.2. A comprehensive update of the ISG proteome
SILAC coupled to a more stringent preparation of the
granules revealed itself as a powerful strategy to increase our
knowledge on ISGs and their biogenesis. Actually, 140 pro-
teins can be considered to be enriched in mature ISG fractions
(Table 1, Supplemental data 2). Among them 35 were already
identified in the study by Brunner et al. (mainly intravesicular
proteins) and 7 in the work of Hickey et al. Interestingly, 98
proteins represent newly described ISG proteins. The 140
proteins comprise 18% of intravesicular proteins and 34% of
membrane proteins. The remaining 48% include proteins with
unspecific localizations or for which the localization was
unknown. Characteristic proteins of ISGs such as insulin, PC1
and 2, but also Vamp2 and Vamp3, Chromogranins A, B, and C
and Secretogranin 3 were identified [6,2224].
A second group assembles 447 proteins coming from ISGs,
but potentially also present in other co-purifying organelles
such as lysosomes or mitochondria. Fifty one of them were
already described in Brunner et al., and 24 in Hickey et al.
Syntaxin-1A, 3, 7 and 13, but also SNAP-25 and Vamp8 are
found in this group. Many Ras-related Rab proteins were also
identified as members of this class (Supplemental data 3).
The last group contains 81 proteins, which are enriched in
the upper fractions of the last sucrose gradient of the
purification procedure. Thereby, they are either proteins
from contaminant organelles or proteins from immature
ISGs that are sorted out of the granules during the maturation
process. Actually, 26% of these proteins are known to be
localized within lysosomes such as Cathepsins L1, D and F.
Fifteen of these proteins were already identified in Brunner et
al., and 2 in Hickey et al. (Supplemental data 4).
3.3. Validation of the granular localization of ProSAAS
Our work allowed identifying potential new mature ISG-
associated proteins. Among them, ProSAAS is the most
Insulin concentration
Percent intensity
Vamp 4
Cathepsin L
Fig. 1 Distribution of markers of ISGs and co-purifying
organelles in the third sucrose gradient used for purification
of ISGs.A) Beta-granin, Vamp4, Cathepsin L and GDH levels
were assessed by Western blot as markers of mature ISGs,
immature ISGs, lysosomes and mitochondria, respectively;
B) Insulin concentration (μg/L) in each fraction was
measured by ELISA.
4623JOURNAL OF PROTEOMICS 75 (2012) 4620 4631
Page 4
enriched one in the mature ISG fraction. This protein was
previously shown to be present in neuronal cells, and to
inhibit in vitro PC1 [25], an enzyme involved in the processing
of several prohormones including proinsulin [26]. More
recently, ProSAAS was identified in ß-cells and in pancreatic
islets [2730]. However, its precise localization was never
In order to gain insight in the localization of ProSAAS in
ß-cells, immunofluorescence experiments were performed
using polyclonal rabbit anti-ProSAAS antibodies. This con-
firmed the presence of ProSAAS in INS-1E cells and showed its
partial but clear co-localization with insulin (Fig. 3). The
specificity of the anti-ProSAAS antibody in immunofluores-
cence studies was demonstrated by using the peptide used for
rabbit immunization in a competition experiment. Two other
ProSAAS peptides (PEN and LEN) were also tested as negative
controls. The peptide used for the production of the anti-
ProSAAS antibodies completely abolished the fluorescence
whereas the use of PEN and LEN did not affect the labeling
(Fig. 3).
Immuno-electron microscopy analyses were also con-
ducted. These experiments showed that dots corresponding
to anti-ProSAAS detection were mainly localized within
structures that strongly looked like ISGs (Fig. 4A). These
Fig. 2 Coupling quantitative proteomics and improved ISG purification.A). ISGs were prepared from INS-1E cells grown in
either light or heavy SILAC media using a two-step gradient protocol [6]. ISG fractions from each condition were then loaded on
sucrose gradients and high speed centrifuged. Upper fractions of the gradient containing light organelles were pooled with
lower fractions of the gradient containing heavy organelles. This pool was analyzed by quantitative proteomics, as well as
the reverse combination of fractions; B) 3 groups of proteins can be distinguished: 1/ enriched mature ISG proteins (purple
triangles), 2/ ISG proteins with multiple localizations (orange rounds), and 3/ immature ISG proteins or contaminants (pink
4624 JOURNAL OF PROTEOMICS 75 (2012) 4620 4631
Page 5
Table 1 Proteins enriched in the mature ISG fraction.
The 140 proteins which were found to be enriched in mature ISGs according to their SILAC ratio are listed. The second and third columns indicate
if proteins were also identified in Brunner et al. and Hickey et al. The fourth column indicates their localization as described in Gene Ontology and
the last column exhibit their SILAC ratio.
