Clinical findings and predictive factors for positive anti-interferon-γ autoantibodies in patients suffering from a non-tuberculosis mycobacteria or talaromyces marneffei infection: a multicenter prospective cohort study

ABSTRACT We investigated the clinical features and screened for predictive factors of anti-interferon-γ autoantibody (AIGA) positivity. We enrolled 63 AIGA-positive (group 1) and 29

AIGA-negative (group 2) HIV-negative patients. White blood cell (WBC) and neutrophil counts, erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP), globulin, immunoglobulin (Ig)

G, and IgM levels were higher, whereas CD4+T cell count and hemoglobin level were lower in group 1 than in group 2. Co-infections, multiple infections, and disseminated infections were

significantly higher in group 1 than in group 2. Prognosis was worse in group 1 than in group 2, especially for relapse and persistent infections. The number of infecting pathogens and sites

involved; WBC and neutrophil counts; globulin, IgG, IgM, and CRP levels; and ESR were significantly positively correlated with AIGA titers; however, CD4+T cell count was significantly

negatively correlated with AIGA titers. Therefore, IgG, globulin, and CRP levels; CD4+T cell and WBC counts; the number of infecting pathogens and sites involved; and ESR were considered

potential predictors for AIGA positivity. For HIV-negative hosts with double or multiple opportunistic, disseminated infections and high serum IgG and globulin levels, low CD4+T cell count,

and an increase in inflammatory marker levels, positive AIGA-associated immunodeficiency should be considered. SIMILAR CONTENT BEING VIEWED BY OTHERS _TALAROMYCES MARNEFFEI_ AND

NONTUBERCULOUS MYCOBACTERIA CO-INFECTION IN HIV-NEGATIVE PATIENTS Article Open access 10 August 2021 LATENT AND ACTIVE TUBERCULOSIS INFECTION IN ALLOGENEIC HEMATOPOIETIC STEM CELL TRANSPLANT

RECIPIENTS: A PROSPECTIVE COHORT STUDY Article 08 May 2021 UNDERSTANDING AND INTERPRETING ANTINUCLEAR ANTIBODY TESTS IN SYSTEMIC RHEUMATIC DISEASES Article 05 November 2020 INTRODUCTION

Adult-onset immunodeficiency syndrome (AOID), caused by anti-interferon-γ autoantibody (AIGA), has been strongly associated with intracellular opportunistic infections in human

immunodeficiency virus (HIV)-negative adults1,2,3,4,5,6. In patients with positive AIGAs, several molecular mechanisms underlie interferon (IFN)-γ dysfunction, considered to primarily

inhibit signal transducer and activator of transcription 1 (STAT1) phosphorylation and interleukin-12 production, resulting in severe dysfunction of the Th1 response1,7. Patients with

positive AIGAs manifest clinically severe mycobacterial, _Talaromyces marneffei_ (TM), and salmonella infections, with double or multiple infections1,2,3,4,5,6. Since 2012, an increasing

number of patients have been diagnosed with AIGA, suggesting that AIGA production was previously underestimated. To date, more than 800 patients have been diagnosed with AIGAs7. The

diagnosis of AIGA disease requires the following two steps: 1) the detection of the AIGA titer by enzyme-linked immunosorbent assay (ELISA), particle-based assay, flow cytometry analysis, or

western blotting; and 2) an assessment of the neutralizing activity of these AIGAs: STAT-1 phosphorylation or human leukocyte antigen (HLA)-DR expression on IFN-γ-responsive cell assays are

the most widely used methods for assessing the neutralization potential of AIGA1,7,8. AOID has always been misdiagnosed due to the lack of clinical knowledge about it. However, the AIGA

titer is closely related to poor infection prognosis, especially in relapsed and refractory infections6. Thus, the timely detection of AIGA and diagnosis of AOID is essential to improve the

prognosis of infected patients. However, these methods of diagnosing positive AIGAs are time-consuming, require expensive instruments, and are used only for scientific research rather than a

routine clinical test. Therefore, in this study, we (1) compared AIGA-positive and AIGA-negative patients to elucidate the clinical characteristics of AIOD and (2) screened existing

clinical routine indicators to identify potential predictors of AIGAs for timely identification of AIGA positivity to evaluate host immunity and improve prognosis. RESULTS BASELINE

