Sepsis occurs when a host’s normal immune response to infection becomes dysregulated, leading to life-threatening organ dysfunction.1 In human medicine, it is estimated that there are approximately 30 million cases of sepsis worldwide.2 Determining the prevalence of sepsis in veterinary medicine remains challenging due to the diverse etiologies, heterogeneous clinical manifestations, and lack of standardized diagnostic criteria for defining sepsis in animals. Despite continued efforts in human and veterinary medicine to reduce mortality from sepsis worldwide, mortality rates remain high, at around 30% to 50% in people diagnosed with septic shock.1 Mortality rates in veterinary medicine are variable but similarly high across species, ranging from approximately 21% to 68% in dogs, approximately 40% in cats, and approximately 20% to 86% in neonatal foals.3–9 Studies investigating sepsis in large animals are more limited; however, several studies have found evidence of sepsis in approximately one-third of critically ill calves and calves with diarrhea.3,4 In a population of critically ill calves, sepsis was associated with an increased mortality of approximately 77% compared to a mortality rate of approximately 54% in critically ill calves without sepsis.3
Coagulopathies are conditions resulting in disorders of hemostasis, or the body’s ability to form blood clots in response to bleeding, and they encompass disorders resulting in excessive bleeding as well as excessive coagulation. Coagulopathies are common in both human and veterinary patients suffering from sepsis and are often associated with increased mortality.10 Coagulopathies in patients with sepsis can range in severity, but the most severe change is known as disseminated intravascular coagulation (DIC). Disseminated intravascular coagulation is a clotting disorder characterized by widespread clotting in small-to-mid-sized vessels and simultaneous hemorrhage.11 In a study12 of 20 dogs with naturally occurring sepsis, 5 (25%) fulfilled the criteria for DIC, defined as the presence of thrombocytopenia and 2 of the following criteria: 25% prolongation in prothrombin time or partial thromboplastin time, reduced antithrombin activity, abnormally high fibrin(ogen) degradation products concentration, or evidence of red blood cell fragmentation. Out of 2 studies investigating coagulation abnormalities in septic foals, one found evidence of coagulopathy in approximately 7 of 28 (25%) foals with septic shock, and the other study found that 20 of 40 (50%) foals with sepsis fit the criteria for DIC.13,14 In one of these studies,13 foals who succumbed to their disease more frequently had coagulopathy and clinical evidence of bleeding, although this did not reach a level of significance.
The complex interplay between sepsis and coagulation disorders has been a central focus of intensive research efforts. In the past decade, the intricate link between the immune system and coagulation pathways has been elucidated through the concept of immunothrombosis. This phenomenon is characterized as a crucial component of the innate immune response, arising from the intimate crosstalk between the coagulation cascade and the innate immune system.15 Immunothrombosis involves a complex milieu of cellular constituents, effector molecules, and innate immune cells, of which the most well-characterized are monocytes/macrophages, neutrophils, platelets, endothelial cells, tissue factor (F3), inflammatory cytokines, and anticoagulant proteins.16 When well-regulated, immunothrombosis is thought to provide protection against invading pathogens through limiting the movement of infectious agents via thrombi or fibrin networks.17 Aberrant activation or dysregulation of immunothrombosis has been implicated in thrombotic disorders, perhaps most notably DIC, in patients with a variety of infectious or inflammatory conditions, including sepsis.15,18
