Genome
Identification of potential proteases for abdominal aortic aneurysm by weighted gene coexpression network analysis
Journal: Genome
Manuscript ID gen-2020-0041.R1
Manuscript Type: Article
Date Submitted by the 28-Jun-2020 Author:
Complete List of Authors: Zhang, Hui; Peking Union Medical College Hospital, Department of Vascular Surgery Yang, Dan; Chinese Academy of Medical Sciences and Peking Union Medical College, Department of Computational Biology and Bioinformatics,Draft Institute of Medicinal Plant Development Chen, Siliang; Peking Union Medical College Hospital, Department of Vascular Surgery Li, Fangda; Peking Union Medical College Hospital, Department of Vascular Surgery Cui, Liqiang; Peking Union Medical College Hospital, Department of Vascular Surgery Liu, Zhili; Peking Union Medical College Hospital, Department of Vascular Surgery Shao, Jiang; Peking Union Medical College Hospital, Department of Vascular Surgery Chen, Yuexin; Peking Union Medical College Hospital, Department of Vascular Surgery Liu, Bao; Peking Union Medical College Hospital, Department of Vascular Surgery Zheng, Yuehong; Peking Union Medical College Hospital, Department of Vascular Surgery
Abdominal aortic aneurysm, next-generation sequencing, WGCNA, Keyword: proteases, matrix metalloproteinase
Is the invited manuscript for consideration in a Special Not applicable (regular submission) Issue? :
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1 Identification of potential proteases for abdominal aortic aneurysm by weighted gene
2 coexpression network analysis
3 Short title: WGCNA identifies crucial proteases in AAA
4
5 Hui Zhang1, Dan Yang2, Siliang Chen1, Fangda Li1, Liqiang Cui1, Zhili Liu1, Jiang Shao1, Yuexin
6 Chen1, Bao Liu1, Yuehong Zheng1.
7 1Department of Vascular Surgery, Peking Union Medical College Hospital, Beijing 100730, PR
8 China; 2Department of Computational Biology and Bioinformatics, Institute of Medicinal Plant
9 Development, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing
10 100730, PR China.
11 Correspondence: Draft
12 Yuehong Zheng, MD, Department of Vascular Surgery, Peking Union Medical College Hospital,
13 Shuaifuyuan #1, Dongcheng District, Beijing 100730, China.
14 Phone #: +8613811015811
15 Email: [email protected]
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16 Abstract
17 Proteases are involved in the degradation of the extracellular matrix, which contributes to the
18 formation of abdominal aortic aneurysm (AAA). To identify new disease targets in addition to the
19 results of previous microarray studies, we performed next-generation sequencing (NGS) of the
20 whole transcriptome of Angiotensin II-treated Apo E-/- male mice (n=4) and control mice (n=4) to
21 obtain differentially expressed genes (DEGs). Identified DEGs of proteases were analyzed using
22 weighted gene coexpression network analysis (WGCNA). RT-qPCR was conducted to validate
23 the differential expression of selected hub genes. We found that 43 DEGs were correlated with
24 the expression of the protease profile, and most were clustered in immune response module.
25 Among 26 hub genes, we found that Mmp16 and Mmp17 were significantly downregulated in AAA 26 mice, while Ctsa, Ctsc and Ctsw were Draftupregulated. Our functional annotation analysis of genes 27 coexpressed with the five hub genes indicated that Ctsw and Mmp17 were involved in T cell
28 regulation and Cell adhesion molecule pathway, respectively, and that both were involved in
29 general regulation of the cell cycle and gene expression. Overall, our data suggest that these
30 ectopic genes are potentially crucial to AAA formation and may act as biomarkers for the diagnosis
31 of AAA.
32 Key words: Abdominal aortic aneurysm, next-generation sequencing, transcriptome, WGCNA,
33 extracellular matrix, proteases, matrix metalloproteinase, cathepsin.
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34 Introduction
35 Abdominal aortic aneurysm (AAA) is defined as a 50% increase in the diameter of the infrarenal
36 aorta and is an age-related degenerative disease(B Timothy, Terrin, & Dalman, 2008). The only
37 treatment option for AAA is surgical repair(Schermerhorn et al., 2008). It is necessary to explore
38 suitable noninvasive pharmacological treatments for this disease from a health economics
39 perspective. The pathogenesis of AAA formation, development and rupture is not fully understood.
40 Aortic wall inflammation is considered one of the major causes of AAA development and
41 progression(Goldstone, Malone, & Moore, 1978). After infiltration of immune inflammatory cells,
42 a wide range of immunoreactive mediators are released, which leads to direct degradation of the
43 aortic wall. Similarly, the development of intraluminal thrombi in the aortic wall is a source of 44 immune-derived proteases that exacerbateDraft pathogenesis(Jean-Baptiste et al., 2011). Collectively, 45 these events result in significant changes in the medial layer, including degradation of
46 extracellular matrix (ECM) structural proteins such as elastin and collagen fibers, which ultimately
47 lead to aortic wall expansion and rupture(Jean-Baptiste et al., 2011).
48 Proteases are known to be involved in AAA by degradation of the ECM during tissue
49 remodeling(Haiying, Sasaki, Jin, Kuzuya, & Cheng, 2018). The role of ECM proteases and their
50 role in AAA development has been extensively studied because they may serve as suitable
51 candidates for pharmacological treatments and can be specifically targeted without impairing
52 overall immunity. Key proteases that play pathological roles in AAA include elastase, chymase,
53 tryptase, matrix metalloproteinases (MMPs), cathepsins, granzyme B, etc.(Alon, Lisa S, & David
54 J, 2012). Elastases induce aneurysms via cleavage of elastin and destruction of elastic lamellae,
55 as well as generating proinflammatory elastin fragments (elastokines)(Folkesson, Silveira,
56 Eriksson, & Swedenborg, 2011). Chymases and tryptases can activate MMPs and cathepsins,
57 resulting in elastic lamellae fragmentation. They also promote immune infiltration and smooth
58 muscle cell apoptosis(Jiusong et al., 2009; Zhang, Sun, Lindholt, Sukhova, & Shi, 2011). MMPs
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59 are a large class of endopeptidases that are essential in physiological homeostasis and
60 remodeling processes in the ECM. Deciphering the specific contribution of ECM proteases may
61 provide valuable insights into the development of therapeutic approaches for AAA(Alon et al.,
62 2012). There are supporting data that MMP-1, 2, 3, 9, 12, and 13 are increased in the aortic wall
63 of AAA(Rabkin, 2017). Cathepsins, or cysteine proteases, are a family of lysosomal proteases
64 that act effectively as elastolytic and collagenolytic enzymes and therefore play a significant role
65 in the degradation of the ECM(Schulte et al.; Sun et al.). Increased activity of cathepsins B, H, L,
66 and S has been detected in the aortic wall with AAA(Abisi et al., 2007). In addition, cathepsin C,
67 a protease responsible for activating zymogen granzyme, may also contribute to the development
68 of AAA by increasing the activation and release of granzyme and promoting the recruitment of
69 neutrophils to the affected aorta(Pagano et al., 2007). The data are weaker or insufficient for other
70 MMPs or cathepsins. Therefore, further Draftresearch is necessary to explore other possibly important
71 but neglected proteases.
