Immune status changing helps diagnose osteoarticular tuberculosis.

OBJECTIVE: This study is aimed to develop a new nomogram for the clinical diagnosis of osteoarticular tuberculosis (TB).
METHODS: xCell score estimation to obtained the immune cell type abundance scores. We downloaded the expression profile of GSE83456 from GEO and proceed xCell score estimation. The routine blood examinations of 326 patients were collected for further validation. We analyzed univariate and multivariate logistic regression to identified independent predicted factor for developing the nomogram. The performance of the nomogram was assessed using the receiver operating characteristic (ROC) curves. The correlation of ESR with lymphocytes, monocytes, and ML ratio was performed and visualized in osteoarticular TB patients.
RESULTS: Compared with the healthy control group in the dataset GSE83456, the xCell score of basophils, monocytes, neutrophils, and platelets was higher, while lymphoid was lower in the EPTB group. The clinical data showed that the cell count of monocytes were much higher, while the cell counts of lymphocytes were lower in the osteoarticular TB group. AUCs of the nomogram was 0.798 for the dataset GSE83456, and 0.737 for the clinical data. We identified the ML ratio, BMI, and ESR as the independent predictive factors for osteoarticular TB diagnosis and constructed a nomogram for the clinical diagnosis of osteoarticular TB. AUCs of this nomogram was 0.843.
CONCLUSIONS: We demonstrated a significant change between the ML ratio of the EPTB and non-TB patients. Moreover, we constructed a nomogram for the clinical diagnosis of the osteoarticular TB diagnosis, which works satisfactorily.

Introduction

World health organization (WHO) estimates that 1.8 billion people, about one-fourth of the global population, are Mycobacterium tuberculosis (M.tb) infected, out of which roughly 10 million contracted tuberculosis (TB) and 1.6 million died of the disease [1-3]. Therefore, TB remains a leading global public health problem [4, 5]. Till now, TB has been described in all virtual tissues or organs, including the spine, lymph nodes, abdomen, genitourinary tract, skin, joints, and meningeal [6, 7]. Extrapulmonary tuberculosis (EPTB) incidents are higher in immune-compromised individuals and represents 15% of the global TB cases [8]. However, an individual’s immunity status decides whether patients with M.tb would develop active TB or not [9].

Despite the existence of guidelines for the diagnosis and treatment of TB [10], there tend to be cases of EPTB that present with atypical manifestations such as local pain, weight loss, night sweat and fever, which make the diagnosis difficult [11]. In addition, the anatomical sites frequently involves in EPTB are not easily accessible and require invasive procedures for diagnostic confirmation. The availability of little information is the reason for the difficulty and delay in the diagnosis of EPTB. Therefore, an urgent need arises to invent new methods for EPTB diagnosis.

The absolute number of monocytes or lymphocytes in peripheral blood or yet the ratio of monocytes to lymphocytes (ML ratio) has prognostic value in diseases such as hematopathy and tumors [12]. Previous studies have demonstrated that the ML ratio shows a predictive value for active TB [13]. The development of osteoarticular TB is the result of an immune system disorder. However, spine TB is the most common kind of osteoarticular TB, which influences the proportion of immune cells. Therefore, the discovery of immune status associated with osteoarticular TB can advance osteoarticular TB diagnosis.

In this study, the expression profile of dataset GSE83456 was downloaded to obtain the xCell score of the cell type, including lymphocytes, monocytes, neutrophils, eosinophils, basophils, erythrocyte, and platelet that belongs to the blood routine examination items. We found that the immune status of individuals changes during TB infection. ML ratio was found significantly up-regulated in the EPTB group than that in the non-TB group. Based on this result, we constructed a novel nomogram, which incorporated easy access to clinical characteristics like ML raito, erythrocyte sedimentation rate (ESR) and body mass index (BMI), for osteoarticular TB diagnosis.

