Immune system expression profiling in patients experiencing low back pain: A pilot study

Lauren E Lisiewski; Xiaoning Yuan; Joseph Chin; Nadia Kiridly; George C Christolias; Clark C Smith; Nadeen O Chahine

doi:10.1002/pmrj.70067

. 2025 Dec 30;18(7):735–747. doi: 10.1002/pmrj.70067

Immune system expression profiling in patients experiencing low back pain: A pilot study

Lauren E Lisiewski ^1,², Xiaoning Yuan ³, Joseph Chin ³, Nadia Kiridly ², George C Christolias ³, Clark C Smith ³, Nadeen O Chahine ^1,^2,^✉

PMCID: PMC13358407 PMID: 41467468

Abstract

Background

Spine pathologies are associated with an inflammatory microenvironment, with previous studies demonstrating systemic inflammation based on analysis of serum from patients with low back pain (LBP). This pilot study used gene expression profiling to identify candidate cellular mechanisms mediating systemic inflammation in patients with LBP, with and without radicular pain, compared to asymptomatic controls.

Objective

To identify differences in expression of inflammation‐ and immune‐related genes in whole blood from patients with LBP and spine pathology compared to control participants. Also, identify relationships between differentially expressed (DE) genes and patient‐reported outcomes (PROs) for pain (Visual Analog Scale [VAS]) and disability (Oswestry Disability Index [ODI]).

Design

Case control, observational study.

Setting

Outpatient clinic at academic institution.

Patients

Nine participants with LBP who were 18 years or older and diagnosed with lumbar disc herniation, spinal stenosis, or disc degeneration. Eight control participants who were 18 years or older and had no history of LBP or spinal treatment.

Interventions

N/A.

Main outcome measure

Expression levels of inflammation‐ and immune‐related genes in patients with LBP compared to control participants measured using Nanostring nCounter analysis.

Results

Analysis of patients with LBP versus controls resulted in 11 DE genes (p ‐adj < .05) and 9 trending genes (p ‐adj < .1) falling into three clusters: “Interferon α/β Signaling,” “Immunoglobulin Binding,” and “Toll‐like Receptor (TLR) Binding.” VAS scores were also associated with expression level of FCER1G, FCGR3A/B, and KLRK1. Further analysis showed significantly lower expression of FCER1G in patients with high versus low VAS (p < .05). No relationships between DE gene expression and ODI was identified; however, a significant correlation between VAS and ODI scores was observed (p < .05).

Conclusions

Clustering of DE and trending genes suggested increased interferon α/β signaling and TLR signaling may contribute to systemic inflammation related to LBP. Immunoglobulin binding was also implicated and varied with pain severity in patients with LBP.

INTRODUCTION

Low back pain (LBP) is the leading cause of disability worldwide and is estimated to affect 60%–80% of the population at some point in their life. ¹ , ² Consequently, LBP contributes substantially to the health care burden and costs globally, justifying a need for better understanding and maintenance of the pathologies that cause the condition. ³ LBP is commonly associated with lumbar spine pathologies, including intervertebral disc (IVD) degeneration, disc herniation, and spinal stenosis. ¹ , ⁴ , ⁵ , ⁶ , ⁷ Currently, conservative LBP therapies focus on pain management using medications, physical therapy, or, in more severe cases where radicular pain is present, epidural steroid injections (ESI) and other minimally invasive interventions. ⁸ , ⁹ However, these treatment approaches focus on managing the symptoms, rather than treating the origins of patient pain to prevent disease progression and pain recurrence. Therefore, identification of the cellular mechanisms mediating LBP is of great interest to inform potential disease‐modifying therapies and precision medicine approaches.

Importantly, disc pathologies are associated with local inflammation and immune activation at the site of degeneration or injury. ¹⁰ Prior studies have also characterized systemic inflammation in this patient population, demonstrating elevated levels of inflammatory cytokines and chemokines in the blood. Evidence of inflammation was observed in patients with multiple diagnoses compared to controls, with cytokine and chemokine levels increasing with disease severity. ¹¹ , ¹² Although these findings establish that systemic inflammation and the immune response are dysregulated in patients with LBP and spine pathology, they are also limited in their focus on a subset of inflammatory cytokines. Alternatively, analysis of transcription at the gene level allows for inference of related proteins, as well as additional information on the signaling mechanisms directing protein translation.