CHARACTERISTICS AND AIGA TITERS Based on the 99th percentile of the AIGA titers in 103 healthy volunteers, the cutoff for positivity was 6402.28 ng/ml. During the study, 63 AIGA-positive

cases (group 1) and 29 AIGA-negative cases (group 2) with TM and/or nontuberculosis mycobacteria (NTM) infection were enrolled. All 93 participants were HIV-negative. Sex distribution, age,

and body mass index (BMI) did not differ significantly between the groups (Table 1). The AIGA titers in group 1 (median 32,343.8 ng/ml with interquartile range 19,712.8–58,117.3 ng/ml) were

significantly higher than those in group 2 (median 3452.9 ng/ml with interquartile range 1985.7–3983.2 ng/ml) (_P_ < 0.001). COMPARISON OF CLINICAL FEATURES AND OUTCOMES BETWEEN THE

AIGA-POSITIVE AND AIGA-NEGATIVE GROUPS A comparison of the biomarkers and clinical parameters between the groups (Table 1) revealed that the AIGA titer, white blood cell (WBC) count,

neutrophil (N) count, erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP) level in group 1 were higher than those in group 2 (_P_ < 0.00). The hemoglobin (HGB) level in

group 1 was lower than that in group 2 (_P_ < 0.01). Furthermore, the globulin, immunoglobulin (Ig) G, and IgM levels were higher in group 1 than in group 2 (_P_ < 0.05). In group 1,

there were 29 (45.3%) patients with co-infections or multiple infections, with the co-infection rate being significantly higher than that in group 2 (_P_ < 0.001). Besides, the number of

sites involved in group 1 was significantly higher than that in group 2 (_P_ < 0.001) (4 sites vs. 1 site, respectively) (Table 1). In group 1, the median CD4+ T lymphocyte count was 484

(with interquartile range 365–654) cells/µL, lower than the normal range. In addition, the CD4+ T lymphocyte count in group 1 was lower than that in group 2 (_P_ < 0.01). The CD4+ T

lymphocyte count of nine patients in group 1 was below the normal level (CD4+T cells < 410 cells/µL), and all nine patients had disseminated infections, persistent infections, and

co-infections. The prognosis and outcomes of patients in group 1 were worse than those of patients in group 2, especially in the case of persistent and relapse infections (_P_ < 0.001)

(Table 1). Nineteen (29.7%) patients had a persistent infection, 21 (32.8%) had a relapse infection, 9 (14.1%) died, and 15 (23.4%) were cured in group 1. In contrast, 5 (17.2%) patients had

a persistent infection, 6 (20.7%) had a relapse infection, 3 (10.3%) died, and 15 (51.7%) were cured in group 2. PEARSON CORRELATION AND UNIVARIATE LOGISTIC REGRESSION ANALYSIS FOR

PREDICTIVE FACTORS OF AIGA POSITIVITY Pearson correlation analysis with a two-tailed test was used to correlate between AIGAs and clinical indices. The number of different types of infecting

pathogens (_P_ = 0.000, _r_ = 0.480) and number of sites involved (_P_ = 0.000, _r_ = 0.532) (Fig. 1); WBC (_P_ = 0.012, _r_ = 0.328) and neutrophil counts (_P_ = 0.027, _r_ = 0.291); ESR

(_P_ = 0.002, _r_ = 0.425); and CRP (_P_ = 0.000, _r_ = 0.528) (Fig. 2), globulin (_P_ = 0.000, _r_ = 0.589), IgG (_P_ = 0.000, _r_ = 0.755), and IgM (_P_ = 0.010, r = 0.388) (Fig. 3) levels

significantly positively correlated with AIGA titers. The CD4+T cell count significantly negatively correlated with AIGA titers (_P_ = 0.000, _r_ =  − 0.596) (Fig. 3). The univariate

logistic regression analysis showed that the CD4+T cell; the number of types of infecting pathogens and sites involved; IgG, globulin, and CRP levels of patients are potential predictors for

