Keywords
ADAM9, Data mining, Transcriptomics, RNAseq, Microarray
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This article is included in the Neglected Tropical Diseases collection.
ADAM9, Data mining, Transcriptomics, RNAseq, Microarray
“ADAM metallopeptidase 9 (ADAM9) is a member of the ADAM (a disintegrin and metalloprotease domain) family. Members of this family are membrane-anchored proteins structurally related to snake venom disintegrins, and have been implicated in a variety of biological processes involving cell-cell and cell-matrix interactions, including fertilization, muscle development, and neurogenesis. The protein encoded by this gene interacts with SH3 domain-containing proteins, binds mitotic arrest deficient 2 beta protein, and is also involved in TPA-induced ectodomain shedding of membrane-anchored heparin-binding EGF-like growth factor. Several alternatively spliced transcript variants have been identified for this gene.” (Quoted from RefSeq1).
ADAM9 top functions include cellular adhesion, protein cleavage and shedding. (Supplementary Figure 1). Human ADAM9 protein cleaves and releases collagen XVII from the surface of skin keratinocytes2. This activity is enhanced in the presence of reactive oxygen species. Mouse ADAM9 protein cleaves and releases epidermal growth factor (EGF) and fibroblast growth factor receptor 2IIIb (FGFR2IIIb) from the surface of prostate epithelial cells3. Following LPS treatment, ADAM9 protein catalytic domain cleaves Angiotensin-I converting enzyme (ACE) from the surface of endothelial cells4. Human ADAM9 protein disintegrin-cysteine-rich domain binds integrins and thus mediates cell adhesion5. Human ADAM9 protein enhances adhesion and invasion of non-small lung tumors which mediates tumor metastasis6. Mouse ADAM9 protein enhances tissue plasminogen activator (TPA)-mediated cleavage of CUB domain-containing protein 1 (CDCP1)7. This activity mediates lung tumor metastasis. Human ADAM9 protein mediates cell-cell contact interaction between stromal fibroblasts and melanoma cells at the tumor-stroma border, thus contributing to proteolytic activities required during invasion of melanoma cells8.
ADAM9 expression and regulation. ADAM9 has been reported as being expressed in various cell populations including monocytes9, activated macrophages10, epithelial cells, activated vascular smooth muscle cells, fibroblasts8, keratinocytes and tumor cells. The abundance of ADAM9 RNA measured by RT-PCR is decreased in vitro in human melanoma cells after culture with collagen type I or with Interleukin 1 alpha (IL1α) compared to mock stimulated conditions11.
ADAM9 has been involved in disease processes including cancer, cone rod dystrophy and atherosclerosis. Homozygous mutation of the human ADAM9 gene results in severe cone rod dystrophy and cataract12. Mutation of the mouse ADAM9 gene results in no major abnormalities during development and adult life13. The abundance of ADAM9 RNA and protein measured by immunostaining and RT-PCR is increased in vivo in human prostate tumors compared to normal tissue14. The abundance of ADAM9 RNA measured by microarray and RT-PCR is increased in vivo in human advanced atherosclerotic plaque macrophages compared to normal tissue15. This increase is predictive of Prostate Specific Antigen (PSA) relapse.
It is known that ADAM9 is upregulated in some tumor cells during pathologic processes and also contributes to the formation of multinucleate giant cells from monocytes and macrophages10. However, little is known about the activities of ADAM9 in regulating physiologic or pathologic processes, especially during acute infection or in response to tissue damage.
Existing knowledge pertaining to ADAM9 was retrieved using NCBI’s National Library of Medicine’s Pubmed search engine with a query that included official gene symbol and name as well as aliases: “ADAM9 OR ADAM-9 OR "ADAM metallopeptidase domain 9" OR MCMP OR MDC9 OR CORD9”. As of January of 2015, 287 papers were returned when running this query. By reviewing this literature keywords were identified that were classified under six categories corresponding to cell types, diseases, functions, tissues, molecules or processes. Frequencies of these keywords were then determined for the ADAM9 bibliography as shown in Supplementary Figure 1. This literature screen identified and prioritized existing knowledge about the gene ADAM9 and was used to prepare the background section of this manuscript and provided the necessary perspective for the interpretation of ADAM9 profiles across other large-scale datasets.
We employed a resource that is described in details in a separate manuscript (submitted) and is available publicly: https://gxb.benaroyaresearch.org/dm3/landing.gsp. Briefly: we have assembled and curated a collection of 172 datasets that are relevant to human immunology, representing a total of 12,886 unique transcriptome profiles. These sets were selected among studies currently available in NCBI’s Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/).