Genetic changes in peripheral blood have been previously examined through identification of polymorphisms associated with pain phenotypes. ¹³ Other studies focused on evaluating differences in expression levels for a selection of pain‐related genes in patients experiencing acute versus chronic pain. ¹⁴ , ¹⁵ Most broadly, bulk RNA sequencing has been used to study the whole genome, with whole blood transcriptomic analysis known to reflect genetic changes in the nervous system, making it an attractive approach for unbiased characterization of pain. ¹⁶ Analysis of whole blood from patients with disc pathology, sciatica, or spinal cord injury, in comparison to healthy controls, revealed differential expression of genes related to inflammation and altered immune cell prevalence. ¹² , ¹⁷ , ¹⁸ , ¹⁹ , ²⁰ Additionally, another study identified an upregulation of genes related to pain and the immune response in patients with chronic pain compared to healthy controls, with these genes also implicated in the transition from acute to chronic pain. ¹⁶ Taken together, these studies support the premise that gene expression analysis of whole blood is effective for identifying transcriptional changes related to pain, inflammation, and the immune response.

This pilot study focused on identifying gene expression changes specific to inflammation and the immune response in patients experiencing chronic LBP due to lumbar spine pathologies, with and without radicular pain, in comparison to healthy controls. It aimed to fill a critical gap in the identification of cellular mechanisms mediating the inflammatory and immune response associated with LBP in the context of multiple lumbar spine pathologies, contrary to previous studies that focused on a singular diagnosis (eg, disc herniation). Correlations of inflammation‐ and immune‐related genes with patient‐reported pain and disability severity provide additional insights into underlying systemic, biological variables associated with LBP and related pathology.

METHODS

LBP patient participants

This study was approved by the institutional review board of Columbia University. Patients with LBP were recruited in person at the physician's office in the Department of Rehabilitation and Regenerative Medicine from January 2019 to May 2019 (n = 9). Informed consent was obtained before enrollment of all patients. Patients were eligible for the study if they were 18 years or older and presenting with LBP, with or without radicular pain. All patients had a diagnosis of lumbar disc herniation, spinal stenosis, or disc degeneration in the lumbar spine based on clinical examination and magnetic resonance imaging, and were recruited after being referred for ESIs, with samples collected prior to injection treatment. Patients with prior history of lumbar spine surgery, previous ESI within the last 6 months, known inflammatory conditions, including rheumatoid arthritis (RA), osteomyelitis, discitis, gout, ankylosing spondylitis, arachnoiditis, or other infections, as well as a history of cancer, patients pregnant or breastfeeding, and those with decision impairment were excluded from the study.

Data collected from patients with LBP included demographic information (gender, ethnicity, race, age, and body mass index [BMI]), diagnosis, and duration of symptoms. BMI was categorized as “normal” (18.5–24.9), “overweight” (25.0–29.9), or “obese” (≥30.0). Pain level using the 10‐point Visual Analog Scale (VAS) and disability level using the Oswestry Disability Index (ODI) out of a total score of 100 were also collected and categorized as “high” (VAS >5 or ODI > 30) or “low” (VAS≤5 or ODI≤30) based on median values. Age, duration of symptoms, VAS, and ODI are represented as mean ± SD.

Control participants

Control participants were recruited as volunteers from the Columbia University Irving Medical Center (CUIMC) campus in January 2020 (n = 8). Informed consent was obtained before enrollment of all control participants. Participants were included if they were 18 years or older and had no self‐reported history of back pain or ESIs. Demographic data was collected from control participants with age represented as mean ± SD. Statistical differences in age between patients with LBP and control participants were assessed using a Student's t‐test. Differences in the gender, ethnicity, race, and BMI distributions between groups were compared with a $χ^{2}$ test. In all statistical analyses, p < .05 was considered statistically significant.

Blood sample collection and processing

Blood (2.5 mL) was collected from all participants using venipuncture into BD Paxgene RNA tubes, which contain a reagent that lyses all cells and immediately stabilizes RNA, minimizing gene expression changes often observed soon after blood collection. Samples were incubated at room temperature for at least 2 hours, stored at −20°C overnight, then transferred to −80°C for long‐term storage. Tubes were removed from the freezer and incubated for 2 more hours at room temperature before RNA extraction using the Paxgene Blood RNA Kit, according to the manufacturer's protocols.

Nanostring preparation

The quality of RNA from 17 samples, 9 LBP patient and 8 control participants, was analyzed by the Molecular Pathology Shared Resource facilities at Columbia University using an Agilent Bioanalyzer. All samples were confirmed to have an RNA integrity number (RIN) >8.2 before proceeding with further analyses. High quality RNA samples were provided to the Human Immune Monitoring Core at Columbia University, where samples were analyzed using nCounter Analysis Systems (Nanostring Technologies) according to the manufacturer's protocol. The Immunology V2 panel was used, which contained 579 human immunology‐related genes, 15 housekeeping genes, 8 negative controls, and 6 positive controls.

