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\begin{document}

\title{Effect of treatment with GLP-1R agonists on the urinary peptidome of
T2DM patients}



\author[1]{Sonnal Lohia}%
\author[2]{Justyna Siwy}%
\author[3]{Emmanouil Mavrogeorgis}%
\author[4]{Susanne  Eder}%
\author[4]{Stephanie Thoeni}%
\author[4]{Gert Mayer}%
\author[2]{Harald Mischak}%
\author[1]{Antonia Vlahou}%
\author[3]{Vera Jankowski}%
\affil[1]{Biomedical Research Foundation of the Academy of Athens}%
\affil[2]{Mosaiques-diagnostics}%
\affil[3]{RWTH Aachen University}%
\affil[4]{Medical University of Innsbruck}%


\vspace{-1em}



  
  \date{June 12, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Type II diabetes mellitus (T2DM) accounts for approximately 90\% of all
diabetes mellitus cases in the world. Glucagon-like peptide-1 receptor
(GLP-1R) agonists have established an increased capability to target
directly or indirectly six core defects associated with T2DM, while, the
underlying molecular mechanisms of these pharmacological effects are not
fully known. This exploratory study was conducted to analyze the effect
of treatment with GLP-1R agonists on urinary peptidome of T2DM patients.
Urine samples of thirty-two T2DM patients from the PROVALID study (A
Prospective Cohort Study in Patients with T2DM for Validation of
Biomarkers) collected at pre- and post-treatment with GLP-1R agonist
drugs were analyzed by CE-MS. In total, 70 urinary peptides were
significantly affected by GLP-1R agonist treatment; generating from 26
different proteins. The downregulation of MMP proteases, based on the
concordant downregulation of urinary collagen peptides was highlighted.
Treatment also resulted in downregulation of peptides from SERPINA1,
APOC3, CD99, CPSF6, CRNN, SERPINA6, HBA2, MB, VGF, PIGR and TTR, many of
which were previously found to be associated with increased insulin
resistance and inflammation. The findings indicate potential molecular
mechanisms of GLP-1R agonists in the context of management of T2DM and
prevention or delaying the progression of its associated diseases.%
\end{abstract}%



\sloppy


\emph{Effect of treatment with GLP-1R agonists on the urinary peptidome
of T2DM patients}

Sonnal Lohia\textsuperscript{1,2}, Justyna Siwy\textsuperscript{3},
Emmanouil Mavrogeorgis\textsuperscript{2,3}, Susanne
Eder\textsuperscript{4}, Stephanie Thoeni\textsuperscript{4}, Gert
Mayer\textsuperscript{4}, Harald Mischak\textsuperscript{3}, Antonia
Vlahou\textsuperscript{1} and Vera Jankowski\textsuperscript{2*}

\textsuperscript{1}Center of Systems Biology, Biomedical Research
Foundation of the Academy of Athens, 11527 Athens, Greece;
slohia@bioacademy.gr (S.L.); vlahoua@bioacademy.gr (A.V.)

\textsuperscript{2}Institute for Molecular Cardiovascular Research, RWTH
Aachen University Hospital, 52074 Aachen, Germany;
mavrogeorgis@mosaiques.de (E.M.); vjankowski@ukaachen.de (V.J.)

\textsuperscript{3}Mosaiques Diagnostics GmbH, 30659 Hannover, Germany;
siwy@mosaiques-diagnostics.com (J.S.); mischak@mosaiques.de (H.M.)

\textsuperscript{4}Department of Internal Medicine IV (Nephrology and
Hypertension), Medical University Innsbruck, 6020 Innsbruck, Austria;
susanne.eder@i-med.ac.at (S.E.); stefanie.thoeni@i-med.ac.at (S.T.);
gert.mayer@i-med.ac.at (G.M.)

*Correspondence: vjankowski@ukaachen.de; Tel.: +49-(0241)-80-80580; Fax:
+49-0241

\emph{Abbreviations:}

ADAMTS5, A disintegrin and metalloproteinase with thrombospondin motifs
5; APOC3, apolipoprotein C-III; AUC, area under the curve; BMI, body
mass index; CD99, CD99 antigen; CE-MS, capillary electrophoresis coupled
to mass spectrometry; COL1A1, collagen alpha-1(I) chain; COL1A2,
collagen alpha-2(I) chain; COL3A1, collagen alpha-1(III) chain; CRNN,
Cornulin; CPSF6, cleavage and polyadenylation specificity factor subunit
6; eGFR, estimated glomerular filtration rate; FDR, false discovery
rates; GLP-1R, Glucagon-like peptide-1 receptor; HbA1C, Hemoglobin A1C;
HBA, hemoglobin subunit alpha; KEGG, Kyoto Encyclopedia of Genes and
Genomes; MB, myoglobin; MMP, matrix metalloproteinase proteases; PIGR,
polymeric immunoglobulin receptor; PROVALID, Prospective Cohort Study in
Patients with T2DM for Validation of Biomarkers study; SBP/DBP,
systolic/diastolic blood pressure SERPINA1, alpha-1-antitrypsin;
SERPINA6, corticosteroid-binding globulin; T2DM, Type II diabetes
mellitus; TTR, transthyretin; VGF, neurosecretory protein VGF

\emph{Keywords}\textbf{:} CE-MS, GLP-1R agonists, peptidomics, T2DM,
urine biomarker