The custom software interface provides the user with a means to easily navigate and filter the compendium of available datasets (https://gxb.benaroyaresearch.org/dm3/geneBrowser/list). Datasets of interest can be quickly identified either by filtering on criteria from pre-defined lists on the left or by entering a query term in the search box at the top of the dataset navigation page.
Clicking on one of the studies listed in the dataset navigation page opens a viewer designed to provide interactive browsing and graphic representations of large-scale data in an interpretable format. This interface is designed to navigate ranked gene lists and display expression results graphically in a context-rich environment. Selecting a gene from the rank ordered list on the left of the data-viewing interface will display its expression values graphically in the screen’s central panel. Directly above the graphical display drop down menus give users the ability: a) To change how the gene list is ranked; this allows the user to change the method used to rank the genes, or to include only genes that are selected for specific biological interest. b) To change sample grouping (Group Set button); in some datasets, a user can switch between groups based on cell type to groups based on disease type, for example. c) To sort individual samples within a group based on associated categorical or continuous variables (e.g. gender or age). d) To toggle between the histogram view and a box plot view, with expression values represented as a single point for each sample. Samples are split into the same groups whether displayed as a histogram or box plot. e) To provide a color legend for the sample groups. f) To select categorical information that is to be overlaid at the bottom of the graph. For example, the user can display gender or smoking status in this manner. g) To provide a color legend for the categorical information overlaid at the bottom of the graph. h) To download the graph as a jpeg image.
Measurements have no intrinsic utility in absence of contextual information. It is this contextual information that makes the results of a study or experiment interpretable. It is therefore important to capture, integrate and display information that will give users the ability to interpret data and gain new insights from it. We have organized this information under different tabs directly above the graphical display. The tabs can be hidden to make more room for displaying the data plots, or revealed by clicking on the blue “show info panel” button on the top right corner of the display. Information about the gene selected from the list on the left side of the display is available under the “Gene” tab. Information about the study is available under the “Study” tab. Information available about individual samples is provided under the “Sample” tab. Rolling the mouse cursor over a histogram bar while displaying the “Sample” tab lists any clinical, demographic, or laboratory information available for the selected sample. Finally, the “Downloads” tab allows advanced users to retrieve the original dataset for analysis outside this tool. It also provides all available sample annotation data for use alongside the expression data in third party analysis software.
The seminal discovery was made while examining RNAseq transcriptional profiles. A knowledge gap was exposed when those results were interpreted in light of existing knowledge reported in the literature. Next, the initial observation was validated and further extended by examining profiles of the gene of interest, ADAM9, across a large number of independent publically available transcriptome datasets. The completion of these tasks was aided by a custom data browsing application loaded with a curated compendium of 172 datasets relevant to human immunology sourced from the National Center for Biotechnology Information’s (NCBI) Gene Expression Omnibus (GEO) (https://gxb.benaroyaresearch.org/dm3/landing.gsp, manuscript submitted). Briefly, ADAM9 transcript was identified as a potential early stage discovery while browsing RNA-sequencing profiles of blood leukocyte populations (https://gxb.benaroyaresearch.org/dm3/geneBrowser/show/396), with the genes being ranked in alphabetical order. In this particular dataset whole blood sample of healthy donors, patients during acute infections (meningococcal sepsis, E. coli sepsis, C. difficile colitis), multiple sclerosis patients pre- and post- interferon treatment, patients with Type 1 diabetes and patients with amyotrophic lateral sclerosis (ALS) were obtained and monocyte, neutrophil, CD4 T cell, CD8 T cells, B cell, NK Cell isolated prior to profiling via RNA sequencing16. The abundance of ADAM9 RNA measured by RNA-seq in human blood neutrophils and monocyte samples from subjects with sepsis was found to be markedly increased as compared to uninfected controls (Figure 1; [iFigure/GSE60424]16). By comparison levels of abundance of ADAM9 RNA in lymphocytes and Natural Killer (NK) cells were low and no changes were observed in subjects with sepsis in these cell populations. Despite the small number of septic subjects included in the study (N=3) the robust increase in abundance that was observed prompted attempts to validate and further extend this initial observation in independent public datasets that were part of the compendium.
Our data browsing tool allows the assessment of expression profiles across transcriptome datasets (https://gxb.benaroyaresearch.org/dm3/geneBrowser/list). In order to validate and extend our original observation we looked up ADAM9 transcriptome profiles for all available 172 datasets (https://gxb.benaroyaresearch.org/dm3/geneBrowser/crossProject?probeID=ENSG00000168615&geneSymbol=ADAM9&geneID=8754studies).