More work is needed to understand the specific connections between the cells of the innate immune system, including neutrophils and monocytes/macrophages, and the initiation of coagulation. Current work has focused on interactions between platelets and neutrophils. Extensive research has examined the linkage between platelets and the formation of neutrophilic extracellular traps through neutrophil binding of platelet toll-like receptor 4 (TLR-4).19 Neutrophil extracellular traps are comprised of a combination of extracellular chromatin, histones, myeloperoxidase, lactoferrin, and antimicrobial peptides, including defensins, all of which contribute to their antimicrobial properties, and neutrophil extracellular traps have been described in vivo in dogs with sepsis and other inflammatory conditions.20–22 Neutrophil extracellular trap formation can increase fibrin deposition, and they serve as a platform for F3 accumulation.23,24 Furthermore, neutrophils can form interactions with platelets through P-selectin (SELP), promoting thrombosis and thromboembolism.25
Monocytes and macrophages likewise play an important role in the initiation of coagulation, most notably through the expression of F3, which as a component of the F3-FVIIa complex ultimately results in the cleavage of fibrinogen by thrombin to form a fibrin clot.26 Macrophages are a key component of the innate immune system and play an important role in the initiation of the adaptive immune response through the secretion of cytokines and adaptor molecules. Much work has looked to investigate the response of macrophages to lipopolysaccharide (LPS), a major component of the outer cell wall of gram-negative bacteria, as a model for sepsis in animals and humans.27 Recognition of LPS through binding of the membrane-bound receptor TLR-4 on macrophages and monocytes, in combination with other membrane bound and cytosolic receptors, leads to the activation of multiple pathways that ultimately results in the production of effector molecules and proinflammatory cytokines, including IL-1, IL-6, and tumor necrosis factor (TNF)-α.1 Many of the proinflammatory changes induced by macrophages in response to LPS play a role in the initiation of coagulation pathways, most notably the activation of F3 and downregulation of anticoagulant proteins, including antithrombin, protein C, and F3 pathway inhibitor. Altogether, these changes are thought to contribute to a procoagulant state in sepsis.1
Our study aims to develop a more comprehensive understanding of the pathophysiological mechanisms underlying coagulation disorders associated with sepsis, using cattle as a model species. Cattle are a key livestock species and have been used extensively as a model species in biomedical research. Sepsis is an important cause of morbidity and mortality in calves and adult cattle, but information remains limited on the development of their immune response to infectious disease and the relationship between inflammation and coagulation in this species,3,4,28 The choice of cattle as a model species for this study is supported by previous research investigating the response of bone marrow–derived macrophages to LPS across 9 species, including humans, sheep, goats, cattle, water buffalo, horses, pigs, mice, and rats. That study29 found an overall conservation of constitutive and LPS-inducible transcription factor transcripts across all 9 species, with the primary differences between species relating to immunometabolism. The present study will build on this previous work by analyzing the early transcriptomic response of monocyte-derived macrophages (MoMΦ) exposed to LPS in relationship to coagulation. We hypothesized that there would be an increased expression of procoagulant genes and a decreased expression of genes that protect against coagulation in the macrophages exposed to LPS relative to a control group. Our results may grant new insights into the mechanisms underlying coagulation abnormalities in sepsis, which may help provide routes for the discovery of novel prognostic markers or therapeutic targets.
Methods
Animals
There were 2 rounds of data collection in this experiment. In the first set of data collection, blood was collected from 2 Holstein steer (Bos taurus) from the Washington State University (WSU) dairy herd free from Mycobacterium avium subsp paratuberculosis. This herd was held in an open feedlot and managed by the college Animal Resource Unit staff. For the first data set (biological replicates 1 and 2), the collection of blood for in vitro experiments was approved by the WSU IACUC in association with another study (Animal Subject Approval Form 6542). For the second set of data collection (biological replicate 3), blood from 10 Holstein steers from the same herd was collected and pooled. For the second data set, collection of blood from multiple species for research was approved by the WSU IACUC in association with another study (Animal Subject Approval Form 6148).