Protein names Brunner,
MCP 2007 [6]
JPR 2009 [16]
Ratio H/L
ProSAAS x Intravesicular 45.1
Similar to G protein-regulated inducer of neurite outgrowth 1 Other 38.9
Eukaryotic translation initiation factor 3 subunit B Other 38.3
Insulin-1 x x Intravesicular 27.2
Insulin-2 x x Intravesicular 21.4
Eukaryotic translation initiation factor 2, subunit 2 (Beta) Other 19.6
Nucleobindin-1 x Membrane 18.7
Wnt inhibitory factor 1 x Intravesicular 18.4
Glucagon Intravesicular 17.8
FAS-associated factor 2 Other 17.6
Procollagen-lysine; Lysyl hydroxylase 1 Membrane 17.3
Chromogranin-A;Beta-granin x Intravesicular 16.0
Vitamin D-binding protein x Intravesicular 15.7
Carboxypeptidase E x x Intravesicular 15.6
Sialyltransferase ST3Gal-I Membrane 15.5
Prohormone convertase 1 Intravesicular 15.4
Fibronectin x Intravesicular 15.3
Eukaryotic translation elonga tion factor 1 beta 2 Other 15.2
Importin subunit beta-1 Membrane 15.2
Secretogranin V x Intravesicular 15.1
BTF3L4 protein-like Other 15.1
Cystatin N Membrane 14.7
Intelectin 1 (Galactofuranose binding) x Other 14.6
Dystroglycan 1 Membrane 14.5
Clusterin x Intravesicular 13.8
Rac GTPase-activating protein 1 (Predicted) Other 13.0
60S ribosomal protein L11 Membrane 12.7
Transforming growth factor, beta induced Other 12.6
prosaposin Unknown 12.6
von Willebrand factor A domain containing 5B2 Unknown 12.5
T-complex protein 1 subunit beta;CCT-beta Other 12.2
Kinesin-1 heavy chain Other 12.1
Eukaryotic translation initiation factor 2 subunit 1 Other 11.9
Follistatin-like 4 Unknown 11.8
Secretogranin-1;Chromogranin-B x x Intravesicular 11.7
Guanine nucleotide-binding protein G(s) subunit alpha isoforms Xlas x Membrane 11.6
Secretogranin III x Intravesicular 11.6
IMPORTIN 5 Other 11.4
Putative uncharacterized protein HAPLN4 Other 11.4
Neuronal differentiation-related gene protein Unknown 11.2
Vitamin K-dependent protein S Intravesicular 10.9
Transcobalamin-2 x Intravesicular 10.9
Stanniocalcin-1 x Other 10.8
ADP-ribosylation factor 6 Membrane 10.7
A regulatory subunit of protein phosphatase 2A Other 10.6
Renin receptor x Membrane 10.5
Adenylyl cyclase-associated protein 1 Membrane 10.3
Prohormone convertase 2 x x Intravesicular 10.3
Proteasome (prosome, macropain) 26S subunit, non-ATPase, 11 Unknown 10.0
Sulfated glycoprotein 1 x Intravesicular 10.0
Family with sequence similarity 20, member C Other 10.0
Peptidyl-glycine alpha-amidating monooxygenase x Membrane 10.0
Putative uncharacterized protein ENSRNOP00000015381 Other 9.9
Secretogranin II;Chromogranin-C x x Intravesicular 9.9
Nucleobindin-2 x Intravesicular 9.7
40S ribosomal protein S10 Other 9.7
Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 x Intravesicular 9.7
40S ribosomal protein S18 Other 9.7
N-myc downstream-regulated gene 1 protein Membrane 9.6
Adenine phosphoribosyltransferase Other 9.5
(continued on next page)
4625JOURNAL OF PROTEOMICS 75 (2012) 4620 4631
Page 6
Table 1 (continued)
Protein names Brunner,
MCP 2007 [6]
JPR 2009 [16]
Ratio H/L
Erythrocyte membrane protein band 4.1-like 2 Unknown 9.5
Cullin-associated NEDD8-dissociated protein 1 Membrane 9.5
Guanine nucleotide-binding protein G(q) subunit alpha Membrane 9.3
40S ribosomal protein S20 Other 9.1
Kinesin family member 23 Unknown 9.1
Septin 11 isoform IV Other 9.1
Rho GTPase activating protein 1 Unknown 9.0
Tln1 protein Membrane 8.8
Cystatin-C;Cystatin-3 x Intravesicular 8.8
chromosome 9 open reading frame 46 Unknown 8.7
tolloid-like 1 Unknown 8.7
sparc/osteonectin, cwcv and kazal-like domains proteoglycan (testican) 1 Unknown 8.5
Heat shock 70 kDa protein 13 Membrane 8.5
40S ribosomal protein S28 Other 8.3
Iqgap1 protein Other 8.3
Myosin-9 Membrane 8.2
Leucine-rich repeat-containing protein 59 Membrane 8.0
Procollagen-lysine; Lysyl hydroxylase 3 x Membrane 8.0
V-type proton ATPase subunit S1 Membrane 7.9
N-acetylglucosamine-1-phosphotransferase, gamma subunit, isoform CRA_a Membrane 7.8
Eukaryotic translation initiation factor 3 subunit J Other 7.8
Heterogeneous nuclear ribonucleoprotein R Other 7.7
Receptor-type tyrosine-protein phosphatase N2 x Membrane 7.6
Clathrin heavy chain 1 Membrane 7.6
Dihydropyrimidinase-related protein 1 Other 7.5
Guanine nucleotide binding protein gamma 4 subunit Membrane 7.4