\emph{Abstract word count: 200 words}

\emph{Total number of words: 6340 words}

\emph{Abstract}\textbf{:}

Type II diabetes mellitus (T2DM) accounts for approximately 90\% of all
diabetes mellitus cases in the world. Glucagon-like peptide-1 receptor
(GLP-1R) agonists have established an increased capability to target
directly or indirectly six core defects associated with T2DM, while, the
underlying molecular mechanisms of these pharmacological effects are not
fully known. This exploratory study was conducted to analyze the effect
of treatment with GLP-1R agonists on urinary peptidome of T2DM patients.
Urine samples of thirty-two T2DM patients from the PROVALID study (A
Prospective Cohort Study in Patients with T2DM for Validation of
Biomarkers) collected at pre- and post-treatment with GLP-1R agonist
drugs were analyzed by CE-MS. In total, 70 urinary peptides were
significantly affected by GLP-1R agonist treatment; generating from 26
different proteins. The downregulation of MMP proteases, based on the
concordant downregulation of urinary collagen peptides was highlighted.
Treatment also resulted in downregulation of peptides from SERPINA1,
APOC3, CD99, CPSF6, CRNN, SERPINA6, HBA2, MB, VGF, PIGR and TTR, many of
which were previously found to be associated with increased insulin
resistance and inflammation. The findings indicate potential molecular
mechanisms of GLP-1R agonists in the context of management of T2DM and
prevention or delaying the progression of its associated diseases.

\emph{Statement of significance}\textbf{:}

In 2021, the International Diabetes Federation (IDF) predicted a
five-fold increase in the worldwide adult population of diabetes
mellitus to 783 million in 2045; of which type II diabetes mellitus
(T2DM) is said to account for approximately 90\% of all cases. T2DM is
characterized as a chronic disease and is diagnosed with increased
levels of blood glucose or hyperglycemia. Since no cure exists, efforts
to control and treat T2DM have escalated. Treatment with glucagon-like
peptide-1 receptor (GLP-1R) agonists have dramatically transformed
patient care guidelines for T2DM. In comparison to the other
anti-hyperglycemic medications, GLP-1R agonists have established an
increased capability to target directly or indirectly six out of the
eight core defects associated with T2DM, also portraying beneficial
cardiovascular and renal outcomes. The focus of research thus needs to
shift from investigating the pharmacological effects of GLP-1R agonists,
to understanding the underlying molecular mechanisms. In this
exploratory study, we aimed to assess the effect of treatment with
GLP-1R agonists on the urinary peptidome of T2DM patients, in an
untargeted peptidomics approach. The urinary peptides identified
indicate potential effect of GLP-1R agonists on reduction of insulin
resistance and inflammation in the context of prevention, delaying the
progression and management of T2DM.

\emph{Introduction} \textbf{}

In 2021, the International Diabetes Federation predicted a five-fold
increase in the worldwide adult population of diabetes mellitus, from
150 million in 2000{[}1{]} to 783 million in 2045{[}2{]}; of which type
II diabetes mellitus (T2DM) is said to account for approximately 90\% of
all cases{[}3{]}. T2DM is characterized as a chronic disease{[}4{]} and
is diagnosed with increased levels of blood glucose or
hyperglycemia{[}5{]}. T2DM has been associated with macrovascular
complications, such as atherosclerosis, myocardial infarction and
diabetic foot syndrome; as well as microvascular complications, such as
neuropathy, retinopathy, and nephropathy{[}6-8{]}. Approximately 44\% of
all T2DM cases have been linked to obesity, correlating an exponential
increase in their worldwide prevalence{[}9, 10{]}. Additionally, the
World Health Organization predicts 57.8\% of the world population to be
overweight and obese by 2030{[}11{]}.

Since no cure exists, efforts to control and treat T2DM have
escalated{[}12{]}. The American Diabetes Association recommends healthy
lifestyle changes as the first-in-line response, followed by
metformin-based pharmaceutical interventions{[}13{]}. The approach of
lowering glucose plasma levels remains the most sought-after treatment
and has recently witnessed novel and effective advances. These alongside
metformin, commonly include treatment with glucagon-like peptide-1
receptor (GLP-1R) agonists, sodium-glucose co-transporter-2 (SGLT-2)
inhibitors, dipeptidyl peptidase-4 (DPP-4) inhibitors and insulin pumps
followed by continuous monitoring of glucose levels{[}8{]}. Furthermore,
research focusing on treatment paradigms targeting the `ominous octet'
of T2DM, implicated in the development and progression of hyperglycemia,
has gained momentum in the last couple of decades.

The GLP-1R agonists have dramatically transformed patient care
guidelines for T2DM{[}14{]}. In comparison to the other
antihyperglycemic medications, GLP-1R agonists have established an
increased capability to target directly or indirectly six out of the
eight core defects (`ominous octet') associated with T2DM{[}15-17{]}. In
response to food intake, the intestinal L-cells dependent secretion of
GLP-1 is significantly impaired in T2DM patients, which affects the
pancreatic \selectlanguage{greek}β \selectlanguage{english}cells dependent insulin secretion; eventually resulting in
hyperglycemia{[}18-20{]}. The synthetically produced GLP-1R agonists
stimulate GLP-1R, in a similar fashion as the native GLP-1. Treatment
benefits of peptide-based GLP-1R agonists include glycemic control,
weight loss, delayed onset of macroalbuminuria, prevention of clinical
hyperglycemic episodes and cardiovascular events, and reduced estimated
glomerular filtration rate (eGFR) decline; that in turn indirectly
result in the inhibition of glucagon secretion and increased secretion
of insulin with minimal hypoglycemic risks{[}8, 21-23{]}.