The abundance of ADAM9 RNA measured by microarrays in human blood samples was significantly increased as compared to uninfected controls in subjects with sepsis [iFigure/GSE28750]17 & [iFigure/GSE29536]18, in subjects with bacterial and influenza pneumonia [iFigure/GSE34205]19, [iFigure/GSE40012]20, in subjects with respiratory syncytial virus (RSV) infection [iFigure/GSE34205]19 & [iFigure/GSE17156]19 and in subjects with tuberculosis [iFigure/GSE19439]21 & [iFigure/GSE34608]22. Aggregated findings were reported in the form of flow charts that were generated using google docs presentations, with links to the source interactive graphs systematically provided as hyperlinks (Figure 2, Supplementary Figure 2 and Table 1). Altogether these data indicate that increase in abundance of ADAM9 can be detected in blood leukocytes, including monocytes and neutrophils fractions during bacterial and viral infection.
Next, we investigated the regulation of ADAM9 transcription following leukocyte exposure to pathogens and pathogen-associated molecules. The abundance of ADAM9 RNA measured by microarrays in human blood cultures treated with Heat Killed E.coli, Heat Killed Staphylococcus aureus (HKSA) or Heat Killed Legionella pneumophillum (HKLP) for 6 hours was increased marginally as compared to unstimulated conditions [iFigure/GSE30101]23. The abundance of ADAM9 RNA measured by microarrays in human blood cultures treated with Heat Killed Acholeplasma laidlawii (HKAS), E. coli LPS (E-LPS), Flagellin, PAM3, R837, Zymosan, Influenza virus, RSV, CpG, Poly:IC, for 6 hours was not changed as compared to unstimulated conditions (Ex-vivo) [iFigure/GSE30101]23 ; IL8 [iFigure] and CXCL10 [iFigure] serve as positive controls. The abundance of ADAM9 RNA measured by microarrays in human blood samples from subjects treated with poly:IC for 1 day was marginally increased as compared to baseline samples [iFigure/GSE32862]24; CXCL10 [iFigure] serves as a positive control (Figure 3 and Supplementary Figure 3). Statistical analysis results are shown in Table 2. Taken together, these results showed that the abundance of ADAM9 was not changed or changed only marginally after stimulation with purified molecules bearing Pathogen Associated Molecular Patterns (PAMPs). These finding raised the question as to whether ADAM9 transcription might be activated instead by host-derived Damage-Associated Molecular Pattern molecule (DAMPs)25,26.
Our dataset screen revealed in addition that changes in abundance of ADAM9 could be associated with tissue remodeling. The abundance of ADAM9 RNA measured by microarrays in human skin biopsy samples of subjects with lepromatous leprosy was significantly increased as compared to controls in subjects with tuberculoid leprosy [iFigure/GSE17763]27. The abundance of ADAM9 RNA measured by microarrays in human blood samples was significantly increased as compared to controls in pregnant subjects [iFigure/GSE17449]28. The abundance of ADAM9 RNA measured by microarrays in human blood monocytes samples from subjects with filariasis was significantly increased as compared to uninfected controls [iFigure/GSE2135]29. These results are shown in Table 3, Figure 4 and Supplementary Figure 4. A common thread between these different states is that they involve extensive tissue remodeling, whether it involves the skin (leprosy), placental tissue (pregnancy) or lymphatic tissues (filariasis).
GEO ID | A vs B | Avg A-Avg B | Avg A/Avg B | P value |
---|---|---|---|---|
GSE17763 | Lepromatous leprosy VS Tuberculoid leprosy | 13164.0 | 2.2 | 0.0012 |
GSE17449 | Non pregnancy VS Pregnancy | 51.3 | 1.4 | 0.0366 |
GSE2135 | Filariasis VS Post Treatment | 251.1 | 2.4 | 0.0313* |
Filariasis VS Healthy Control | 283.6 | 2.9 | 0.0197 | |
Post Treatment VS Healthy Control | 32.5 | 1.2 | 0.2143 |
Changes in ADAM9 transcript abundance were observed in additional datasets: The abundance of ADAM9 RNA measured by microarrays in human blood samples was significantly increased as compared to healthy controls in subjects with sarcoidosis [iFigure/GSE34608]22, in subjects after severe blunt trauma [iFigure/GSE11375]30, in subjects with chronic kidney disease [iFigure/GSE15072]31, and in subjects who have undergone elective thoracic or abdominal surgery [iFigure/GSE28750]17. The abundance of ADAM9 RNA measured by microarrays in human blood samples from subjects treated with localized external beam radiation therapy for 42 days was significantly increased as compared to baseline samples [iFigure/GSE30174]32. The abundance of ADAM9 RNA measured by microarrays in human blood monocytes samples from obese subjects was significantly increased as compared to lean controls [iFigure/GSE32575]33. Finally, the abundance of ADAM9 RNA measured by microarrays in human blood monocytes samples from subjects after severe trauma was significantly increased as compared to healthy controls [iFigure/GSE5580]34. These results showed that increase in ADAM9 transcript abundance was associated with tissue injury and sterile inflammation (Table 4, Figure 5 and Supplementary Figure 5) and thus are consistent with the observations that are reported above associating increase in ADAM9 RNA with responses to Damage-Associated Molecular Pattern molecules (DAMPs) and tissue remodeling.