Differentially expressed genes

Impact of covariates, including gender, age, and BMI, was evaluated using the nSolver software (Nanostring Technologies), and the condition resulting in the greatest number of differentially expressed (DE) genes was further analyzed. In summary, any genes or controls that frequently fell below the background threshold level were eliminated from further analysis. Raw counts for each gene above the background threshold were multiplied by a normalization factor, determined by the positive and negative controls, that accounted for technical variability. Fold change and t‐statistics were calculated for each gene, while controlling for the determined covariates (ie. age, BMI) as potential confounding variables, using one of three differential expression algorithm models: mixture negative binomial, simplified negative binomial, or log‐linear/linear regression. Statistical significance of DE and trending genes was determined based on Benjamini–Hochberg‐adjusted p value (p‐adj) < .05 and p‐adj < .1, respectively.

The STRING online database was used for analysis of predicted associations between protein products of DE and trending genes. ²¹ Cluster analysis was performed using Markov clustering with an inflation parameter of three. Confidence of the predicted associations in network clusters is indicated by the thickness and color intensity of the lines connecting proteins within the network.

DE gene relationship with VAS and ODI

Linear normalized count data, calculated by multiplying raw counts by a normalization factor that controls for technical variability, were compared against VAS and ODI. ρ and p values were calculated using a Spearman correlation of each gene with VAS and ODI, separately. DE gene linear normalized counts were also compared between high and low VAS and ODI groups using a Student's t‐test. A Pearson correlation of VAS and ODI values was also performed, and R² and p value were calculated.

DE gene relationship with diagnosis

Linear normalized counts of each DE gene were plotted based on the diagnoses (disc herniation, disc degeneration, and spinal stenosis) of patients with LBP. A Student's t‐test was used to determine statistical significance between disc degeneration and spinal stenosis with p < .05 considered statistically significant. No statistical analysis was conducted for disc herniation samples due to low sample size of 1.

Age as a confounding variable

Age was correlated with linear normalized counts of DE genes for all LBP patients and control participants. Correlation coefficient (ρ) and p values were calculated for each gene using a Spearman correlation. Simple linear regressions were also calculated between age and VAS, and age and ODI to determine if a statistically significant relationship was present between variables.

RESULTS

Participant demographic data

All participant information and corresponding p values are shown in Table 1. The gender distributions of the two groups were similar, with 56% and 50% male participants in LBP and control participants, respectively (p = .82). Analyzing BMI, ethnicity, and race categorically, there were no statistical differences between LBP patients and control participants (BMI: p = .67; ethnicity: p = .31; race: p = .69). Age was the only demographic measurement that was significantly different between the groups, with a mean of 54 ± 15 years in LBP patients and 38 ± 15 years in control participants (p = .04).

TABLE 1.

Demographic data for LBP patients and control participants.

		LBP Patient (n = 9)	Control (n = 8)	p value
Age, year	mean ± SD	54 ± 15	38 ± 15	.04
Gender distribution	Male: N (%)	5 (56%)	4 (50%)	.82
Gender distribution	Female: N (%)	4 (44%)	4 (50%)	.82
Ethnicity	Hispanic or Latino	3 (33%)	1 (12.5%)	.31
Ethnicity	Non‐Hispanic or Latino	6 (67%)	7 (86.5%)	.31
Race	White	5 (55%)	6 (75%)
	American Indian or Alaska Native	1 (11%)	0 (0%)
	Asian	1 (11%)	1 (12.5%)	.69
	Black or African American	1 (11%)	0 (0.00%)
	Prefer not to answer	2 (22%)	1 (12.5%)
BMI distribution	Normal (18.5–24.9)	3 (33%)	3 (37.5%)
	Overweight (25.0–29.9)	4 (45%)	2 (25%)	.67
	Obese (≥30.0)	2 (22%)	3 (37.5%)
Diagnosis distribution	Spinal stenosis	5 (56%)
	Disc degeneration	3 (33%)
	Disc herniation	1 (11%)
Duration of symptoms (Years)	mean ± SD	4.4 ± 4.4
VAS (out of 10)	mean ± SD	5.3 ± 2.4
ODI (out of 100)	mean ± SD	30 ± 12

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Abbreviations: BMI, body mass index; LBP, low back pain; ODI, Oswestry disability index; VAS, Visual Analog Scale.