Since their first approval in 2005, seven GLP-1R agonists drugs have
been developed for glycemic control in T2DM patients. In 2021,
Trujillo\emph{et al.} , described fourteen head-to-head trials conducted
on these seven GLP-1R agonists, the results of which continue to
establish the benefits of this class of drugs in treatment of T2DM
patients. The authors further highlighted studies that have published
beneficial cardiovascular and renal outcomes of the treatment; however,
the results, in terms of the magnitude for glycemic control and adverse
effects are not consistent{[}24{]}. The focus of research thus needs to
shift from investigating the pharmacological effects of GLP-1R agonists,
to understanding the underlying molecular mechanisms.

Recent advances in the analyses of urinary peptidome, have paved way for
the detection of alterations at both pathological as well as
physiological levels in chronic diseases{[}25{]}. Urine-based omics
studies have especially garnered relevance, due to the ease of sample
collection, longer stability of the peptides, complex composition (from
blood, kidney, and bladder) and the possibility of larger cohort studies
because of its non-invasive approach. In addition, the water soluble and
charged nature of urinary peptides enable their uncomplicated mass
spectrometric (MS) based detections{[}26{]}. The urinary peptidomic
analysis in this study was performed by a capillary electrophoresis
coupled to mass spectrometry (CE-MS) technique, which supports the
identification of naturally occurring urinary peptides and peptidomic
changes in response to drug interventions{[}27{]}. Therefore, in this
exploratory study, we aimed to assess the effect of treatment with
GLP-1R agonists on the urinary peptidome of T2DM patients, in an
untargeted peptidomics approach.

\emph{Materials and Methods}

Study population and sample collection

Used were urine samples of thirty-two T2DM patients, administering
anti-hypertensive medications at the primary health care level,
recruited within the Prospective Cohort Study in Patients with T2DM for
Validation of Biomarkers (PROVALID) study. PROVALID is an observational,
prospective cohort study in five European countries with detailed
information on inclusion and exclusion criteria of the patient selection
previously provided{[}28{]}. The study was approved by the Ethics
Committee of the Medical University of Innsbruck (Nr. 1188/2020).
Consent was obtained from all the patients. Urine samples were collected
from the patients at their visit just before GLP-1R agonists
prescription and labelled as pre-treatment samples. The urine samples
collected at the first visit after the treatment initiation were
labelled as post-treatment samples. All the sixty-four urine samples
were stored at -20\selectlanguage{ngerman}°C until peptidomic analysis.

Sample preparation and CE-MS analysis

The standard operating protocols describing urine sample preparation and
extraction of peptides followed in this study, have been applied in
numerous studies as reviewed previously {[}29, 30{]}. The CE-MS analysis
was conducted as described in detail by Zurbig \emph{et al.} {[}31{]},
utilizing a P/ACE MDQ capillary electrophoresis system (Beckman Coulter,
Fullerton, CA, USA) coupled with a Micro-TOF II MS (Bruker Daltonic,
Bremen, Germany) instrument. Literary evidence on the advantages of
CE-MS analysis in terms of reproducibility, sensitivity, precision and
accuracy are extensively available{[}32{]}. The relative peptide
intensities were normalized based on an internal standard of 29 stable
collagen peptides, that can be detected naturally in urine samples of
healthy and diseased. This calibration was performed for normalizing the
variability in peptide intensities{[}33{]}. The resulting peptides and
their normalized intensity values were stored in an internal Microsoft
SQL database{[}34{]}, which enabled the comparison of the pre- and
post-treatment urinary peptide profiles. For identification of the
peptide sequences, MS/MS based analysis by a Dionex Ultimate 3000 RSLS
nanoflow system (Dionex, Camberley, UK) or a Beckman P/ACE MDQ CE that
was coupled to an Orbitrap Velos MS instrument (Thermo Fisher Scientific
Inc., Boston, MA, USA) was performed, as described previously{[}35{]}.

Statistical analysis

All statistical analyses in this study were performed using R
programming (R version 4.2.2, R Foundation for Statistical Computing,
Vienna, Austria with IDE: R Studio Version 1.2.5, Boston, MA, USA). As a
pre-requisite for the analysis, thresholds of 30\% (i.e., \selectlanguage{english}[?] 10 out of 32
datasets) peptide frequency (in at least one treatment group) were
applied. Alongside, area under the receiver operating curve (ROC) curve
(AUC) values were calculated by the DeLong approach, to compare the
urinary peptide profiles between the pre- and post-treated samples; the
selected urinary peptides passed a threshold of AUC \selectlanguage{english}[?] 0.60. The normally
distributed and continuous datasets generated from CE-MS based peptide
profiles of the urine samples, obtained from pre-treatment (\emph{n}
=32) and post-treatment (\emph{n} =32); were compared by a paired
Wilcoxon rank-sum test, using the row\_wilcoxon\_paired() function from
the matrixTests package. A \emph{p} -value \textless{} 0.05 considered
statistically significant, was further adjusted for false discovery
rates (FDR) by the Benjamini-Hochberg method{[}36{]}. All the plots in
this manuscript were created using the ggplot() function from the
ggplot2 package{[}37{]}.