72 The rapidly increasing availability of transcriptomics data generated by RNA sequencing (RNA-
73 seq) provides us with the opportunity to use this information to generate testable hypotheses to
74 understand the molecular mechanisms that control gene expression and biological processes.
75 With the latest developments in transcriptomics and next-generation sequencing technology,
76 coexpression networks constructed from RNA-seq data can also identify or infer the functional
77 status of genes from a systematic perspective(Sipko, Thomas, & Pedro, 2014). One way to infer
78 gene function and gene-disease associations from genome-wide gene expression is weighted
79 gene coexpression network analysis (WGCNA), which builds a gene network with a tendency to
80 coactivate across a group of samples and analyzes this network(van Dam, Võsa, van der Graaf,
81 Franke, & de Magalhães, 2018). The application of the above technology will help us discover
82 new AAA-related proteases and potential biomarkers associated with the diagnosis and treatment
83 of AAA.
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84 In this study, we first identified novel genes that might underlie the aneurysm formation of AAA in
85 ApoE-/- mice via high-throughput or next-generation sequencing (NGS) of the transcriptome. Then
86 we explored the gene expression modules that correlate with protease activity in AAA progression
87 by WGCNA based on the RNA-seq data. Furthermore, we selected specific genes from protease
88 family for RT-qPCR analyses to examine whether the differences in gene expression associated
89 with aneurysm formation translated into similar changes in protein expression.
90 Materials and Methods
91 Animals and treatment
92 Male Apo E-/- mice were obtained from the Jackson Laboratory (Bar Harbor, ME). All mice were 93 bred as littermate controls and housedDraft in pathogen-free barrier cages. They were fed a normal 94 laboratory diet. Mice at 10-12 weeks of age were infused subcutaneously for 4 weeks with saline
-1 -1 95 vehicle or angiotensin II (AngII) at a dose of 1000 ng.kg .min (Sigma-Aldrich, St. Louis, MO)
96 with Alzet osmotic minipumps (Model 2004, Durect Corporation). After anesthetization and
97 perfusion with 4% paraformaldehyde, the aorta was exposed and the periadventitial tissue was
98 carefully removed from the aortic wall. The aortic tissues were prepared for RNA isolation as
99 described previously(Zheng et al., 2013). All animal protocols were approved by the Animal Care
100 and Use Committee of Capital Medical University (20120109) and experiments conformed to the
101 Guide for the Care and Use of Laboratory Animals (National Institutes of Health publication No.85-
102 23,1996).
103 RNA isolation
104 Total RNA was isolated from abdominal aorta samples using TRIzol reagent (Invitrogen, Life
105 Technologies) following the manufacturer’s protocol. Then evaluation of RNA purity,
106 concentration, and integrity was performed by a combination of a Nanodrop 2000
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107 spectrophotometer (Thermo Scientific), 2100 Bioanalyzer (RNA6000 Nano LabChip; Agilent
108 Technologies, Santa Clara, CA), and/or agarose gel electrophoresis.
109 Next-generation sequencing and gene expression analysis
110 Ribosomal RNA was digested using a Ribo-zero kit. cDNA libraries of all samples were
111 constructed and analyzed with an Agilent 2100 Bioanalyzer, and sequenced by the Illumina
112 HiSeqTM 2500 machine. After quality control (QC) of the raw reads, clean reads were obtained
113 and compared to the reference sequence with tophat/bowtie2. The results of the comparison were
114 passed through the second qualitative control (QC of alignment) by counting the distribution and
115 coverage of the reads on the reference sequence. Follow-up analyses, including gene expression,
116 gene structure optimization, variable splicing, new transcript discovery and coding ability 117 prediction, and SNP detection were processedDraft afterwards. RNA-seq data comparison analysis 118 was used to demonstrate whether there was differential expression in a unigene among samples.
119 The DEseq R package was used to conduct differentially expressed gene screening(Anders &
120 Huber, 2010). Differentially expressed genes (DEGs) were initially identified if their fold changes
121 were ≥2 and P value <0.05. Furthermore, the false discovery rate (FDR) error control method was
122 used for multiple hypothesis test correction of the P value. DEGs were screened out and classified
123 by gene ontology (GO) functional significance enrichment analysis. Cellular pathway association
124 was analyzed according to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.
125 Both used the Database for Annotation, Visualization and Integrated Discovery v6.8
126 (https://david.ncifcrf.gov/). The total protease concentration was measured using a Pierce
127 protease assay kit following the manufacturer’s protocol.
128 Coexpression network construction by WGCNA
129 Weighted gene coexpression network analysis (WGCNA, v1.49) was applied to identify global
130 gene expression profiles and coexpressed genes. The WGCNA package was installed from
131 Bioconductor (http://bioconductor.org/biocLite.R). The soft threshold method for Pearson
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132 correlation analysis of the expression profiles was used to determine the connection strengths
133 between two transcripts to construct a weighted network. Average linkage hierarchical clustering
134 was carried out for group transcripts based on topological overlap dissimilarity in network
135 connection strengths and then gene modules were detected. Genes in each module were
136 subjected to GO analysis and KEGG pathway analysis. The threshold was set as count >2 and P
137 < 0.05.
138 Real-time quantitative PCR (RT-qPCR) and ROC analysis
139 Total RNA isolated from abdominal aortic tissues obtained from five AngII mice and five control
140 mice was included in the RT-qPCR validation study. These animals were obtained under the exact
141 same condition as the eight samples used in the RNA-seq experiment. RT-qPCR was performed
142 with an iCycler IQ system (Bio-Rad) (Li Draftet al., 2012; Yang et al., 2012). We diluted cDNA samples
143 20-fold and performed RT-qPCR reaction using SYBR green JumpStart Taq ReadyMix (Sigma-
144 Aldrich) with 100 μM of primer (SINO biological). Then we amplified the above product in the ABI
145 Prism 7000 Sequence Detection System (Applied Biosystems). Thermal cycler conditions were
146 50°C for 2 minutes and 95°C for 10 minutes, then 40 cycles of 15 seconds at 95°C followed by 1
147 minute at 59°C. Glyceral-dehyde 3-phosphate dehydrogenase was used as reference gene to
148 normalize all samples. Then, ROC analysis was conducted with these 5 selected hub genes using
149 SPSS 25.0.
150 Statistical analysis
151 Normally distributed data are reported as the mean and standard deviation. Two-tailed unpaired
152 Student’s t-test using Welch’s correction was performed to evaluate data with unequal variance.
153 The statistical analysis of RT-qPCR data was carried out at the normalized level of relative
154 expression using the Kruskal-Wallis test and the Mann-Whitney unpaired t test for comparisons
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155 between two groups. A P value lower than 0.05 was considered statistically significant. Statistical
156 analysis was performed using GraphPad Prism v.6.0b (GraphPad Software, La Jolla, CA).