Among patients with LBP, 56% of participants were diagnosed with spinal stenosis, 33% with disc degeneration, and 11% with disc herniation. The duration of LBP symptoms was 4.4 ± 4.4 years on average, with all individuals in the LBP patient group experiencing pain for at least 3 months and up to 13.3 years. Patients with LBP reported an average VAS of 5.3 ± 2.4 and ODI of 30 ± 12.

Differential gene expression in LBP patients and control participants

Raw count data of 579 immune‐related genes was collected with 190 of the genes falling below the background threshold level. Out of the remaining 389 genes of interest, controlling for the covariates of BMI and age resulted in the greatest number of DE genes, prompting further analysis. In the comparison of LBP patients versus control participants, eleven significant DE genes were identified, including IFITM1, GBP1, MYD88, FCER1G, IFI16, FCGR3A/B, TNFSF10, TAP1, TOLLIP, KLRK1, and HLA‐A, in order of decreasing significance based on p‐adj. All DE genes were significantly greater in patients with LBP versus control participants, except KLRK1, which was significantly lower in LBP patients compared to control participants (Figure 1A,B). Nonsignificant trends were also observed in nine additional genes, including upregulation of FCER1A, CASP1, IRF1, GP1BB, IFIT2, and HLA‐B and downregulation of KLRB1, CD96, and FYN, in LBP patients versus control participants.

(A) Volcano plot of all genes. (B) Heat map of DE genes in the comparison of LBP patients and control participants. (C) Cluster analysis of DE and trending genes in LBP patients versus control participants yielding three functional clusters: Cluster 1 (orange): Interferon ⍺/β Signaling, TAP Binding; Cluster 2 (yellow): Immunoglobulin Binding; Cluster 3 (blue): TLR Binding. DE, differentially expressed; LBP, low back pain; TAP, transporter associated with antigen processing; TLR, toll‐like receptor.

Cluster analysis of differential gene expression

Cluster analysis using the STRING database was performed on all DE and trending genes together, yielding three clusters. Cluster 1 was defined by nine genes (IFITM1, GBP1, IFI16, TNFSF10, TAP1, HLA‐A, IRF1, IFIT2, HLA‐B) related to “Interferon α/β Signaling” and “Transporter Associated with Antigen Processing (TAP) Binding.” Cluster 2 was composed of seven genes (FCER1G, FCGR3A, FCGR3B, KLRK1, FCER1A, KLRB1, CD96, FYN) related to “Immunoglobulin Binding.” Cluster 3 contained three genes (MYD88, TOLLIP, CASP1) related to “Toll‐like Receptor (TLR) Binding” (Figure 1C).

Correlation of DE genes with ODI and VAS scores

Linear normalized count data for DE genes was analyzed against VAS scores within the LBP patient group to determine potential relationships. Trending correlations were observed between VAS scores and linear normalized counts for FCER1G (ρ = −0.67; p = .053; Figure 2A), FCGR3A/B (ρ = −0.66; p = .056; Figure 2B), and KLRK1 (ρ = 0.63; p = .074; Figure 2C). Further comparisons of DE gene linear normalized counts between categorized high and low VAS showed significantly lower FCER1G expression in patients with LBP with high compared to low VAS scores (p < .05, Figure 2A). Nonsignificant trends toward lower expression of TNFSF10 (p = .080, Figure 2D) and IFI16 (p = .069, Figure 2E) were observed in high versus low VAS, with no relationships with VAS identified for the remaining DE genes (Figure 2F–K).

(**A–K**) Differentially expressed gene normalized count correlations with VAS scores of LBP patients and comparisons between categorized high and low VAS groups. VAS, Visual Analog Scale.

Investigation of relationships between DE gene linear normalized counts and ODI yielded no significant correlations. Moreover, no significant differences in the comparison of linear normalized counts between high and low ODI were observed (Figure 3A–K). However, ODI scores were significantly correlated with patients' corresponding VAS scores (R ² = 0.58; p < .05; Figure 3L).

(**A–K**) Differentially expressed gene normalized count correlations with ODI scores of LBP patients and comparisons between categorized high and low ODI groups. (L) Correlation between ODI and VAS scores of LBP patient participants. LBP, low back pain; ODI, Oswestry disability index; VAS, Visual Analog Scale.

Effect of diagnosis on gene counts

In the comparison of DE gene normalized counts between spinal stenosis and disc degeneration, significant differences were observed in IFI16 (p < .05; Figure S1E) and GBP1 (p < .05; Figure S1F) with a nonsignificant trend in FCER1G (p = .091; Figure S1A). Low sample size for disc herniation precluded statistical analysis; nevertheless, the normalized counts for disc herniation were greater than the mean for disc degeneration and spinal stenosis in all upregulated DE genes, and was the lowest normalized count for the downregulated DE gene, KLRK1 (Figure S1A–K).