Generation of MoMΦ
For the first data set (biological replicates 1 and 2), whole blood (500 mL from each steer) was collected from the jugular vein into a bottle containing 70 mL acid citrate dextrose for a final acid citrate dextrose percentage of 14% and stored at 4 °C until processing. For the second data set (biological replicate 3), whole blood (125 mL from each steer) was collected and pooled into a bottle containing 14% acid citrate dextrose and stored at 4 °C until processing. Peripheral blood mononuclear cells were isolated by density gradient centrifugation using Ficoll-Paque density 1.077 (GE Healthcare Life Sciences). The isolated peripheral blood mononuclear cells were then incubated with anti-CD14 magnetic cell-sorting microbeads (MACS; Miltenyi Biotec) and passed through a magnet to collect the CD14+ monocytes. The monocytes were counted using an automated cell counter (Moxi OS 4.4; Orflo) and suspended in culture medium at a concentration of 1 X 106 cells/mL. The culture medium was prepared using Dulbecco’s Modified Eagle Medium; high glucose, 4-(2-hydroxyethyl)-1-piperazineethanesfulonic acid; no phenol red (Thermo Fisher Scientific); 2.7 mL/250 mL medium of penicillin-streptomycin (stock solutions 10,000 U/mL penicillin and 10,000 µg/mL streptomycin); 27.5 mL/250 mL medium of heat inactivated calf bovine serum; 500 µL/50 mL β-mercaptoethanol; and glutamine (0.5 mM). The cells were plated in 6-well plates, 3 mL in each well (for an initial seeding of 3 million cells/well), and 25 ng/mL of recombinant bovine granulocyte-macrophage colony-stimulating factor (Catalog #RP0871B-005; Kingfisher Biotech) was added to each well. The initial seeding of 3 million cells/well was chosen to compensate for cell loss (approximately 25%) during the 6-day differentiation period. Cells were then incubated at 37 °C with 5% CO2, and media was changed on days 3 and 6 to maintain cell health and remove nonadherent or apoptotic cells. This approach minimized the potential for prestimulation of macrophages by dying cells in culture. Prior to the addition of the treatments on day 6 of culture, cells were visualized with phase contrast microscopy to assess cell viability and confirm the transition from monocytes to macrophages, characterized by increased cytoplasmic processes.30
Treatments
On day 6 of culture, 1 mL of medium was removed and replaced with fresh warmed complete cell medium. A control plate was treated with 50 µL of PBS/well, and the other plate was treated with a final concentration of 5 µg/mL of LPS (Escherichia coli O111:B4, Catalog #L2630; Sigma-Aldrich)/well. Lipopolysaccharide was diluted in endotoxin-free PBS to achieve the desired concentration, and PBS also served as the vehicle control. The final concentration of 5 µg/mL for LPS was chosen based on previous work in our lab demonstrating consistent and reliable activation of bovine macrophages without excessive cell death (unpublished data).31 There were 2 plates in total (1 for the LPS treatment and the other for the control treatment with PBS), with 6 wells each for each biological replicate. The plates were then gently mixed for 45 seconds and placed back in the incubator for 3 hours. The time point of 3 hours was selected to characterize the early gene expression profile of LPS-exposed macrophages, which has been described as an appropriate early time point.32
RNA isolation
Each well was rinsed twice with 5 mL of PBS. The RNA was isolated using the Aurum Total RNA Mini Kit (Bio-Rad Laboratories) procedure for adherent cell cultures, and 2 wells were used for each RNA sample. Lysis solution was added to the well and pipetted up and down to lyse cells thoroughly, then transferred to a second well and again pipetted up and down to lyse the cells. Next, 70% ethanol was added and mixed, and the mixture was transferred to an RNA binding column and centrifuged (14,000 X g for 1 minute). The column was washed with low stringency wash solution and centrifuged (14,000 X g for 1 minute). Diluted DNase I was then added directly to the membrane stack at the bottom of each column and incubated for 15 minutes. Afterwards, the column was washed first with high-stringency wash solution and centrifuged (14,000 X g for 1 minute) and then with low-stringency wash solution and centrifuged (14,000 X g for 1 minute). The column was then centrifuged for an additional 2 minutes (14,000 X g). The RNA binding column was then transferred to a capped microcentrifuge tube, and warmed elution solution was then added directly to the membrane stack and allowed to sit for 1 minute. Afterwards, the sample was centrifuged for 3 minutes at 8,000 X g to elute the RNA. The quantity and quality of the RNA sample was assessed using a NanoDrop One/OneC spectrophotometer, version 2.4.0.37 (Thermo Fisher Scientific). At the end, samples consisted of 3 technical replicates for each of the 3 biological replicates (2 biological replicates in the first set of data collection and 1 biological replicate in the second set of data collection). OD260/280 ratios > 2.0 were obtained for all total RNA samples, indicating high RNA purity and no genomic DNA contamination. The samples were stored at −80 °C until they were sent to Novogene for processing.