AIGA positivity (Table 2). DISCUSSION Until now, the diagnostic criteria for Adult-onset immunodeficiency syndrome (AOID) have been unavailable, the clinical knowledge and attention about

AOID have been limited, and routine clinical tests for detecting AIGAs have been lacking. These resulted in the misdiagnosis of AOID . Therefore, finding potential clinical predictors of

AIGA positivity from existing clinical indicators is important for timely detection and diagnosis of AIGAs to evaluate host immunity and improve prognosis. In this study, we determined

significant predictors for AIGAs that could play a role in controlling the disease. This study provides the first evidence that high globulin and IgG levels, low CD4+T levels, double or

multiple infections, and disseminated infections could be potentially effective predictive factors of AIGA positivity in HIV-negative patients with TM and/or NTM infections. In this study,

the inflammatory markers for leukocyte count, CRP level, and ESR in AIGA-positive patients were significantly higher than those in AIGA-negative patients. A notable bone marrow response

prone to anemia and leukocytosis development in AIGA-positive patients was also observed. In addition, most patients who were AIGA-positive had systemic dissemination and double or multiple

infections frequently. These markers indicate more exuberant dissemination and multiple infections in patients with AIGA positivity. We found that globulin, IgG, and IgM levels in

AIGA-positive patients were higher than those in AIGA-negative cases. Furthermore, the AIGA titer was significantly positively correlated with IgG and serum globulin levels in patients. The

globulin level was high because the essence of AIGAs is immunoglobulins. After total immunoglobulin subclass evaluation and functional verification, including AIGA IgG, IgM, and IgA

subclasses, Browne et al. found that the isotypes and subtypes of AIGAs appear to be heterogeneous, and IgG1 and IgG4 were the most frequent subtypes in the population1,6,8. Moreover, AIGA

IgG can inhibit IFN-γ-dependent STAT1 phosphorylation, especially IgG48. However, AIGA IgA and IgM have binding activity; they do not neutralize inhibitory interferon-γ-dependent STAT1

phosphorylation1,8. These issues indicated that IgG-AIGA is the major subtype of AIGAs and neutralizing component to prevent interferon-γ-induced STAT1 phosphorylation. Therefore, the high

IgG level in peripheral blood can be used as an effective predictive factor in AIGA-positive patients, and the level of IgG can be used to evaluate the intensity of immunosuppression in

serum. The number of infecting pathogens is significantly positively correlated with AIGA titers, which may be associated with pathogens expressing the Noc2 protein. Lin CH et al. found that

pathogenic AIGAs may be produced from cross-reactivity between _Aspergillus spp._ and _M. intracellulare_ ribosomal assembly protein Noc2, especially Noc2 in Aspergillus spp. Furthermore,

these autoantibodies target a major epitope (P123–135 AAKTGKRKRSQML) in the C-terminal region of IFN-γ5. To investigate whether the Noc2 protein in TM has a similarly form of molecular

mimicry that could trigger the development of AIGAs, we searched in NCBI BLAST for sequences displaying homology to P123–135, which sequence was unique to IFN-γ in humans. Surprisingly, the

P123–135 epitope had 80%-positive homology to amino acids 98–107 (AKTGKRKRID) of the ribosome assembly protein Noc2 of TM. At the same time, the two sequences are similar in spatial

structure. This region in Noc2 is highly conserved across all of the species in the databases (UniProt). Therefore, when infected with NTM, TM, _Aspergillus_, and other pathogens with Noc2

sequences, cross-reactivity with Noc2 might occur, stimulating the production of AIGAs, leading to further impaired immunity. In the present study, the AIGA-positive titer of patients with

co-infection and multiple infections was significantly higher than that in AIGA-negative patients. Moreover, the AIGA titer was significantly positively correlated with the number of

pathogens that infected the patients. These findings are consistent with those of previous studies and our hypothesis. However, further investigation is required to determine whether the