Age as a potential confounding variable

No significant correlations were identified between age and DE gene normalized counts from all LBP patients and control participants (Figure S2A–K). Additionally, linear regression of age with VAS and ODI did not demonstrate a statistically significant relationship between these variables (Figure S2L,M).

DISCUSSION

This pilot study aimed to identify systemic gene expression differences in whole blood of patients with LBP and lumbar spine pathology, compared to asymptomatic controls. Using Nanostring technology to focus analyses on inflammatory and immune‐related differences, DE and trending genes were identified in LBP patients versus control participants. Cluster analysis of DE and trending genes yielded three clusters representing the molecular functions of “Interferon α/β Signaling”, “Immunoglobulin Binding”, and “TLR Binding”. Additionally, correlations between DE genes and patient‐reported outcomes (PROs) revealed relationships between pain and expression levels for a subset of “Immunoglobulin Binding” genes.

Interferon α/β signaling

Interferon signaling is a known regulator of inflammation and the innate immune response, and consists of two primary signaling pathways: Type I most commonly regulated by IFNα and IFNβ, and Type II controlled by IFNγ binding. ²² Previous studies have demonstrated a relationship between Type II Interferon signaling and LBP, with IFNγ concentrations in serum correlating with pain severity in patients with lumbar disc herniation. ²³ However, the role of Type I Interferon signaling, also known as Interferon α/β signaling, in LBP is less established. Interestingly, in the current study a cluster related to “Interferon α/β Signaling” containing the DE and trending genes, IFITM1, GBP1, IFI16, TNFSF10, TAP1, HLA‐A, IRF1, IFIT2, and HLA‐B was identified, with all genes in the cluster upregulated in whole blood of LBP patients compared to control participants (Figure 1).

IFN regulatory factors (IRFs) are transcription factors that can dictate expression of IFNα and IFNβ. ²⁴ Specifically, IRF1 functions to polarize macrophages toward a proinflammatory phenotype. ²⁵ , ²⁶ Upregulation of IRF1 expression in this study implicates Interferon α/β signaling in inflammation and immune activation in LBP. Additionally, GBP1 expression is IRF1‐dependent and has been shown to have a diverse set of regulatory functions in inflammatory environments with a focus on immune cell viability and proliferation. ²⁷ , ²⁸ , ²⁹ In the context of pain, increased expression of GBP1 has also been observed in whole blood from humans with self‐reported high levels of pain. ³⁰ , ³¹ Upregulation of other genes downstream of IFNα and IFNβ, including IFITM1, IFI16, TNFSF10 (TRAIL), and IFIT2, provides additional evidence of elevated Interferon α/β signaling in patients with LBP (Figure 1). ³² , ³³ , ³⁴ , ³⁵ Interferon signaling has been studied mostly in the context of viral infection, where chronic upregulation of Interferon α/β signaling has been associated with consistently increased systemic inflammation, ultimately causing dysregulated T‐cell function. ³⁶

T cells are important components of the adaptive immune response and consist of two main subtypes: CD8+ and CD4+ T cells. ³⁷ In humans, the major histocompatibility complex (MHC) system of immune complexes are called the human leukocyte antigens (HLAs), with CD8+ T cells activated by binding to endogenous peptides presented by HLA class I molecules, and CD4+ T cells activated by exogenous peptides presented by HLA class II molecules. ³⁸ Interestingly, upregulation of genes encoding the HLA class I complexes in the blood, including HLA‐A, HLA‐B, and HLA‐C, has been reported in the transition from acute to chronic pain in LBP, as well as other chronic pain conditions. ¹⁶ , ³⁷ , ³⁹ TAP1 also encodes a protein that plays a critical role in antigen processing for presentation on HLA class I molecules. ⁴⁰ In the current study, the increased expression of HLA‐A, HLA‐B, and TAP1 in patients with LBP suggests that adaptive immune responses, specifically antigen presentation by HLA class I molecules for activation of CD8+ T cells, may play a role in the molecular mechanisms associated with LBP (Figure 1). Elevated expression of genes related to MHC Class I signaling may be due to a higher burden of endogenous peptides for processing and presentation to CD8+ T cells. Such peptides are typically not derived from pathogens, but from cytosolic proteins or ribosomal products produced as a result of defective protein translation, which can increase in response to inflammation. ³⁸ , ⁴¹ Previous studies have demonstrated that Interferon α/β signaling affects assembly of MHC Class I molecules, including HLA‐A and HLA‐B. ⁴² , ⁴³ Additionally, in this study, IRF1 expression was upregulated in patients with LBP and has been shown to initiate expression of genes related to MHC class I antigen presentation. ⁴⁴ These findings suggest potential alterations in activation, or exhaustion, of CD8+ T cells and indicate interplay between Interferon α/β signaling and the adaptive immune response in LBP. ³⁹ Exhausted CD8+ T cells, identified by expression of PD‐1, are often increased in chronic inflammatory states. ⁴⁵ Anti‐PD‐1 therapies designed to target exhausted immune cells have been successfully used clinically to combat immune dysfunction resulting from viral infection. Given the findings of the current study, suggesting CD8+ T‐cell exhaustion in LBP patients compared to control participants, anti‐PD‐1 therapies could be a potential future treatment modality for LBP; however, the impact of this treatment on chronic inflammation of nonviral origins has yet to be elucidated. ⁴⁶ , ⁴⁷