26S protease regulatory subunit 6B Membrane 7.4
60S ribosomal protein L12 Membrane 7.3
Myosin-Ib Other 7.3
60S acidic ribosomal protein P2 Membrane 7.2
Synaptotagmin V x Membrane 7.1
GTPase activating protein (SH3 domain) binding protein 1 Unknown 7.0
ATP-citrate synthase x Other 6.9
Alpha-centractin Membrane 6.9
Guanine nucleotide-binding protein G(o) subunit alpha Membrane 6.8
Uqcrb protein;RCG60159 Other 6.8
Postsynaptic density protein Other 6.8
Glucose-6-phosphate isomerase Intravesicular 6.8
Fxr2 protein Other 6.8
Receptor-type tyrosine-protein phosphatase-like N x Membrane 6.7
Vesicle-associated membrane protein 3;Synaptobrevin-3;Cellubrevin x Membrane 6.7
Vascular endothelial growth factor A Intravesicular 6.6
Ras-related protein Rab-27B Membrane 6.5
4 F2 cell-surface antigen heavy chain Membrane 6.5
Drebrin Membrane 6.4
Immunoglobulin heavy constant mu Unknown 6.4
Myosin regulatory light chain 12B Other 6.4
40S ribosomal protein S15a Other 6.4
Sulfhydryl oxidase 1 Intravesicular 6.4
Macrophage migration inhibitory factor Other 6.4
Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-1 Membrane 6.4
Isocitrate dehydrogenase [NAD] subunit beta, mitochondrial Mitochondria 6.3
RCG41580, isoform CRA_a;Ribosomal protein L22 like 1 Membrane 6.3
GLI pathogenesis-related 2 Unknown 6.2
ATPase, H+ transporting, lysosomal 38 kDa, V0 subunit d1 x Membrane 6.2
High mobility group protein B2 Membrane 6.2
Casein kinase II subunit alpha Other 6.2
Eprs protein Unknown 6.2
Rab effector Noc2;Rabphilin-3A-like protein Membrane 6.1
Spectrin alpha chain, brain Membrane 6.1
Cytoskeleton-associated protein 4 Unknown 6.1
Protein regulator of cytokinesis 1 Unknown 6.0
Putative uncharacterized protein Ndufc2 Mitochondria 6.0
Vesicle-associated membrane protein 2;Synaptobrevin-2 x Membrane 6.0
4626 JOURNAL OF PROTEOMICS 75 (2012) 4620 4631
Page 7
results were confirmed using double immunostaining exper-
iments showing the colocalization of insulin and ProSAAS
(Fig. 4B). Interestingly, only a part of ISGs were labeled by the
anti-ProSAAS antibody.
3.4. Modulation of ProSAAS mRNA level by glucose
Since ProSAAS is localized in ISGs, we investigated its
behavior in ß-cells grown with various concentrations of
glucose. We exposed cells to 5, 11 or 30 mM of glucose for 24 h.
In comparison to our previous experimental conditions
(11 mM), quantitative RT-PCR experiments indicated that
proSAAS mRNA expression is favored in cells exposed to low
level of glucose (5 mM) whereas it is inhibited in cells grown
with high levels of glucose (30 mM) (Fig. 5).
4. Discussion
Organellar proteomics was greatly improved by MS-based
methods in the past decade [3133]. Classical subcellular
fractionation strategies are now coupled with the highest
sensitivity MS instruments, allowing an increase in the
number of identified proteins that are then attributed to
specific organelles. However, specialists have now to face the
consequent downsides of high-tech MS instruments, as it also
leads to a larger coverage of co-purifying contaminants.
Thereby, subcellular proteomics now needs to improve
purification strategies and develop quantitative pipelines to
discriminate contaminant proteins from genuine members of
the targeted organelle [34]. Such methods are mainly based on
the combination of proteomics, bioinformatics, and statistics
such as the localization of organelle protein by isotope
tagging (LOPIT) [35], or the Protein Correlation Profiling PCP
described in [36].
Another important issue is the inclusion of the biological
context in the study since organelles are dynamic entities.
Their proteome content tends to change under various
physiological and pathological conditions. This is precisely
the case of ß-cell ISGs. It is well described that their protein
content changes during their maturation into dense core
vesicles from their budding at the TGN to the release of their
content at the plasma membrane [37].
Two main proteomic studies were conducted to decipher
ISG proteomes, leading to the identification of 130 and 51
proteins [6,16]. However, Suckdale noticed that some main
ISG proteins were missing, pointing out the deficit of
sensitivity in both approaches used for these studies [17].