AIGA titer positively correlates with the number of pathogens or the Noc2 level. As the clinical manifestations and recurrent opportunistic infection of AOID are similar to those if AIDS,

AOID caused by AIGAs has previously been defined as an “AIDS-like syndrome”9,10. However, the mechanisms of immune deficiency between them are completely different. Previously, studies have

found that persistent exposure to antigen continuously stimulates T lymphocytes leading to prolonged inflammation. During chronic infections, memory T lymphocytes enter an entirely different

differentiation program that ends in T cell exhaustion11,12. Here, the CD4+ T cell count was lower in AIGA-positive patients than in AIGA-negative patients. Meanwhile, there was a

significant negative correlation between the AIGA titer and CD4+ T cell count. These may be related to the following reasons. First, post-infection immunosuppression. The CD4+ T cell count

was selected during the active stage of infection. Co-infection and multiple, persistent, and disseminated infections were more common in AIGA-positive patients than in AIGA-negative

patients. Second, because of its ability to regulate various protective functions and sustain the activity of both CD4+ and CD8+T cells, IFN-γ is essential for fighting infections13. Third,

AIGAs can underlie IFN-γ dysfunction by inhibiting STAT1 phosphorylation, interleukin-12 production, and severe dysfunction of the Th1 response1,7,14 and affect the differentiation of CD4+ T

cell subsets, leading to a decrease in Th1 cell differentiation and proliferation. Thus, there may be a vicious circle among these factors, severe co-infection, multiple, persistent, and

disseminated infections, AIGA titer, and CD4 + T cells level. This observation in our study is consistent with those of previous studies, including the study of Browne et al.1. CONCLUSIONS

This study provides the first evidence that high globulin and IgG levels, low CD4+T levels, double or multiple infections, and disseminated infections could be potentially effective

predictive factors for AIGA positivity in HIV-negative patients with TM and/or NTM infections. Monitoring and predicting the AIGA titer is crucial to assess patient prognosis and host

immunodeficiency severity. Methods for detecting AIGAs, including ELISA, particle-based assay, and flow cytometry, should be widely used in the clinical setting. A feasible, precise, and

standardized protocol should be established to improve the diagnosis of AIGA in clinical practice. This study had some limitations. First, this study was a multicenter retrospective study

conducted in Guangxi, China, and its conclusions may not apply to other countries and provinces. The sample size was too small to perform multivariate analysis. However, this study makes a

novel contribution to the literature by comparing AIGA-positive and AIGA-negative patients to elucidate the clinical significance of AIGAs and screening existing clinical indicators to

identify potential predictors of AIGA for timely identification of AIGA positivity to evaluate host immunity and improve prognosis. METHODS We performed this multicenter prospective cohort

study in Guangxi Province in the south of China between January 1, 2017, and December 31, 2019, from 13 centers [(1) The Eighth Affiliated Hospital of Sun Yat-Sen University; (2) The First

Affiliated Hospital of Guangxi Medical University; (3) The Affiliated Tumor Hospital of Guangxi Medical University; (4) The Second Affiliated Hospital of Guangxi Medical University; (5) The

Hospital of Guangxi Zhuang Autonomous Region; (6) Nan Xishan Hospital of Guangxi Zhuang Autonomous Region; (7) Nanning Second People's Hospital; (8) Nanning Fourth People's

Hospital; (9) Nanning Eighth People's Hospital; (10) Yiyang Central Hospital; (11) Liuzhou First People's Hospital; (12) Guigang First People's Hospital; and (13) Guilin First

People's Hospital]. We selected and collected peripheral blood serum from patients with fungal and bacterial infections (tuberculosis, NTM, TM, NTM, cryptococcus and other bacterial

infections) during the study period. After detecting AIGA in the serum, we assigned patients into the AIGA positive group and AIGA negative group according to AIGA titers. Participants with

AIGA titers exceeding the 99th percentile of the 103 healthy controls were defined as AIGA-positive. To eliminate selection bias, after matching the patients’ sex, age, HIV and the type of

pathogenic microorganism infection, we assigned patients into the AIGA negative group from AIGA negative patients. We excluded participants less than 18 years of age; those with autoimmune

disease, cancer, or immunodeficiency; or those who had received immunosuppressive medications in the past 3 months. Finally, we enrolled 63 AIGA-positive (group 1) and 29 AIGA-negative