157 Results
158 Differentially expressed genes in the AAA model and gene expression analysis
159 The expression patterns of AAA mice and the control mice are shown in the heatmap (Figure 1).
160 Overall, 2059 differentially expressed genes were obtained from each group by RNA‐Seq short
161 sequence read analysis. A total of 1108 and 951 genes in the AAA group were revealed to be up-
162 and downregulated, respectively, compared with the control group (fold change ≥2 or ≤0.5, P
163 <0.05; see Table S1 and S2 for details). The top 10 up- and downregulated genes with FDR <0.05
164 are listed in Tables S3 and S4. 165 Gene expression profiles: functionalDraft cluster analysis 166 To assess the function of the DEGs, GO classes were determined with DAVID. The identified
167 genes in the AAA group were associated with 539 enriched GO terms, of which 395 were
168 associated with biological process (BP), 66 were associated with cellular component (CC), and
169 78 were associated with molecular function (MF). The top 20 most significantly enriched terms of
170 BP, MF and CC are presented in Figure 2, respectively. We conducted functional enrichment
171 analysis of KEGG pathways as well based on DEGs. KEGG analysis demonstrated that a total of
172 51 and 48 gene pathways were significantly up- and downregulated in the AAA group,
173 respectively. The top 20 up- and downregulated pathways in the AAA group are presented in
174 Figure 3. Details of the related pathways are shown in Figure S1.
175 Exploration of GO terms and KEGG pathways revealed processes and pathways mainly related
176 to (a) inflammatory response and signaling pathways in inflammation (NF-kappa B signaling
177 pathway, Chemokine signaling pathway, Cytokine-cytokine receptor interaction), (b) muscle
178 contraction and focal adhesion (muscle attachment, ECM-receptor interaction, Vascular smooth
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179 muscle contraction, Cell adhesion molecules), and (c) immune responses and T/B cell activation
180 (B cell receptor signaling pathway, T cell activation, alpha-beta T cell differentiation).
181 Identification of potential proteases involved in AAA
182 Since chronic inflammation is considered to be one of the driving forces of AAA pathogenesis and
183 ECM proteases are critical in aortic wall degradation, we measured the protease concentration of
184 both groups (Figure S2). The total protease activity of the AAA group was significantly higher than
185 that of the control groups. Furthermore, 43 DEGs (Table S5) were found to be correlated with the
186 expression of the protease family, which corresponds to previous studies that link protease
187 dysfunction with AAA onset(Trott & Harrison, 2014). These genes are mainly clustered in
188 proteolysis involved in cellular protein catabolic process, cysteine-type endopeptidase activity,
189 apoptotic process, integral component of membrane of GO enrichment, and NF-kappa B signaling
190 pathway in KEGG analysis (data not shown).Draft Notably, we found that cathepsin C was among the
191 DEGs coding proteases. This is consistent with the view that cathepsin C is related to the
192 development of AAA. Moreover, deficiency of this protease results in reduced aneurysm formation,
193 preservation of elastic lamellae and reduced inflammation(Pagano et al., 2007).
194 Biological Analysis of proteases by Construction of the weighted coexpression network
195 Hub gene analysis
196 To further analyze the biological characteristics of these proteases, we established a weighted
197 coexpression network and investigated their function based on the features of the network. First
198 of all, the coexpression network of the 2059 DEGs was constructed by WGCNA. Network topology
199 was screened using a soft thresholding power of 8 (cutoff = 0.8) (Figures 4A, 4B). The network
200 heatmap (Figure 4C) showed the independence of the gene modules we detected. To identify the
201 role of genes in the protease profile in AAA progression, the 26 most connected protease genes
202 of the 43 proteases were classified as candidate hub genes, including Adam6b, Adam9,
203 Adamdec1, Adamts20, Adamtsl5, Bace1, Clec4a3, Cpe, Ctsa, Ctsc, Ctsw, Dpp6, Dpp10, F8,
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204 Ggt1, Ihh, Mmp16, Mmp17, Otub2, Prss23, Pycard, Slpi, Thsd4, Tinagl1, Tnfaip3, and Usp18
205 (Figure 5A).
206 Functional module analysis
207 To obtain the biological functions and signaling pathways involved in each module, genes of each
208 module were subjected to GO-BP and KEGG pathway analysis. These 43 proteases were found
209 to be clustered mainly in two modules. Module 1 focused on immune processes including adaptive
210 and innate immune responses and the regulation of T cell activation and migration. The top 10
211 enriched processes are demonstrated in Table S6. Module 2 (Table S7) included a variety of BPs,
212 such as cell adhesion, actomyosin structure organization, extracellular matrix organization, heart
213 development, actin crosslink formation, and muscle contraction, indicating that these hub genes 214 are possibly involved in the development,Draft migration, proliferation and apoptosis of muscle cells.
215 Functional analysis of genes coexpressed with five hub proteases
216 ECM proteases, including elastase, chymase, tryptase, cathepsins, MMPs, and granzyme B,
217 have been reported to play a certain role in AAA development(Alon et al., 2012). For example,
218 cathepsin C is responsible for the activation of zymogen granule enzymes, which means it may
219 also contribute to AAA pathogenesis by releasing more of granule enzymes and promoting
220 neutrophil recruitment to the affected aorta wall(Pagano et al., 2007). Notably, Ctsw (Figure 5B),
221 Ctsa (Figure 5C) and Ctsc (Figure 5D) were among the 26 detected hub genes in our study. The
222 list of genes coexpressed with Ctsa, Ctsc and Ctsw is shown in Table 1. In addition, Mmp16 and
223 Mmp17 were coexpressed with 20 DEGs respectively (Figure 5E and 5B). Unlike cathepsin C,
224 MMP16 and MMP17 have not been widely reported to be responsible for the generation of AAAs.
225 The list of genes coexpressed with Mmp16 and Mmp17 can be found in Table 2. Martin-Alonso
226 et al.(Martín-Alonso et al., 2015) reported a missense mutation of Mmp17 in acute ascending
227 aortic aneurysms. Although previous literature has confirmed that proteases such as MMP9 play
228 an important role in the pathogenesis and development of AAA, MMP9 demonstrated no
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229 significant change at the mRNA level in this study, which does not negate the role of MMP9 in
230 AAA, since MMP9 may still be modified at the translational level. To explore the possible function
231 of these five hub genes, we functionally annotated the 20 genes coexpressed with their hub
232 genes, respectively. Terms and genes clustered in them are presented in Table 3. The biological
233 functions of the hub genes can be reasonably inferred through the functional analysis of these
234 coexpressed genes.
235 Validation of RNA-seq data
236 Although certain MMPs and cathepsins have been demonstrated to be associated with the
237 generation of AAA, most hub genes we detected in these two families have not yet been widely
238 explored. Therefore, we selected Ctsa, Ctsw, Mmp16 and Mmp17, hub genes from the protease
239 profile, for further RT-qPCR analyses. DraftCtsc was also included to confirm the previous research
240 results and increase the credibility of this study. Mmp16 and Mmp17 showed significant changes
241 in both transcription levels and translation levels, suggesting that they might be associated with
242 the pathogenesis of AAA or play certain roles in AAA formation. The RT-qPCR results, shown in
243 Figure 6, were consistent with the results of the gene expression profiles determined by
244 sequencing. This indicates relatively good agreement between the RNA-seq and RT-qPCR
245 results. qPCR analysis confirmed that Ctsa, Ctsc and Ctsw were significantly upregulated in AAA
246 mice compared with control mice (Figure 6A), while Mmp16 and Mmp17 were significantly
247 downregulated in AAA mice. Western blot analysis (Figure 6B) further demonstrated that the
248 transcription of MMP16 and MMP17 proteins was significantly lower in AAA mice than in control
249 mice. The low expression level of Mmp17 in the aortic aneurysm model is supported by previous
250 literature(Martín-Alonso et al., 2015; Papke, Yamashiro, & Yanagisawa, 2015), while the
251 correlation between MMP16 and AAA has not yet been reported. Furthermore, all five genes were
252 selected for ROC analysis. The area under the curve (AUC) of each gene is listed in Table 4.