Patients and methods

Microarray data and xCell score estimation

Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/) is an open database from where we downloaded the gene expression profiles. The GSE83456 with platform GPL10558 (Illumina HumanHT-12 V4.0 expression beadchip) was downloaded and used to extracted blood sample profiles of 47 EPTB and 61 healthy controls (HCs) from the total 202 samples for further analysis. We analyzed the xCell score using the R package ‘xCell’ (https://github.com/dviraran/xCell), which allowed us to obtained 64 immune cell type abundance scores [14]. Next, the xCell score of lymphocytes, monocytes, neutrophils, eosinophils, basophils, erythrocyte, and platelet belonging to the blood routine examination items was picked out. The xCell score of lymphocytes was a constitution of 21 subtypes (S1 Fig) of lymphoid cells (B cells, T cells and NK cells). This score was consistent with the method of lymphocyte count in the routine blood examination.

Patients

Subjects volunteering for the study had signed informed consent forms. In addition, the Ethics Committee of The First Affiliated Hospital of Guangxi Medical University approved this study.

From 2012 to 2018, we consecutively screened out 173 osteoarticular TB patients from The First Affiliated Hospital of Guangxi Medical University. Following were the included diagnostic criteria for osteoarticular TB: (1) patients with typical symptoms of tuberculous infection, including low fever, night sweats, weight loss, and fatigue; (2) patients with positive M.tb antibody; (3) patients with spinal cord compression symptoms, such as pain, myodynamia, muscle tension, tendon reflexes, limited activity, and spinal deformities; (4) patients with the typical features of spinal TB on imageology, such as bone marrow edema, endplate erosion, vertebral destruction, and spinal compression; and (5) patients with tuberculous granuloma [15]. Exclusion criteria were set for osteoarticular TB patients: (1) co-infection with other types of bacteria, virus, and co-morbidities; (2) co-occurrence with tumor (3) already receiving anti-tuberculosis drug treatment; and (4) recurrent tuberculosis.

In The First Affiliated Hospital of Guangxi Medical University, from 2012 and 2018, we randomly screened out the non-TB patients from all the inpatients diagnosed with lumbar disc herniation or lumbar spinal stenosis. After identifying patients, 326 eligible patients were enrolled, including 164 osteoarticular TB patients and 162 non-TB patients.

Statistical analysis

Using the Student’s t-test, we analyzed continuous variables such as xCell score of monocytes, peripheral blood of monocytes counts, or lymphocytes. To identify the independent risk factors in the dataset GSE83456 and the clinical data, we performed the univariate and multivariate logistics regression analyses using the R package ‘rms’ (https://cran.rstudio.com/bin/windows/contrib/4.0/rms_6.1-0.zip). Also, we constructed and compared the nomograms based on the logistics regression result obtained. The performance of the nomogram were assessed using the receiver operating characteristic (ROC) curves. Furthermore, factors in the clinical cohort sample were evaluated via univariate and multivariate logistic regression. The correlation of ESR with lymphocytes, monocytes, and ML ratio in osteoarticular TB patients was performed and visualized using the R package ‘corrplot’ (https://cran.rstudio.com/bin/windows/contrib/4.0/corrplot_0.84.zip). The value of P < 0.05 was considered a significant difference.

Gene Set Enrichment Analysis (GSEA)

Using GSEA (4.0.3), we identified the potential biological mechanisms of ML ratio, which were involved in the impact of osteoarticular TB [16]. The gene set permutations with 1000-times were conducted to acquire the normalized enrichment score (NES). The normal P-value < 0.05 and false discovery rate (FDR) < 0.25 was used to quantify statistically significant enrichment.

Results

Patient baseline characteristics

Table 1 illustrates the baseline characteristics collected for the 326 patients, such as age, sex, profession, nationality, BMI, ESR. In this study, spine TB accounted for 93.9% and constituted the majority of osteoarticular TB. BMI and hemoglobin were much lower in the osteoarticular TB group than in the non-TB group. However, ESR was much higher in the osteoarticular TB group (Table 1).

Table 1

Baseline characteristics of patients.