(group 2) patients with TM and NTM infections. Demographic and clinical data were recorded in standardized forms. The comparison of biomarkers or clinical parameters between the groups was

performed at the active infection stage. All patients were followed-up until October 1, 2020, or until the time of death. The clinical course of the infected patients was classified into the

following four categories: cured (no recurrence of infection for at least 6 months after the discontinuation of antimicrobial therapy); persistent infection (no improvement of clinical

symptoms after antimicrobial therapy); relapse infection (improvement of clinical symptoms, negative pathogen detection after antimicrobial therapy, followed by the reappearance of

pathogen-associated infectious signs and/or positive pathogen testing); and death15. Disseminated disease was defined as an infection in two noncontiguous and sterile sites. Antimicrobial

therapies included antifungal, anti-NTM, anti-tuberculosis, and anti-bacterium therapies. This study was approved by the Ethical Review Committee of the First Affiliated Hospital of Guangxi

Medical University (2020.KY-E-044). Written informed consent was provided by all participants enrolled in this study. DIAGNOSTIC CRITERIA FOR NTM, TM, AND OTHER PATHOGENIC INFECTIONS Each

patient fulfilled the diagnostic criteria for each disease. TM infection was diagnosed as follows: 1) positive cultures of TM, characterized by dimorphic fungi that grew as a mold at 25 °C

and yeast at 37 °C; 2) characteristic morphology of the yeast form of TM, confirmed by cytology and histopathology from tissues and secretions by Periodic Acid–Schiff staining or Wright’s

staining, including a transverse septum7,15; or 3) TM isolated by metagenomic next-generation sequencing from clinical specimens. NTM was diagnosed following the 2007 American Thoracic

Society (ATS)/Infectious Disease Society of America guidelines16. Positive cultures, cytology, and histopathology of the clinical specimens were used to diagnose _S. aureus_, _Aspergillus_,

_Salmonella_, _Burkholderia_, as well as _Candida albicans_, _Klebsiella pneumoniae_, _Providencia rettgeri_, and _Citrobacter_ infections. HEALTHY VOLUNTEER INCLUSION CRITERIA Healthy

volunteers were enrolled over the same period. Healthy volunteers were defined as individuals without infection, underlying diseases, immunodeficiency conditions (diseases that could lead to

or are associated with immunosuppression), including autoimmune diseases, malignancy, and primary immunodeficiency, or chronic diseases such as chronic renal failure, liver cirrhosis,

diabetes mellitus, hypertension, or solid organ transplantation. Moreover, only those who had not received glucocorticoid and/or immunosuppressive therapy were included. One hundred three

healthy volunteers with normal routine blood and chest radiography findings were recruited from the health checkup center in The First Affiliated Hospital of Guangxi Medical University. All

participants were HIV-negative. AIGA ASSAY Serum samples were obtained under sterile conditions before the patients received antimicrobial therapy and during the active stage of infection.

Serum samples were retrieved from a serum bank and stored at − 80 °C. AIGAs were detected in all participants. All serum samples were tested at the first thaw. The detection of AIGAs was

performed using an ELISA kit (Cloud-Clone Corp., Wuhan, China), whose detection range is 12–200 ng/ml. According to the manufacturer’s protocols, the serum samples from patients were 1:1500

diluted, and serum samples from a healthy control were 1:600 diluted by phosphate-buffered saline (PBS)15. The normal range for the anti-IFN-γ-autoantibody concentration was defined by the

99th percentile for the 103 healthy controls and was estimated using log-normal distribution. Outlying concentrations were classified as positive for anti-IFN-γ autoantibodies1,6,15.