RNA sequencing
The samples were sent to Novogene for processing. The RNA quality was assessed using the Invitrogen Nanodrop One Spectrophotometer (Thermo Scientific) and Qubit 4 Fluorometer (Thermo Fisher), and samples with purity OD260/280 ≥ 2 were used for library preparation. Per correspondence with Novogene, the quality control for the RNA samples was performed using the Qubit 2.0 Spectrophotometer (Thermo Fischer) and the Bioanalyzer Instrument (Agilent). Libraries were prepared from 1 µg of total RNA per sample using the NEBNext Ultra II Non-Directional RNA Library Prep Kit (New England Biolabs) according to the manufacturer’s protocols. The quality and concentration of the library were assessed with Labchip (PerkinElmer) and quantitative PCR. Libraries were sequenced on a NovaSeq 6000 (Illumina) platform using the S4 flow cell and paired-end 150-bp sequencing mode, generating an average of 56 million raw reads per sample.
Data analysis on iDEP 2.0
The read count data from the 3 technical replicates for each of the 6 biological replicates was averaged. The averaged read count data from Novogene were uploaded to iDEP 2.0 and aligned to a recent bovine reference genome (ARS-UCD1.2 Ensembl).33 Genes with counts per million (CPM) less than 0.5 CPM were filtered out, and data were normalized to CPM with Edge R (log2 [CPM + 4]). Differential expression data between the control and LPS (treatment) groups, including log2 fold change and false discovery rate (FDR)-adjusted P values, were extracted using “DESeq2” with an FDR cutoff of 0.1.34 Differentially expressed genes were defined by an adjusted P value of less than .05 and a minimum log fold change of 2.
Hierarchical clustering of the top 1000 genes and generation of heat maps was conducted using the “heatmap.2” function in iDEP 2.0, and the graph was generated using ggplot2. Principal component analysis (PCA) was performed in iDEP 2.0. Enrichment Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of differentially expressed genes was generated from this data set using the generally applicable gene set enrichment for pathway analysis with an FDR significance cutoff ≤ 0.2. Gene expression data were visualized on the KEGG pathway diagrams using Pathview.35,36
Statistical analysis
A power analysis was run by Novogene (Sacramento, CA) based on 3 experimental replicates with runs in triplicates with acceptable statistical power. False discovery rate–adjusted P values extracted from iDEP are calculated using the Benjamini-Hochberg method with an FDR-adjusted P value threshold of < .05.
Results
Comparison of differentially expressed genes and genes related to coagulation in LPS-exposed monocyte-derived bovine macrophages
The transcriptome of the MoMΦ exposed to LPS (treatment) for 3 hours was analyzed for differentially expressed genes and compared to the MoMΦ exposed to PBS (control). Using an FDR ≤ 0.05 and minimum fold change of 2, there were 1,602 upregulated genes and 1,209 downregulated genes (Figure 1).
Heat map visualization displaying the 1,000 most variable differentially expressed genes in bovine monocyte-derived macrophages (MoMΦ) exposed to 5 µg/mL lipopolysaccharide (LPS) for 3 hours versus control MoMΦ treated with an equal volume of PBS for the same length of time. Three biological replicates are shown in each treatment group. Increased expression is shown in red, and decreased expression is shown in green. Logarithmic fold change (red-green scale) values describe gene expression values. C = Control group.
Citation: American Journal of Veterinary Research 85, 12; 10.2460/ajvr.24.04.0116
Of the genes with significant up- or downregulation, we analyzed a panel of 16 genes identified as procoagulant (SELP, F3, factor V [F5], factor XI [F11], thromboxane A2 receptor [TBXA2R], plasminogen activator inhibitor 1 [SERPINE1], plasminogen activator inhibitor 2 [SERPINB2], VEGFA, thrombospondin 1 [THBS1], factor VIII, coagulation factor II thrombin receptor, coagulation factor XIII A chain, fibrinogen γ chain, factor XII, von Willebrand factor, and serpin family F member 2) and 9 genes identified as protective against coagulation (heparan sulfate-glucosamine 3-sulfotransferase [HS3ST1], prostaglandin I2 receptor [PTGIR], protein C receptor [PROCR], urokinase plasminogen activator [PLAU] receptor [PLAUR], F3 pathway inhibitor 2 [TFPI2], protein S, PLAU, thrombomodulin, and antithrombin) (Tables 1 and 2). These genes were selected based on a review of previous literature suggesting their role in coagulation and genes identified in the KEGG pathway complement and coagulation cascade.37–52 Additionally, we investigated 4 proinflammatory genes (TNF, IL-6, IL-1A, and IL-1B) and 3 anti-inflammatory genes (TGF-B1, IL-10, and IL-1RN) (Tables 3 and 4).