GEO ID | A vs B | Avg A-Avg B | Avg A/Avg B | P value |
---|---|---|---|---|
GSE34608 | Sarcoidosis VS Control | 56.4 | 1.9 | < 0.0001 |
Tuberculosis VS control | 56.9 | 1.9 | < 0.0001 | |
GSE11375 | Trauma VS Healthy | 58.6 | 2.5 | < 0.0001 |
GSE15072 | HD VS Healthy | 545.6 | 7.6 | < 0.0001 |
CKD VS Healthy | 94.3 | 2.1 | 0.0359 | |
GSE28750 | Post surgery VS Healthy | 153.2 | 5.1 | < 0.0001 |
Sepsis VS Healthy | 281.8 | 8.5 | < 0.0001 | |
GSE30174** | Healthy VS Baseline | 35.8 | 1.1 | 0.7243 |
Healthy VS 1h EBRT | -91.3 | 0.9 | 0.4727 | |
Healthy VS D7 EBRT | 236.5 | 1.4 | 0.1419 | |
Healthy VS D14 EBRT | 455.8 | 1.7 | 0.0068 | |
Healthy VS D21 EBRT | 643.8 | 2.0 | 0.0021 | |
Healthy VS D42 EBRT | 272.1 | 1.4 | 0.2150 | |
Healthy VS 1 mo Post Tx | 85.8 | 1.1 | 0.5678 | |
GSE32575 | Obese before surgery VS Obese post surgery | 15.1 | 1.1 | 0.0369 |
Obese before surgery VS control | 34.1 | 1.3 | < 0.0001 | |
Obese post surgery VS control | 19.0 | 1.2 | 0.0001 | |
GSE5580 | TP mono VS HC mono | 247.1 | 1.7 | 0.0070 |
TP Leukocyte VS HC Leukocyte | 233.2 | 2.9 | 0.0006 | |
TP T cell VS HC T cell | 57.9 | 3.0 | 0.0175 |
This study is the first report describing the modulation of levels of ADAM9 transcripts in human whole blood and showing restriction of its expression to neutrophils and monocytes. In addition we observed that the abundance of ADAM9 was increased during acute infection but did not change after stimulation with pathogen-derived molecules. It was not changed in vivo following administration of synthetic double stranded RNA (polyIC), a treatment that mimics viral exposure. Notably, it was not increased either in patients during the early acute phase of HIV infection when an intense immunological response is detected in absence of clinical symptoms iFigure/GSE29536]18. However, ADAM9 transcript abundance was increased in the blood of patients as a result of tissue damage, sterile inflammation and tissue remodeling. Therefore, in addition to its widely reported role in the pathogenesis of cancer the constellation of findings that we are reporting point towards the involvement of ADAM9 in immune-mediated processes and suggest that ADAM9 may constitute a valuable marker for assessing tissue damage, whether it occurs as result of acute infection, traumatic injury or medical procedures such as surgery or radiation therapy. Indeed, these findings may be of especially high significance in the context of acute infections since unlike “generic” markers of inflammation, that could also be used to assess tissue injury in other settings, ADAM9 would not be confounded by the host responses to the pathogen and may therefore accurately reflect damage to the patient tissues or organs (Figure 6). Thus ADAM9 blood transcript levels, or possibly levels of circulating proteins, could potentially be employed for triage of patients presenting with symptoms of infection in the emergency room or for monitoring of patients in intensive care units.
All primary data presented in this manuscript can be accessed along with contextual information via the data browsing application described above and is also available in NCBI’s GEO public repository. GEO accession numbers (starting with GSE) are provided where appropriate throughout this manuscript along with the primary reference associated with the GEO record.
F1000Research: Dataset 1. Raw data of ADAM9 transcripts in blood in response to tissue damage, 10.5256/f1000research.6241.d4506135
DR and DC designed the analytic approach, mined the data, prepared figures and drafted the manuscript. CK, BK, GL participated in the mining of the dataset compendium. All authors read and approved manuscript.
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Competing Interests: No competing interests were disclosed.
Competing Interests: No competing interests were disclosed.
Competing Interests: No competing interests were disclosed.
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