Characteristics	Osteoarticular TB	HC	Overall	P value
	(N = 164)	(N = 162)	(N = 326)
Age
Mean (SD)	45.2 (17.0)	48.0 (17.2)	46.6 (17.2)	0.137
Median [Min, Max]	46.5 [3.00, 78.0]	51.0 [5.00, 86.0]	49.0 [3.00, 86.0]
Sex
Female	69 (42.1%)	69 (42.6%)	138 (42.3%)	1
Male	95 (57.9%)	93 (57.4%)	188 (57.7%)
Nationality
Han	80 (48.8%)	97 (59.9%)	177 (54.3%)	0.158
Zhuang	75 (45.7%)	59 (36.4%)	134 (41.1%)
Yao	8 (4.9%)	4 (2.5%)	12 (3.7%)
Others	1 (0.6%)	2 (1.2%)	3 (0.9%)
Profession
Farmer/Worker	100 (61.0%)	86 (53.1%)	186 (57.1%)	0.153
Office clerk	9 (5.5%)	10 (6.2%)	19 (5.8%)
Student	7 (4.3%)	17 (10.5%)	24 (7.4%)
Others	48 (29.3%)	49 (30.2%)	97 (29.8%)
BMI
Mean (SD)	20.1 (2.79)	22.5 (4.17)	21.3 (3.76)	<0.001
Median [Min, Max]	19.8 [13.2, 29.4]	22.7 [13.5, 36.9]	21.0 [13.2, 36.9]
Missing	20 (12.2%)	10 (6.2%)	30 (9.2%)
Location
-	0 (0%)	162 (100%)	162 (49.7%)	<0.001
joint tuberculosis	10 (6.1%)	0 (0%)	10 (3.1%)
spine tuberculosis	154 (93.9%)	0 (0%)	154 (47.2%)
Hemoglobin
Mean (SD)	119 (17.6)	127 (16.7)	123 (17.6)	<0.001
Median [Min, Max]	121 [76.4, 153]	128 [75.8, 167]	124 [75.8, 167]
Erythrocyte
Mean (SD)	4.71 (3.01)	4.60 (0.663)	4.65 (2.19)	0.646
Median [Min, Max]	4.46 [2.83, 42.0]	4.56 [3.14, 7.07]	4.51 [2.83, 42.0]
White blood cell
Mean (SD)	7.21 (2.71)	7.62 (2.57)	7.41 (2.65)	0.163
Median [Min, Max]	6.78 [2.55, 17.2]	7.08 [3.10, 21.2]	6.90 [2.55, 21.2]
Lymphocytes
Mean (SD)	1.52 (0.798)	2.06 (0.780)	1.79 (0.834)	<0.001
Median [Min, Max]	1.38 [0.268, 7.21]	1.95 [0.382, 5.55]	1.67 [0.268, 7.21]
Monocytes
Mean (SD)	0.645 (0.269)	0.582 (0.205)	0.614 (0.241)	0.017
Median [Min, Max]	0.589 [0.20, 1.72]	0.553 [0.04, 1.52]	0.568 [0.04, 1.72]
Neutrophil
Mean (SD)	4.73 (2.25)	4.72 (2.49)	4.72 (2.37)	0.964
Median [Min, Max]	4.24 [1.16, 14.7]	4.07 [1.55, 19.5]	4.14 [1.16, 19.5]
Eosinophils
Mean (SD)	0.278 (0.474)	0.229 (0.172)	0.254 (0.357)	0.219
Median [Min, Max]	0.189 [0, 5.73]	0.201 [0, 0.976]	0.198 [0, 5.73]
Basophils
Mean (SD)	0.034 (0.021)	0.036 (0.019)	0.035 (0.020)	0.225
Median [Min, Max]	0.031 [0, 0.162]	0.033 [0, 0.133]	0.032 [0, 0.162]
Platelet
Mean (SD)	290 (85.3)	250 (80.1)	270 (85.1)	<0.001
Median [Min, Max]	274 [78.5, 562]	239 [98.0, 630]	256 [78.5, 630]
ESR
Mean (SD)	38.6 (23.6)	19.5 (19.6)	30.0 (23.8)	<0.001
Median [Min, Max]	34.0 [1.00, 109]	11.0 [2.00, 94.0]	24.0 [1.00, 109]
Missing	6 (3.7%)	34 (21.0%)	40 (12.3%)

Osteoarticular tuberculosis group vs. non-TB group

The xCell score of 64 subtypes for each sample was calculated based on the expression file (S1 File). The xCell score of lymphocytes was sum of all xCell score of lymphoid cells (B cells, T cells and NK cell) in the dataset GSE83456 (S1 Fig). Compared with the HC group in the dataset GSE83456, the EPTB group has a higher xCell score of basophils, monocytes, neutrophils, and platelets, but lower xCell score of lymphocytes (Fig 1A). The cell counts of monocytes and platelets were much higher while that of lymphocytes were lower in the osteoarticular TB group of the clinical data (Fig 1B). Monocytes, lymphocytes and platelet were significantly different between the osteoarticular TB and non-TB group.

