Whole blood transcriptional profiles and the pathogenesis of tuberculous meningitis

Background Mortality and morbidity from tuberculous meningitis (TBM) are frequent and strongly associated with the inflammatory response to Mycobacterium tuberculosis infection. However, the mechanisms driving the associations are uncertain. We sought to identify the gene modules, hubs and pathways associated with the pathogenesis and mortality from TBM, and to identify which best-predicted death. Methods We used whole blood RNA sequencing to obtain transcriptional profiles from 281 Vietnamese adults with TBM (207 HIV-negative; 74 HIV-positive), 295 with pulmonary TB (PTB), and 30 healthy controls. The TBM cohort was divided randomly into a discovery cohort (n=142) and a validation cohort (n=139). Weighted gene co-expression network analysis identified clusters of genes (or ‘modules’) and hub genes associated with death or disease severity. An overrepresentation analysis identified pathways associated with TBM mortality, with a consensus analysis identifying consensual patterns between HIV-positive and HIV-negative individuals. A multivariate elastic-net Cox regression model selected the candidate predictors of TBM mortality, then model prediction performance using logistic regression and internal bootstrap validation to choose best predictors. Results Overall, TBM mortality was associated with increased neutrophil activation and decreased T and B cell activation pathways. Death from TBM was associated with increased angiogenesis in HIV-positive adults, and with activated TNF signaling and down-regulated extracellular matrix organization in HIV-negative adults. PTB and TBM have similar transcriptional profiles compared to healthy controls, although inflammatory genes were more activated in HIV-positive than HIV-negative TBM. The expression of four hub genes – MCEMP1, NELL2, ZNF354C and CD4 – were strongly predictive of death from TBM (AUC 0.80 and 0.86 for HIV-negative and HIV-positive, respectively). Conclusions Whole blood transcriptional profiling revealed that TBM is associated with a characteristic systemic inflammatory response, similar to that invoked by pulmonary tuberculosis, but with key gene modules, hubs and pathways strongly associated with death. Our analysis suggests a novel 4-gene biomarker for predicting death from TBM, but also opens a new window into TBM pathogenesis that may reveal novel therapeutic targets for this lethal disease.


Introduction
Of 7.1 million new tuberculosis (TB) cases in 2019, tuberculous meningitis (TBM) is estimated to have developed in 164,000 adults, around 25% of whom were living with HIV 1 .TBM is the most severe form of TB, causing death or neurological disability in half of all cases.Overall, TBM mortality is around 25%, but rises to around 50% in those with HIV, with most deaths occurring in the first 3 months of treatment 1,2 .
The poor outcomes from TBM are strongly associated with the inflammatory response 3,4 , with both a paucity and an excess of inflammation linked to death from TBM [5][6][7][8][9][10][11] .However, the mechanisms behind these observations remain uncertain.Immune responses in TBM are thought to be compartmentalized within the central nervous system.Studies have shown that immune cell counts, cytokine concentrations, metabolites and transcriptional responses differ between the peripheral blood and the cerebrospinal fluid (CSF) 10,12,13 .In adults with TBM, leukocyte activation is higher in the CSF than in peripheral blood, although a marked myeloid response in peripheral blood has been reported 10 .Blood transcriptomic analysis has found increased neutrophil-associated transcripts and inflammasome signaling in those with HIV-associated TBM and immune reconstitution inflammatory syndrome 5 .In children with TBM, whole blood transcriptional profiles showed increased inflammasome activation and decreased T-cell activation 12 .Taken together, these studies suggest the inflammatory response associated with TBM may have a greater systemic component than originally thought and its characterization may help define the immune mechanisms leading to death and disability.
The accurate and early detection of patients at highest risk of complications and death from TBM may help to target treatment for those most in need.The British Medical Research Council (MRC) grades have been used to categorize TBM severity for almost 80 years 14 , and the system strongly predicts TBM mortality 15 .Previously, we and others developed new prognostic models from studies of 1699 adults with TBM using clinical and laboratory parameters, including MRC grade 4,15 .These models predicted outcome more accurately than MRC grade alone.However, they might be improved by measures of host inflammatory response.Host-based peripheral blood gene expression analysis has been used to identify active or progressive pulmonary TB and in pulmonary TB treatment monitoring 16,17 , but has yet to be applied to TBM.
In the current study, we investigated whole blood transcriptional profiles in 281 Vietnamese adults with TBM, 295 with pulmonary TB (PTB), and 30 healthy controls.Our objective was to use weighted gene coexpression network analysis, an unbiased and well-evaluated approach, to identify the biological pathways and hub genes associated with TBM pathogenesis and assess the predictive value of gene expression for early mortality from TBM.

