Abstract We have investigated Amblyomin-X-treated horse melanomas to better understand its mode of action through transcriptome analysis and the in vivo model. Amblyomin-X is a Kunitz-type homologous protein that selectively leads to the death of tumor cells via ER stress and apoptosis, currently under investigation as a new drug candidate for cancer treatment. Melanomas are immunogenic tumors, and a better understanding of the immune responses is warranted. Equine melanomas are spontaneous and not so aggressive as human melanomas are, as this study shows that the in vivo treatment of encapsulated horse melanoma tumors led to a significant reduction in the tumor size or even the complete disappearance of the tumor mass through intratumoral injections of Amblyomin-X. Transcriptome analysis identified ER- and mitochondria-stress, modulation of the innate immune system, apoptosis, and possibly immunogenic cell death activation. Interactome analysis showed that Amblyomin-X potentially interacts with key elements found in transcriptomics. Taken together, Amblyomin-X modulated the tumor immune microenvironment in different ways, at least contributing to induce tumor cell death. Subject terms: Bioinformatics, Melanoma, Cellular signalling networks, High-throughput screening, Translational research Introduction Melanoma is a type of cancer arising from the malignant transformation of melanocytes, pigment producing-cells found predominantly in the basal layer of the epidermis and eyes. Cutaneous melanoma is the most aggressive and treatment-resistant form of skin cancer responsible for the vast majority of skin cancer-related deaths in the Caucasian population^[58]1. The global incidence of melanoma continues to increase at an alarming rate, despite decades of public prevention programs in many countries. Around 232,000 new cases of skin cancer were recorded worldwide in 2012, accounting for 1.6% of all new cases of cancer back then, while over 300,000 new cases of melanoma were diagnosed worldwide in 2018, according to the World Cancer Research Foundation^[59]2,[60]3. In Brazil, 1,547 deaths were recorded in 2013 due to melanoma, with around 5,690 new cases reported back then, while around 6,260 new cases were expected due in 2018, according to the National Cancer Institute (INCA)^[61]4. Cutaneous melanoma usually affects a higher proportion of patients, in the age range 40–60 years. They can be treated by surgical excision when detected in the early stage (0, I, II and resectable III), however, in the later stages (unresectable III, IV and recurrent melanoma) the treatment options are chemotherapy, target therapy (BRAF/MEK pathway inhibitors), immunotherapy (checkpoint blockade CTLA-4 receptor inhibition, PD-1 ↔ PD-L1 axis inhibition, and interferon-gamma immunotherapy), or a combination of them. Death in most patients is caused by metastatic disease which may have evolved from the primary tumor. Therefore, there is a need for new strategies in melanoma therapy because many patients do not respond to these treatments due to mutations in their genome, microbiota profile, response to neo-antigens, and resistance to drugs^[62]5. In horses, skin tumors are the most common among neoplasms. Horse melanomas are encapsulated complex systems surrounded by a hard peel and represent 5 to 14% of equine skin neoplasms^[63]6,[64]7. These melanomas are usually benign tumors; however, they may have an unpredictable malignancy, progressing to malignant forms, and metastasize^[65]6. Dark-haired horses feature higher melanoma malignancy as they get older compared to white-haired horses^[66]7. Malignant transformations are caused by driver mutations. The acknowledged mutated genes in human melanoma are BRAF and NRAS, as well as the newly discovered PPP6C, RAC1, SNX31, TACC1, STK19, and ARID2^[67]8. In horses, the only recognized driver mutation hitherto is caused by a 4.6 Kb duplication in intron 6 of Syntaxin 17 (STX17)^[68]9,[69]10^. As is known, spontaneous horse melanoma tumors have a different natural history and many anatomical differences, compared to human melanoma tumors, but for some^[70]11,[71]12 it is considered a good translational model. In the present study, we treated horse melanoma tumors with Amblyomin-X, a Kunitz-type recombinant protein identified in a cDNA library from the tick Amblyomma sculptum salivary glands^[72]13. This molecule has the ability to inhibit Factor Xa in