Fig 1

Different cell type between EPTB and healthy controls.

(A) Violin plot showed the xCell score of 7 kinds of blood cells in dataset GSE83456. (B) Violin plot showed the cell counting of 7 kinds of blood cells in clinical data. (C) Violin plot showed the cell rate of 5 kinds of blood cells in clinical data.

A high ML ratio is associated with osteoarticular TB

The ML ratio in the dataset GSE83456 represents the ratio of xCell score of monocytes to lymphocytes. The ML ratio in the clinical data represents the ratio of cell counts of monocytes to lymphocytes. In the EPTB group, the ML ratio was significantly higher in the dataset GSE83456 (Fig 2A) and clinical data (Fig 2B). We identified the ML ratio as an independent risk factor for the diagnosis of EPTB; therefore, nomograms (S2 Fig) based on the ML ratio were constructed for both dataset GSE83456 and clinical data. ROC analysis showed the AUC of the nomogram was 0.798 for the dataset GSE83456, and 0.737 for the clinical dataset (Fig 2C).

Fig 2

T test of ML ratio between EPTB and healthy controls and GSEA analysis.

(A) Violin plot showed the ML ratio between EPTB and healthy controls in dataset GSE83456. (B) Violin plot showed the ML ratio between EPTB and healthy controls in clinical data. (C) AUCs of the nomogram based on ML ratio in GSE83456 and clinical data. (D) The enriched gene sets in HALLMARK collection by the EPTB samples. (E) The enriched gene sets in HALLMARK collection by the high ML ratio samples.

GSEA analysis showed that 22 hallmarks pathways were significantly enriched in the EPTB group, while none enriched in the HC group. Fig 2D shows 12 representative pathways, including apoptosis, reactive oxygen species (ROS), inflammatory response, interferon-α/γ response, complement, IL6/JAK/STAT3, and TNFα via NF-κB. Moreover, GSEA analysis also showed that nine significant enriched pathways in the high ML ratio phenotype were consistent with those of the EPTB group (Fig 2E).

Nomogram for osteoarticular TB diagnosis

Fig 3A shows a nomogram based on three independent risk factors (ML ratio, ESR, and BMI), which we constructed for advancing clinical diagnosis of osteoarticular TB (Table 2). AUC of this nomogram was 0.843 (Fig 3B); also, the calibration curves indicated a satisfactory agreement between nomogram prediction and actual probabilities (Fig 3C). We further evaluated a correlation between monocytes, lymphocytes, and ESR. Monocytes and ML ratio was positively correlated with ESR while lymphocytes was negatively correlated with ESR. Fig 3D illustrates ML ratio was positively correlated with ESR (cor = 0.439, P < 0.0001).

Fig 3

Nomogram and correlation analysis.

(A) Nomogram for predicting osteoarticular TB probability for clinical data; The red line represents an osteoarticular TB patient while the green line represents an non-TB patient. (B) AUCs of the nomogram for clinical data. (C) Calibration curves for predicting osteoarticular TB probability for clinical data. (D) The correlation of ML ratio with ESR.

Table 2

Univariate and multivariate logistic regression of the clinical data.

Characteristics	Univariate logistic		Multivariate logistic
	HR	P	HR	P
Age	0.99 [0.98, 1.00]	0.137
Sex	1.02 [0.66, 1.59]	0.924
BMI	0.82 [0.75, 0.88]	p<0.0001	0.80 [0.72, 0.88]	p<0.0001
White blood cell	0.94 [0.87, 1.02]	0.165
Erythrocyte	1.03 [0.92, 1.21]	0.656
ESR	1.05 [1.03, 1.06]	p<0.0001	1.03 [1.01, 1.05]	0.001
Hemoglobin	0.97 [0.96, 0.99]	p<0.0001	1.00 [0.98, 1.03]	0.663
ML ratio	45.39 [13.37, 173.58]	p<0.0001	21.41 [2.42,222.93]	0.008
PL ratio	1.01 [1.01, 1.01]	p<0.0001	1.00 [1.00, 1.01]	0.1
PM ratio	1.00 [1.00, 1.00]	0.384