Characteristics and outcomes of the cohorts
Four cohorts (all ≥18 years) were used in the study, representing a total of 606 participants.The characteristics of these cohorts are provided in Table 1.There were 281 adults with TBM; 207 HIVnegative and 74 HIV-positive.In the HIV-negative TBM adults, the median age was 46 years (IQR 34, 58), 127 (61%) were male, and the median Body Mass Index (BMI) was 20.2 (IQR 18.2, 22.3).HIV-positive adults were more likely than HIV-negative adults to be male, younger, have lower BMI, have previously received TB treatment, and to have microbiologically confirmed TBM.Total white cell counts in blood and CSF in HIV-positive adults were lower than in HIV-negative adults.The PTB cohort consisted of 295 adults with the median age of 44 years (IQR 31, 52), 228 (77%) were male, the median of BMI was 19.4  (IQR 17.7, 21.6) and 129 (48%) had pulmonary cavities on chest X-ray.Of the 30 healthy controls, 11 (37%) were male, and the median age was 33 (IQR 29, 37) (Table 1).
The clinical variables associated with three-month mortality of the 281 adults with TBM are given in Table 2.The discovery (n=142) and validation (n=139) cohorts had similar characteristics (Table 2).47.5% (133/280) had definite TBM 18 , with microbiologically confirmed disease, accounting for 45.9% (101/220) of survivors and 52.4% (32/61) of those who died.The overall three-month mortality rate was 21.7% (61/281): 16.4% (34/207) in HIV-negative and 36.5% (27/74) in HIV-positive (p<0.001).We did not observe differences in mortality by sex, age and diagnostic category.Greater disease severity, MRC grades 2 and 3 at enrolment, was associated with increased mortality compared to grade 1 (p<0.001).In those who died, CSF and peripheral blood neutrophil counts were higher and peripheral blood lymphocyte count lower, compared to those who survived.

Whole blood transcriptional profiles of the four cohorts
We analyzed the whole blood transcriptomics, using bulk RNA sequencing from 606 participants in the 4 cohorts.On average 35.1 million reads/sample and 57% reads mapping accuracy to human reference genome (GRCh.38 release 99) was obtained.The study objectives and cohorts flow are presented in Figure 1.Principal component analysis on the transcriptomic profile of the top 20,000 genes with most variation across 4 studies showed distinct separation between healthy controls from both PTB and TBM cases (Figure 2).The PTB profile substantively overlapped with TBM, with some separation between HIV-negative and HIV-positive TBM (Figure 2).
Enrichment scores from single sample gene set enrichment analysis (ssGSEA), which based on expression rank of genes relevant to pathways, were measured in some pathways already known to be important mediators of TB or TBM pathogenesis (Figure 2).As anticipated, inflammatory response, cytokine signaling, interferon signaling, TNF signaling, and inflammasome activation pathways, were enriched in PTB and TBM cohorts as compared to healthy controls.In TBM, enrichment of genes in these pathways were generally higher in HIV-positive than in HIV-negative individuals (Table S1, Table S5).

Transcriptional gene modules associated with TBM severity and mortality
Transcriptional profiles associated with TBM mortality were generated by identifying differentially expressed genes.Of the top 20,000 genes with most variation, we observed 724 (3.6%) genes that were differentially expressed (FDR <0.05, absolute fold change (FC) >1.5) in all those with TBM (Figure 3, Table S2).Next, we applied weighted gene co-expression network analysis (WGCNA) to 5000 most variable genes from 281 TBM samples (n= 207, HIV-negative; n=74, HIV-positive) to define clusters of highly correlated genes (modules) associated with TBM severity and mortality.Gene modules are clusters of genes that have highly interconnected expression, usually related to their biological functions.Hub genes are genes with high connectivity to other genes within a respective module.First, we used WGCNA to construct a network of gene modules in the discovery cohort.Then we validated the presence of these transcriptional modules in the validation cohort, labelling the modules with different colors.In the discovery cohort (n=142), 15 modules were identified overall, consisting of 44 to 1350 genes per module (Figure S1, Table S6).All 15 modules were preserved in the validation cohort (n=139) through the preservation analysis (Figure S2).
The associations between the 15 modules and TBM severity and mortality are presented in Figure 4 for both the discovery and validation cohorts.Modules were linked to each other in a hierarchical structure, with major biological processes annotated.Associations of the modules with TBM disease severity (MRC grade) at baseline were estimated by Spearman correlations between MRC grade and the first principle component (PC1) of each module.Similarly, associations of the modules with mortality were measured by hazard ratio (HR) per increase 1 unit of PC1 using Cox regression model adjusted for age, HIV status and dexamethasone treatment (Figure 4) in both discovery and validation cohorts.
Of the 15 preserved modules, five modules were significantly associated with mortality in the discovery and validation cohorts, with false discovery rate (FDR) < 0.05 (Figure 4).These five modules were separated into two big module clusters.The first cluster contained the blue module (n=799 genes), involved in inflammatory and innate immune responses, and the cyan module (n=44 genes) with unknown biological function.These modules were upregulated in those who died, as shown in the heatmap in Figure 4 (HR: 3.0 and 2.2 for the blue module, and 2.2 and 1.7 for the cyan module, FDR < 0.05 for all comparisons).The black (n= 207 genes), brown (n=698 genes) and red (n=229 genes) modules were in the second cluster and were generally involved in adaptive immunity including T and B cell signaling pathways.These three modules were down-regulated in those who died (HR: 0.43 and 0.36 for brown, 0.46 and 0.39 for red, 0.59 and 0.49 for black) (Figure 4).
It is known that TBM MRC grade before treatment initiation strongly predicts outcome from TBM.Here, we investigated correlations between each module and MRC grade and their association with mortality.
The pink module, involved in hemostasis and platelet activation, was positively correlated with MRC grade, but not mortality.Of the five modules associated with death from TBM, all were correlated with MRC grade (Figure 4).Four of these five modules, were enriched for immune responses.