the blood coagulation cascade and triggers apoptosis by activating its intrinsic pathway in tumor cells^[73]14–[74]16. In previous studies, Amblyomin-X showed more avidity in the recognition of tumor cells, and rapid excretion in healthy murines^[75]17. We have already shown that Amblyomin-X causes cell death via induction of both endoplasmic reticulum (ER) stress and proteasome inhibition (PI) in renal adenocarcinoma (murine RENCA), in melanoma (murine B16F10 and human SK-MEL-28)^[76]18, as well as in pancreatic (human Mia-Paca-2) tumor cell lines^[77]17,[78]19,[79]20. Over the past ten years, preclinical studies have shown the potential of proteasome inhibition as an effective therapeutic strategy for treating different types of cancers^[80]21–[81]24 including melanoma^[82]25–[83]29. Bortezomib and Amblyomin-X are proteasome inhibitors (PIs) activating different innate immune pathways, the former via MAVS-NOXA^[84]25, and the latter via RIG-I-MAVS, as suggested by our transcriptome analysis. Trials with Bortezomib^[85]30–[86]35 alone have shown insufficient clinical efficacy, and in combination with paclitaxel and carboplatin, limited clinical benefit and significant toxicity^[87]30. Therefore, the effort to study proteasome inhibitors in different cancer therapies progresses^[88]31,[89]36. Herein, we investigated the effect of Amblyomin-X intratumoral injection in horse melanomas, followed by interactome and transcriptome analyses conducted to understand genes modulation and respective enriched pathways. The data suggest that Amblyomin-X modulates several pathways responsible for tumor cell death and its immune microenvironment. Moreover, transcriptome analysis suggests potentially yet unknown DEGs and novel target pathways, mainly in relation to the responses of the immune system. Results Equine melanoma regression upon treatment with Amblyomin-X We injected Amblyomin-X intratumorally, at 1 mg/kg of the tumor mass, on each third day, during 28 days, and followed the tumor volume evolution and clinical animal conditions by over a period of 5 months. We treated some selected tumors in the ventral tail of four animals by following three groups: treated (Amblyomin-X), and two control groups, vehicle (PBS) and untreated (see Materials and Methods). Tumor dimensions were recorded before each injection and calculated the relative volume means related to the initial volume (Fig. [90]1). In all cases Amblyomin-X treatment promoted a reduction of at least 75% in tumor volume at the end of the treatment period (one month). Tumor #5 (from animal number 417) completely disappeared after 12 days under treatment, while tumors from another animal (number 795) continued to show volume reduction, even one month after the end of treatment. Interestingly some animal control tumors (untreated and PBS-injected vehicle), that were in proximity to tumors treated with Amblyomin-X, also showed regressions in tumor size, which might suggest an indirect effect that could be related to the lymphatic system or recruitment of the immune system cells. In two other animals, control tumors maintained or increased their initial volumes. The evolution curves of each tumor per animal are shown in Fig. [91]2 (see also, Table [92]S1). It is worth to mention, that each horse tumor is presented as an independent capsule. We believe that long-term development and shrinkage are similar to all tail horse melanoma tumors, but experiments showed that each tumor responds differently, and we don’t know the reason. Figure 1. [93]Figure 1 [94]Open in a new tab In vivo animal 797 tumor treatment and excision. We can see on the left (a) animal 795 tail and chosen tumors: 1 and 8 as control, 2, 3 and 5 as vehicle, 4, 6, and 7 as treatment; (b) horse tail and tumor evolution on day 0, 15, 26 and 90 (excision); (c) veterinarian injecting Amblyomin-X solution or PBS in a tumor; and (d) T6, one of the excised tumors. Tumors were chosen related to distance and size. Three tumors, with a diameter of around 1.5 cm, were chosen for treatment. About three farthest tumors were chosen as vehicle and other three tumors as control, whenever possible. Figure 2. [95]Figure 2 [96]Open in a new tab This plot shows the percentage volume curves related to the initial volume (0 h). Every three days, Amblyomin-X was injected for about one month’s time. Animals 795 and 797 and their tumors were followed for at most 5 months. Treated tumor