253 Although limited sample size makes it difficult to obtain a statistically significant prediction, the
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254 ROC analysis showed that these genes are potentially crucial genes in the pathogenesis of AAA
255 and showed diagnostic value (Figure 7).
256 Discussion
257 In the current study, we first validated novel genes involved in AAA pathogenesis using NGS-
258 based expression profiling. We screened 2059 genes that were differentially expressed in AngII-
259 induced Apo E-/- mice compared to the control mice, where 1108 DEGs were upregulated in the
260 AAA model mice (Tables S1 and S2). Genes such as il1b, cd4, cxcr2, ccl19, ccl21a and several
261 other chemokine/receptor family members previously implicated in the pathogenesis of
262 AAA(Hinterseher et al., 2013; Rush et al., 2009) were among the 2059 DEGs.
263 We then conducted functional clustering analysis of GO enrichment and KEGG pathways, in
264 which VSMC contraction, immune responseDraft and inflammatory signaling pathways were mainly
265 presented (Figure 2, 3 and S1). Notably, GO-BP analysis mainly revealed immune responses
266 including immune system process, adaptive immune response, T cell activation, T cell receptor
267 signaling pathway, CD8−positive alpha−beta T cell differentiation, positive regulation of T cell
268 migration, and CD4−positive alpha−beta T cell differentiation, which was consistent with the
269 previous understanding that chronic activation of the immune system is closely related to the
270 occurrence of AAA. Concerning KEGG pathways, DEGs were mostly enriched in inflammatory
271 pathways, including the NF-kappa B signaling pathway, Chemokine signaling pathway, and
272 Cytokine-cytokine receptor interaction. Immune-related pathways, such as primary
273 immunodeficiency, natural killer cell-mediated cytotoxicity, the T cell receptor signaling pathway,
274 the B cell receptor signaling pathway, leukocyte transendothelial migration, and antigen
275 processing and presentation, were also involved. Moreover, ECM-receptor interaction, muscle
276 attachment, VSMC contraction, and cell adhesion molecules were enriched in GO analysis and
277 KEGG pathways, which led us to another important pathology of AAA, apoptosis of VSMCs in the
278 aortic wall and degradation of the ECM. These findings were also consistent with a previous study
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279 using microarray data from GEO datasets(Chen et al., 2019). Aortic wall inflammation is
280 considered one of the main causes of the occurrence and development of AAA(Golledge, 2013).
281 The constant activation of inflammatory status is characterized by elevated cytokine production,
282 including interleukin-6, tumor necrosis factor-α, interleukin-1 β, and increased circulating immune
283 cells that are highly associated with the aging process(Helle, 2006). One of the causes of chronic,
284 long-term, low-level activation of the immune system is redox imbalance. Increased production of
285 reactive oxygen species (ROS) and a corresponding reduction in antioxidant defense systems
286 result in a net increase in ROS, which activates proinflammatory genes primarily through
287 activation of the nuclear factor kappa B (NFκB) pathway(Chung et al., 2009). An increase in NFkB
288 activation has been observed in AAA. Inhibition of NFkB activity suppresses the formation of
289 aneurysms in experiments partly by reducing MMP activity(Hideki et al., 2004). Draft 290 As mentioned before, we focused on DEGs expressing key proteases that potentially play critical
291 roles in the generation of AAA. Among all 43 proteases we identified, most were clustered in
292 multiple GO processes of Module 1 (Table S6), suggesting a clear tendency for these proteases
293 to be associated with immune responses and therefore to be possibly correlated with the
294 pathogenesis of AAA. Moreover, several proteases were clustered in Module 2 (Table S7).
295 However, their functional clustering was rather scattered and not as integrated as those in Module
296 1. For example, MMP16 was found to be clustered in GO-BP chondrocyte proliferation and
297 ossification. This implies that MMP16 may be involved in certain processes of osteogenesis, but
298 it remains unclear whether MMP16 is connected to the pathological process of AAA. While Ihh
299 was clustered in Module 2 in cell differentiation, chondrocyte proliferation, epithelial cell-cell
300 adhesion and ossification.
301 We obtained 26 candidate hub genes that are associated with the expression of proteases
302 through coexpression network analysis (Figure 5). In the coexpression network constructed in this
303 study, multiple genes expressing proteases were found to be differentially expressed, such as
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304 Mmp16 and Mmp17 (Figure 5E and 5B) from the MMP profile and Ctsw (Figure 5B), Ctsa (Figure
305 5C) and Ctsc (Figure 5D) from the cathepsin profile. Functional analysis of their coexpressed
306 genes (Table 3) provided us with a potential direction of how they work in the formation of AAA
307 and other processes. According to the functional annotation, Ctsw is possibly associated with the
308 regulation of regulatory T cell differentiation and the T cell receptor signaling pathway in KEGG.
309 Mmp17 was also related to the KEGG pathway of cell adhesion molecules. In addition, Ctsw and
310 Mmp17 may both be involved in general regulation of the cell cycle and gene expression, such
311 as microtubule-based movement, mitotic nuclear division, cell division, chromosome segregation,
312 DNA metabolic process, mitotic sister chromatid segregation, negative regulation of DNA binding,
313 positive regulation of gene expression and DNA topological change. This may establish a certain
314 link between cell cycle regulation and AAA. Genes coexpressed with Mmp16 were clustered in
315 the regulation of dendritic spine morphogenesisDraft and protein phosphorylation. The variety of
316 functional aggregates indicates the diversity of the biological roles of these proteases. Further
317 research is needed to discover their possible role in the formation and development of AAA.
318 Additionally, we used RT-qPCR analyses to study the corresponding proteases and confirmed
319 that the differences in gene expression translated into similar changes in protein expression.
320 These genes are potentially crucial in the pathogenesis of AAA and showed diagnostic value
321 (Figure 7 and Table 4).
322 Dysregulation of ECM turnover is a hallmark of AAA pathogenesis. MMPs are a large family of
323 endopeptidases that are involved in the breakdown of ECM in normal physiological processes.
324 Both MMP-2 and MMP-9 were able to cleave elastin, which created the link between MMPs and
325 aortic degeneration(Senior et al., 1991), and initiated the study of MMPs in aneurysm disease,
326 mainly focusing on these two proteases and their corresponding endogenous inhibitors. However,
327 there is limited understanding of the mechanisms by which other MMPs are involved in the
328 pathogenesis of aneurysms. MMP16 and MMP17 are proteinases that bind to the cell membrane,
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329 and normal expression promotes functional differentiation of VSMCs, while low expression leads
330 to dysfunction of VSMCs, which then possibly promotes the development of aortic aneurysms.