Immunoglobulin binding

Fc receptors (FcRs) are immunoglobulin‐binding structures that play a vital role in regulating inflammation and the immune system. They are expressed on many immune cell types, where FcR engagement leads to signaling that triggers a multitude of biological functions including antigen presentation, phagocytosis, cell lysis, and initiation of inflammatory cascades. ⁴⁸ Additionally, FYN is a protein kinase that regulates many of these functions, operating as a molecular switch by modulating phosphorylation. ⁴⁹ In the current study, cluster analysis revealed a subset of DE and trending genes related to “Immunoglobulin Binding” including FCER1G, FCGR3A, FCGR3B, KLRK1, FCER1A, KLRB1, CD96, and FYN (Figure 1). Specifically, upregulated expression of FCER1G, FCGR3A/B, FCER1A, and FYN in LBP patients compared to control participants suggests a systemic microenvironment with broader potential for immune cell activation and resulting effector functions due to immunoglobulin binding with FcRs (Figure 1).

A specific subset of FcRs are also expressed on natural killer (NK) cells. NK cells can function through multiple mechanisms, including binding of target cells to FCGR3A causing cell lysis antibody‐dependent cellular cytotoxicity (ADCC). ⁵⁰ , ⁵¹ , ⁵² , ⁵³ Markers most commonly expressed on NK cells, including KLRK1, KLRB1, and CD96, were primarily downregulated in LBP patients compared to control participants, suggesting a decrease in NK cell number or cell death through NK cell‐mediated mechanisms (Figure 1). ⁵² , ⁵⁴ A similar decrease in NK cells was previously observed in patients with chronic LBP. ⁵⁵ , ⁵⁶

FcRs have also been shown to play a role in other chronic inflammatory conditions, including increased expression of FCER1G in whole blood from patients with RA compared to healthy controls, as well as involvement in synovium from patients with osteoarthritis (OA). ⁵⁷ , ⁵⁸ Moreover, signaling through FcRs has shown potential as a therapeutic target for systemic inflammatory diseases and could indicate another therapeutic avenue for LBP based on the suggested evidence of increased FcR involvement in the current study. ⁵⁹

TLR binding

TLRs are members of a receptor family activated by binding of pathogen‐associated molecular patterns (PAMPs) or damage‐associated molecular patterns (DAMPs), commonly produced with tissue and cellular injury. ⁶⁰ , ⁶¹ , ⁶² , ⁶³ Most prominently, TLR4 signaling has been shown to contribute to the progression of IVD degeneration and LBP. ⁶⁴ , ⁶⁵ , ⁶⁶ , ⁶⁷ When activated, signaling downstream of TLR4 can occur through both the myeloid differentiation primary response 88 (MyD88)‐dependent and MyD88‐independent pathways. ⁶⁸ , ⁶⁹ , ⁷⁰ , ⁷¹ The MyD88‐dependent pathway leads to downstream activation of the nuclear factor kappa‐light‐chain‐enhancer of activated B cells (NF‐κB) and mitogen‐activated protein kinase (MAPK) pathways leading to expression and production of many inflammatory cytokines, chemokines, and matrix degrading enzymes. Meanwhile, the MyD88‐independent pathway relies on signaling through IRFs leading to Interferon α/β signaling. In this study, cluster analysis revealed upregulated expression of genes related to “TLR Binding”. Increased expression of TOLLIP, MYD88, and CASP1 in the LBP patient group supports the role of TLR signaling in LBP, specifically through the MYD88‐dependent pathway (Figure 1). ⁷² Additionally, the upregulated expression of genes within the Interferon α/β signaling cluster provides further evidence for the involvement of TLR signaling in LBP through the MyD88‐independent pathway as well (Figure 1C).