Transcriptional hub genes associated with TBM severity and mortality
We next identified hub genes within the four biologically functional modules associated with TBM mortality in both the discovery and validation cohorts.Hub genes showed higher connectivity within the modules, and stronger association with TBM mortality as compared to less connected genes within a module (Figure S3).Seven hub genes (ETS2, PGD, UPP1, CYSTM1, FCAR, KIF1B and MCEMP1) were upregulated in death, all from the acute inflammation (blue) module.Hub genes from the brown, red and black modules were downregulated in death and were involved in adaptive immune response.Ten hub genes associated with mortality (CD96, TNFRSF25, TBC1D4, CD28, ABLIM1, RASGRP1, NELL2, TRAF5, TESPA1, TRABD2A) were from the brown module, three (EVL, PLCG1, NLRC3) from the red, and six (CD2, CD247, TGFBR3, ARL4C, KLRK1, MATK) from the black module (Figure 5, Table S7).
We examined patterns of shared and distinct gene expression of some hub across the four cohorts (healthy controls, PTB, and TBM with and without HIV-infection) to reveal disease progression and pathogenesis of different TB forms (Figure 6).There were two upregulated genes from the acute inflammation module (FCAR and MCEMP1) and six downregulated genes (NELL2, TRABD2A, PLCG1, NLRC3, CD247 and MATK) from the other three adaptive immunity modules.The patterns of up and downregulation, relative to healthy controls, were similar in PTB and TBM, although tended to be more pronounced in those with TBM as well as those with HIV (Figure 6).

Transcriptional immune pathways associated with TBM mortality
To better understand the biological functions of the five modules associated with TBM mortality, a gene set associated with mortality in each module was functionally annotated using known pathway databases, such as gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) 19 .
Pathway enrichment analysis was performed using fold enrichment to determine if the prevalence of genes in a pathway were different from that expected by chance.We also used ssGSEA enrichment scores based on gene expression ranking of genes in a particular pathway within a single individual, to show the activity of this pathway between death and survival TBM as well as across 4 cohorts.These analyses helped to identify the pathways linked to mortality within the gene modules.
In the blue module, we found TBM mortality was associated with upregulated inflammatory and innate immune response transcripts, particularly in pathways involved in the acute inflammatory response and the regulation of inflammatory responses, including responses to bacteria and neutrophil activation.
KEGG pathway analysis suggested this signal was associated with transduction and immune system pathways, such as toll-like receptor signaling, TNF signaling, NF-kappa B signaling and neutrophil extracellular trap formation (Figure 5, Table S8).
We did not find any pathway significantly associated with mortality in the upregulated cyan gene module, although in the hierarchical clustering the cyan module was highly correlated with the blueinflammatory response module (Figure 4).In contrast, analyses of the down-regulated brown, red and black modules highlighted pathways involved in adaptive immunity, predominantly those mediated by lymphocytes.These included downregulations of lymphocyte proliferation, T cell activation, B cell activation, natural killer cell mediated cytotoxicity and their signaling pathways such as antigen receptor-mediated, T and B cell receptor (Figure 5, Table S8).
We also investigated the association between pathways known to be important to TB pathogenesis and mortality (Figure 7).Looking at those who died vs. survived amongst HIV-positive TBM, there was little difference between the groups with respect to inflammatory response, cytokine signaling, and icosanoid and inflammasome activation.This was also the case for HIV-negative individuals.However, interferon and TNF pathways were more active in those with HIV, and TNF signaling expression was significantly higher in HIV-negative adults who died rather than survived.