volume curves were colored red, control tumor volumes green, and vehicle tumor volumes blue, for animals 417 in (a), 438 in (b), 795 in (c) and 797 in (d). Each treated curve shows a decrease in tumor volumes. Vehicle and control animals also showed a decrease in tumor volumes for animals 417 and 438. Animals 795 and 797 showed either a constant or increased volume related to the initial volume. Since each tumor has its own history and fate, the rate of decrease is unpredictable. Clinical evaluation and biochemical parameters Global biochemical parameters i.e. urea, creatinine, TGO (AST, aspartate aminotransferase), GGT (gamma-glutamyl transferase), BT (total bilirubin), BD (direct bilirubin), BI (indirect bilirubin), FAL (alkaline phosphatase) and ALB (Albumin) were all within normal limits during and after treatment termination. We have observed no adverse effects in the animals used in our study (Table [97]S2). Histological analysis Histological analysis of untreated and treated tumors showed proliferation of atypical and hyperpigmented melanocytes, presence of numerous macrophages phagocytizing melanin, but only a few perivascular lymphocytes (Fig. [98]3). Tumor treated for 28 days (animal 797, tumor 6), showed regression areas represented by the absence of atypical melanocytes, only remaining melanophages (Fig. [99]3d). Figure 3. [100]Figure 3 [101]Open in a new tab In vivo histology. Distinct histopathological slides are presented. (a) animal 438, tumor 2, vehicle excised on day 28 – an untreated tumor showing melanophages and many atypical melanocytes. (b) and (c) animal 797, tumor 3, vehicle excised at day 28 – an untreated tumor showing less atypical melanocytic cells. (d) animal 797, tumor 6, treated with Amblyomin-X and excised at day 28 – regression areas showing only melanophages can be seen. Bioinformatics Mapping and differential expression Alignment and read count resulted in 269,991 transcripts, displayed in a matrix with horse Ensembl IDs as rows and 18 samples as columns, of which we removed two with low library size, both related to 12 h samples. We also excluded lowly expressed genes. We used edgeR to calculate the normalized expression table in CPM and BioMaRt to find horse-to-human ortholog coding genes, which yielded 14.414 transcripts, of which 13,138 transcripts had valid gene symbols for horse and 13,943 for human. There were more symbols for human genes due to paralog-ortholog conversion. For horse transcripts, there were 546 DEGs for 6hx0h and 259 DEGs for 12hx0h (Table [102]S3). Those DEGs having human orthologs were 546 for 6hx0h and 259 for 12hx0h. We could not find DEGs for the 12hx6h comparison, probably because gene expressions between these time points are somehow very close. Pathway analyses Metacore pathway enrichment analysis found 196 pathways for 6hx0h and 67 pathways 12hx0h. The same analysis can be seen using Reactome, String-db with KEGG, and String-db with GO Biological Processes (Table [103]S4). We classified the enriched pathways according to the words in each pathway description name that may represent a function or have a gene symbol (Fig. [104]S1a). We also ranked these pathways regarding how strongly modulated they were (delta of the sum of LFC = sum of LFC for 6hx0h minus sum of LFC for 12hx0h, for all genes in the pathway), resulting in positive values for pathways more strongly modulated for 6hx0h compared to 12hx0h, negative if more strongly modulated for 12hx0h compared to 6hx0h, and similar if the modulations were close to each other (Table [105]S5). For instance, the Innate Immune System (IIS) class comprised 26 pathways, of which 19 had genes more strongly modulated for 6hx0h, 3 for 12hx0h, and 4 were equally modulated (Fig. [106]S1b–d). According to the pathway classification, we have found that “immune system” was the class with more enriched pathways (80 pathways), according to Metacore, followed by “signaling” (55 pathways), “lung” (37 pathways), “canonical pathway” (31 pathways), “innate immune system” (26 pathways), “inflammation” (23 pathways), “cancer” (22 pathways), “muscle” (18 pathways), and “adhesion/ECM/cytoskeleton” (15 pathways). Figure [107]S1b shows 6hx0h higher modulated pathway classes, where immune system, pathway signaling, (lung) inflammation, innate immune system, canonical pathway, cancer, disease, migration/invasion/motility, adhesion/ECM/cytoskeleton, apoptosis, muscle, immunogenic