None of these studies addressed the modulation of ISG
proteome during maturation steps. Here, by combining an
improved ISG preparation (triple density gradient) and quan-
titative proteomics, we succeeded to obtain more precise
information on the ISG proteome composition, but also
preliminary data on its potential changes during maturation
from the Golgi to the plasma membrane. Three groups
of proteins were segregated, corresponding to 1/ proteins
enriched in mature ISG, 2/ ISG proteins with multiple
localization, and 3/ immature ISG proteins or contaminant.
The first group contains 140 proteins enriched in mature
ISG fractions, and therefore not present in any of the potential
other co-purifying organelles. This was the case for well-
known ISG proteins such as insulin, carboxipeptidase E, PC2,
Vamps, secretogranins and chromogranins. Vacuolar ATPases
involved in the regulated exocytosis, and G-protein family
members described to play an important role in insulin
exocytosis such as the different Guanine nucleotide-binding
proteins belong to this group as well [38,39]. We also identified
Vamp2 and Vamp3 (Cellubrevin), two members of the
v-SNARE complex that is specific to secretory granules and
required for effective secretion events in ß-cells [40,41].
Finally, the group of proteins enriched in mature ISG also
includes PC1, a key enzyme for proinsulin processing, or Noc2
and RhoG known to participate in exocytosis events [23],as
well as many other proteins, which were not described in
previous proteomic studies. However, this strategy does not
allow differentiating proteins, which are ISG specific and
therefore enriched in the lower fractions, from proteins which
are recruited along the maturation process, and therefore
present only in mature ISG.
Table 1 (continued)
Protein names Brunner,
MCP 2007 [6]
JPR 2009 [16]
Ratio H/L
40S ribosomal protein S21 Other 6.0
Nucleosome assembly protein 1-like Membrane 6.0
Protein kinase, cAMP-dependent, catalytic, alpha Membrane 6.0
Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-2 x Membrane 5.9
39S ribosomal protein L41, mitochondrial Mitochondria 5.9
Eukaryotic translation initiation factor 3, subunit F Unknown 5.8
Caprin-1 Other 5.8
Plasminogen activator inhibitor 1 RNA-binding protein Membrane 5.8
Tryptophanyl-tRNA synthetase, cytoplasmic Other 5.8
Ras homolog gene family, member G (Rho G) Membrane 5.8
Tubulin beta-2A chain x Other 5.8
Cytochrome bc1 complex subunit 2, mitochondrial Mitochondria 5.7
Serine hydroxymethyltransferase Mitochondria 5.7
Annexin A11 Other 5.7
cytochrome c-1 Unknown 5.7
4627JOURNAL OF PROTEOMICS 75 (2012) 4620 4631
Page 8
The second group defined by the results of the SILAC
experiment is constituted of 447 proteins present in several
organelles including ISGs. This result is in line with previous
findings indicating that 39% of organellar proteins share
several localizations [42]. This group contains several syntax-
ins, but also Vamp 8 and SNAP-25 that are involved in
t-SNARE complexes related to docking and fusion mecha-
nisms of secretory organelles [43]. Syntaxin 7 and Vamp 8 are
well-known to be located also in endosomes and lysosomes
[4446], which is consistent with their presence in this group.
The third group of proteins contains 81 proteins, which are
likely immature ISG sorted proteins or potential contaminant
proteins. For example, many lysosomal proteins (26%) were
present in this group. However, several of them have been
previously described to be localized within immature ISG as
well. For instance, Kuliawat and co-workers showed that
some cathepsins are present in immature ISGs and are then
sorted to lysosomes during the maturation process [47].
These results represent a first im portant step in the
understanding of ISG dynamics along maturation. They open
Fig. 3 Colocalization study of ProSAAS and insulin using immunofluorescence.INS-1E cells were analyzed by confocal
microscopy after double labeling with antibodies directed against insulin and ProSAAS. The cells were then exposed
respectively to rhodamine- or FITC-conjugated secondary antibodies. The merged images are presented in the right panels.
A) Localization of insulin and ProSAAS; B) same experiment but with the addition of the peptide used to immunize the rabbit
for the production of ProSAAS antibody; C) same experiment but with the addition of the LEN peptide; D) same experiment but
with the addition of the PENpeptide.
4628 JOURNAL OF PROTEOMICS 75 (2012) 4620 4631
Page 9
the possibility to monitor ISG proteome modulation under
physiological and pathological conditio ns. Notably, type 2
diabetes resulting in part from a default in ß-cell insuli n
secretion, possibly through to the modification of the ISG
proteome [48]. It would be therefore essential to unravel the
precise mechanisms affected during ISG biogenesis and exocy-
tosis in this pathology. In this view, ProSAAS, a protein found to
be more abundant in the mature ISG fraction has been described
for the first time in mice brain [49] and found in ß-cells in
previous studies [6,28,29]. Using immunofluorescence and
electron microscopy, we demonstrated a partial but clear co-
localization with ISGs. ProSAAS represents a target of choice due
to its potentia l role in prohormone processing in neuronal cells.