Bioinformatic analysis

Bioinformatics analysis was employed to place the findings of urinary
peptidomics within the biological context. Given that changes in peptide
levels might be indicative of alterations in protease activity, the
proteolytic events responsible for the secretion of the statistically
significant GLP-1R agonists based urinary peptides were investigated by
the open-source online tool ``Proteasix'' (http://www.proteasix.org,
accessed on 6 March 2023){[}38{]}. In brief, Proteasix retrieves
information from literature and databases like MEROPs, UniProt
Knoweldgebase (KB) and CutDB. The ``Observed Prediction tool'' of
Proteasix with search parameters set to default, was utilized in this
study. In addition, the relevant parental proteins corresponding to the
statistically significant urinary peptides were subjected to network and
pathway enrichment analysis utilizing the default settings of the online
STRING database{[}39, 40{]} (http://www.string-db.org, accessed on 15
May 2023). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway
database, which contains pathway maps representing the current
understanding of molecular interaction and reaction networks, was
utilized for enrichment analysis. Default settings were used, and
pathways with a predicted significance of \emph{p-value} \textless{}
0.05 (FDR) were considered statistically significant.

\emph{Results} \textbf{}

Study design

The exploratory and untargeted urinary peptidomic approach in this
study, was conducted to analyze the effect of GLP-1R agonist treatment
on the urinary peptidome of T2DM patients, as illustrated in Figure 1.
Briefly, urine samples from thirty-two T2DM patients collected at two
time points: pre-treatment and post-treatment, with a time interval of
11.5 +- 3.72 months in between were analyzed. Intervention with GLP-1R
agonist was introduced at 4.4 +- 4.11 months from pre-treatment visit and
the treatment duration until the post-treatment visit was 6.9 +- 3.59
months.

Clinical information

The clinical information of all the thirty-two T2DM patients
participating in this study, from pre- and post-treatment visits with
GLP-1R agonists are provided in the Table 1. The mean age for the
patients at first sample collection was 63.7 +- 7.25 years and 56.3\% of
them were females. As depicted in the Table 1, no statistically
significant difference between the clinical levels of Hemoglobin A1C
(HbA1C), body weight, body mass index (BMI), systolic and diastolic
blood pressure (SBP/DBP), eGFR, albuminuria to creatinine ratio and
urinary creatinine was observed after the treatment with GLP-1R
agonists.

Peptidomic analysis

This exploratory urinary peptidomic analysis resulted in a list of 329
sequenced peptides to be further investigated. Statistical assessment
including Benjamini-Hochberg FDR adjustment yielded a list of 70
statistically significant peptides (adjusted \emph{p} -value \textless{}
0.05) affected by the GLP-1R agonist treatment, detailed information of
the peptide identifications is provided in Table S1. The distribution of
peptide intensity of these 70 statistically significant peptides (red
spots) amongst the 329 peptides was also examined (Figure 2A). Volcano
plot assessing the regulation of all the 329 peptides in response to the
treatment (Figure 2B), highlighted the downregulation of urinary
peptides in majority of the statistically significant peptides (66 out
of 70 peptides), the vast majority in downregulation could also be
visualized on comparison of the urinary peptide profiles from
pre-treatment (Figure 2C) and post-treatment (Figure 2D), as obtained
from the CE-MS analysis.

The 70 statistically significant peptides were identified as fragments
of 26 proteins, as shown in Table 2. Most of the peptides (59 out of 70)
originated from the collagen family of proteins and 41 out of the 59
collagen peptides belonged in majority to three collagen proteins,
namely, collagen alpha-1(III) chain (P02461; COL3A1; \emph{n} =16),
collagen alpha-1(I) chain (P02452; COL1A1; \emph{n} =15) and collagen
alpha-2(I) chain (P08123; COL1A2; \emph{n} =10). All the remaining
identified 11 (out of 70) urinary peptides came from different proteins,
alpha-1-antitrypsin (P01009; SERPINA1), apolipoprotein C-III (P02656;
APOC3), CD99 antigen (P14209; CD99), cleavage and polyadenylation
specificity factor subunit 6 (Q16630; CPSF6), Cornulin (Q9UBG3; CRNN),
corticosteroid-binding globulin (P08185; SERPINA6), hemoglobin subunit
alpha (P69905; HBA1; HBA2), myoglobin (P02144; MB), neurosecretory
protein VGF (O15240; VGF), polymeric immunoglobulin receptor (P01833;
PIGR) and transthyretin (P02766; TTR). Four out of the 70 urinary
peptides showing a statistically significant upregulation on treatment
with GLP-1R agonists in T2DM patients, as observed in Figure 2B, were
characterized as COL3A1 (\emph{n} =3) and COL1A2 (\emph{n} =1) peptides
(Table 2).

Since several peptides from COL1A1 and COL3A1 were found significantly
associated with treatment (Figure 2E and 2F, respectively), we
investigated the alignment of the peptides in their protein structures.
The identified peptide sequences were aligned with the primary structure
of the proteins, as shown in Figure 2G and Figure 2H, respectively for
COL1A1 and COL3A1. For both proteins, peptides appeared equally
distributed and no specific hot spot became apparent.