331 MMP-17 is also known as membrane-type matrix metalloproteinase 4 (MT-MMP 4)(Wang,
332 Johnson, Ye, & Dyer, 1999). However, this protein differs from most MT-MMPs in that it is a GPI-
333 anchored protein instead of a transmembrane protein. MMP17 mRNA levels were previously
334 analyzed in tissue samples and VSMCs from patients with aortic aneurysms and other arterial
335 lesions, but these studies produced inconsistent results for the pathogenic effects of MMP17
336 levels in these diseases(Carrell, Burnand, Wells, Clements, & Alberto, 2002; Jackson et al., 2011;
337 Sakiko et al., 2010). Martín-Alonso et al.(Martín-Alonso et al., 2015) demonstrated the pathogenic
338 role of MMP17 in aortic pathology by using a genetic loss-of-function mouse model, although they
339 mainly focused on studies of thoracic aortic aneurysms instead of AAA. Aortic dilation occurred
340 in Mmp17-deficient mice. MMP17 wasDraft required for the development of contractile filaments,
341 correct positioning and orientation of SMCs within the elastic lamella. It was also needed in the
342 formation of connections to elastic fibers. In the dilated aorta, morphological and distributional
343 changes and related ECM changes led to VSMC dysfunction. Remodeling of the ECM and
344 liberation of cleaved fragments by MMP17 during embryogenesis plays a vital role in establishing
345 and maintaining vessel integrity (Martín-Alonso et al., 2015). The identification of MMP17
346 substrates other than osteopontin can promote our understanding of the possible pathological
347 mechanisms of aortic aneurysm production, that is, aortic remodeling under pathological
348 conditions with an increased inflammatory response. Further consideration needs to be given to
349 the balance between ECM degradation and ECM fragment production to develop an effective
350 therapeutic strategy for the treatment of aortic aneurysms using MMP inhibitors(Papke et al.,
351 2015).
352 Limitations
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353 Although potential DEGs that are possibly correlated with AAA generation were selected through
354 NGS and coexpression analysis, further studies including overexpression or knockdown of
355 candidate DEGs need to be performed to confirm the biological function of these possible AAA-
356 connected genes.
357 Several proteases that were previously confirmed to be crucial in the pathogenesis of AAA were
358 not spotted in this study. This may be due to multiple reasons, such as heterogeneity of the AAA
359 tissue samples, including samples from AAAs of different sizes and grades of inflammation,
360 adherent material of thrombi and presence of atherosclerosis.
361 We explored the diagnostic significance of these genes by ROC analysis. The limitation of
362 insufficient sample size makes a statistically significant conclusion not rigorous. We will expand
363 the sample size in further studies and ultimatelyDraft verify them in AAA patients.
364 Conclusions
365 This study presents a global comparative transcriptome profiling of AAA mice compared with wild-
366 type mice using next-generation sequencing. Based on WGCNA, we identified novel hub genes
367 of the protease profile that likely contribute to the progression of AAA. The expression of these
368 genes demonstrated a significant correlation with the functional differentiation of VSMCs.
369 Dysfunction of VSMCs may promote AAA clinical stage and prognosis. Thus, these genes may
370 act as potential biomarkers in predicting clinical outcomes and diagnosis of AAA and could
371 potentially facilitate the development of new therapies that reduce the overall health burden of
372 AAA. Further research is required to extend these findings.
373 Authors' contributions
374 All authors participated in the design, interpretation of the studies and analysis of the data and
375 review of the manuscript. YD, SJ and ZY conceived and designed the experiments. LF performed
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376 the animal experiments and collected tissue samples. CS and ZH run bioinformatics analysis and
377 analyzed the data with CL and LZ. ZH and YD drafted the manuscript. CY and LB supervised
378 data analysis. ZY supervised the study and revised the paper. All authors read and approved the
379 final manuscript.
380 Acknowledgements
381 We thank Dr. Cao from Institute of Basic Medical Sciences, Chinese Academy of Medical
382 Sciences for providing technical support for co-expression network analysis.
383 Declaration of Conflicting Interests
384 The authors declared no potential conflicts of interest with respect to the research, authorship, 385 and/or publication of this article. Draft
386 Funding
387 This work was supported by grants from the Major Research Program of National Natural Science
388 Foundation of China (grant number 51890892) and National Natural Science Foundation of China
389 (grant number 81770481).
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496 Table 1. list of DEGs coexpressed with Ctsa, Ctsc, and Ctsw.
DEGs coexpressed with ctsa DEGs coexpressed with ctsc DEGs coexpressed with ctsw
No. Gene symbol Gene Name Gene symbol Gene Name Gene symbol Gene Name
RIKEN cDNA 4933402N22 amyloid beta (A4) precursor protein- asp (abnormal spindle)-like, microcephaly
1 4933402N22Rik gene(4933402N22Rik) Apbb1 binding, family B, member 1(Apbb1) Aspm associated (Drosophila)(Aspm)
C2 calcium-dependent domain containing C-type lectin domain family 4, member
2 C2cd4a 4A(C2cd4a) Clec4a3 a3(Clec4a3) Cd28 CD28 antigen(Cd28)
DNA segment, Chr 3, ERATO Doi 751,
3 Cdkl3 cyclin-dependent kinase-like 3(Cdkl3) D3Ertd751e expressed(D3Ertd751e) Grap2 GRB2-related adaptor protein 2(Grap2)
DALR anticodon binding domain containing 4 Dap3 death associated protein 3(Dap3) Dalrd3 Draft3(Dalrd3) Gtse1 G two S phase expressed protein 1(Gtse1) 5 Egr2 early growth response 2(Egr2) Dnah2 dynein, axonemal, heavy chain 2(Dnah2) Incenp inner centromere protein(Incenp)
family with sequence similarity 135,
6 Elmo2 engulfment and cell motility 2(Elmo2) Fam135a member A(Fam135a) Kif15 kinesin family member 15(Kif15)
heterogeneous nuclear ribonucleoprotein F-box and leucine-rich repeat protein
7 Gm17190 A3 pseudogene(Gm17190) Fbxl5 5(Fbxl5) Kif18b kinesin family member 18B(Kif18b)
8 Gm20687 predicted gene 20687(Gm20687) Fhit fragile histidine triad gene(Fhit) Kif20b kinesin family member 20B(Kif20b)
microtubule associated monooxygenase,
calponin and LIM domain containing paraneoplastic antigen MA3
9 Mical1 1(Mical1) Gm18336 pseudogene(Gm18336) Kif21b kinesin family member 21B(Kif21b)
MpV17 mitochondrial inner membrane killer cell lectin-like receptor, subfamily D,
10 Mpv17 protein(Mpv17) Gpr68 G protein-coupled receptor 68(Gpr68) Klrd1 member 1(Klrd1)
N(alpha)-acetyltransferase 35, NatC
11 Naa35 auxiliary subunit(Naa35) Mmp7 matrix metallopeptidase 7(Mmp7) Lck lymphocyte protein tyrosine kinase(Lck)
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12 Nlgn3 neuroligin 3(Nlgn3) Nat6 N-acetyltransferase 6(Nat6) Lef1 lymphoid enhancer binding factor 1(Lef1)
13 Oas1c 2'-5' oligoadenylate synthetase 1C(Oas1c) Polm polymerase (DNA directed), mu(Polm) Mmp17 matrix metallopeptidase 17(Mmp17)
prickle planar cell polarity protein
14 Oas1e 2'-5' oligoadenylate synthetase 1E(Oas1e) Prickle4 4(Prickle4) Ms4a4b MAS-related GPR, member F(Mrgprf)
phosphatidylinositol 4-kinase, catalytic, ras responsive element binding protein membrane-spanning 4-domains, subfamily
15 Pi4kb beta polypeptide(Pi4kb) Rreb1 1(Rreb1) Neil3 A, member 4B(Ms4a4b)
synaptotagmin binding, cytoplasmic RNA
16 Scyl3 SCY1-like 3 (S. cerevisiae)(Scyl3) Syncrip interacting protein(Syncrip) Nek2 numb homolog (Drosophila)(Numb)
peptidylglycine alpha-amidating
17 Sphk1 sphingosine kinase 1(Sphk1) Tnk1 tyrosine kinase, non-receptor, 1(Tnk1) SellIfi209 monooxygenase(Pam)
tumor necrosis factor, alpha-induced 18 Tmem234 transmembrane protein 234(Tmem234) Ubxn11 DraftUBX domain protein 11(Ubxn11) Tk1 protein 8(Tnfaip8) WNK lysine deficient protein kinase
19 Tulp1 tubby like protein 1(Tulp1) Wnk1 1(Wnk1) Tnfaip8 TNNI3 interacting kinase(Tnni3k)
ubiquinol-cytochrome c reductase
20 Uqcc1 complex assembly factor 1(Uqcc1) Zfp729b zinc finger protein 729b(Zfp729b) Top2a topoisomerase (DNA) II alpha(Top2a) 497
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498 Table 2. list of DEGs coexpressed with MMP16 and MMP17.