Indeed,itwasshownthatitisabletospecificallyinhibitin vitro
PC1, which is one of the proteins responsible for the processing
of proinsulin [8,9]. PC1 being able to auto-activa te through a
cleavage of its own inhib itory C-term part [25], ProSAAS could be
an inhibitor of this auto-activation avoiding an early activation
Fig. 4 Colocalization study of ProSAAS and insulin using immuno-electron microscopy.INS-1E cells were prepared and
analyzed by electron microscopy as described in Section 2. A) Immuno-labelling of ProSAAS. Secondary antibody was
conjugated to 10 nm gold particules. Some immunoreactive ProSAAS dots are represented by black arrows; B) Double
immuno-labeling for insulin (10 nm gold particules) and ProSAAS (15 nm gold particules represented by black arrows).
4629JOURNAL OF PROTEOMICS 75 (2012) 4620 4631
Page 10
of PC1 in the Golgi. During ISG maturation, the decreased pH
becomes favorable to the auto-activation of PC1, and therefore
insulin processing. ProSAAS may be involved in the regulation
of this process. For this reason, we preliminarily assessed
ProSAAS transcript levels at low, medium and high concentra-
tions of glucose for 24 h and showed that the increase of glucose
concentration leads to a decrease of ProSAAS mRNA expression.
Considering ProSAAS role in neurons, this decrease would have
an effect on proinsulin processing in ß-cells. Actually, a
corresponding decrease at the protein level might be related to
a reduced inhibition of PC1 auto-activation. This early auto-
activation of PC1, would lead to its degradation as it is instable
at the neutral pH observed at the TGN. It would then cause a
decrease of PC1 available in ISG for insulin processing, explain-
ing the decrease of in sulin available for secretion.
5. Conclusions
We conducted here a powerful proteomic study to decipher ß-
cell ISG proteomes from INS-1E cells. Triple density gradient
purification was combined with SILAC to discriminate poten-
tial protein co-purifying organelles from bona-fide ISG pro-
teins. We obtained 3 categories of ISG proteins: 1/ 140 proteins
enriched in mature ISG, 2/ 447 ISG proteins with multiple
localizations and 3/ 81 immature ISG proteins or contaminant.
We strongly believe that the proper discovery of the identity
and function of these 668 ISG proteins would be of a
considerable help to further understand some of the under-
lying mechanisms implicated in ß-cell dysfunction associated
to type 2 diabetes.
Supplementary materials related to this article can be
found online at
We thank Alexander Scherl (Geneva University Medical
Center) for his help on LTQ-Orbitrap analyses of ISGs. We are
grateful Florence Armand (Proteomics Core Facility of the
EPFL) for her kind help in bioinformatics. This work was
supported by the Swiss National Science Foundation (grant
no. 310030-135727/1), The Fondation Romande pour la
Recherche sur le Diabète and the EFSD Research Programme
in Diabetes and Cancer to MF.
[1] Straub SG, Shanmugam G, Sharp GW. Stimulation of insulin
release by glucose is associated with an increase in the
number of docked granules in the beta-cells of rat pancreatic
islets. Diabetes 2004;53:317983.
[2] Dunn MF. Zinc-ligand interactions modulate assembly and
stability of the insulin hexamer a review. Biometals
[3] Arvan P, Halban PA. Sorting ourselves out: seeking consensus
on trafficking in the beta-cell . Traffic 2004;5:5361.
[4] Blazquez M, Shennan KI. Basic mechanisms of secretion:
sorting into the regulated secretory pathway. Biochem Cell
Biol 2000;78:18191.
[5] Tooze SA. Biogenesis of secretory granules in the trans-Golgi
network of neuroendocrine and endocrine cells. Biochim
Biophys Acta 1998;1404:23144.
[6] Brunner Y, Coute Y, Iezzi M, Foti M, Fukuda M, Hochstrasser
DF, et al. Proteomics analysis of insulin secretory granules.
Mol Cell Proteomics 2007;6:100717.
[7] Colomer V, Kicska GA, Rindler MJ. Secretory granule content
proteins and the luminal domains of granule membrane
proteins aggregate in vitro at mildly acidic pH. J Biol Chem
[8] Orci L, Ravazzola M, Amherdt M, Madsen O, Perrelet A,
Vassalli JD, et al. Conversion of proinsulin to insulin occurs
coordinately with acidification of maturing secretory
vesicles. J Cell Biol 1986;103:227381.
[9] Steiner DF, Park SY, Stoy J, Philipson LH, Bell GI. A brief
perspective on insulin production. Diabetes Obes Metab
2009;11(Suppl. 4):18996.
[10] Klumperman J, Kuliawat R, Griffith JM, Geuze HJ, Arvan P.
Mannose 6-phosphate receptors are sorted from immature
secretory granules via adaptor protein AP-1, clathrin, and
syntaxin 6-positive vesicles. J Cell Biol 1998;141:35971.