HIV influence on modules, hub genes, and pathways associated with TBM mortality
Transcriptional profiles associated with TBM mortality stratified by HIV status are shown in Figure 3.In HIV-negative individuals, 786 (3.9%) genes were differentially expressed, whereas in HIV-positive the number was 1620 (8.1%) genes (Figure 3, Table S2 & S3).We hypothesized that host transcriptional signatures associated with TBM mortality differ according to HIV status.To test this hypothesis, we constructed gene co-expression networks from all genes in HIV-negative (n=207) and HIV-positive (n=74) individuals separately, then performed consensus gene co-expression network analysis (Figure S4).
Modules were identified with colors and to discriminate them from the modules linked to mortality alone we added an annotated function on the module name.We focused on modules that failed to form consensus associations with mortality due to opposite associations in the two cohorts.
Sixteen gene co-expression modules (Figure 8, Table S9), ranging from 60 to 958 genes, were obtained from the HIV-negative and positive cohorts.Of these, 12 modules formed consensus association with mortality (Figure 8).Of the 4 modules which failed to form consensus association, two modules were significantly associated with mortality (FDR<0.05).The blue-angiogenesis module was up-regulated in death in HIV-positive adults (HR: 1.7) and the yellow-extracellular matrix organization (EMO) module was down-regulated in death in HIV-negative adults (HR: 0.31) (Figure 8).
Hub genes associated with mortality, which are highly correlated with other genes in the module, were identified in the two modules.Five hub genes, with three downregulated (ZNF354C, ZNF721 and ETAA1) and two upregulated (CEP295NL and RNF24), were identified in the blue-angiogenesis module.Other five hub genes, with four downregulated (IL11RA, CD4, ZNF395 and BCL9L) and one upregulated (PPP4R2), were identified in the yellow-EMO module (Figure 8, Table S10).Expression of some hub genes across the four cohorts showed the patterns of up (RNF24) and downregulation (ZNF721, BCL9L and IL11RA), and relative to healthy controls they were similar in PTB and TBM.Expression of RNF24 and ZNF721 genes were significantly associated with death in HIV-positive adults, whereas expression of BCL9L and IL11RA genes were significantly associated with death in HIV-negative adults (Figure S5).
Pathway enrichment analysis showed genes in the blue-angiogenesis module were significantly enriched for angiogenesis or blood vessel development, leukocyte and neutrophil activation, and signal transduction pathways (Figure 8, Table S11).Gene expression in the yellow-module was strongly enriched in extracellular matrix organization, cell-substrate adhesion and protein localization pathways (Figure 8, Table S11).Hierarchical clustering of modules indicated that these two modules were highly correlated (Figure 8) suggesting they share similar functions, which appeared in some pathways such as angiogenesis and extracellular matrix organization.
In addition, almost all of the pathways significantly associated with TBM mortality were similar in HIVnegative and positive cohorts, confirming that these important pathways were common to all TBM (Figure S6).However, pathway enrichment scores were higher in HIV-positive than in HIV-negative, especially for the TNF signaling pathway and TNF transcripts (Figure 9).Death in HIV-negative TBM was associated with increased enrichment of these pathways, but with less TNF transcript compared to HIVpositive individuals.The enrichment scores indicated mortality was strongly associated with downregulation of adaptive immune responses, including T cell activation, T and B cell receptor signaling pathways, with little impact of HIV status on these pathways (Figure 9).These data suggest an inadequate adaptive immune response contributes to disease pathogenesis and mortality in all those with TBM, regardless of HIV status.

Predictors of TBM mortality
We aimed to identify baseline gene signatures that might help predict death or survival from TBM.We selected potential gene predictors from 26 common and 10 HIV-specific hub genes; these hub genes represented the dominant modules associated with mortality.Three predefined clinical factors known to be associated with outcome (age, MRC grade and CSF lymphocyte count) were also used for candidate predictor selection.Using a multivariate Cox elastic-net regression model for 39 predictors, six predictors were selected for HIV-negative TBM (MRC grade, age, CSF lymphocyte count, gene set 1: MCEMP1, TRABD2A and CD4), and 6 predictors were selected for HIV-positive TBM (MRC grade, age, CSF lymphocyte count, gene set 2: NELL2, MCEMP1 and ZNF354C) (Table S12).Gene expression and association with mortality of five distinct genes in gene sets 1 and 2 were presented in Figure S7.By combining the two gene sets above and reducing genes in the same module, we generated gene set 3 (MCEMP1, TRABD2A, CD4 and ZNF354C) and gene set 4 (MCEMP1, NELL2, ZNF354C and CD4).These two gene sets, with and without clinical factors (MRC grade and age), were tested for their performance in HIV-negative and HIV-positive cohorts together with a reference model 15 .Model 7 from gene set 4 and clinical factors outperformed other gene sets and reference model, with the best overall model performance (lowest optimism-corrected Brier score, 0.11 and 0.14 for HIV-negative and HIV-positive, respectively) and the best discriminant performance (highest optimism-corrected AUC 0.80 and 0.86 for HIV-negative and positive) (Table 3, Figure S8).