DEGs coexpressed with MMP16 DEGs coexpressed with MMP17
No. Gene symbol Gene Name No. Gene symbol Gene Name
1 Add2 adducin 2 1 Armc7 armadillo repeat containing 7 UDP-GlcNAc:betaGal beta-1,3-N- 2 Epha4 EPH receptor A4 2 B3gnt5 acetylglucosaminyltransferase 5 3 Erg ETS transcription factor ERG 3 Bmp2k BMP2 inducible kinase 4 Fars2 phenylalanyl-tRNA synthetase 2, mitochondrial 4 Cd28 CD28 molecule 5 Fmo3 flavin containing monooxygenase 3 5 Cyfip2 cytoplasmic FMR1 interacting protein 2 6 Gal3st3 galactose-3-O-sulfotransferase 3 6 Dennd1c DENN domain containing 1C potassium voltage-gated channel modifier 7 Kcng2 subfamily G member 2 Draft7 Dok3 docking protein 3 establishment of sister chromatid cohesion N- 8 Lrrc10 leucine rich repeat containing 10 8 Esco2 acetyltransferase 2 major facilitator superfamily domain containing 9 Mfsd2a 2A 9 Hmgb2 high mobility group box 2 10 Mrgprf MAS related GPR family member F 10 Mdn1 midasin AAA ATPase 1 11 Nebl nebulette 11 Neil3 nei like DNA glycosylase 3 12 Nek10 NIMA related kinase 10 12 Pdcd1 programmed cell death 1 13 Ntrk3 neurotrophic receptor tyrosine kinase 3 13 Rexo1 RNA exonuclease 1 homolog 14 Numb NUMB endocytic adaptor protein 14 Rhoh ras homolog family member H 15 Pam peptidylglycine alpha-amidating monooxygenase 15 Rnf213 ring finger protein 213 16 Pdlim5 PDZ and LIM domain 5 16 Sell selectin L spindle and kinetochore associated complex 17 Pex7 peroxisomal biogenesis factor 7 17 Ska3 subunit 3 18 Taf6l TATA-box binding protein associated factor 6 like 18 Stap1 signal transducing adaptor family member 1 19 Tnni3k TNNI3 interacting kinase 19 Tmem156 transmembrane protein 156 20 Tprkb TP53RK binding protein 20 Top2a topoisomerase (DNA) II alpha
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499 Table 3. Functional annotation of genes coexpressed with 5 hub genes.
Hub gene Term P Value Related genes Ctsa GO:0006909~phagocytosis 0.039530986 TULP1, ELMO2 Ctsc GO:0030308~negative regulation of cell growth 0.092582342 TNK1, APBB1 Ctsw GO:0007018~microtubule-based movement 3.38E-05 KIF15, KIF20B, KIF18B, KIF21B GO:0007067~mitotic nuclear division 6.44E-05 NEK2, INCENP, KIF20B, KIF18B, ASPM GO:0051301~cell division 2.05E-04 NEK2, INCENP, KIF20B, KIF18B, ASPM GO:0007049~cell cycle 0.001332699 NEK2, INCENP, KIF20B, KIF18B, ASPM GO:0021873~forebrain neuroblast division 0.001658466 LEF1, ASPM GO:0007059~chromosome segregation 0.002412636 NEK2, INCENP, TOP2A mmu04660:T cell receptor signaling pathway 0.003435176 LCK, GRAP2, CD28 GO:0045589~regulation of regulatory T cell differentiationDraft0.006618475 LCK, CD28 GO:0006259~DNA metabolic process 0.01646957 TOP2A, TK1 GO:0000070~mitotic sister chromatid segregation 0.018918056 NEK2, KIF18B GO:0043392~negative regulation of DNA binding 0.023797972 NEK2, LEF1 GO:0010628~positive regulation of gene expression 0.042193198 LCK, LEF1, CD28 Mmp16 GO:0061001~regulation of dendritic spine morphogenesis 0.012540401 EPHA4, PDLIM5 GO:0006468~protein phosphorylation 0.021308139 NTRK3, EPHA4, NEK10, TNNI3K GO:0016310~phosphorylation 0.024963682 NTRK3, EPHA4, NEK10, TNNI3K GO:0050775~positive regulation of dendrite 0.031071994 EPHA4, NUMB morphogenesis Mmp17 GO:0007059~chromosome segregation 0.002412636 SKA3, TOP2A, ESCO2 GO:0006265~DNA topological change 0.006618475 HMGB2, TOP2A mmu04514:Cell adhesion molecules (CAMs) 0.014403887 SELL, PDCD1, CD28 GO:0010628~positive regulation of gene expression 0.042193198 HMGB2, STAP1, CD28 500
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501 Table 4. The AUC of 5 hub genes.
Gene symbol AUC P value
Ctsa .938 .083
Ctsc .875 .043
Ctsw .938 .043
Mmp16 1.000 .021
Mmp17 .938 .043 502 Draft
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503 Figure captions
504 Figure 1. Clustering analysis of DEGs in AAA group vs. control. The clustering analysis results of
505 DEGs is demonstrated as a heatmap. The color red means up-regulated, while green represents
506 for down-regulated genes.
507 Figure 2. Enrichment analysis of the DEGs in AAA group vs. control according to gene ontology
508 (GO) annotation, including molecular function (MF), cellular component (CC) and biological
509 process (BP). All GO terms were sorted by -log10Pvalue for each entry, and entries containing
510 less than 3 DEGs were filtered out.