Differential expression of procoagulant genes in bovine monocyte-derived macrophages (MoMΦ) exposed to 5 µg/mL lipopolysaccharide (LPS) for 3 hours versus control MoMΦ treated with an equal volume of PBS for the same length of time ranked by fold-change.
| Ensembl ID | Gene symbol | Gene name | Log2 fold change | FDR-adjusted P value |
|---|---|---|---|---|
| ENSBTAG00000020755 | SELP | P-selectin | +7.43 | 2.02E-12 |
| ENSBTAG00000007101 | F3 | Tissue factor | +4.32 | 2.77E-06 |
| ENSBTAG00000014465 | SERPINE1 | Plasminogen activator inhibitor 1 | +3.63 | .000392 |
| ENSBTAG00000023198 | SERPINB2 | Plasminogen activator inhibitor 2 | +3.08 | 1.47E-06 |
| ENSBTAG00000002006 | THBS1 | Thrombospondin 1 | +2.46 | .00505 |
| ENSBTAG00000017722 | F5 | Factor V | +2.33 | 1.50E-05 |
| ENSBTAG00000003572 | F11 | Factor XI | +1.56 | .00175 |
| ENSBTAG00000005339 | VEGFA | Vascular endothelial growth factor A | +1.47 | 2.55E-05 |
| ENSBTAG00000014944 | TBXA2R | Thromboxane A2 receptor | −3.19 | .000474 |
FDR = False discovery rate.
Differential expression of genes considered protective against coagulation in MoMΦ exposed to 5 µg/mL LPS for 3 hours versus control MoMΦ treated with an equal volume of PBS for the same length of time ranked by fold change.
| Ensembl ID | Gene symbol | Gene name | Log2 fold change | FDR-adjusted P value |
|---|---|---|---|---|
| ENSBTAG00000007449 | HS3ST1 | Heparan sulfate-glucosamine 3-sulfotransferase 1 | +6.23 | 5.51E-10 |
| ENSBTAG00000015844 | TFPI2 | Tissue factor pathway inhibitor 2 | +5.51 | 7.14E-19 |
| ENSBTAG00000014585 | PTGIR | Prostaglandin I2 receptor | +4.68 | .000232 |
| ENSBTAG00000008291 | PROCR | Protein C receptor | +2.38 | .00166 |
| ENSBTAG00000013125 | PLAUR | Urokinase-type plasminogen activator receptor | +0.95 | .00625 |
Differential expression of proinflammatory genes in MoMΦ exposed to 5 µg/mL LPS for 3 hours versus control MoMΦ treated with an equal volume of PBS for the same length of time ranked by fold change.
| Ensembl ID | Gene symbol | Gene name | Log2 fold change | FDR-adjusted P value |
|---|---|---|---|---|
| ENSBTAG00000001321 | IL-1B | Interleukin 1 β | +6.14 | 1.78E-77 |
| ENSBTAG00000025471 | TNF | Tumor necrosis factor α | +5.64 | 3.54E-70 |
| ENSBTAG00000014921 | IL-6 | Interleukin 6 | +5.33 | 1.79E-08 |
| ENSBTAG00000010349 | IL-1A | Interleukin 1 α | +3.50 | 2.19E-36 |
Differential expression of anti-inflammatory genes in MoMΦ exposed to 5 µg/mL LPS for 3 hours versus control MoMΦ treated with an equal volume of PBS for the same length of time ranked by fold change.