Discussion

At present, TB poses a global threat for both developing and developed countries [17, 18], the innate immune response representing one of the most critical determinants associated with the outcome of EPTB infection [19]. In this study, the xCell score of monocytes and platelets is significantly higher, while that of lymphocytes is lower in the EPTB group in dataset GSE83456. Moreover, the ML ratio of the xCell score is significantly higher in the EPTB group. GSEA results showed pathways closely related to the inflammation, such as apoptosis, reactive oxygen species (ROS), inflammatory response, and interferon-α/γ response, were significantly enriched. Previous studies suggest that ROS and interferon-α/γ might be associated with M.tb immune escape and disease progression in infected humans [20, 21]. Micheliolide and nitric oxide play an anti-inflammatory role in M.tb infection by inhibiting NF-κB that is a ubiquitously existed transcription factor family [22, 23]. These results demonstrated M.tb infection activates these inflammation-related pathways and plays an significant role in the pathogenesis of EPTB. The pathways significantly enriched in the EPTB group were also enriched in the high ML ratio phenotype, which indicates that a high ML ratio is a crucial characteristic of EPTB and is consistent with a previous study [13].

Understanding the immune responses protecting from infection or progression to disease is crucial to allow the development of diagnostic tools for the efficient prevention and management of EPTB. The assumption is further confirmed by the analysis of the clinical data. It had been proved that the cell counts and frequency of monocytes and lymphocytes were significantly different between TB disease and HCs [24], which were consistent with our results. Lymphocytes are thought to be the primary effector cells in TB immunity, while myeloid cells as the primary host cell for infection. Therefore, the relative abundance may reflect a balance between effector and target cells. An alternative explanation is that the relative abundance of these cell types could be a marker of hematopoietic parameters associated with TB. The ratio of myeloid transcripts to lymphoid transcripts were altered in inflammation-associated disease. Altogether, these data support the hypothesis that a high or a low ML ratio may be a correlate of risk for TB. Naranbhai and colleagues demonstrated that the ML ratio may be a readily available tool to identify the risk of TB during HIV infection of patients with acceptable combination of antiretroviral therapy [25, 26].

So far, the osteoarticular TB diagnosis relies on the comprehensive analysis of the history, imaging, and blood test. However, the challenges in the clinical identification of osteoarticular TB are compounded by a diagnostic armamentarium with significant limitations. Based on the just mentioned facts, we try to benefit from ML ratio, which can help us with osteoarticular TB diagnosis since it is difficult to obtain pus or tissue for M.tb culture [27]. Besides, a bacterial culture is frequently culture-negative. In this study, the ML ratio was an independent predictive factor for osteoarticular TB diagnosis in both the dataset GSE83456 and clinical datasets. Higher ML ratio was associated with osteoarticular TB through bioinformatic analysis and clinical validation. ESR is one of the blood markers monitor TB and infection [28]. Moreover, ESR is positively related to the ML ratio. As the results are shown in this studies, osteoarticular TB will lead to anemia, low albumin, and low BMI [29, 30]. Moreover, nutritional deficiencies predispose to a worse osteoarticular TB infection [31]. Results of the study indicate that the ML ratio, BMI, and ESR are independent predictive factors for osteoarticular TB diagnosis. Therefore, we constructed a nomogram to improve the diagnosis accuracy of osteoarticular TB. The AUC of the nomogram, which demonstrated more accurate and practical performance, is 0.843. Our study provides clear indicators, easily acquired in clinical work, for improvement of osteoarticular TB diagnosis. Nonetheless, the osteoarticular TB diagnosis also depends on the imaging. Combine our nomogram with imaging may significantly improve the diagnosis of osteoarticular TB. Therefore, our future research will focus on this combination.

There are three important potential limitations of this study: 1) since this is a retrospective study of osteoarticular TB and non-TB group, there is a possibility of selection bias; 2) The participants of this study did not have TSTs or interferon γ–release assays performed; 3) Most of the diagnosed patients were typically confirmed to be pathologically positive but microbiologically negative.