[11] Molinete M, Dupuis S, Brodsky FM, Halban PA. Role of clathrin
in the regulated secretory pathway of pancreatic beta-cells.
J Cell Sci 2001;114:305966.
[12] Sollner TH. Regulated exocytosis and SNARE function
(review). Mol Membr Biol 2003;20:209 20.
[13] Tooze SA, Martens GJ, Huttner WB. Secretory granule biogenesis:
rafting to the SNARE. Trends Cell Biol 2001;11:11622.
[14] Chapman ER. Synaptotagmin: a Ca(2+) sensor that triggers
exocytosis? Nat Rev Mol Cell Biol 2002;3:498508.
[15] Seabra MC, Mules EH, Hume AN. Rab GTPases, intracellular
traffic and disease. Trends Mol Med 2002;8:2330.
[16] Hickey AJ, Bradley JW, Skea GL, Middleditch MJ, Buchanan
CM, Phillips AR, et al. Proteins associated with
immunopurified granules from a model pancreatic islet
beta-cell system: proteomic snapshot of an endocrine
secretory granule. J Proteome Res 2009;8:17886.
[17] Suckale J, Solimena M. The insulin secretory granule as a
signaling hub. Trends Endocrinol Metab 2010;21:599609.
Fig. 5 Modulation of ProSAAS mRNA expression in
response to different glucose concentrations. Quantitative
RT-PCR for ProSAAS: INS-1E cells were incubated 24 h with 5,
11 or 30 mM of glucose. INS-1E cells incubated 24 h with
11 mM glucose were used as control. Each condition was
done in triplicate. Statistical analysis was performed with an
unpaired t-test. Glc= glucose.
4630 JOURNAL OF PROTEOMICS 75 (2012) 4620 4631
Page 11
[18] Ong SE, Blagoev B, Kratchmarova I, Kristensen DB, Steen H,
Pandey A, et al. Stable isotope labeling by amino acids in cell
culture, SILAC, as a simple and accurate approach to
expression proteomics. Mol Cell Proteomics 2002;1:37686.
[19] Laemmli UK.Cleavage of structural proteins during the assembly
of the head of bacteriophage T4. Nature 1970;227:6805.
[20] Cox J, Matic I, Hilger M, Nagaraj N, Selbach M, Olsen JV, et al. A
practical guide to the MaxQuant computational platform for
SILAC-based quantitative proteomics. Nat Protoc 2009;4:
[21] Liou W, Geuze HJ, Slot JW. Improving structural integrity of
cryosections for immunogold labeling. Histochem Cell Biol
[22] Itoh Y, Tanaka S, Takekoshi S, Itoh J, Osamura RY.
Prohormone convertases (PC1/3 and PC2) in rat and human
pancreas and islet cell tumors: subcellular
immunohistochemical analysis. Pathol Int 1996;46:72637.
[23] Matsumoto M, Miki T, Shibasaki T, Kawaguchi M, Shinozaki
H, Nio J, et al. Noc2 is essential in normal regulation of
exocytosis in endocrine and exocrine cells. Proc Natl Acad Sci
U S A 2004;101:8313 8.
[24] Schmid GM, Meda P, Caille D, Wargent E, O'Dowd J,
Hochstrasser DF, et al. Inhi bition of insulin secretion by
betagranin, an N-terminal chromogranin A fragment. J Biol
Chem 2007;282:1271724.
[25] Qian Y, Devi LA, Mzhavia N, Munzer S, Seidah NG, Fricker LD.
The C-terminal region of proSAAS is a potent inhibitor of
prohormone convertase 1. J Biol Chem 2000;275:23596601.
[26] Steiner DF. The proprotein convertases. Curr Opin Chem Biol
[27] Brunner Y, Schvartz D, Priego-Capote F, Coute Y, Sanchez JC.
Glucotoxicity and pancreatic proteomics. J Proteomics
[28] Cras-Meneur C, Inoue H, Zhou Y, Ohsugi M, Bernal-Mizrachi
E, Pape D, et al. An expression profile of human pancreatic
islet mRNAs by Serial Analysis of Gene Expression (SAGE).
Diabetologia 2004;47:28499.
[29] Guest PC, Abdel-Halim SM, Gross DJ, Clark A, Poitout V,
Amaria R, et al. Proinsu lin processing in the diabetic
GotoKakizaki rat. J Endocrinol 2002;175:63747.
[30] Waanders LF, Chwalek K, Monetti M, Kumar C, Lammert E,
Mann M. Quantitative proteomic analysis of single pancreatic
islets. Proc Natl Acad Sci U S A 2009;106:189027.
[31] Aebersold R, Mann M. Mass spectrometry-based proteomics.
Nature 2003;422:198207.
[32] Brunet S, Thibault P, Gagnon E, Kearney P, Bergeron JJ,
Desjardins M. Organelle proteomics: looking at less to see
more. Trends Cell Biol 2003;13: 62938.