| Ensembl ID | Gene symbol | Gene name | Log2 fold change | FDR-adjusted i value |
|---|---|---|---|---|
| ENSBTAG00000006685 | IL-10 | Interleukin 10 | +4.14 | 2.74E-52 |
| ENSBTAG00000019665 | IL-1RN | Interleukin 1 receptor antagonist | +2.51 | 2.98E-14 |
The addition of LPS to the MoMΦ resulted in significant upregulation of 4 of 4 proinflammatory genes (TNF, IL-6, IL-1A, IL-1B), 2 of 3 anti-inflammatory genes (IL-10 and IL-1RN), 8 of 16 genes identified as procoagulant (SELP, F3, F5, F11, SERPINE1, SERPINB2, VEGFA, THBS1), and 5 of 9 genes identified as protective against coagulation (HS3ST1, PTGIR, PROCR, PLAUR, TFPI2). There was significant downregulation of 1 of 16 procoagulant genes (TBXA2R). Procoagulant genes that were investigated and were not significantly different between treatment groups included factor VIII, coagulation factor II thrombin receptor, coagulation factor XIII A chain, fibrinogen γ chain, factor XII, von Willebrand factor, and SERPINF2. Genes identified as protective against coagulation that were not significantly different between treatment groups included protein S, PLAU, thrombomodulin, and antithrombin. The anti-inflammatory gene TGFB1 was not significantly different between treatment groups.
Enriched KEGG pathways
Categorization of differently expressed genes was accomplished using the generally applicable gene set enrichment pathway analysis method to generate KEGG gene set pathways. The top 5 upregulated pathways in the KEGG pathway based on FDR-adjusted P values included cytokine/cytokine receptor interaction, TNF signaling pathway, nucleotide oligomerization domain–like-receptor signaling pathway, influenza A, and the IL-17 signaling pathway. When interrogating pathways related to coagulation using the KEGG gene set, the complement and coagulation cascades pathway was significantly upregulated in the LPS-exposed MoMΦ (FDR-adjusted P value of .027) (Figure 2).
Enrichment Kyoto Encyclopedia of Genes and Genomes analysis of differentially expressed genes engaged in the complement and coagulation cascades signaling pathway. Red represents upregulated genes and blue represents downregulated genes in MoMΦ to 5 µg/mL LPS for 3 hours relative to the control group, consisting of MoMΦ exposed to an equal volume of PBS for the same length of time. Logarithmic fold change (red-green scale) values describe gene expression values.
Citation: American Journal of Veterinary Research 85, 12; 10.2460/ajvr.24.04.0116
Discussion
The present study aimed to further the understanding of the early transcriptomic response to LPS using MoMΦ as an in vitro model for sepsis. Specifically, we explored the early changes in genes that might be associated with coagulation to better understand the development of coagulopathies in patients with sepsis. We hypothesized that genes promoting coagulation would be upregulated and that those genes protecting against coagulation would be downregulated relative to a control. However, the results showed increased expression of a majority of genes investigated that both promote and protect against coagulation as well as increased expression in genes promoting inflammation. These findings suggest that in the early response to LPS, macrophages may exhibit a multifaceted response, simultaneously promoting and protecting against excessive coagulation. This multifaceted response highlights the complex interplay between different pathways and mechanisms involved in the early stages of septicemia. These data further support the link between inflammation and coagulation in macrophages exposed to LPS and demonstrate the utility of using MoMΦ as a model system to investigate sepsis-associated coagulopathies. These insights into the early transcriptomic changes in response to LPS may help guide future research on the development and prevention of thrombotic complications in sepsis.
We found increased expression levels of 8 genes associated with coagulation in the LPS-exposed MoMΦ relative to a control, including F3, SELP, SERPINE1, SERPINB2, THBS1, F5, F11, and VEGFA. Upregulation of F3, SERPINE1, SERPINB2, SELP, thrombospondin-1 THBS1, and VEGFA have been well documented in in vitro and in vivo models of sepsis in animals and/or human patients with sepsis.26,46,51,53–57 The consistency of these findings in the present study highlights the potential of MoMΦ as a model system to investigate the pathophysiology of coagulopathies and sepsis.