511 Figure 3. Enrichment analysis of top 10 up- and downregulated KEGG pathways. All DEGs were 512 sorted by the -log10Pvalue correspondingDraft to each entry, while filtering out entries that contain 513 less than 3 DEGs.
514 Figure 4. WGCNA analysis of DEGs in AAA group vs. control. (A, B) Soft-thresholding power
515 analysis was used to obtain the scale-free fit index of network topology. (C) Heatmap depicts the
516 Topological Overlap Matrix (TOM) among the DEGs weighted coexpression network.
517 Figure 5. (A) Co-expression network of DEGs that are correlated with expression of proteases.
518 Network of certain DEGs are amplified and demonstrated separately, including Cathepsin w plus
519 Mmp17 (B), Cathepsin a (C), Cathepsin c (D), and Mmp16 (E).
520 Figure 6. The confirmation of the different expression of protease profile in tissues from AAA
521 group and control. (A) The expression of Mmp16, Mmp17, Cathepsin a, Cathepsin c, and
522 Cathepsin w was analyzed by qPCR analysis in tissues from AAA and control ones. Data were
523 presented as mean±SEM (n=5). **P < 0.01, vs. control group. (B) The expressions of MMP16
524 and MMP17 were examined by Western blot analysis in tissues from AAA mice and control ones.
525 A representative blot was shown. (C) Quantitative analysis of MMP16 and MMP17 was expressed
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526 as fold change over control group. Data were presented as mean±SEM (n=5). **P < 0.01 vs.
527 control group.
528 Figure 7. ROC curves of selected hub genes. (A–E) ROC curves for Ctsa, Ctsc, and Ctsw, Mmp16
529 and Mmp17, respectively. All five genes have AUC exceeding 0.8.
Draft
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2 1 0 -1 -2
Draft
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Control AAA Genome Page 30 of 35 A BP muscle attachment olfactory nerve structural organization establishment of glial blood−brain barrier regulation of cellular response to growth factor stimulus immune system process immune response adaptive immune response cell adhesion T cell activation innate immune response defense response to virus T cell receptor signaling pathway CD8−positive, alpha−beta T cell differentiation positive regulation of T cell migration reflex negative regulation of peptidyl−cysteine S−nitrosylation CD4−positive, alpha−beta T cell differentiation integrin−mediated signaling pathway positive regulation of transcription from RNA polymerase II ... positive regulation of transcription, DNA−templated 0 5 10 15 20 B CC cytoplasmic side of membrane membrane plasma membrane external side of plasma membrane Myb complex immunological synapse cell−cell junction Draft integral component of plasma membrane cytoplasm extracellular matrix focal adhesion cell junction smooth muscle contractile fiber cell surface cytoskeleton basement membrane T cell receptor complex actin filament bundle extracellular region Z disc 0 5 10 15 C MF beta−galactosyl−N−acetylglucosaminylgalactosylglucosyl−ceramide ... lactosylceramide 1,3−N−acetyl−beta−D−glucosaminyltransferase activity protein binding actin binding SH3/SH2 adaptor activity regulatory region DNA binding leptin receptor activity core promoter binding Fc−gamma receptor I complex binding complement receptor activity myosin phosphatase regulator activity ligand−dependent nuclear receptor binding peptidase inhibitor activity bioactive lipid receptor activity chemokine activity serine−type endopeptidase inhibitor activity carbohydrate binding endopeptidase inhibitor activity chemokine receptor activity microtubule motor activity 0.0 2.5 5.0 7.5 10.0 12.5 https://mc06.manuscriptcentral.com/genome-pubs −log10Pvalue Page 31 of 35 Genome
A Up Cell adhesion molecules (CAMs) Primary immunodeficiency Natural killer cell mediated cytotoxicity Cytokine−cytokine receptor interaction T cell receptor signaling pathway Hematopoietic cell lineage B cell receptor signaling pathway NF−kappa B signaling pathway Intestinal immune network for IgA production Leukocyte transendothelial migration Measles Chemokine signaling pathway Graft−versus−host disease Antigen processing and presentation NOD−like receptor signaling pathway Cytosolic DNA−sensing pathway Viral myocarditis Influenza A Herpes simplex infection Epstein−Barr virus infection 0 5 10 −log10Pvalue B DraftDown Vascular smooth muscle contraction cGMP − PKG signaling pathway Hypertrophic cardiomyopathy (HCM) Dilated cardiomyopathy (DCM) Arrhythmogenic right ventricular cardiomyopathy (ARVC) ECM−receptor interaction Renin secretion Regulation of actin cytoskeleton Focal adhesion GABAergic synapse MAPK signaling pathway Adrenergic signaling in cardiomyocytes PI3K−Akt signaling pathway Butanoate metabolism Oxytocin signaling pathway Cardiac muscle contraction Salivary secretion Aldosterone synthesis and secretion Insulin secretion TGF−beta signaling pathway 0.0 2.5 5.0 7.5 10.0