Bioinformatic analysis

To uncover plausible molecular mechanisms responsible for the observed
impact of GLP-1R agonists treatment on urinary peptides in T2DM
patients, the proteases potentially responsible for cleavage of the 70
statistically significant peptides were investigated using Proteasix. In
total, 10 endopeptidases were retrieved as a result of the default
search with the ``Observed Prediction tool'' of Proteasix, putatively
responsible for cleaving 38 urinary peptides (36 downregulated and 2
upregulated) out of the 70 urinary peptides. The results are provided in
Table S2. Most of the predicted endopeptidases belonged to the matrix
metalloproteinase (MMP) family of proteases (7 out of 10 proteases),
responsible for cleaving peptides at both the N' and C' terminals.
Further proteases predicted as potentially responsible for cleaving the
N' terminal belonged to the cathepsin family (CTSL and CTSD) while that
cleaving at the C' terminus was A disintegrin and metalloproteinase with
thrombospondin motifs 5 (ADAMTS5). Notably, the proteases MMP2, MMP9 and
MMP13 were mapped to at least 6 cleavage sites each.

The protein-protein interactome was constructed using 26 parental
proteins identified from 70 urinary GLP-1R agonist-associated peptides,
using the STRING database. The network consisted of 27 nodes and 113
edges, as depicted in Figure 2I. The protein-protein interaction
enrichment yielded a significant \emph{p-value} \textless{}
1.0e\textsuperscript{--16}. While, most of the collagen proteins can be
observed to interact with all the other collagen proteins, interestingly
none of these interacted with the non-collagen proteins. Furthermore, a
protein-protein interaction network within the non-collagen proteins
could also be observed, indicating their involvement in the
pathophysiology of T2DM. Within this network, a total of 9 KEGG pathways
were predicted to be significantly enriched, including pathways related
to protein digestion and absorption, ECM-receptor interaction, AGE-RAGE
signaling pathway in diabetic complications, amoebiasis, relaxin
signaling, focal adhesion, human papillomavirus infection, PI3K-Akt
signaling pathway, small cell lung cancer and platelet activation,
detailed results are provided in Table S3.

\emph{Discussion} \textbf{}

In the last decade, GLP-1R agonists have been the recommended and
preferred second line treatment for T2DM patients. Despite the various
advantages of GLP-1R agonists over other anti-hyperglycemic drugs, the
underlying molecular mechanisms of treatment with GLP-1R agonists have
not been studied in-depth. Aiming to understand the effect of GLP-1R
agonist treatment on T2DM patients, the urinary peptidome of thirty-two
T2DM patients were analyzed for the first time in this study with CE-MS.
The untargeted peptidomic analysis coupled with statistical tools
identified 70 statistically significant (adjusted for multiple testing)
urinary peptide fragments in abundance between the pre- and
post-treatment samples. These urinary peptides generated from 26
parental proteins. Uniform distribution of intensity of the 70 peptides
(red spots in Figure 2A), emphasized that their observed significant
change with GLP-1R agonist treatment was not a function of abundance in
the urine samples. For most of these 70 peptides, a comparative analysis
further revealed a combined downregulation with GLP-1R agonist treatment
(66/70 peptides).

In total, 59 out of the 70 statistically significant urinary peptides,
majorly generated from three prominent collagen proteins COL3A1
(\emph{n} =16), COL1A1 (\emph{n} =15) and COL1A2 (\emph{n} =10).
Recently, He \emph{et al.} ,{[}41{]} reported about the high abundance
of collagen peptides observed in urine samples, as a result of proline
hydroxylation which plausibly inhibits its reabsorption in the kidney.
55 out of the 59 collagen peptides were observed to be significantly
downregulated with GLP-1R agonists treatment in the T2DM urinary
proteome, while 4 peptides (COL3A1;\emph{n} =3 and COL1A2; \emph{n} =1)
showed a significant upregulation on treatment which could plausibly be
attributed to the variation in the post-translational modification by
hydroxylation of proline residues in the peptides and varied proteolytic
cleavage. In-line with the extensive recent report by Mavrogeorgis
\emph{et al.} ,{[}42{]} all the urinary collagen peptides identified in
this study were devoid of the signal peptide, N-terminal pro-peptide and
C-terminal pro-peptide; corresponding only to the mature protein region
(Figure 2G and 2H). The observed downregulation of the collagen peptides
in our study could potentially represent rather an attenuated
degradation of the mature collagen protein, instead of resulting from
protein synthesis or protein assembling processes. Rossing \emph{et
al.{[}43{]}} and Genovese \emph{et al.} ,{[}44{]} have earlier
speculated that the decrease of urinary collagen proteins could
attribute to decreased proteolysis of collagen molecules, resulting from
an increased resistance to proteolytic cleavage or an increased
expression of protease inhibition.