Activation of the MyD88‐dependent pathway through TLR4 signaling can also lead to transcription of pro‐interleukin (IL)‐1β, which can be processed to its active form by inflammasomes. ⁷³ Inflammasomes are protein complexes that, when activated, cleave the inactive protein, procaspase 1, to the active form, CASP1. ⁷⁴ CASP1 then acts on pro‐IL‐1β causing secretion of the pro‐inflammatory cytokine, IL‐1β, leading to robust downstream effects on inflammation and the innate immune response. Additionally, CASP1 activation increases the incidence of pyroptosis, a form of cell death. ⁷⁴ Significant upregulation of CASP1 in LBP patients compared to control participants in this study implicates inflammasome formation and IL‐1β secretion in perpetuating inflammation and innate immune activation (Figure 1).

Increased concentrations of IL‐1β have also been observed in serum of patients with IVD pathologies supporting its role in LBP. ¹⁰ , ⁷⁵ , ⁷⁶ IL‐1β has also been shown to play a role in regulating both inflammation and pain in other inflammatory conditions, including RA and OA. ⁷⁷ Multiple treatments blocking IL‐1β signaling have been approved by the United States Food and Drug Administration (FDA) for local or intravenous administration, and have been shown to be effective at treating the pain associated with OA, RA, and many other conditions. ⁷⁸ , ⁷⁹ The known role of IL‐1β in LBP, and suggested activation of this signaling cascade in the current study, indicates that IL‐1β blockade could be a promising therapy for LBP as well.

Relationship of pain, disability, and diagnosis with the immune system

A relationship between pain and disability has been firmly established in the literature with increasing pain correlating with increased disability, consistent with the observations in the LBP patient group in the current study (Figure 3L). ⁸⁰ , ⁸¹ Interestingly, although no relationships were observed between DE gene normalized counts and ODI (Figure 3A–K), the relationship between VAS and expression levels of three DE genes, FCER1G, FCGR3A/B, and KLRK1, was unexpected, when compared to the relationship between LBP patients and control participants. For example, FCER1G and FCGR3A/B expression decreased with increasing VAS, although expression levels were higher in LBP versus control (Figures 1B and 2A,B). Similarly, KLRK1 expression increased with increasing VAS, but was lower in LBP versus control (Figures 1B and 2C). Upon further investigation of these genes in patients divided into high and low VAS groups, a significant difference in FCER1G normalized counts was observed, indicating a relationship between FCER1G and pain in this study (Figure 2A). Additional studies will be required to determine the potential biological implications of the relationship between DE genes and VAS; however, FCER1G, FCGR3A/B, and KLRK1 are all contained within the “Immunoglobulin Binding” cluster suggesting a possible mechanism specific to the regulation of pain in LBP patients (Figure 1C). A similar relationship was observed between serum cytokine levels and pain intensity in a previous study of patients with LBP. ⁷ Although correlation does not equate to causation, the relationship between DE gene expression levels and VAS demonstrates the complexity of interactions between spine pathology and pain and suggests potential immune suppression or dysfunction with increased pain levels within patients with LBP.

In the context of pathological diagnosis, many studies have investigated relationships between inflammatory markers and degeneration severity in human IVD tissue or in the blood of patients with LBP. ⁷ , ⁷⁵ , ⁷⁶ However, few studies have investigated gene expression changes related to clinical diagnosis. In this study, higher expression of GBP1 and IFI16 was observed in patients diagnosed with spinal stenosis compared to disc degeneration (Figure S1E,F). Both of these genes play a role in the innate immune response suggesting that innate immune effector cells may be differentially activated based on diagnosis and corresponding pathological mechanisms.

LIMITATIONS

The small sample size may have limited the number of DE genes identified and precluded statistical analysis with disc herniation in the comparison of diagnoses (Figure S1). Additionally, the significant difference in age between the LBP patients and control participants could have confounded the results, as levels of inflammatory cytokines have been shown to vary with age. ⁷⁶ However, potential confounders (ie. age, BMI) were controlled for in the differential expression analysis. Moreover, no significant correlations were present between age and DE gene normalized counts, and age did not have a significant relationship with VAS or ODI (Figure S2). A healthy population without prior history of LBP is also more likely to consist of younger participants because the prevalence of LBP increases with age, so this is not unexpected. ³ Additionally, although patients with known inflammatory diseases (ie. RA, osteomyelitis, discitis, gout, ankylosing spondylitis, arachnoiditis, other infections) were excluded and LBP patient participants were confirmed to have back or leg pain related to a discogenic or degenerative pathology by a clinician, it cannot be ruled out that other conditions that may be associated with inflammation, such as OA, may have effected study findings. However, these conditions often coexist with LBP; therefore, inclusion of these patients is representative of the typical patient population in the clinic. Also, VAS was used for measurement of pain in this study for ease of data collection in an active clinical setting, but the addition of more extensive pain measurements or physical pain evaluations, such as the Patient‐Reported Outcomes Measurement Information System (PROMIS), McGill Pain Questionnaire, or somatosensory function, could allow for more standardized quantification of pain. ¹⁵ , ¹⁶