Discussion
The biological pathways involved in pathogenesis of TBM are unclear.In general, previous studies investigating TBM pathogenesis have been small, testing for relatively small numbers of selected genes or molecules, and have been unable to take an unbiased and broader view of the inflammatory response.This study investigated the pathways associated with death from TBM at a whole-genome transcriptome level, characterizing a global dysregulation in immune responses, including inflammation, and revealing specific functional pathways and hub genes involved in TBM and the mechanisms leading to death.
We sought to understand better the pathogenesis of TBM by identifying pretreatment blood transcriptional gene modules associated TBM disease severity and three-month mortality.Four out of five identified modules were involved in immunological functions, indicating multiple functional pathways of systemic immunity are involved in the pathogenesis of TBM.In particular, mortality was strongly associated with increased acute inflammation and neutrophil activation, and decreased adaptive immunity and T and B cell activation.Whilst there appeared to be many common pathways involved in TBM mortality in HIV-positive and negative individuals, there were differences: death was associated with increased expression of angiogenesis genes in HIV-positive adults, and with TNF signaling and down regulated extracellular matrix organisation in HIV-negative adults.We also identified a four-gene signature as a potential host response biomarker for mortality, regardless of HIV status.
TBM mortality was associated with increased acute inflammatory responses, the regulation of inflammatory responses, and neutrophil activation.Previous blood transcriptome studies have shown that IFN-inducible neutrophil-driven transcripts were over-expressed in blood neutrophils from active TB patients compared to those with latent TB 20,21 .Another study described that TBM adults who developed IRIS during treatment, also had significantly more abundant neutrophil-associated transcripts that preceded the development of TBM-IRIS 5 .Our own earlier studies have also suggest a role for neutrophils in TBM pathogenesis, with high pretreatment CSF bacterial loads being correlated with high neutrophil numbers in both CSF and blood and more frequent new neurological events or paradoxical inflammatory complications 8 .
Taken together with previous studies, our findings support an important role for over-activation of neutrophil-mediated inflammatory responses in TBM pathogenesis and its lethal complications.Looking further into specific pathways, TBM mortality was neither associated with inflammatory response and cytokine signaling pathways in general, nor with interferon or inflammasome activation.However, increased transcripts in some specific immunity pathways, including TNF signaling, Toll-like receptor, NFkappa B and neutrophil extracellular trap formation, were associated with TBM mortality.These pathways are involved as activators or effectors in the process of neutrophil activation and regulation, leading to an exacerbated inflammatory response [22][23][24] .
We found blood transcriptional responses of T cells and B cells were under-expressed in those who died from TBM, indicating an impairment in adaptive immunity in fatal disease.A reduction of activities in both T cell and B cell receptor signaling pathways in death were identified, independent of overexpression of neutrophil-mediated immune responses, indicating that multiple functional pathways influence TBM mortality.TBM pathogenesis is known to be associated with T cell impairment.Previous studies have shown lower numbers of T cells, reduced ability to respond to Mycobacterium tuberculosis antigens or reduced expression of activation markers and cytokine production in TBM compared to PTB and healthy individuals 10,25,26 .This impaired T cell function has correlated with disease severity and poor clinical outcomes in participants with PTB and TBM 10,27,28 .
B cells and antibodies also influence humoral immunity against Mycobacterium tuberculosis.Studies have shown a decreased memory B cell proportion, and lower levels of IgG, IgM antibodies and Fcγ receptors binding capacity in plasma in those with active lung TB compared to those with latent TB or healthy volunteers 29,30 .But little is known about the role of B cells in TBM.The observed association between TBM mortality and decreased transcriptional responses in B cell activation and B cell receptor signaling pathways suggest an unanticipated role for B cells and humoral immunity in TBM pathogenesis that needs further investigation.