cell death, virus responses, and stress, stand out as the most important pathway classes for this condition. Figure [108]S1c shows 12hx0h higher modulated pathway classes, however only a few pathways can be seen such as adhesion/ECM/cytoskeleton, lung, muscle, immune system, receptor pathways, signaling pathways, innate immune system, migration/invasion/motility, and cancer, resulting in a weaker response for 12hx0h when compared to 6hx0h. Finally, fig. [109]S1d shows classes with similar responses for 6hx0h and 12×0h, including immune system, pathway signaling, canonical pathways, development, complement, muscle, cancer, dermis, and innate immune system. In summary, most of the pathways were related to cell stress, innate immune response/inflammation, cytokines, cell damage/endothelial, neutrophil, apoptosis, ECM, cell adhesion, muscle, T-help, complement, and coagulation. Therefore, pathway classes were divided into three groups: a) first responses (ER-stress and IIS); b) confounding factors; and c) secondary responses (Table [110]1). Herein, confounding factors are denoted as enriched pathways, and part of the expression of their modulated genes, observed not probably due to the drug action, but mainly due to the stress and wounds caused by the injections. Next, we summarize the results highlighting pathways. In the text below, all pathway classes are written in bold. Table 1. Selected enriched pathways that represent genes and important functions related to the ex vivo experiment. a – first responses Functional Pathway DEGs(6hx0h) DEGs(12hx0h) ER-stress HCV-mediated liver damage and predisposition to HCC via cell stress CALR, CASP3, CYCS, HSP90B1, HSPA5, HSPD1, IL1R1, IL6, IL6ST, ITPR1, SOD2, STAT3, XBP1 Endoplasmic reticulum stress response pathway CYCS, HSP90B1, HSPA5, ITPR1, SOD2, XBP1 Role of PKR in stress-induced antiviral cell response CASP3, EIF2AK2, IL1B, IL1R1, IL6, IL8, STAT1, TLR2 EIF2AK2, IL1B, IRF7, STAT1, TLR2 TLR (IIS) HSP60 and HSP70 - TLR signaling pathway HSPA5, HSPA6, HSPA8, HSPD1, IL1B, IL6, IL8, TLR2 RLR (IIS) Innate immune response to RNA viral infection DDX58, DHX58, IFIH1, IRF7 OSM (IIS) Oncostatin M signaling via JAK-Stat IL6ST, OSMR, SERPINA3, SOCS3, STAT1, STAT3, TIMP1 SOCS3, STAT1, TIMP1 Oncostatin M signaling via MAPK IL6ST, LDLR, MMP3, OSMR, STAT1, TIMP1 OAS/RNase L (IIS) Antiviral actions of Interferons EIF2AK2, IRF9, OAS2, STAT1, WARS EIF2AK2, IRF9, MX1, OAS1, OAS2, STAT1 b – confounding factors Functional Pathway DEGs(6hx0h) DEGs(12hx0h) Neutrophil Inhibition of neutrophil migration by proresolving lipid mediators in COPD ACTN2, C5AR1, CD34, CXCR2, FPR2, IL1B, IL1R1, IL8, ITPR1, PRKCQ, PTAFR, TLR2 ACTN2, CXCR2, IL1B, PRKCQ, TLR2 Eosinophil Eosinophil chemotaxis in asthma C3, CCL7, CXCL10, CXCR2, NGF Cell adhesion ECM remodeling IGFBP4, IL8, MMP3, PLAUR, SERPINE1, TIMP1 Integrin inside-out signaling in neutrophils CXCL1, CXCR2, IL8, ITPR1, LYN, PTAFR, RASGRP2, SELE, SELP Muscle Muscle contraction: Delta-type opioid receptor in smooth muscle contraction ITPR1, MYH8, PENK MYH8, PENK c – secondary responses Functional Pathway DEGs(6hx0h) DEGs(12hx0h) Inflammation Release of pro-inflammatory factors and proteases by alveolar macrophages in asthma CXCL1, IL1B, IL6, IL8, MMP3, STAT1, TIMP1, TLR2 CXCL10, IL1B, STAT1, TIMP1, TLR2 Release of pro-inflammatory mediators and elastolytic enzymes by alveolar macrophages in COPD CTSL, IL1B, IL6, IL8, STAT1, TLR2 CTSL, CXCL10, IL1B, STAT1, TLR2 Inflammatory mechanisms of pancreatic cancerogenesis CD46, CXCL1, CXCR2, IL1B, IL1R1, IL6, IL8, STAT1, STAT3 IL-1 IL-1 signaling pathway BIRC3, CCL7, CXCL1, IL1B, IL1R1, IL6, IL8, PTGES, STAT1, ZC3H12A The innate immune response to contact allergens HSPA5, HSPA6, HSPA8, IL1B, IL1R1, IL6, IL8, TLR2 Cell migration Chemotaxis: CCL16-, CCL20-, CXCL16- and CCL25-mediated cell migration ITPR1, MMP11, MMP3, MMP8 Cytoskeleton remodeling Cytoskeleton remodeling: Regulation of actin cytoskeleton organization by the kinase effectors of Rho GTPases ACTN2, MYH8 ACTN2, MYH8 Apoptosis & Survival Endoplasmic reticulum stress response pathway CYCS, HSP90B1, HSPA5, ITPR1, SOD2, XBP1 Apoptosis and survival: CXCR3-B signaling CASP3, CYCS, HMOX1, ITPR1, RYR1, STAT3 Apo-2L(TNFSF10)-induced apoptosis in melanoma CASP3, CASP4, CYCS, IL8, XBP1 Interferon-alpha/beta IFN-alpha/beta signaling via JAK/STAT EIF2AK2, IFIT3, IL1RN, IRF9, ISG15, SOCS3, STAT1, STAT3 