CONCLUSIONS

This pilot study identified significant differences in inflammation‐ and immune‐related gene expression in the blood of patients with LBP and various spine pathologies, compared to asymptomatic controls. DE genes and cluster analysis implicated Interferon α/β signaling, immunoglobulin binding, and TLR signaling in the immune response related to LBP, despite heterogeneity in underlying pathological diagnosis. Overall, the observed expression changes suggest that patients with LBP may have elevated host defense activity compared to healthy controls. This was demonstrated by elevated expression of genes related to MHC‐I antigen presentation that suggests increased propensity for CD8+ T‐cell exhaustion, greater immunoglobulin binding with FcRs, and TLR signaling. The relationship between multiple DE genes related to immunoglobulin binding and VAS also indicates a potential mechanism mediating pain severity in patients with LBP. These findings extend the knowledge of systemic inflammatory and immune changes that occur in the blood of patients with chronic LBP and are relevant to multiple spine pathologies.

DISCLOSURE

The authors have no conflicts of interest to disclose.

PATIENT CONSENT STATEMENT

Informed consent was obtained before enrollment of all study participants.

Supporting information

Figure S1. (A–K) Normalized count distribution based on LBP patient participant diagnoses for all DE genes. *p < .05 Disc degeneration versus spinal stenosis.

PMRJ-18-735-s002.pdf^{(415.4KB, pdf)}

Figure S2. (A–K) Correlations of age with DE gene normalized count for all LBP patient and control participants. Correlation of age with (L) VAS and (M) ODI.

PMRJ-18-735-s001.pdf^{(579.6KB, pdf)}

ACKNOWLEDGMENTS

The authors thank those members of the Department of Rehabilitation and Regenerative Medicine who assisted in patient interactions, as well as the Molecular Pathology Shared Resource (MPSR) facilities and Human Immune Monitoring Core (HIMC) for their help with sample analysis. This study was supported in part by grants, including National Institutes of Health (NIH) R01AR069668, NIH R01AR077760, and NIH R21AR080516.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1. (A–K) Normalized count distribution based on LBP patient participant diagnoses for all DE genes. *p < .05 Disc degeneration versus spinal stenosis.

PMRJ-18-735-s002.pdf^{(415.4KB, pdf)}

Figure S2. (A–K) Correlations of age with DE gene normalized count for all LBP patient and control participants. Correlation of age with (L) VAS and (M) ODI.

PMRJ-18-735-s001.pdf^{(579.6KB, pdf)}

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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PERMALINK

Immune system expression profiling in patients experiencing low back pain: A pilot study

Lauren E Lisiewski, PhD

Xiaoning Yuan, MD, PhD

Joseph Chin, MD

Nadia Kiridly, BS

George C Christolias, MD

Clark C Smith, MD

Nadeen O Chahine, PhD

Abstract

Background

Objective

Design

Setting

Patients

Interventions

Main outcome measure

Results

Conclusions

INTRODUCTION

METHODS

LBP patient participants

Control participants

Blood sample collection and processing

Nanostring preparation

Differentially expressed genes

DE gene relationship with VAS and ODI

DE gene relationship with diagnosis

Age as a confounding variable

RESULTS

Participant demographic data

TABLE 1.

Differential gene expression in LBP patients and control participants

FIGURE 1.

Cluster analysis of differential gene expression

Correlation of DE genes with ODI and VAS scores

FIGURE 2.

FIGURE 3.

Effect of diagnosis on gene counts

Age as a potential confounding variable

DISCUSSION

Interferon α/β signaling

Immunoglobulin binding

TLR binding

Relationship of pain, disability, and diagnosis with the immune system

LIMITATIONS

CONCLUSIONS

DISCLOSURE

PATIENT CONSENT STATEMENT

Supporting information

ACKNOWLEDGMENTS

DATA AVAILABILITY STATEMENT

REFERENCES

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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