To further corroborate the above speculation, endopeptidases responsible
for putatively cleaving 38 statistically significant collagen peptides
were majorly accounted to the MMP family of proteases (89\% of the
cleavage sites, i.e., 34 out of the 38 urinary peptides) by MMP2
(21.1\%), MMP9 (21.1\%) and MMP13 (15.5\%). The suggested downregulation
in the activity of MMP peptidases as observed by decrease in intensity
of collagen peptides in this study, is in-line with literature.
Down-regulation in expression of MMP9 was observed on treatment with
Liraglutide in a study that included induced-DM rabbit models{[}45{]}.
In an another study, 45\% and 60\% reduction in activity of MMP2 and
MMP9, respectively, in addition to 60\% reduced COL1A1 levels, was
observed in a male C57BL/6 mice on treatment with Semaglutide (GLP-1R
agonist type){[}46{]}. Research groups exploring the effect of Exenatide
(GLP-1R agonist type) treatment on tumor necrosis factor-\selectlanguage{greek}α \selectlanguage{english}human
coronary artery smooth muscle cells{[}47{]} and human retinal pigment
epithelium cells{[}48{]}, reported the downregulated expression of MMP2
and MMP9, respectively on treatment. Another study analyzing
atherosclerosis associated biomarkers in T2DM female subjects, reported
decrease in MMP2 and MMP9 levels, with an increase in GLP-1 and GLP-1R
levels{[}49{]}. Interestingly, treatment of Human SW1353 with
Dulaglutide (GLP-1R agonist type){[}50{]} and Fibroblast-like
synoviocytes cell lines with Exenatide{[}51{]}, also resulted in the
downregulation of MMP13 and ADAMTS5 proteases.

On the other hand, collectively, all the non-collagen peptides (11 out
of 70) showed lower abundance after GLP-1R agonist treatment and each
generated from a different protein namely, SERPINA1, APOC3, CD99, CPSF6,
CRNN, SERPINA6, HBA2, MB, VGF, PIGR and TTR. In studies analyzing the
effect of Liraglutide (GLP-1R agonist type) on T2DM patients,
Rafiullah\emph{et al.} ,{[}52{]} reported the downregulation of the
urinary protein SERPINA1 and Adiels \emph{et al.} ,{[}53{]} reported
decreased secretion of APOC3, respectively with treatment. No literature
was found reporting regulation of the other non-collagen peptides by
GLP-1R agonist treatment. However, impact of diabetes and/or obesity on
these molecules has been reported in the literature. CD99 transcripts
have been stated to up-regulate in T2DM profiles{[}54{]} and Pasello
\emph{et al.} ,{[}55{]} reported the proteins involvement in biological
processes such as cell death and inflammation. CPSF6 indirectly
modulates glucose homeostasis and insulin secretion{[}56{]}; while,
HBA2, a commonly known marker for anemia and \selectlanguage{greek}β-\selectlanguage{english}thalassaemia, interferes
with glycemic markers of T2DM patients{[}57{]}.

Increased levels of TTR have been associated with glucose intolerance,
obesity and decreased pancreatic \selectlanguage{greek}β-\selectlanguage{english}cells percentage in T2DM{[}58, 59{]}.
Along the same lines, increased levels of SERPINA6 have been identified
in obese patients and is reported to play a crucial role in glucose
homeostasis, along with reducing insulin resistance and
inflammation{[}60, 61{]}. Benchoula \emph{et al.} ,{[}62{]} in their
extensive review reported that VGF is expected to induce obesity, while
also playing a role in lipolysis and insulin secretion, hence, acting as
a potential target in T2DM therapy. CRNN is reportedly associated with
the immune system and acts as an inflammation marker in chronic
diseases{[}63, 64{]}. In addition, elevated levels of MB, a known
cardiac marker, were reported in T2DM patients{[}65{]} and has been
associated with insulin resistance, dyslipidemia and abnormal glucose
metabolism with elevated levels acting as a biomarker for diabetic
kidney disease{[}66{]}. Similarly, inflammatory mediators have been
reported to increase PIGR protein levels in the renal tubular cells,
linking its role in renal injury{[}67{]}. As a result of this
exploratory study and literature search, we report the plausible
molecular mechanisms affected by the treatment of GLP-1R agonists on the
pathophysiology of T2DM, as hypothesized from the functions of the
down-regulated non-collagen proteins in Figure 3. The results in this
study may therefore indicate towards the beneficial effect of GLP-1R
agonists in the context of management of T2DM and prevention or delaying
the progression of its associated diseases.

Regardless of the novel findings, this study comes with its own
limitations. Firstly, the large difference in time points between the
pre-treatment and administration of GLP-1R agonists of 4.4 \selectlanguage{ngerman}± 4.11 may
have resulted in unidentified variations of clinical parameters as well
as the composition of urinary peptides, which were not accounted in this
study. Secondly, the administering of multiple anti-hypertensives and
GLP-1R agonist drugs at varied dosages and different combinations to the
T2DM patients may have produced different effects of the treatment,
which were also not analyzed in this study. Thirdly and surprisingly,
within the follow-up we did not observe significant changes in Hb1Ac and
BMI. The study was not powered to detect such changes, which would
require about 10 times the number of subjects to be included, however,
this does help in eliminating the argument that the reported changes in
this study could be a result of weight loss, and instead support the
proteins' role in T2DM pathophysiological mechanisms. Fourthly, since
only one urinary peptide was identified per non-collagen protein, the
reported effects of GLP-1R agonist treatment on these proteins cannot be
definitive and require further experimental studies with increased
power. However, to overcome this short-coming, we additionally,
performed a paired Wilcoxon test on a cohort of thirty-two T2DM patients
administering only the anti-hypertensive drugs and no GLP-1R agonists
drugs from the same PROVALID study, that were matched to the GLP-1R
agonist treated cohort by age, sex, BMI, SBP, DBP and eGFR.
Interestingly, we did not identify any statistically significant urinary
peptides between the paired urine samples collected at a similar time
difference as in this study. Finally, we could identify 15 urinary
peptides downregulated by GLP-1R agonist treatment in our study, whose
elevated levels have been previously reported as markers of heart
failure{[}68{]}. This observation may further indicate the positive
effect of GLP-1R agonist treatment.