CXCL10, EIF2AK2, IFIT3, IFITM1, IL1RN, IRF7, IRF9, ISG15, ISG20, OAS1, SOCS3, STAT1, XAF1 IFN-alpha/beta signaling via MAPKs EIF2AK2, IFIT3, IRF9, ISG15, PRKCQ, STAT1, ZBTB16 CXCL10, EIF2AK2, IFIT3, IRF7, IRF9, ISG15, PRKCQ, STAT1 [111]Open in a new tab The four columns are the functions, pathways, enriched DEGs for 6hx0h comparison and for 12hx0h comparison, respectively. The table was divided into A, B, and C, for pathways classified as “ER-Stress and IIS”, “confounding factors”, and “secondary responses”, respectively. ER-stress was inferred due to the enriched pathways such as “HCV-mediated liver damage and predisposition to HCC via cell stress”, “Endoplasmic reticulum stress response pathway”, and “Role of PKR in stress-induced antiviral cell response”. For Immune System, six innate immune system responses were found: TLR pathway, RIG-I-like Receptors (RLR), Oncostatin-M pathway, OAS/RNase L pathway, Neutrophil, Eosinophil pathways. TLR was inferred due to the enriched “HSP60 and HSP70 - TLR signaling pathway”; RLR due to the enriched “Innate immune response to RNA viral infection” pathway; and Oncostatin-M due to the enriched “Antiviral actions of Interferons” pathway. For the present study, Neutrophil, Eosinophil and Complement are termed as confounding factors, since these biological functions are related to the damage caused by the intratumoral injections, activating “endothelial damage” and “wound healing” pathways. Another confounding factor is Muscle contraction, to be discussed later. All data related to these pathways can be seen in Table [112]1B. In addition, ER-stress and Innate Immune Responses should activate many secondary responses like Inflammation, IL-1, Cell migration, Cytoskeleton remodeling, Apoptosis & Survival, and Interferon-alpha/beta. Inflammation was inferred due to the enriched pathways related to macrophages: “Release of pro-inflammatory factors and proteases by alveolar macrophages in asthma”, “Release of pro-inflammatory mediators and elastolytic enzymes by alveolar macrophages in COPD”, “Inflammatory mechanisms of pancreatic cancerogenesis”, “Immune response_IL-6-induced acute-phase response in hepatocytes”, and “Immune response_TREM1 signaling pathway”; and IL-1 was inferred due to enriched pathways: “IL-1 signaling pathway” and “The innate immune response to contact allergens”. Moreover, Cell Migration was inferred due to the enriched “Chemotaxis: CCL16-, CCL20-, CXCL16- and CCL25-mediated cell migration” pathway; and Cytoskeleton Remodeling was inferred due to the enriched “Cytoskeleton remodeling: Regulation of actin cytoskeleton organization by the kinase effectors of Rho GTPases” pathway. Finally, Apoptosis & Survival was inferred due to enriched “Endoplasmic reticulum stress response pathway”, “Apoptosis and survival: CXCR3-B signaling”, and “Apo-2L(TNFSF10)-induced apoptosis in melanoma” pathways; and Interferon-alpha/beta was inferred due to the enriched “Immune response: IFN-alpha/beta signaling via JAK/STAT” and “Immune response: IFN-alpha/beta signaling via MAPKs” pathways. Network analyses With the application of String-db, 93 enriched pathways for 6hx0h and 59 for 12hx0h were found. String-db allows for the calculation of protein-protein interaction (PPI) networks, and with igraph^[113]37 it was possible to figure out the connectivity index (k) and betweenness centrality (g) (Table [114]S6). Next, Gephi was used to cluster genes according to the modularity index (community detection algorithm) with a resolution of 1.2 and the use of weights. The result can be seen in Table [115]S7 and respective networks can be seen in Fig. [116]4, where the main enriched pathways could be achieved using all clustered DEGs and Reactome to find the enriched pathways. Node colors were obtained using modularity classes for nodes clusterization, their sizes are proportional to the degree of connectivity and the label size is proportional to the betweenness centrality. Figure 4. [117]Figure 4 [118]Open in a new tab Network analysis. Gephi clusterized DEGs according to the modularity index with resolution of 1.2 and using weights, and respective enrichment analyses were done using Reactome. On the left (a) are all DEGs related to 6hx0h comparison, and 9 clusters could be defined related to Immune System, Transport & Cytochrome C, Antiviral pathways (RLR), GPCR pathway, UPR, rRNA processing & metabolism of nucleotides, Cell junction & cell-cell communication, muscle & ion homeostasis, and Circadian pathway & Ubiquitination. On the right (b) are DEGs related to 12hx0h comparison, resulting in a smaller network, with seven clusters: Innate Immune System responses, Cell cycle pathways, Neutrophil Degranulation, ESR-mediated signaling, Muscle, Circadian Clock, and rRNA processing. In-silico crosstalk simulation Crosstalk between pathways Five different possible crosstalks were simulated: a) adhesion-ECM-cytoskeleton versus remodeling versus stress, b) ICD versus apoptosis versus ER-chaperone-Golgi, c) cancer versus complement versus inflammation, d) IIS versus apoptosis” versus autophagy, and e) angiogenesis-vascular versus hypoxia versus stress. For this purpose we used Circlize R package. All five crosstalks showed a strong interaction between genes related to different biological functions; additionally, there were genes that participate simultaneously in different functions (pleiotropy). Only autophagy was not seen, possibly because it is not based on a phenomenon of transcriptional modulation. Crosstalk analysis between Innate Immune System x Apoptosis x Autophagy can be seen in Fig. [119]5, and respective enriched gene modulations in Fig. [120]6 (other crosstalks can be seen in Fig. [121]S2, and respective heatmap, dotplot, and spaghetti plots in Fig. [122]S3). There is a stronger interaction between genes for 6hx0h compared to 12hx0h. All circlize plots have more genes and are more connected for 6hx0h compared to 12hx0h, demonstrating a strong response at 6 h, diminishing at 12 h. Figure 5. [123]Figure 5 [124]Open in a new tab Crosstalk between three pathway classes. To better visualize the crosstalk between DEGs, we downloaded the String-db PPI table and filtered only interactions with scores greater than or equal to 0.4. On the left (a) is the crosstalk between DEGs related to innate Immune system, apoptosis and autophagy, with all 59 DEGs well connected, which leads to the inference that IIS and Apoptosis have common genes to work properly, or to be activated. Only autophagy was a less significant pathway, not observed until 12 h by transcriptome analysis. We can see on the right (b) all 28 DEGs for 12hx0h, with fewer genes and interactions comparing to 6hx0h. Near each gene are its possible pathways like ‘iis’ (innate immune system), ‘ap’ (apoptosis), and ‘aut’ (autophagy). In supplementary material other four distinct crosstalks can be seen (Fig. S2). Figure 6. [125]Figure 6 [126]Open in a new tab Heatmap and dotplot for the innate immune system (a,b), apoptosis (c,d), and autophagy (e,f). There are 59 genes related to the Innate Immune System and most of them were upregulated. Here, hierarchical cluster analysis shows 6 h samples mixed with 12 h samples, but completely apart form 0 h. Genes like CXCL10, IRF7, OAS21, IFH1, IFITM1, MX1, OAS2, XAF1, DDX58, IRF9, and ISG20 are more upregulated at 12 h than 6 h, meaning that RLR and OAS pathways responses are increasing in time. Genes like MYL1, MYH8, and ACTN2 are downregulated, the first two with higher modulation at 6 h, and the latter at 12 h. Apoptosis split very well the 3 time points, besides sample A4T2h6 sample, related to 6 h, is closer to 12 h. Many apoptotic genes were upregulated, except RYR1, with a high modulation for SOD2, HSPA6 and IL1β. Genes related to autophagy could not enrich the “autophagy pathway”, but IL6, IL1β, CASP3, IL6ST, ALPL, and IL8 were upregulated DEGs, and MYL1 and MYH8 were downregulated DEGs. CEMiTool/WGCNA coexpression analysis Performing coexpression of genes modulated by Amblyomin-X treatment using CEMiTool, yielded 33 modules named M1 to M32, and an additional one called not-correlated (Table [127]S8). Enrichment analysis was calculated on these modules using KEGG, Reactome, WikiPathways and Gene Ontology (GO), which resulted in a predominance of signaling pathways related to muscle, interleukins, and response to virus infection. Interestingly, some of the genes enriched for these pathways were positioned just after the DEG definition threshold. For instance, 7 genes (ISG15, CASP10, CASP8, TNF, DHX58, TRAF3, and IRF7) were found, related to KEGG RIG-I-like receptor signaling pathway/RIG-I-like Receptor Signaling WP3865 at M13, although only 3 (ISG15, IRF7 and DHX58) out of those 7 were DEGs for 12hx0h and only one (ISG15) for 06hx0h. Since these genes are clustered in the same module, they showed the same expression pattern. In this same