[33] Brunner Y, Schvartz D, Coute Y, Sanchez JC. Proteomics of
regulated secretory organelles. Mass Spectrom Rev 2009;28:
[34] Andersen JS, Mann M. Organellar proteomics: turning
inventories into insights. EMBO Rep 2006;7:8749.
[35] Dunkley TP, Watson R, Griffin JL, Dupree P, Lilley KS.
Localization of organelle proteins by isotop e tagging (LOPIT).
Mol Cell Proteomics 2004;3:112834.
[36] Andersen JS, Wilkinson CJ, Mayor T, Mortensen P, Nigg EA, Mann
M. Proteomic characterization of the human centrosome by
protein correlation profiling. Nature 2003;426:5704.
[37] Dittie AS, Klumperman J, Tooze SA. Differential distribution
of mannose-6-phosphate receptors and furin in immature
secretory granules. J Cell Sci 1999;112(Pt. 22):395566.
[38] Kowluru A, Seavey SE, Rhodes CJ, Metz SA. A novel regulatory
mechanism for trimeric GTP-binding proteins in the
membrane and secretory granule fractions of human and
rodent beta cells. Biochem J 1996;313(Pt. 1):97107.
[39] Lang J. Molecular mechanisms and regulation of insulin
exocytosis as a paradigm of endocrine secretion. Eur J
Biochem 1999;259:317.
[40] Easom RA. Beta-granule transport and exocytosis. Semin Cell
Dev Biol 2000;11:25366.
[41] Regazzi R, Wollheim CB, Lang J, Theler JM, Rossetto O,
Montecucco C, et al. VAMP-2 and cellubrevin are expressed in
pancreatic beta-cells and are essential for Ca(2+)-but not for GTP
gamma S-induced insulin secretion. EMBO J 1995;14:272330.
[42] Foster LJ, de Hoog CL, Zhang Y, Xie X, Mootha VK, Mann M. A
mammalian organelle map by protein correla tion profiling.
Cell 2006;125:18799.
[43] Fasshauer D, Antonin W, Margittai M, Pabst S, Jahn R. Mixed and
non-cognate SNARE complexes. Characterization of assembly
and biophysical properties. J Biol Chem 1999;274:154406.
[44] Antonin W, Holroyd C, Tikkanen R, Honing S, Jahn R. The
R-SNARE endobrevin/VAMP-8 mediates homotypic fusion of
early endosomes and late endosome s. Mol Biol Cell 2000;11:
[45] Wong SH, Xu Y, Zhang T, Hong W. Syntaxin 7, a novel
syntaxin member associated with the early endosomal
compartment. J Biol Chem 1998;273:37580.
[46] Wong SH, Zhang T, Xu Y, Subramaniam VN, Griffiths G, Hong
W. Endobrevin, a novel synaptobrevin/VAMP-like protein
preferentially associated with the early endosome. Mol Biol
Cell 1998;9:154963.
[47] Kuliawat R, Klumperman J, Ludwig T, Arvan P. Differential
sorting of lysosomal enzymes out of the regulated secretory
pathway in pancreatic beta-cells. J Cell Biol 1997;137:595608.
[48] Poitout V, Robertson RP. Glucolipotoxicity: fuel excess and
beta-cell dysfunction. Endocr Rev 2008;29:35166.
[49] Fricker LD, McKinzie AA, Sun J, Curran E, Qian Y, Yan L, et al.
Identification and characterization of proSAAS, a granin-like
neuroendocrine peptide precursor that inhibits prohormone
processing. J Neurosci 2000;20:63948.
4631JOURNAL OF PROTEOMICS 75 (2012) 4620 4631
Page 12
  • Source
    • "In the first work, they only enriched total ISGs, without further distinguishing between iISGs and mISGs (Brunner et al. 2007). In the second study, they used a three-step gradient purification procedure to obtain both iISGs and mISGs; however, the properties and purity of the final fractions were not well characterized (Schvartz et al. 2012). Here, we developed a simpler enrichment method and achieved better separation, as verified by many specific markers. "
    [Show abstract] [Hide abstract] ABSTRACT: Insulin is one of the key regulators for blood glucose homeostasis. More than 99% of insulin is secreted from the pancreatic β-cells. Within each β-cell, insulin is packaged and processed in insulin secretary granules (ISGs) before its exocytosis. Insulin secretion is a complicated but well-organized dynamic process that includes the budding of immature ISGs (iISGs) from the trans-Golgi network, iISG maturation, and mature ISG (mISG) fusion with plasma membrane. However, the molecular mechanisms involved in this process are largely unknown. It is therefore crucial to separate and enrich iISGs and mISGs before determining their distinct characteristics and protein contents. Here, we developed an efficient two-step subcellular fractionation method for the enrichment of iISGs and mISGs from INS-1 cells: OptiPrep gradient purification followed by Percoll solution purification. We demonstrated that by using this method, iISGs and mISGs can be successfully distinguished and enriched. This method can be easily adapted to investigate SGs in other cells or tissues, thereby providing a useful tool for elucidating the mechanisms of granule maturation and secretion.