Transcriptomic profiles from four cohorts, including healthy controls and PTB, provide a broad view of host responses in different TB clinical forms.Our data showed common transcriptional pathways and genes between PTB and TBM.A range of immune responses, involving in inflammation, cytokines, interferon, inflammasome and neutrophil signaling pathways, were activated in both PTB and TBM, but with significantly greater activation in HIV-positive TBM than HIV-negative TBM.This finding aligns with our previous data showing a dyregulated hyper-inflammation in HIV-associated TBM, with significantly higher CSF cytokine concentrations than in those without HIV infection 7 .These data suggest that different forms of TB are associated with similar inflammatory responses, but with different degrees of host responses, exemplified by hyper-inflammation in those with HIV.
The HIV-driven differences in TBM-associated inflammation appear sufficient to influence response to adjunctive anti-inflammatory therapy with corticosteroids.An earlier randomized controlled trial of corticosteroids in 545 predominantly HIV-negative Vietnamese adults showed they significantly improved survival 31 .Our group recently completed a similar sized trial exclusively in HIV-positive adults without any clear benefit upon mortality (NEJM 22-16218.R2, in press).Therefore, the differences in gene expression associated with TBM mortality in the current study -with angiogenesis activation linked to HIV-positive TBM mortality only, for example -may provide an explanation for the poor response and offer alternative therapeutic strategies.Recent studies have shown that angiogenesis is induced by Mycobacterium tuberculosis infection, which then contributes to inflammation, tissue damage and is correlated with disease severity 32 .
Developing prognostic models for TBM is important for guiding clinical decision making and improving outcomes.Several studies have developed and validated prognostic models for TBM using clinical, laboratory and radiological variables.These models have demonstrated moderate to high accuracy in predicting mortality and functional outcomes from TBM patients 15,[33][34][35] .In this study, we used blood transcriptional signatures and co-expression network analysis to identify module-representative hub genes.We identified a four-gene set at the start of treatment (MCEMP1, NELL2, ZNF354C and CD4) whose expression strongly predicted three-month TBM mortality.Our prognostic models combining this four-gene host response in blood and two routine clinical predictors achieved very good performance, with AUC 0.80 and 0.86 for HIV-negative and HIV-positive individuals with TBM, respectively.This is proof-of-concept that whole blood RNA host response might be a useful pre-treatment biomarker to predict early TBM mortality.Although further investigation and validation is needed, we have identified potential gene candidates for future development as prognostic biomarkers.
In summary, we present a comprehensive and unbiased analysis of the gene transcripts associated with TBM severity and mortality.Our data open a new window on TBM pathogenesis, with dysregulation in both innate and adaptive immune responses strongly associated with death from TBM.Furthermore, we have identified similarities and differences in the inflammatory responses associated with TBM in HIVpositive and negative adults, which may explain the different therapeutic effects of adjunctive corticosteroid treatment.We also revealed a 4-gene host response signature in blood that might represent a novel biomarker for defining those at highest risk of death, regardless of their HIV status.
or a new diagnosis of multidrug-resistant TB.All participants in this cohort had <7 days of anti-TB drugs at enrolment and did not have clinical evidence of extra-pulmonary TB.Healthy controls were enrolled in Hospital for Tropical Diseases in Ho Chi Minh city, from a prospective study for epidemiological characteristics of human resistance to Mycobacterium tuberculosis infection.Participants in this cohort were adults without signs or symptoms of TB nor history of TB contact within the last two years.
Ethics approval was obtained from the institutional review board at the Hospital for Tropical Diseases, Pham Ngoc Thach Hospital and the ethics committee of the Ministry of Health in Vietnam, and the Oxford Tropical Research Ethics Committee, UK (OxTREC 52 -16, 36-16, 24-17, 33-17 and 532-22).All participants provided their written informed consent to take part in the study, or from their relatives if they were incapacitated.