module M13, seven genes (IRF1, CASP10, CASP8, TNF, CDKN2A, TRAF3, and IRF7) were related to apoptosis and suggesting that Amblyomin-X has triggered this pathway. HSH2D, coding for a protein that may be a modulator of the apoptotic response through its ability to affect mitochondrial stability^[128]38, was one of the main intramodular hubs in this module. Another intramodular hub in module M13 is ORM1, coding for a member of the lipocalin family of proteins, with a function in modulating the innate immune system in events of acute phase inflammation, and known to bind synthetic drugs^[129]39. ORM1 is also a DEG for 06hx0h and 12hx0h comparisons. The interaction hub in module M13 was SMURF1, which promotes ubiquitination and subsequent proteasomal degradation of MAVS^[130]40 and plays a role in dendrite formation by melanocytes^[131]41. In module M9, 13 genes (H2AFZ, HLA-B, HLA-C, HLA-F, PSMA7, PSMB1, PSMB2, PSMB3, PSMB6, PSMC3, PSMC4, PSMD8, and UBE2D2) were found, enriched by WikiPathways for “Proteasome Degradation” (WP183 from WikiPathways), and 16 (ASB1, ASB8, KEAP1 PSMA7, PSMB1, PSMB2, PSMB3, PSMB6, PSMC3, PSMC4, PSMD8, RNF138, RNF217, UBE2D2, UBE2M, and TRAF7) enriched by Reactome for “Antigen processing: Ubiquitination & Proteasome degradation”. None of which are DEGs, but still following the same pattern of increase in expression over time. We expected such a phenomenon with Amblyomin-X treatment^[132]19. In module 9, the major intramodular hub is PRELID1, involved in the modulation of the mitochondrial apoptotic pathway by ensuring the accumulation of cardiolipin (CL) in mitochondrial membranes. One of the interaction hubs in this module is UBE2M, one of the ubiquitin-conjugating enzymes. The encoded protein is linked with the ubiquitin-like protein NEDD8, which can be conjugated to cellular proteins, such as Cdc53/Culin. Cellular interaction partners of Amblyomin-X Identification of cellular interacting partners is critical to understand the initial process originated by Amblyomin-X, resulting in modulation of pathways at the later stage of the route. Therefore, we obtained the equine melanoma cellular interactomics profile for Amblyomin-X by co-precipitation. Multiple potentially interacting proteins were identified in eluent fraction that bound to immobilized Amblyomin-X. The list of proteins is shown in Table [133]S9, including unique identifiers and the number of peptides. Interestingly, we found proteins related to biological effect previously reported in Amblyomin-X studies, such as apoptosis, mitochondrial dysfunction, regulation of cell migration and protein clearance, which includes cytochrome-c, plasminogen, actin cytoplasmic 1, fibronectin, heat shock protein HSP 90-alpha, E3 ubiquitin-protein ligase Mdm2, mitochondrial superoxide dismutase, and vimentin^[134]15,[135]18,[136]42,[137]43. Furthermore, we identified immunogenic proteins such as Toll-like receptor 2 (TLR2) and T-cell surface antigen CD2 (CD2) as interacting partners of Amblyomin-X. Validation of RNA-Seq results through qRT-PCR This study selected 56 DEGs related to innate immune response, apoptosis and inflammation for validation with qRT-PCR. Next, these genes were classified into 3 groups, according to the median expression: highly expressed (one of the median expressions greater or equal to 50 CPM), moderately expressed (not lowly neither highly expressed) and lowly expressed (all expressions less than 5 CPM) (Table [138]S10). Forty-two (42) out of these 56 genes were chosen for qRT-PCR validation (Fig. [139]S4). The correlation between qRT-PCR LFC for 6hx0h and RNA-Seq LFC for 6hx0h (Table [140]S11) was 80.2%, and the correlation between qRT-PCR LFC for 12hx0h and RNA-Seq LFC for 12hx0h was 81.5% (Fig. [141]S5). Discussion Amblyomin-X treatment reduced equine melanoma tumor size, after which we investigated modulated genes and enriched pathways. The study considered as confounding factors: the pathways related to needle tissue injury as wound healing, the increase in tumor volume as hypoxia, and the vein injury as vascular endothelial cell damage. Although real, these processes were probably not the reason for the decrease in tumor volumes since many tumors treated with vehicle did not show a decrease in size. Our results validated previous uses of Amblyomin-X against several tumor cell lines including skin melanoma^[142]15–[143]18,[144]42 like ER-stress, Mitochondria-stress, and