Conclusion

We can conclude that the immune status of individuals changes during TB infection. Nonetheless, the ML ratio was identified as an independent predictive factor for EPTB diagnosis in the dataset GSE83456 and was considered a significant characteristic of osteoarticular TB. Lastly, the nomogram we constructed showed satisfactory ability to diagnose osteoarticular TB.

Cell types in the microenvironment.

(A, B, C, D, E) xCell score of sixty-four cell types in GSE83456 were grouped into five groups: lymphocytes, myeloids, stem, stromal, and other cells.

(TIF)

Click here for additional data file.

Nomograms.

(A) Nomogram for predicting EPTB probability base on ML ratio in dataset GSE83456. (B) Nomogram for predicting osteoarticular TB probability base on ML ratio in clinical data.

(TIF)

Click here for additional data file.

xCell score of 64 subtypes immune cells of each sample.

(XLSX)

Click here for additional data file.

28 Apr 2021

PONE-D-20-39926

Identification of Immune Status Changing in Patients with Bone Tuberculosis

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Reviewer #1: In this paper, the authors aim to identify economic and easily producible laboratory and clinical parameters, the variation of which can be associated with bone tuberculosis.

For this purpose, the authors first analyze a GSE83456 gene set to evaluate the different scores relating to the leukocyte populations in patients with bone tuberculosis and in healthy subjects. They then perform a similar analysis with the blood counts and clinical data of patients hospitalized for bone tuberculosis or for lumbar disc herniation or lumbar spinal stenosis.

Through a process of uni-variate and then multivariate logistic regression analysis, they first identify the ML ratio and then also the BMI and ESR as independent predictive parameters associated with the probability of bone tuberculosis. From these parameters they obtain more nomograms and test their validity through ROC curves.

The paper shows interesting results and potentially directly applicable to clinical practice; however, there are some points relating to the protocols applied and the results that require improvement before possible publication.

A limitation of this study, in my opinion, is that the group of NPTB was compared with only another group of non-infectious diseases. It would improve the results of the study, the comparison of the NPTB group with other experimental groups like LTBI or patients with non-tubercular infectious disease.

Major points

About the enrolled patients, it is not clear how the diagnosis of bone tuberculosis was made. The authors, in the material and methods section and table 1, should provide data relating to immunological tests, even if partial, or in any case, clarify how the diagnosis of bone tuberculosis was reached, e.g. by imaging.

In lines 27, 57, 71, etc. the authors refer to lymphoid cells and not lymphocytes, in supplementary table 1 there is no a lymphocyte population as a whole but 21 different populations classified as lymphoid; the authors should specify which of these populations were used for the calculation of the ML ratio obtained from the GSE83456 dataset and which differences, in terms of calculation, exist between this ML ratio and that obtained from the clinical data of the patients.

In Figure 2A it is not clear to me how the ML ratio was calculated from the GSE83456 dataset, is it a ratio between the scores?

In lines 105 and 106, the authors refer to healthy controls who actually are subjects suffering from disc herniation or lumbar spinal stenosis, for clarity it would be better to define them as non-TB patients.

In figure 1C the authors refer to a cell ratio, but it is not clear what this parameter indicates, is it perhaps the frequency of the single populations on the total of leukocytes?

In figure S1 there are two nomograms (A and B) but in the text, they are not described and it is not clear what difference there is between the two; the authors should clarify this point.

Minor points

In table 1, please correct the term eosinophil, in general table 1 should be better formatted.

Would be interesting if the authors, in fig 3A show where some NPTB and non TB patient lies in the nomogram.

The right citation for reference n15 is: La Manna MP, Orlando V, Dieli F, Di Carlo P, Cascio A, Cuzzi G, Palmieri F, Goletti D, Caccamo N. Quantitative and qualitative profiles of circulating monocytes may help identifying tuberculosis infection and disease stages. PLoS One. 2017 Feb 16;12(2):e0171358. doi: 10.1371/journal.pone.0171358. Among the authors, there is no Wilkinson KA.

Reviewer #2: Need to be re written. This manuscript does not reflect new findings . In addition there a wrong claim in it. The authors said that bone infection is the most common extrapulmonary Tb. This statement contradict most of the publications which said lymph node is the most dominant extrapulmonary type. The English language is very poor.