Study design
The objectives and cohorts used in this study are presented in Figure 1 and a workflow of data analysis is shown in Figure S9.Briefly, blood transcriptional profiling was generated from four cohorts of 207 TBM HIV-negative, 74 TBM HIV-positive, 279 PTB and 30 healthy controls.To define transcriptional signatures associated with TBM mortality, data from all 281 TBM participants were used.To ensure reproducibility, in our main analysis we randomly split our TBM data into two datasets, a discovery cohort (n=142) and a validation cohort (n=139).After identifying gene modules associated with mortality, related functional pathways and hub genes were determined with more details of the data analysis provided in Figure S9.
In a broader view, gene enrichment and expression from significant pathways and hub genes were illustrated across all four cohorts.Finally, outcome prediction models were developed for TBM.human reference annotation (GRCh38 build 99) 42 .Next, 60,067 genes in the expression matrix were first normalized by variance stabilizing transformation method built in DESeq2 package in R 43 .
Subsequently, the batch effect was removed for 20,000 most variable genes using combat function in the SVA package 44 .

Statistical analysis
The primary outcome examined in this study was TBM three-month mortality.In the descriptive analysis, we summarized and tested association of patient characteristics with three-month mortality using univariate Cox regression analysis.We presented the proportion for binary variables and median (1 st and 3 rd interquartile range) for continuous variables.
To explore transcriptional profiles associated with mortality in TBM, differential expression analysis was performed on the 20,000 normalized gene expression matrix to find differentially expressed genes (DEGs).We constructed the contradicted matrix with survival and death status adjusted for covariates including: age, HIV status, dexamethasone treatment (LAST-ACT trial investigators remained blind), and sequencing batch.Linear models were used to assess DEGs using limma R package.Empirical Bayes moderated-t p-values were computed for each genes and Benjamini-Hochberg were used for correcting multiple testing (FDR).We defined the DEGs with the parameters (fold change > 1.5 and FDR <0.05).To visualize samples clustering in the 4 cohorts, a unsupervised principal component analysis was performed on 20,000 normalized genes, and the first principle component was plotted against the second principle component.The 95% confidence ellipse was drawn for each group using multivariate tdistribution ellipse.To compare enrichment activity of interested pathways between death vs survival, TBM vs PTB or healthy controls, enrichment scores of these pathways were calculated for single patient using single sample Gene Set Enrichment Analysis (GSEA) algorithm (ssGSEA).Pairwise Wilcoxon rank sum test was used for comparison between any two cohorts.
In the primary analysis, we initially conducted weighted gene co-expression network analysis (WGCNA) on the whole-blood transcriptomic profiling of the discovery cohort to identify clusters of genes (or 'modules') in the gene co-expression network among host transcriptomic genes.We then performed a network modules preservation analysis to assess the reproducibility of these modules and the network in the validation cohort.Subsequently, we conducted a module-trait association analysis to identify modules associated with the clinical traits of mortality and TBM severity.For the association analysis with mortality, we used a Cox regression model with the PC1 of the module as an independent variable.We adjusted the model for age, HIV status, and corticosteroid treatment (dexamethasone vs. placebo).
To analyze the association with TBM severity, we calculated the Spearman correlation between TBM severity (MRC grade) and the module's PC1.We corrected for false discovery rate (FDR) for the dependent hypotheses by using the Benjamini-Yekutieli procedure.Target modules were defined as modules associated with mortality in both the discovery and validation cohorts at FDR < 0.05.Hub genes within each targeted module were identified based on 3 criteria: protein coding gene, module membership cut off 0.85 (MM) and high rank of gene significance rank (GS).MM defined as correlation between individual gene and the module's PC1.GS defined as -log 10 p value association of gene with mortality using Cox regression model adjusted for age, HIV status and treatment.Genes were first filtered for protein coding function and MM above 0.85.Genes then was ranked based on its GS and top 3% were selected if number of genes in module above 500, otherwise top 5% were selected.Overlap hub genes between discovery and validation cohort were consider validated.genes and clinical biomarkers: age, TBM severity and CSF lymphocyte count.Similarly, for HIV-positive TBM, candidate predictors consisted of common hub genes, HIV-positive specific hub genes and clinical biomarkers.We performed the analysis with 1,000 bootstrap sampling with replacement.The chosen variables were 1) among the top 75% of selected variables and 2) one representative hub gene per module.Subsequently, we developed a prediction model based on a logistic regression model incorporating only the chosen variables.We also assessed the overall model performance (by optimismcorrected Brier score), the discrimination (by optimism-corrected AUC), and calibration (by optimismcorrected calibration-slope) of the developed model 47 .
More details on performing ORA, SSGSEA, QuSAGE and WGCNA preservation and consensus analyses can be found in the Supplementary method document.

Figure 2 :
Figure 2: Blood transcriptomic profiles of four cohorts: healthy controls (n=30), PTB (n=295), HIV-negative TBM (n=207) and HIV-positive TBM (n=74) (A) Principle component analysis (PCA) of whole transcriptomic profile of HC, PTB PTB and TBM with and without HIV.Each symbol represents one individual with color coding different cohorts.The x-axis represents principle component (PC) 1, while y-axis represents PC2.(B-G) Enrichment scores of known innate immunity pathways associated with TBM pathogenesis.Gene list of these pathway were depicted in additional file 1, tableS5.Pathway enrichment scores were calculated using single sample GSEA algorithm (ssGSEA)(Barbie DA, et al 2009).Each dot represents one participant.The box presents median, 25 th to 75 th percentile and the whiskers present the minimum to the maximum points in the data.

Figure 3 :
Figure 3: Blood transcriptomic profiles of three-month mortality at baseline in all TBM and TBM stratified by HIV status Volcano plot showed differential expression (DE) genes by fold change (FC) between death and survival in all TBM (A), HIVnegative (B) and HIV-positive TBM (C).Each dot represents one gene.The x-axis represents log 2 FC.The y-axis showed -log 10 FDR of genes.DE genes were colored with red indicating up-regulated, blue indicating down-regulated genes which having fold discovery rate (FDR) <0.05 and absolute FC > 1.5.

Figure 4 .
Figure 4. Blood transcriptional modules associated with mortality in TBM (A) Associations between WGCNA modules with two clinical phenotypes TBM disease severity (MRC grade) and three-month mortality in discovery and validation cohorts, and their associated biological processes.The heatmap showed the association between principle component 1 (PC1) of each module and the phenotypes, particularly Spearman correlation r for MRC grade and hazard ratio per increase 1/10 unit of PC1 (HR) for mortality.The HRs were estimated using a Cox regression model adjusted for age, HIV status and dexamethasone treatment.False discovery rate (FDR) corrected based on Benjamini & Yekutieli procedure, with significant level denoted as * < .05,** < .01 and *** <.001.Gradient colors were used to fill the cell with green indicating negative r or HR < 1, red color indicating positive r or HR > 1.The order of modules was based on hierarchical clustering using Pearson correlation distance for module eigengene.On the left, biological processes, corresponding to modules, were identified using Gene Ontology and KEGG database.(B) Validation of the association between WGCNA modules and mortality in discovery and validation cohorts.X-axis represents -log 10 FDR in discovery cohort and Y-axis represents -log 10 FDR in validation cohort.Red dash lines indicate FDR = 0.05 as the threshold for statistically significant in both cohorts.Five modules (blue, brown, red, black and cyan) with FDR < 0.05 were validated.