Apoptosis. It is important to note that the transcriptome results presented herein are the mean cell expression values in the tumor’s core, a complex system composed of many distinct normal cells surrounding tumor cells. It is also necessary to observe that pleiotropic effects can deceive the expression of some DEGs related to these functions. For instance, Actins and Calcium ion play an important role in pathways such as Endoplasmic Reticulum Stress (ER-stress), Cytoskeleton remodeling, and Muscle contraction, and IL-8 plays a role in endothelial damage and inflammation response. Since we are interested in the orchestrated responses between Immune System, Inflammation, Apoptosis, ER-stress and Cytoskeleton remodeling, any actin or calcium ion release, belonging only to muscle processes, would confound our inferences. Taken the above into consideration, we presented the most statistically significant genes and pathways modulated by Amblyomin-X (see also supplementary material). Figure [145]7, a simplified model of the tumor microenvironment, shows the Amblyomin-X molecule in the extracellular medium. That allows us to hypothesize on how some pathways are interrelated and evolve in time. Amblyomin-X is able to enter cancer cells that expose phosphatidylserine on the external membrane, entering by endocytosis, and promoting ER-stress through the proteasome inhibition (PI) or other unknown processes. Next, a series of related phenomena are observed, like Unfolded Protein Response (UPR), Mitochondria-stress and Apoptosis. We also hypothesize that in parallel or due to the cell stress, a second phenomenon of exosomes transport^[146]44 occurs between these cells or, less likely, the activation of retrovirus transcription in cancer cells^[147]45. Both processes release viral RNA that interact with viral RNA sensors (RIG-I, LGP2, MDA5, and DDX60) activating the RLR pathway, which has three outcomes: a) apoptosis through PMAIP1 (NOXA); b) interferon transcription, through TBK1, IRF3, and IRF7; and c) cytokines transcription (IL1β, IL-6, IL-8, IL12, IP10, and TNF-α) through NF-κB. Related to the NF-κB path, IKB must be degraded by the proteasome in order to NF-κB to migrate to the nucleus, however, we just hypothesized that the proteasome was inhibited. Taking this into account, the transcription of these cytokines should decrease in melanoma cells. However, other cells, like macrophages, will be able to transcribe these cytokines after RLR signaling activation. Therefore, some melanoma cells will be unable to sustain the cytokine transcription, but paracrine signaling will be present and RLR sensors transcription will be boosted, as well as the inflammatory response, and probably M1 and M2 polarization starts. As mentioned before, these signaling crosstalk should increase the inflammatory response, and also TREM1 and IL-6-induced acute-phase response (downstream ORM1, SERPINE1, CXCL1, and C3) pathways will be activated. Figure 7. [148]Figure 7 [149]Open in a new tab Schematic overview of a simplified tumor environment model. Melanoma, Macrophage, and Fibroblast (stroma) are possibly the main cells interacting through cytokines, exosomes, and other signaling proteins. Herein, we postulate that some Melanoma cells enter in ER-stress state because Amblyomin-X was internalized, next mitochondria stress and cytoskeleton remodeling occur, and finally, apoptosis and survival duel are the final outcomes. Concomitant, IIS pathways are activated in melanoma cells, first by the RLR responses, possibly sensing RNA molecules originating from fibroblast exosomes^[150]44 or being transcribed from endogenous retroviral elements^[151]45. IFN type I and NOXA transcriptions were not seen, but many cytokines (IL1β, IL-6, IL-8, IL12, IP10, and TNF-α) were DEGs, and their proteins were likely to have been produced and released to extracellular medium. Many macrophage signaling pathways were enriched, and their transcribed cytokines are IL1β, IL-6, IL-8, CCL2, CXCL1, CXCL10 (IP10), and TNF-α, all DEGs. Possibly the production of some of these proteins results in M1 and M2 polarization, since STAT1, STAT3, and SOCS3 were also DEGs. These macrophage cytokine releases should lead to many processes, including a crosstalk to Fibroblast, but mainly, the feedback to RLR pathway and the inflammatory response, see text. All these inferences were made according to Metacore algorithms, references, and database.