Unexpectedly, we found that several genes thought protective against coagulation were also increased in the LPS-exposed MoMΦ (HS3ST1, TFPI2, PTGIR, PLAUR, and PROCR), and 1 gene considered procoagulant/prothrombotic was downregulated (TBXA2R). These findings suggest that in the early response to LPS, macrophages may exhibit a complex response, both promoting and protecting against excessive coagulation. Upregulation of HS3ST1, TFPI2, PTGIR, and PROCR or their gene products has been previously documented in various animal models of sepsis as well as human patients with sepsis and supports the results found in this study using MoMΦ as a model.58–61 In addition to their roles in protecting against excessive coagulation, several of these genes or their products have been implicated in either promoting or protecting against the inflammatory response. Heparan HS3ST1 has a proposed anti-inflammatory role as HS3ST1 knockout mice showed a proinflammatory phenotype and increased sensitivity to mortality from LPS-induced septic shock.47 High expression of TFPI2 has been shown in mice after injection of LPS, and TFPI2-deficient mice had an increased susceptibility to gram-negative bacterial pathogens, suggesting an additional immunomodulatory role that may explain the increased levels of TFPI2 in the present study.58 In addition to its role in the formation of plasmin and resolution of clots, the PLAUR is involved in cell signaling and may help reinforce the inflammatory reaction induced by TLR-4 in response to LPS.41,44 Finally, stimulation of TBXA2R in equine monocytes with a thromboxane A2 receptor agonist ([125I]BOP) resulted in suppression of endotoxin-induced TNF-α formation, suggesting that TXA2 production by monocytes may have an autocrine signaling function.62 In the present study, the decreased TBXA2R receptor expression could be related to a proinflammatory function and need for increased TNF expression at this early time point. As these studies show, the upregulation of many of the genes considered protective against coagulation in the present study may reflect the multiple roles of these genes in both coagulation and inflammation rather than coagulation alone.
We also investigated the profiles of several genes associated with inflammation, including proinflammatory and anti-inflammatory genes. There was increased expression of the proinflammatory genes IL-1A, IL-1B, TNF, and IL-6 in MoMΦ exposed to LPS. Interestingly, 2 out of 3 anti-inflammatory genes (IL-1RN and IL-10) were also significantly upregulated. The increased production of proinflammatory genes in MoMΦ exposed to LPS supports a proinflammatory (M1) macrophage phenotype in the present study, but the increased expression of anti-inflammatory genes was unexpected. Although IL-10 is an anti-inflammatory cytokine and more classically associated with an anti-inflammatory (M2) macrophage phenotype, production of IL-10 by alveolar macrophages in response to LPS has been reported.63 Another consideration would be that the population of MoMΦ in this study represented a mixed population of proinflammatory (M1) and anti-inflammatory (M2) macrophages.
To the authors’ knowledge, this is the first study demonstrating upregulation of many of these genes in MoMΦ in response to LPS, which may provide additional insight into the pathophysiology of sepsis and coagulation disorders. In addition, this study demonstrates the utility of MoMΦ as a model system for sepsis, and results from the present study may identify candidate genes or gene products for diagnostic, therapeutic, or prognostic use. Expression levels of genes identified as procoagulant in this study could be used to aid in the early recognition of coagulopathies, and the expression of proinflammatory genes and procoagulant genes could be used to facilitate sepsis diagnosis. The expression levels of VEGFA, F3, and SELP were significantly higher in granulocytes from human patients with polycythemia vera or essential thrombocythemia and a history of thrombosis compared to those without, suggesting a link between these genes and thrombosis and identifying these genes as possible diagnostic markers or therapeutic targets in recognition of or treatment of thrombosis.38 Additionally, increased THBS-1 expression was associated with an increased risk of thrombosis in human patients with Chuvash polycythemia.37 A number of the upregulated genes identified in this study have been successfully used in human medicine to aid in the diagnosis and prognosis of patients with sepsis, including F3, SELP, SERPINE1, VEGFA, and THBS1.46,52,64–66 Plasminogen activator inhibitor 1 has been used to identify human patients with sepsis who go on to develop overt DIC, emphasizing its potential utility as an early marker of the development of coagulopathies.52 Recently, an assay for SERPINE1 has been developed and validated for use in dogs, although further investigation is needed to link increased levels of SERPINE1 with increased risk of coagulopathy in veterinary medicine.67
One of the primary limitations of the current study is the small number of biological replicates, which may decrease the ability to detect statistically significant differences in the differential expression of genes with a smaller fold change (i.e. increased type II error).68 Ideally, 6 biological replicates per treatment group has been recommended; however, as few as 3 biological replicates may be used to identify significantly differently expressed genes that change by more than a factor of 2.69 The tool used for the identification of differentially expressed genes in this paper (DESeq2) had adequate performance (defined as identification of true positives and well-controlled FDR) with a low number of replicates (≤ 3 when the fold-change threshold was set at 2).69 Importantly, even with the small sample size in this study, we identified 1,602 upregulated genes and 1,209 downregulated genes with high statistical significance.
