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Transcriptomic predictors of paradoxical cryptococcosis-associated immune reconstitution inflammatory syndrome

Transcriptomic predictors of paradoxical cryptococcosis-associated immune reconstitution... Background. Paradoxical cryptococcosis-associated immune reconstitution inflammatory syndrome (C-IRIS) affects ~25% of HIV-infected patients with cryptococcal meningitis (CM) after they commence antiretroviral therapy (ART) resulting in significant morbidity and mortality. Genomic studies in cryptococcal meningitis and C-IRIS are rarely performed. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Methods. We assessed whole blood transcriptomic profiles in 54 HIV-infected subjects with CM who developed C-IRIS (27) and compared to control subjects (27) who did not experience neurological deterioration over 24 weeks after ART initiation. Samples were analyzed by whole genome microarrays. Results. The predictor screening algorithms identified the low expression of the components of interferon-driven antiviral defense pathways such as interferon inducible genes, and higher expression of transcripts that encode granulocyte-dependent pro-inflammatory response molecules as predictive biomarkers of subsequent C-IRIS. Subjects who developed early C-IRIS (occurred within 12 weeks of ART initiation) were characterized by upregulation of biomarker transcripts involved in innate immunity such as the inflammasome pathway; while those with late C-IRIS events (after 12 weeks of ART) were characterized by abnormal upregulation of transcripts expressed in T, B, and NK cells, such as IFNG, IL27, KLRB1 and others. The AIM2, BEX1 and C1QB were identified as novel biomarkers for both early and late C-IRIS events. Conclusion. An inability to mount an effective interferon-driven antiviral immune response, accompanied by systemic granulocyte proinflammatory signature, pre-ART, predisposes patients to the development of C-IRIS. Although early and late C-IRIS have seemingly similar clinical manifestations, they have different molecular phenotypes (as categorized by bioinformatics analysis) and are driven by different inflammatory signaling cascades. Background Cryptococcal meningitis (CM) is responsible for 60% of meningitis-associated hospitalizations and deaths in persons with HIV in resource limited settings [1, 2]. Moreover, ~25% of patients with CM who begin antifungal treatment and antiretroviral therapy (ART) experience neurological deterioration characterized by headaches, seizures, or confusion caused by paradoxical Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript cryptococcosis-associated immune reconstitution inflammatory syndrome (C-IRIS) [3-5]. The risk factors associated with C-IRIS include an advanced state of immunosuppression (CD4+ T cell count less than 100 cells/uL) and high cryptococcal fungal burden at ART initiation [5, 6]. The clinical manifestations of C-IRIS usually begin within the first four weeks of ART initiation; however, late presentation beyond 12 weeks has also been reported [4, 7]. The pathogenesis of C-IRIS is still poorly understood. In the setting of immune reconstitution after initiation of effective ART, an exaggerated and dysregulated immune response toward cryptococcal antigens is hypothesized to drive the development of C-IRIS [8, 9]. C-IRIS is associated with activation of multiple immune/inflammatory pathways, but few biomarkers have been reported to define C-IRIS [5, 10, 11]. There are no laboratory tests available in routine clinical practice to confirm C-IRIS, which makes diagnosis and treatment difficult. Thus, identifying a comprehensive transcriptomic signature of C-IRIS is an important step to understanding the pathogenesis of C-IRIS and to identify diagnostic and prognostic biomarkers. In this study, we performed genome-wide transcriptomic analyses using microarrays to characterize ART-induced changes in gene expression in peripheral blood of HIV-infected individuals with CM who did or did not developed C-IRIS, in order to understand the immune dysregulation that causes C-IRIS and identify novel diagnostic and prognostic molecular biomarkers for C-IRIS. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Methods Study setting and sample collection This study utilized de-identified pre-existing samples obtained from the C-IRIS study performed in Durban, South Africa, which followed 130 HIV-infected subjects with confirmed CM for 24 weeks and determined which subjects developed C-IRIS and which did not [5]. Recruitment, follow-up of patients and the criteria for C-IRIS diagnosis have previously been published (see Figure 1) [5]. Ethics approval for the parent study was obtained from University of KwaZulu-Natal (BF053/09, Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Monash University (2009001224, 2016-1197), University of Western Australia (RA/4/1/2541) and University of Minnesota (1007E86393). For the current study, whole blood (2.5mL) was collected into PAXgene tubes (QIAGEN Inc) from 27 subjects who developed C-IRIS in the 6-months following ART commencement and from 27 CD4+ T cell count matched control subjects who did not develop neurological deterioration. Whole blood samples were collected at the time of ART initiation (W00), at week 4 and 12 (W4, W12) post ART commencement and at time of C-IRIS event (Figure 1). We categorized “early C-IRIS” events (21 samples) as those occurring within 12 weeks of ART and those occurring after 12 weeks as “late C-IRIS” (6 samples). RNA extraction and microarray analyses Total RNA was extracted from whole blood using PAXgene Blood RNA kits (QIAGEN Inc) according to the manufacturer’s protocol. The RNA was submitted to University of Minnesota Biomedical Genomics facility for quality controls, assessed by Agilent 2100Bioanalyzer (Agilent Technologies). One ug of total RNA (with the integrity numbers >6.0) was used to prepare amplified and biotinylated antisense complementary RNA targets using the Illumina TotalPrep RNA amplification kit. Seven hundred ng of labeled cRNA were hybridized to human-HT-12v4 BeadChip arrays (Illumina Inc), which contained 47,400 hybridization probes. The arrays were then washed, blocked, stained and scanned on BeadStation500. The microarray data from this study has been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series ticket number #18800677, and available on 01.10.2019. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Microarray signal normalization and statistical data analysis Signal intensity values from the scans were generated using Illumina BeadStudio v2 software. Raw microarray data were background subtracted, and log2 transformed (thus a 1.5-fold change cut offs were chosen for downstream analysis). A stringent filter was implemented to select only those transcripts whose probe sets had at least one present detection above the background detection call, and missing intensity probes values were excluded. Datasets were normalized using the quintile inter- array normalization method and exported into a Partek Genomic Suite v6.6 for batch effect removal and statistical analysis (Partek Inc). Batch effect was removed on random effects, such as date of microarray scan and the position of bead channels within microarray chips. Normalized gene expression values were considered for statistical analysis. Principal component analysis (PCA) visualized specimen-specific gene expression as PC correlations between groups (C-IRIS vs control) and subgroups (time points within each group and IRIS event). In the heat map visualizations of these data sets, the clustering was performed using both Euclidean and Pearson uncentred distance metrics and average linkage based algorithms to demonstrate how cases clustered based on RNA expression. Samples and differentially expressed probe sets were then ordered using hierarchical clustering such that groups and time points (within each group), could be distinguished according to similarities or differences in gene expression patterns. The list of differentially expressed genes was obtained from analysis of variance with restricted maximum likelihood, ANOVA-REML. Significantly changed probe intensities were filtered based on a minimum fold change threshold of 1.5 for up or down regulation, in both groups at any time point. Values of P were corrected for multiple testing using the Benjamini & Hochberg false discovery rate (FDR). Biomarkers search Predictor screening (based on Bootstrap Forest platform) was performed with standard parameters using JMP13 Pro (SAS Institute Inc.). Predictor screening allows simultaneous analysis of all Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript variables for model validation within two groups (IRIS versus no IRIS), or within an analysis of 8 subgroups (control week 0,4,12; and IRIS week 0,4,12, early C-IRIS and late C-IRIS). For C-IRIS biomarker identification, the Nonlinear Iterative Partial Least Squares (NIPALS) algorithm with leave Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript one out cross validation method, available in JMP13 Pro was used, which was carried out on 1700 transcripts. Variable Importance in the Projection (VIP) scores were extracted to reveal relations between predictors and outcome and plotted against coefficient of regression. Predictors and responses were scaled to have a mean of 0 and a standard deviation of 1 (by dividing each column by its standard deviation). To consider the transcript as a potential biomarker, the filters were set as such: the VIP score was >0.8 and the regression correlation coefficient was >0.2 in at least one of the studied subgroups. Model coefficients with their respective ranked VIP contributions are presented in Supplementary Table 3. The algorithm is available upon request. Functional and Pathway Analysis Ingenuity Pathways Analysis, IPA (Ingenuity Systems Inc) software was used to define biological networks, functional analyses of molecular interactions, and upstream regulators for the differentially expressed genes. IPA workflow comprised core, functional and canonical pathway analyses, and was used as a reference data set. Both direct and indirect molecular relationships were included in the analysis settings and the significance of relationships 1700 immune gene transcripts was indicated with z-score and Fisher’s exact test p-values <0.05. qPCR validation assay Quantitative real time PCR was performed in triplicate specimens for each transcript of interest. Relative concentrations were calculated against internally built standard curves. cDNA was synthesized from 200 ng total cellular RNA using Strata Script TM III reverse transcriptase Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript (Stratagene) using transcript-specific primers as described previously [12, 13]. The 600 ng of the pool of total RNA (per plate) was used to create for upper standard, which was titrated 1:3 to generate four point curve. PCR amplifications were then performed using the SYBR® Green PCR Kit (Bio-Rad Laboratories) according to manufacturer’s instructions. Transcript-specific oligonucleotide primers Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript were custom designed and synthesized by IDT Inc. The PCR amplification was done using a Mx3000 Stratagen™ thermocycler and data was analyzed using MxPro v4.01™ software. Relative concentrations of each amplified transcript were normalized to the relative levels of the hypoxanthine phosphoribosyltransferase (HPRT) transcript synthesized from the same specimen. The plots were derived as follows: concentrations of control samples at baseline (W00) were set as 1. The relative fold change expressions for control and C-IRIS groups were plotted longitudinally over 0, 4, 12-week time points; early/later