Figure 5 .
Figure 5. Biological processes, pathways and hub genes of validated modules associated with mortality (A-D) showed biological processes and pathways identified in four mortality associated modules: blue, brown, red and black module, by over representation analysis (ORA).Bar plots show the top representative GO biological processes or KEGG pathways.The bars indicates biological processes or pathways having ORA FDR < 0.05 and size corresponding to fold enrichment calculated as the ratio of gene number of pathway in the input list divided by the ratio of gene number of the pathway in reference.(E-H)showed gene co-expression networks and hub genes of blue, brown, red and black module, respectively.Each node represents one gene.Each edge represents the link between two genes.Hub genes were shown by bigger nodes and bold text.The gradient color of node corresponds to its HR to mortality, with red indicating HR > 1, and blue HR < 1.

Figure 6 .
Figure 6.Gene expression of representative hub genes in healthy controls (n=30), PTB (n=295), HIV-negative TBM (n=207) and HIV-positive TBM (n=74) Each dot represents gene expression from one participant.(A, B) expression of FCAR and MCEMP1 hub genes from the blue module.(C, D) expression of NELL2 and TRABD2A hub genes from the brown modules.(E, F) expression of PLCG1 and NLRC3 hub genes from the red module.(G, H) expression of CD247 and MATK hub genes from the black module.The box presents median, 25 th to 75 th percentile and the whiskers present the minimum to the maximum points in the data.Comparisons were made between dead (red) with survival (blue) or between HIV-negative and HIV-positive TBM by Wilcoxon rank sum test with p-values displayed as significance level above the boxes and the horizontal bars (* < .05,** < .01,*** <.001).

Figure 7 :
Figure 7: Relationship between known pathways associated with TBM pathogenesis and mortality Enrichment scores of known immune pathways associated with TBM pathogenesis.Gene list of these pathway were depicted in additional file 1, table S5.Pathway enrichment scores were calculated using single sample GSEA algorithm (ssGSEA) (Barbie DA, et al 2009).Each dot represents one participant.The box presents median, 25 th to 75 th percentile and the whiskers present the minimum to the maximum points in the data.The comparisons were made between survival and death using Wilcoxon rank sum test.Only significant results are presented with * < .05,** < .01,*** <.001.

Figure 8 .
Figure 8. Consensus transcriptional modules associated with TBM mortality stratified by HIV-infection (A)Associations between 16 consensus WGCNA modules with two clinical phenotypes TBM severity (MRC grade) and mortality in HIV-negative (n=207) and HIV-positive (n=74) TBM participants, and their associated BP Gene ontology or KEEG database.The heatmap showed the association between modules and the phenotypes, with Spearman correlation r for MRC grade and hazard ratio per increase 1 unit of PC1 of module (HR) for mortality in HIV-positive and HIV-negative cohorts.The consensus sub-panel presented associations of the consensus modules and clinical phenotypes with same trend detected in both HIV cohorts, otherwise were annotated with missing (NA) values.False discovery rate (FDR) corrected using Benjamini & Yekutieli procedure, with significant level denoted as * < .05,** < .01 and *** <.001.Gradient colors were used to fill the cell with green indicating negative r or HR < 1, red color indicating positive r or HR > 1.The order of modules was based on hierarchical clustering using Pearson correlation distance for module eigengene.It is noted that these consensus modules were not identical to the identified modules in the primary analysis in Figure1.(B-C) Functional enrichment analysis of HIVpositive pathway (blue module) and HIV-negative pathway (yellow module), respectively.(D-E) Gene co-expression network of blue and yellow modules.Each node represents one gene.Each edge represents the link between two genes.Hub genes were shown by bigger nodes with bold text.The gradient color of node corresponds to its HR to mortality, with red indicating HR>1, and blue HR<1.

Figure 9 .
Figure 9. Enrichment score of immunity pathways in healthy controls (n=30), PTB (n=295), HIV-negative TBM (n=207) and HIV-positive TBM (n=74) Pathway enrichment scores were calculated using single sample GSEA algorithm (ssGSEA) (Barbie DA, et al 2009).Each dot represents one participant.(A-C) showed box-plots depicting enrichment scores of the innate immunity pathways from the blue module.(E-H) enrichment scores of the adaptive immunity pathways from the red and brown modules.(D) normalized expression of TNF.Gene list of these pathway were depicted in additional file 1, table S5.The box presents median, 25 th to 75 th percentile and the whiskers present the minimum to the maximum points in the data.Comparisons were made between dead (red) with survival (blue) or between HIV-negative and HIV-positive TBM by Wilcoxon rank sum test with p-values displayed as significance level above the boxes and the horizontal bars, respectively (* < .05,** < .01,*** <.001).