Reviewer #3: In this manuscript authors have studied immune status of extrapulmonary Tuberculosis patients, using a dataset of gene expression profiling of 47 EPTB and 61 healthy controls(GSE83456 ),which was downloaded from an open data source. The findings are compared with data collected from a hospital (study site) of 166 patient diagnosed having bone TB and 162 non-TB patients.

Furthermore, authors have constructed a nomogram based on three independent risk factors ML ratio, ESR, and BMI for clinical diagnosis of bone TB.

The manuscript requires a lot of English editing, and some sections need revision.

There are many ambiguities in method, result and discussion sections which need careful review and revision, given below are few examples.

Method: Type of demographic, clinical and laboratory data collected from TB and non-TB patient files (2012-2018) and source of cell counts used for comparison with GSE83456 data file

Results: Vague expressions and unspecific determinate words ["much lower”, “much higher” “satisfactory agreement”] are used that are not specific or precise enough for the reader to derive exact meaning.

Minor comments

For some statements, references are not cited (row 50-52 and 214-215)

Tables; Abbreviation used and unit of measurements are not described.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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17 May 2021

Dear Editors and Reviewers:

It is with excitement that I resubmit to you a revised version of the manuscript “Immune Status Changing Helps Diagnose Osteoarticular Tuberculosis” (ID: PONE-D-20-39926) for the “Plos One”. Thank you for giving me the opportunity to revise and resubmit this manuscript. I appreciate the time and detail provided by each reviewer and by you and have incorporated the suggested changes into the manuscript to the best of my ability. We highlight the changes to our manuscript within the document by using the track changes mode in Microsoft Word. The manuscript has certainly benefited from these insightful revision suggestions. I look forward to working with you and the reviewers to move this manuscript closer to publication in the “Plos One”. According to your nice suggestions, we have made extensive corrections to our previous draft. changes to the manuscript are shown in red. Point-by-point responses to the nice associate editor and three nice reviewers are listed below this letter.

Responses to the reviewer’s comments:

Reviewer #1:

Major points

1. About the enrolled patients, it is not clear how the diagnosis of bone tuberculosis was made. The authors, in the material and methods section and table 1, should provide data relating to immunological tests, even if partial, or in any case, clarify how the diagnosis of bone tuberculosis was reached, e.g. by imaging.

Thank you for your valuable comments. We redefined the diagnostic and exclusion criteria for osteoarticular TB (Methods section, line 93-103, page 4).

2. In lines 27, 57, 71, etc. the authors refer to lymphoid cells and not lymphocytes, in supplementary table 1 there is no a lymphocyte population as a whole but 21 different populations classified as lymphoid; the authors should specify which of these populations were used for the calculation of the ML ratio obtained from the GSE83456 dataset and which differences, in terms of calculation, exist between this ML ratio and that obtained from the clinical data of the patients.

Thank you for your valuable comments. Consistenting with the method of lymphocyte count in the routine blood examination, the xCell score of lymphocytes was a constitution of 21 subtypes (Supplemental Figure S1) of lymphoid cells (B cells, T cells and NK cells). We also point out that in the main text (Methods section, line 83-86, page 3). The ML ratio in the dataset GSE83456 represents the ratio of xCell score of monocytes to lymphocytes. The ML ratio in the clinical dataset represents the ratio of cell counts of monocytes to lymphocytes (Results section, line 152-154, page 6).

3. In Figure 2A it is not clear to me how the ML ratio was calculated from the GSE83456 dataset, is it a ratio between the scores?

Thank you for your valuable comments. The ML ratio in the dataset GSE83456 represents the ratio of xCell score of monocytes to lymphocytes. The ML ratio in the clinical dataset represents the ratio of cell counts of monocytes to lymphocytes (Results section, line 152-154, page 6).

4. In lines 105 and 106, the authors refer to healthy controls who actually are subjects suffering from disc herniation or lumbar spinal stenosis, for clarity it would be better to define them as non-TB patients.

Thank you for your valuable comments. In The First Affiliated Hospital of Guangxi Medical University, from 2012 and 2018, we randomly screened out the non-TB patients from all the inpatients diagnosed with lumbar disc herniation or lumbar spinal stenosis (Methods section, line 105-107, page 4).