To conclude, this untargeted peptidomic analysis to identify the effect
of GLP-1R agonists treatment on the urinary peptidome of T2DM patients,
indicated as a prominent finding the downregulation of MMP proteases, as
identified by the downregulation of urinary collagen peptides on GLP-1R
agonists treatment. Treatment with GLP-1R agonists also resulted in the
decrease of SERPINA1, APOC3, CD99, CPSF6, CRNN, SERPINA6, HBA2, MB, VGF,
PIGR and TTR peptides; indicating a potential benefit as many of these
proteins express increased levels in T2DM patients. The results also
merit the possibility of larger cohort studies to further understand the
impact on underlying molecular mechanisms such as insulin resistance and
inflammation, behind the findings.

\emph{Associated data}

Data will be made available upon request directed to the corresponding
author. Proposals will be reviewed and approved by the investigators and
collaborators based on scientific merit. After approval of a proposal,
data will be shared through a secure online platform.

\emph{Acknowledgments:}

Author S.L. would like to extend deepest gratitude to the members of
Vlahou Lab at BRFAA, Greece and Mosaiques Diagnostics, Germany. Author
V.J. is funded by the 'Deutsche Forschunggemeinschaft' (DFG, German
Research Foundation) through the Transregional Collaborative Research
Centre (TRR 219; Project-ID 322900939), (subproject S-03), (INST
948/4S-1); CRU 5011 project number 445703531. Authors H.M., A.V. and
V.J. are funded by the Cost-Action CA 21165, IZKF Multiorgan complexity
in Friedreich Ataxia, CA201165.

\emph{Conflict of Interest Statement:}

The authors declare no conflict of interest. Author H.M. is the founder
and co-owner of Mosaiques Diagnostics (Hannover, Germany). Authors E.M.
and J.S. are employed by Mosaiques Diagnostics.

\emph{Funding:}

This work was supported by the European Union's Horizon 2020 research
and innovation program by grant No 860329 (Marie-Curie ITN
``STRATEGY-CKD'') and No 848011 (``DC-ren'').

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\emph{Legends to Figures}

\textbf{Figure 1:} \textbf{Study design.} Urine samples from thirty-two
T2DM patients were collected at two time points: pre-treatment and
post-treatment with the intervention of GLP-1R agonists at 4.4 +- 4.11
months from first sample collection. Naturally occurring urinary
peptides were quantified in the urine samples by CE-MS analysis,
followed by statistical and bioinformatic analysis of the CE-MS
generated urinary peptide profiles.

\textbf{Figure 2} : \textbf{Results of the urinary peptidomic
analysis.}(A) Distribution of peptide intensity for all the 329
sequenced urinary peptides identified in this study, red dots indicate
the statistically significant peptides. (B) Volcano plot depicting the
regulation of the 329 peptides in response to GLP-1R agonist treatment.
(C) Urinary peptide profiles of the 70 significant peptides in
pre-treatment, as obtained from CE-MS analysis. (D) Urinary peptide
profiles of the 70 significant peptides in post-treatment, as obtained
from CE-MS analysis. (E) Box and Whisker plots depicting the
down-regulation of all the COL1A1 peptides, in response to GLP-1R
agonists treatment. (F) Box and Whisker plots depicting the up- and
down-regulation of all the COL3A1 peptides, in response to GLP-1R
agonists treatment. (G) Alignment of identified peptide sequences in the
primary structure of protein COL1A1. (H) Alignment of identified peptide
sequences in this study in the primary structure of protein COL3A1. (I)
Protein-protein interaction network including the 26 parental proteins
giving the origin to 70 GLP-1R agonists-associated peptides. \emph{In,
(G) and (H), the amino acids in green and red depict the down-regulated
and up-regulated peptide sequences, respectively.}

\textbf{Figure 3:} \textbf{Hypothesis.} The beneficial effects of GLP-1R
agonists treatment on the different pathophysiological pathways
associated with T2DM as suggested by the role of the down-regulated
non-collagen peptides, respectively in each pathway.