In this study, the dosage of LPS used was higher than the levels typically reported for human or bovine MoMΦ. Generally, LPS concentrations in similar studies30,70,71 are around 100 ng/mL, with occasional reports of 500 ng/mL or 1 µg/mL.30,70,71 Our selected concentration was based on previous research from our lab, which demonstrated consistent and reliable activation of bovine macrophages without inducing excessive cell death.31 The higher dosage ensured robust activation of macrophages, allowing for a comprehensive analysis of their response. While the dose used here likely exceeds physiological levels, this approach provides valuable insights into the upper limits of macrophage activation and their potential responses under extreme conditions. This information can be crucial for understanding macrophage behavior in highly inflammatory environments and could inform future studies aiming to modulate macrophage activity in various clinical contexts.
Furthermore, the decision to pool the blood from 10 steers for the third biological replicate introduced an additional variable to the study. Pooling of samples for RNA sequencing has been previously described as a measure to improve cost efficacy and has been shown to identify differentially expressed genes in comparison with individual RNA samples.72,73 However, in studies investigating sample pooling for RNA sequencing, the pooling step occurs after extraction of RNA from individual samples rather than pooling material from individuals and then performing RNA extraction as was done in this study. Despite this approach, the correlation between biological replicates of the same treatment group was still high, and future studies may further explore the implications of pre-extraction pooling to optimize cost and data quality. Furthermore, the patterns of differential gene expression with the genes investigated in this study should ideally be validated with western blotting or reverse-transcription PCR, and further studies are needed to determine if these genes are associated with coagulation defects in vivo.74
The present study sought to further our understanding of the pathophysiology of coagulopathies in sepsis in relation to inflammation by investigating differential gene expression in MoMΦ exposed to LPS at an early time point of 3 hours. This study provided further support that monocytes and macrophages contribute to the procoagulant state found in patients with sepsis through upregulation of specific genes associated with coagulation, including F3, SELP, SERPINE1, SERPINB2, THBS1, F5, F11, and VEGFA. Future studies could investigate the use of the genes and the protein products identified in this study as biomarkers for coagulopathies or to aid in the diagnosis and prognostication of sepsis. The unexpected finding of the upregulation of genes considered protective against coagulation was intriguing. This finding may reflect that there is a multifaceted response both promoting and protecting against coagulation at the early time point that was selected, and further studies investigating these panels of genes at later time points may help better characterize these changes. Furthermore, many of the genes identified as protective against coagulation have dual roles in promoting a proinflammatory state. Thus, the upregulation of these genes may reflect their role in promoting inflammation at this early time point rather than or in addition to their potential role in protecting against coagulation. Given the complex nature of coagulation and inflammation in vivo, it is still uncertain if upregulation of procoagulant genes or genes considered protective against coagulation in MoMΦ contributes to an overall procoagulant state in patients with sepsis. Future in vivo investigations could characterize the gene expression profiles of patients with naturally occurring sepsis to determine if changes in the genes identified in this study are associated with increased mortality or coagulopathies to help identify potential treatment targets or biomarkers to aid in the prognostication of patients with sepsis.
Acknowledgments
The authors would like to acknowledge Emma Karel-Ward for her assistance with blood collection and Maria Clara de Souza for her help with cell isolation. They also thank Dr. Julianne Hwang for her contributions to this work. They are grateful to Dr. Jillian Haines and Dr. Mara S. Varvil for their valuable input on the manuscript.
Disclosures
The authors have nothing to disclose. No AI-assisted technologies were used in the generation of this manuscript.
Funding
This work was supported by funds awarded to HRC by the Washington State University College of Veterinary Medicine intramural grant and the Washington State University Monoclonal Antibody Center Monoclonal Antibody Center, College of Veterinary Medicine, Washington State University (wsu.edu). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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