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C Draft
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Rexo1 B3gnt5 A Dok3 B B Cyfip2 Pdcd1 Tmem156 Ska3 Mdn1 Rnf213 Rexo1 B3gnt5 Rfx7 Dennd1c Map4k2 Stap1 Esco2 Mmp17 Lrrk2 Cd3e Dok3 Mast3 A430078G23Rik Armc7 Bmp2k Cyfip2 Ccdc88c Neil3 Hmgb2 Rhoh Diaph3 Prkcd Nfatc2ip Naip6 Usp18 Top2a Kif21b Smg1 Npnt Dbf4 Zfp407 Tnfsf8 Pdcd1 Kif20b Sell Susd1 Atad5 Cd28 Cbx1 Il21r Nlrc5 Lck Rbm33 Cds2 Nek2 Ikzf1 Tmem156 Gfra2 Itga2b Bbs9 Rad18 Mdn1 Klrd1 Zfp608 Ska3 Ctsw Adam9 Gdf11 Ifi209 Pkib Incenp Samsn1 Rnf213 Vcam1 Il17ra Cst7 Cd33 Dtx1 Btg1 Ccdc9 Tk1 Mcoln2 Cdca3 Gtse1 Tnfaip8 Adam19 Cdk5r1 Sipa1l3 Nup210 Dnaja3 Adam6b Fnip1 Cxcl12 Kif15 Art2b Col26a1 Aspm Kif18b AC125149.3 Arap2 Stk4 Lef1 Ms4a4b Prss23 Nov Cxcl10 Kif11 Grap2 Xlr5c Trim59 Ncr1 Smoc1 Tnfaip3 Bcl7a Wtip Cnga1 Abcg3 Dennd1c Pag1 Lat Hnrnpk Birc2 Trappc6b Dpp10 Cnr2 Ifi208 Aif1 Irgm1 Akna Bcl11a Gab3 Noct Fkbp10 Cenpe Cdca7 Tagap Cr2 Mark1 Gpr174 Kcna3 Slc9a5 Bpnt1 Plk1 Chd7 Dusp16 Oasl1 Esco2 Tyr Pstpip1 Vldlr Stap1 Cdca2 Rel Nlrp1b Fbxo48 Tinagl1 Rgs10 ENSMUST00000037976 Smc4 Ankle1 Glcci1 Eif4g1 Tgfb1i1 Vps4b Csf2rb2 Shank1 Lzts2 Chst3 Adgrg5 Mmp17 Bcl2l14 Btla Kcnj16 Trpm2 Carmil2 Ggt1 Prox1 Arhgef2 Dnajc25 Plagl2 Tnfrsf13c Shcbp1 Pml Nfib Runx3 Fat1 Ccl17 ENSMUST00000113829 Rasgrp3 F8 Zbtb38 Trim30b Ell3 Kcnn4 Cd4 Arid3b Armc7 Rad51 Dennd3 Txk Pglyrp2 Themis Map4k1 Sh2d2a Neurl3 Hvcn1 Havcr1 H2-T24 Crisp3 Nuf2 Tnfsf11 Muc3a Bmp2k Mx2 Tgif1 Hist1h2af Gm6904 B130006D01Rik Nfkbid Ccdc148 Ccdc38 Otub2 Nlrc3 Aoc1 Clec1b
Sema4c Dgka Itgae Sphk1 Naa35 Ccl21a Neil3 C C2cd4a H2-Ob Pif1 Arid2 Il12rb2 4933402N22Rik Dhx32 Uqcc1 Pi4kb Hmgb2 Rhoh D Gm18336 Vpreb3 Grem1 Dap3 Zcchc14 Mpv17 Gm20687 Prickle4 Cebpe Top2a Oas1e Wnk1 Mmp7 Rreb1 Ctsa Dnah2 Elmo2 Kif21b Nat6 Fam135a Scyl3 Egr2 Tmem234 Clec4a3 Hrh1 C530008M17Rik
Mical1 Oas1c Ubxn11 Ctsc Gm17190 Dalrd3 Tulp1 Polm Ift80 Polr2a Gpr68 Mdm1 Cdkl3 Nlgn3 Qrfp Syncrip Tnk1 Zfp729b Apbb1 D3Ertd751e Map3k9 Kif20b Sell Galnt3 Adamts20 Ttk Fbxl5 Fhit
Gse1 Cd28
Btbd11
Oas1b Dsc3 Depdc1b
Ripor2 Slc35f2 Taf6l Numb Bach2 Mrgprf Lck Syngr2 E St14 Gal3st3 Pdlim5 Nek2 Gna15 Epha4 Tut4 Erg Kcns1 Celsr1 Inpp5e Tprkb Ddx43 Gpsm1 Slc4a3 Ntrk3 Add2
Crip3 Tmem267 Capn15 Adamdec1 Mmp16 Adgrb3 Lrrc10 Slc37a2 Lrtm2 Cacna1c Klrd1 Hnrnpr Ctsw Ints11 Tnni3k Camsap3 Fzd10 Nek10 Tjp3 Gm28042 Chn1 Syngr1 Pard6g Il18 Ifi209 Sgsm1 Nbeal2 Elf5 Kcnj5 Mfsd2a Nebl Nsg2 Bace1 Incenp Sympk Nkx2-5 Fmo3 Ttll10 Ccl19 Efcab11 Ptgfr Klra3 Kcng2 Slc24a4 Spo11 Slpi Set Ache Fars2 Lpar3 Ndufb5 Art5 Tk1 Zfp629 Wt1 Gm14147 Il2rb Ets1 Kcnk2 Lmntd1 Gm11756 Slc9a8 Pex7 Atp2a2 Gtse1 Epcam Pam Ihh Tnfaip8 Rnf43 Slc35a2 Slc5a11 Dapl1 Eps8l1 Ccndbp1 Fcnb Spaca9 Mylk3
Col18a1 4430402I18Rik Mfn1 Tg 2610528A11Rik Susd5 Ankrd54 Kctd8 Plac8 Frmd4a Nenf Draft Phldb3 Trim47 Tnfsf12 Nasp Itga9 Mkks Kif15 C1qtnf2 Ptgis Obsl1 Aspm Cpe Kif18b Plekhn1 Myl6 Adamtsl5 Wfdc1 Grhl1 Col4a6 Pde5a Plpp7 Dpp6 Fam81a Klf15 Cd84 Cys1 Csrp2 Crtam Nptn Skint3 Sema3c Mpv17l Lef1 Ms4a4b Ift43 Ndrg2 Eif4enif1 Ap1m2 Cacna2d1 Trim58 Ssbp2 Gstt1 Capn12 Gdpd3 Tcte3 Thsd4 Grap2 Mst1 Dkk3 Cyp2b10 Pla2g4b Sntg2 Gm14137 Serpinb7 Sfn U2af1l4 Fam83d Nkpd1 Sacs Crem Bcl2l1 Vmn2r30 Pycard Wee1 BC016579 Ankrd22 Ppp4c Pdzrn4
Nectin4
Sept1 Ikbkb Crybg2 Exph5 Crybg1
Prpf31 Serpinb10 Col17a1 Spint1
C D E Dhx32 Numb Sphk1 Naa35 Taf6l Mrgprf Gm18336 C2cd4a Prickle4 Cebpe Gal3st3 Pdlim5 4933402N22Rik Epha4 Erg Uqcc1 Pi4kb Wnk1 Mmp7 Rreb1 Dnah2 Tprkb Dap3 Ntrk3 Add2 Mpv17 Gm20687 Nat6 Fam135a Clec4a3 Hrh1 Oas1e C530008M17Rik Mmp16 Ctsa Lrrc10 Ubxn11 Ctsc Tnni3k Elmo2 Dalrd3 Nek10 Polm Scyl3 Egr2 Tmem234 Gpr68
Fars2 Nebl Syncrip Tnk1 Mfsd2a Mical1 Oas1c Zfp729b Apbb1 D3Ertd751e Fmo3 Tulp1 Gm17190 Kcng2 Pex7 Pam Cdkl3 Nlgn3 Fbxl5 https://mc06.manuscriptcentral.com/genome-pubsFhit A Genome Page 34 of 35 3 Control
AAA ** ** ** 2 A level RN A
1
Relative m Relative ** **
0
a c w in in MMP16 MMP17
Catheps Catheps Cathepsin
B Draft ApoE -/+
AngII +- - -+ + WB
MMP16
MMP17
GAPDH
C 2500 Control
2000 AAA
1500 A level RN A **
1000 ** Relative m Relative
500 https://mc06.manuscriptcentral.com/genome-pubs 0 MMP16 MMP17 Page 35 of 35 Csta GenomeCtsc Cstw A 1.0 B 1.0 C 1.0
0.8 0.8 0.8
0.6 0.6 0.6 Sensitivity Sensitivity Sensitivity 0.4 0.4 0.4
0.2 0.2 0.2
0.0 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 1 - Specificity Draft1 - Specificity 1 - Specificity
Mmp16 Mmp17 D 1.0 E 1.0
0.8 0.8
0.6 0.6 Sensitivity Sensitivity 0.4 0.4
0.2 0.2
0.0 0.0 0.0 0.2 0.4 0.6 https://mc06.manuscriptcentral.com/genome-pubs0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0
1 - Specificity 1 - Specificity