\emph{Tables}

\textbf{Table 1:} Clinical information of the T2DM patients in mean +-
SD; from pre- and post-treatment with GLP-1R agonists\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\emph{Clinical information\textsuperscript{a}} & T2DM patients
(\emph{n}=32) & T2DM patients (\emph{n}=32) &
\emph{p-value}\textsuperscript{b}\tabularnewline
\midrule
\endhead
& Pre-treatment & Post-treatment &\tabularnewline
Age (years) & 63.7 \selectlanguage{ngerman}± 7.25 & 63.7 \selectlanguage{ngerman}± 7.25 &\tabularnewline
Sex (\% Females) & 56.3 & 56.3 &\tabularnewline
HbA1C (\%) & 8.0 \selectlanguage{ngerman}± 1.38 & 8.0 \selectlanguage{ngerman}± 1.45 & 0.731\tabularnewline
BW (kg) & 98.3 \selectlanguage{ngerman}± 16.13 & 98.8 \selectlanguage{ngerman}± 20.28 & 0.771\tabularnewline
BMI (kg/m\textsuperscript{2}) & 33.2 \selectlanguage{ngerman}± 6.28 & 33.2 \selectlanguage{ngerman}± 6.93 &
0.915\tabularnewline
SBP (mmHg) & 139.2 \selectlanguage{ngerman}± 14.35 & 137.1 \selectlanguage{ngerman}± 12.61 & 0.385\tabularnewline
DBP (mmHg) & 81.1 \selectlanguage{ngerman}± 7.74 & 81.3 \selectlanguage{ngerman}± 8.21 & 0.906\tabularnewline
eGFR (ml/min/1.73m\textsuperscript{2}) & 67.7 \selectlanguage{ngerman}± 14.46 & 67.6 \selectlanguage{ngerman}± 15.55 &
0.965\tabularnewline
UACR (mg/g) & 33.6 \selectlanguage{ngerman}± 58.55 & 25.5 \selectlanguage{ngerman}± 44.80 & 0.248\tabularnewline
UCREA (mg/dl) & 103.9 \selectlanguage{ngerman}± 41.58 & 96.6 \selectlanguage{ngerman}± 50.72 & 0.429\tabularnewline
\bottomrule
\end{longtable}

\emph{\textsuperscript{a}HbA1C: Hemoglobin A1C; BW: Body weight; BMI:
Body mass index; SBP: Systolic blood pressure; DBP: Diastolic blood
pressure; eGFR: estimated glomerular filtration rate; UACR: Albuminuria
to Creatinine ratio; UCREA: Urinary Creatinine}

\emph{\textsuperscript{b}p-values were obtained from paired Wilcoxon
rank sum (continuous variables) and Chi-squared test (categorical
variable)}

\textbf{Table 2:} List of 26 proteins yielding the 70 statistically
significant urinary peptides, in response to GLP-1R agonists treatment.
(| refers to down regulation and | refers to upregulation)\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{UniProt ID} & \textbf{Gene Symbol} & \textbf{Protein Name} &
\textbf{Number of peptides} & \textbf{Number of peptides}\tabularnewline
\midrule
\endhead
& & & \emph{Total} & \emph{Regulation after treatment}\tabularnewline
P02461 & COL3A1 & Collagen alpha-1(III) chain & 16 & | (13) + |
(3)\tabularnewline
P02452 & COL1A1 & Collagen alpha-1(I) chain & 15 & |\tabularnewline
P08123 & COL1A2 & Collagen alpha-2(I) chain & 10 & | (9) + |
(1)\tabularnewline
P02458 & COL2A1 & Collagen alpha-1(II) chain & 3 & |\tabularnewline
P02462 & COL4A1 & Collagen alpha-1(IV) chain & 2 & |\tabularnewline
P05997 & COL5A2 & Collagen alpha-2(V) chain & 2 & |\tabularnewline
P12107 & COL11A1 & Collagen alpha-1(XI) chain & 2 & |\tabularnewline
P27658 & COL8A1 & Collagen alpha-1(VIII) chain & 1 & |\tabularnewline
Q5TAT6 & COL13A1 & Collagen alpha-1(XIII) chain & 1 & |\tabularnewline
Q05707 & COL14A1 & Collagen alpha-1(XIV) chain & 1 & |\tabularnewline
Q07092 & COL16A1 & Collagen alpha-1(XVI) chain & 1 & |\tabularnewline
P39060 & COL18A1 & Collagen alpha-1(XVIII) chain & 1 & |\tabularnewline
P25067 & COL8A2 & Collagen alpha-2(VIII) chain & 1 & |\tabularnewline
P29400 & COL4A5 & Collagen alpha-5(IV) chain & 1 & |\tabularnewline
Q14031 & COL4A6 & Collagen alpha-6(IV) chain & 1 & |\tabularnewline
P01009 & SERPINA1 & Alpha-1-antitrypsin & 1 & |\tabularnewline
P02656 & APOC3 & Apolipoprotein C-III & 1 & |\tabularnewline
P14209 & CD99 & CD99 antigen & 1 & |\tabularnewline
Q16630 & CPSF6 & Cleavage and polyadenylation specificity factor subunit
6 & 1 & |\tabularnewline
Q9UBG3 & CRNN & Cornulin & 1 & |\tabularnewline
P08185 & SERPINA6 & Corticosteroid-binding globulin & 1 &
|\tabularnewline
P69905 & HBA1; HBA2 & Hemoglobin subunit alpha & 1 & |\tabularnewline
P02144 & MB & Myoglobin & 1 & |\tabularnewline
O15240 & VGF & Neurosecretory protein VGF & 1 & |\tabularnewline
P01833 & PIGR & Polymeric immunoglobulin receptor & 1 & |\tabularnewline
P02766 & TTR & Transthyretin & 1 & |\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure1/Figure1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure2/Figure2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure3/Figure3}

\end{center}
\end{figure}

\selectlanguage{english}
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\end{document}