    Full-text · Article · Aug 2015
  • Source
    • "The strength of our study resides in the use of label-free proteome quantification in combination with a careful normalization strategy, to allow a direct exploration of (dis)similarities in protein abundance between human and rat beta cells. As recently reviewed [24], both descriptive and quantitative proteomic techniques have been used to identify beta cellselective markers, to characterize the beta cell's adaptation to diabetogenic stress conditions or physiological stimuli, and to characterize the beta cell's secretome [25]. Data independent alternate-scanning LC-MS/MS achieves reasonably accurate quantification, based on the observation that the average MS signal response for the three most intense tryptic peptides per mole of protein is constant within a coefficient of variation of less than +/−10% [14, 15]. "
    [Show abstract] [Hide abstract] ABSTRACT: The core proteomes of human and rat pancreatic beta cells were compared by label-free LC-MS/MS: this resulted in quantification of relative molar abundances of 707 proteins belonging to functional pathways of intermediary metabolism, protein synthesis, and cytoskeleton. Relative molar abundances were conserved both within and between pathways enabling the selection of a housekeeping network for geometric normalization and the analysis of potentially relevant differential expressions. Human beta cells differed from rat beta cells in their lower level of enzymes involved in glucose sensing (MDH1, PC, and ACLY) and upregulation of lysosomal enzymes. Human cells also expressed more heat shock proteins and radical scavenging systems: apart from SOD2, they expressed high levels of H2O2-scavenger peroxiredoxin 3 (PRDX3), confirmed by microarray, Western blotting, and microscopy. Besides conferring lower susceptibility to oxidative stress to human cells PRDX3 might also play a role in physiological redox regulation as, in rat, its expression was restricted to a beta cell subset with higher metabolic glucose responsiveness. In conclusion, although their core proteomic architecture is conserved, human and rat beta cells differ in their molar expression of key enzymes involved in glucose sensing and redox control.
    Full-text · Article · Jun 2015 · Journal of Diabetes Research
  • Source
    • "We previously applied this strategy based on label-free LC-MS/MS analysis of unfractionated proteomes of FACS-purified beta cells [8], leading to the description of doublecortin (DCX) [9] and protein phosphatase inhibitor-1 (PPP1R1A) [10] that could be validated respectively in vitro and in vivo in rats and humans, using insensitive immunoprecipation methods. Here we iterated this same quantitative LC-MS/MS analysis on FACS-enriched human beta cell preparations, leading to the prioritization of additional proteins: GDI1, RAB3B, GNAO1, GNAI2 and UCHL1 were all reproducibly detected at relatively high molar abundance, showed a degree of beta cell-selectivity according to the Human Protein Atlas data repository [23] and are non-physiologically secreted [22]. From these, UCHL1 was chosen for further study as it showed theFig. "
    [Show abstract] [Hide abstract] ABSTRACT: Unlabelled: There is a clinical need for plasma tests for real-time detection of beta cell destruction, as surrogate endpoint in islet transplantation and immunoprevention trials in type 1 diabetes. This study reports on the use of label-free LC-MS/MS proteomics for bottom-up selection of candidate biomarkers. Ubiquitin COOH-terminal hydrolase 1 (UCHL1) was identified as abundant protein in rat and human beta cells, showing promising beta cell-selectivity, and was selected for further validation in standardized toxicity models. In vitro, H2O2-induced necrosis of INS-1 cells and human islets resulted in intracellular UCHL1 depletion and its extracellular discharge. In vivo, streptozotocin progressively depleted UCHL1 from islet cores and in 50% of animals, an associated plasma UCHL1 surge was detected preceding the GAD65 peak. UCHL1 was cleared with a half-life of 20min. Whole-body dynamic planar imaging of (99m)-Technetium-labeled UCHL1 indicated a rapid UCHL1 uptake in the liver and spleen, followed by urinary excretion of mainly proteolytic UCHL1 fragments. We conclude that LC-MS/MS proteomics is a useful tool to prioritize biomarkers for beta cell injury with promising molar abundance. Despite its consistent UCHL1 discharge by damaged beta cells in vitro, its in vivo use might be restrained by its rapid elimination from plasma. Biological significance: Our bottom-up LC-MS/MS proteomics represents a pragmatic approach to identify protein-type biomarkers of pancreatic beta cell injury. UCHL1 successfully passed sequential validation steps of beta cell-selectivity, antigenicity and toxic discharge in vitro. Whole-body dynamic planar imaging of radiolabeled recombinant UCHL1 indicated rapid clearance through the liver, spleen and urinary excretion of proteolytic fragments, likely explaining non-consistent detection in vivo. Integration of kinetic biomarker clearance studies in the a priori selection criteria is recommended before engaging in resource-intensive custom development of sensitive immunoassays for clinical translation.
    Full-text · Article · Jan 2015 · Journal of Proteomics
Show more