5. In figure 1C the authors refer to a cell ratio, but it is not clear what this parameter indicates, is it perhaps the frequency of the single populations on the total of leukocytes?

Thank you for your valuable comments. The cell ratio represents the frequency of the single populations on the total of leukocytes in the routine blood examination. Actually, these data had no effect on the results in this study. We have deleted figure 1C from figure 1 in this version of manuscript.

6. In figure S1 there are two nomograms (A and B) but in the text, they are not described and it is not clear what difference there is between the two; the authors should clarify this point.

Thank you for your valuable comments. We identified the ML ratio as an independent risk factor for the diagnosis of EPTB; therefore, nomograms (Supplemental Figure S2) based on the ML ratio were constructed for both dataset GSE83456 and clinical data (Results section, line 155-159, page 6). Figure S2A represents the nomogram for predicting EPTB probability base on ML ratio in dataset GSE83456. Figure S2B represents the nomogram for predicting osteoarticular TB probability base on ML ratio in clinical data (Figure legends, line 442-444, page 16).

Minor points

1. In table 1, please correct the term eosinophil, in general table 1 should be better formatted.

Thank you for your valuable comments. We have modified this part in the text (Table 1).

2. Would be interesting if the authors, in fig 3A show where some NPTB and non TB patient lies in the nomogram.

Thank you for your valuable comments. We have modified this part in the text (Figure 3A). The red line represents an osteoarticular TB patient, while the green line represents a non-TB patient.

3. The right citation for reference n15 is: La Manna MP, Orlando V, Dieli F, Di Carlo P, Cascio A, Cuzzi G, Palmieri F, Goletti D, Caccamo N. Quantitative and qualitative profiles of circulating monocytes may help identifying tuberculosis infection and disease stages. PLoS One. 2017 Feb 16;12(2):e0171358. doi: 10.1371/journal.pone.0171358. Among the authors, there is no Wilkinson KA.

Thank you for your valuable comments. We have modified this part in the text as reference no.13 (Reference section, line 337-341, page 12).

Reviewer #2

Need to be re written. This manuscript does not reflect new findings. In addition, there a wrong claim in it. The authors said that bone infection is the most common extrapulmonary Tb. This statement contradicts most of the publications which said lymph node is the most dominant extrapulmonary type. The English language is very poor.

Thank you for your valuable comments. Due to the lack of writing and English ability, the article before was poorly organized, so we search professional help for improvements to the English language within our manuscript.

Reviewer #3

1.The manuscript requires a lot of English editing, and some sections need revision. There are many ambiguities in method, result and discussion sections which need careful review and revision, given below are few examples.

Method: Type of demographic, clinical and laboratory data collected from TB and non-TB patient files (2012-2018) and source of cell counts used for comparison with GSE83456 data file.

Results: Vague expressions and unspecific determinate words ["much lower”, “much higher” “satisfactory agreement”] are used that are not specific or precise enough for the reader to derive exact meaning.

Thank you for your valuable comments. Due to the lack of writing and English ability, the article before was poorly organized, so we search professional help for improvements to the English language within our manuscript.

Minor comments

1. For some statements, references are not cited (row 50-52 and 214-215)

Thank you for your valuable comments. We have modified this part in the text.

2. Tables; Abbreviation used and unit of measurements are not described.

Thank you for your valuable comments. We have modified this part in the text (Table 1).

We tried our best to improve the manuscript and made some changes in the manuscript. These changes will not influence the content and framework of the paper. And here we did not list the changes but marked in red in revised paper. We appreciate for Editors/Reviewers’ warm work earnestly, and hope that the correction will meet with approval.

Once again, thank you very much for your comments and suggestions.

Submitted filename: revision letter.docx

Click here for additional data file.

25 May 2021

Immune Status changing Helps Diagnose Osteoarticular Tuberculosis

PONE-D-20-39926R1

Dear Dr. Zhan,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Colin Johnson, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have checked and improved almost all the critical points that I had noticed, they have also improved the writing. In my opinion, the paper is now more clear and intelligible.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

7 Jun 2021

PONE-D-20-39926R1

Immune Status changing Helps Diagnose Osteoarticular Tuberculosis

Dear Dr. Zhan:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Colin Johnson

Academic Editor

PLOS ONE