STATISTICAL ANALYSIS Data are expressed as median ± interquartile range. Differences between groups were compared using Kruskal–Wallis H or Mann–Whitney U test. Dunn–Bonferroni test was used

for post-hoc analysis. Chi-square or Fisher’s exact test was used to compare categorical variables. Pearson correlation analysis was used for the correlation between AIGAs and clinical

indices. Univariate logistic regression analysis was used for predicting factors of AIGA positivity. The normal range for the AIGA concentration was defined by the 99th percentile for the

103 healthy controls and was estimated using log-normal distribution. Outlying concentrations were classified as positive for AIGAs1,6. We used SPSS (version 25.0) and GraphPad Prism

(version 7, La Jolla, CA, USA) for statistical analysis and preparing graphs. Results with _P_ < 0.05 were considered significant. ETHICS APPROVAL AND CONSENT TO PARTICIPATE This study

was approved by the Ethical Review Committee of the First Affiliated Hospital of Guangxi Medical University (2020.KY-E-044). Written informed consent was provided by all participants in the

prospective cohort study. All patients were followed-up until July 1, 2020, or until the date of death. All methods were carried out in accordance with the Declaration of Helsinki guidelines

and regulations. DATA AVAILABILITY The datasets used or analyzed during the current study are available from the corresponding author on reasonable request. ABBREVIATIONS * BMI: Body mass

index * AIGA: Anti-IFN-γ autoantibody * ND: Not done * HLA: Human leukocyte antigen * WBC: White blood cell * N: Neutrophil count * L: Lymphocyte count * HGB: Hemoglobin * ESR: Erythrocyte

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by grants from the Natural Science Foundation of China [NSFC81760010 and 82060364], the Science and Technology Department of Guangxi Zhuang Autonomous Foundation of Guangxi Key Research and

Development Program (No. GuikeAB20238025 and No. GuikeAB16380228), and Guangxi Natural Science Foundation (NO. 2021GXNSFBA220064). AUTHOR INFORMATION Author notes * These authors contributed

equally: Ye Qiu, Mengxin Tang and Wen Zeng. AUTHORS AND AFFILIATIONS * Department of Respiratory and Critical Medicine, The Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen,

518000, Guangdong, China Ye Qiu, Mengxin Tang, Xin Feng, Mianluan Pan & Jianquan Zhang * State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory

Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China Ye Qiu * Department of Respiratory and Critical Medicine, The First Affiliated Hospital of

Guangxi Medical University, Nanning, 530021, Guangxi, China Ye Qiu, Mengxin Tang, Wen Zeng & Xin Feng * Department of Nephrology, The Second Affiliated Hospital of Guangxi Medical

University, Nanning, 530021, Guangxi, China Wei Li Authors * Ye Qiu View author publications You can also search for this author inPubMed Google Scholar * Mengxin Tang View author

publications You can also search for this author inPubMed Google Scholar * Wen Zeng View author publications You can also search for this author inPubMed Google Scholar * Xin Feng View

author publications You can also search for this author inPubMed Google Scholar * Mianluan Pan View author publications You can also search for this author inPubMed Google Scholar * Wei Li

View author publications You can also search for this author inPubMed Google Scholar * Jianquan Zhang View author publications You can also search for this author inPubMed Google Scholar

CONTRIBUTIONS Y.Q. conceived and designed the study; acquired, analyzed, and interpreted the data; drafted the manuscript. J.Z. conceived of the study and critically revised the manuscript

for important intellectual content. X.F. and M.T. analyzed and interpreted the data. W.L. conceived of the study and revised the manuscript. M.P. acquired and analyzed the data and agreed to

be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Y.Q. and W.Z.

agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy and integrity of any part of the work are appropriately investigated and resolved.

All authors read and approved the final manuscript. CORRESPONDING AUTHORS Correspondence to Wei Li or Jianquan Zhang. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing

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Tang, M., Zeng, W. _et al._ Clinical findings and predictive factors for positive anti-interferon-γ autoantibodies in patients suffering from a non-tuberculosis mycobacteria or _Talaromyces

marneffei_ infection: a multicenter prospective cohort study. _Sci Rep_ 12, 9069 (2022). https://doi.org/10.1038/s41598-022-13160-x Download citation * Received: 04 September 2021 *

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