C-IRIS events were correspondingly assigned to W03/W16. Results Subjects enrolled into this study underwent approximately 2 weeks of induction anti-fungal therapy with Amphotericin B and subsequently started ART. Figure 1 shows the time line of antifungal therapy, ART, C-IRIS events, and sample collection. Comparative demographic details of the original cohort of 130 participants, and current sub study are found in Chang et al. [5], where the C-IRIS group and the control group without neurological deterioration showed no significant difference in age, gender, baseline HIV viral load or serum cryptococcal antigen titers. At the time of ART initiation, transcriptome profiles differed between C-IRIS and control groups We hypothesized that HIV-infected patients with CM who developed C-IRIS would show a transcriptomic signature of immune deactivation at the time of ART initiation. ANOVA-REML analysis, using criteria described in the methods, identified over 1000 differentially expressed Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript transcripts between the C-IRIS and the control groups at the time of ART initiation. C-IRIS samples exhibited decreased expression of transcripts associated with antiviral responses (Figure 2), specifically those that lead to downregulation of viral replication including interferon induced protein 44 (IFI44), and interferon induced proteins with tetratricopeptide repeats (IFIT1-5). In addition, the Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript DExH and DExD/H-Box helicases (DDX58, DHX58), and transcripts encoding enzymes that directly restrict or inhibit viral replication (e.g. oligoadenylate synthases OIS1-3) were also downregulated in C-IRIS group. This peripheral blood transcriptomic signature in the C-IRIS group reflected a paucity of expression of genes that play a significant role in cellular resistance to viral infections. In contrast, the expression of transcripts encoding markers of activated granulocytes, tissue infiltration and destruction were significantly higher expressed in subjects who subsequently developed C-IRIS (e.g. matrix metallopeptidases, or myeloperoxidases, see table 1). Transcripts encoding C-type lectin domain family members (CLEC 5A,1B,4D) and the family of small antimicrobial cytotoxic peptides defensins (DEFA1,3,4,5) also exhibited significantly increased expression in the group at baseline. Taken into account that defensins are more abundant in 2+ neutrophil granules, and CLECs play a role in MHCI Ca -dependent pathogen recognition, this baseline transcriptomic signature suggests an activation of granulocyte-mediated inflammation, which may put patients at risk for the development of C-IRIS. ART-associated down-regulation of antiviral pro-inflammatory responses in the control group is seen over 12 weeks Longitudinal data analysis in the control group identified 588 and 1,412 differentially abundant transcripts at week 4 and 12, compared to W00 (pre-ART commencement). We detected longitudinal upregulation of transcripts such as IL7R, numerous cluster differentiation and HLA molecules during 12 weeks on ART (see Supplementary table 1). Such longitudinal increases in expression are a reflection of the effective immune recovery in lymphocyte populations and improvement in the antigen-presentation function in the control group. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Functional analysis of down-regulated transcripts indicated progressive decline of two predominant pathways: type I/II IFN–STAT signaling and NF-kB signaling (see Supplementary Figure 1). Activation of these pro-inflammatory genes is often associated with cell death, thus the reduction of Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript expression of these transcripts is beneficial for the effective immune reconstitution on ART [14]. On the contrary, the abundance of some interferon I/II-STAT and NF-kB signaling components had increased in the C-IRIS group at week 4 post-ART, suggesting a switch to aberrant immune activation as presented below. Altered blood transcriptomic profiles in subjects who developed C-IRIS We hypothesized that HIV-infected patients with CM show a transcriptomic signature of aberrant immune activation in the blood during C-IRIS event. We performed principal component analysis (PCA) to visualize the differences in gene expression levels in the entire dataset. Figure 3A depicts distinct separation between C-IRIS and control samples (PCA1). The PCA2 segregated samples collected at scheduled visits (week 0, 4, and 12) longitudinally within C-IRIS and control groups, and at time of C-IRIS event. Moreover, PCA3 clustered together samples from IRIS events that occurred within first 12 weeks on ART (early C-IRIS), and separately from C-IRIS events that occurred after 12 weeks (late C-IRIS). This segregation of transcriptomic profiles suggests that although early and late C-IRIS events have seemingly similar clinical manifestations, they differ significantly in transcriptomic signature. Early C-IRIS events were characterized by upregulation of innate immune response genes while late C-IRIS events showed deregulation of genes in adaptive immune response pathways. Using Partek Genomics Suite software, we obtained a similarity matrix based on the hierarchical clustering (as described in methods) to identify differences in transcript expression at early versus late C-IRIS events (Figure 3B, and supplementary Figure 3). ANOVA-REML analysis identified 539 and Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript 1143 transcripts that were differentially expressed at the time of early or late C-IRIS events, respectively, as compared to W00. Early C-IRIS events were characterized by up-regulation of numerous transcripts involved in innate immune responses: for example, upregulation of transcripts Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript involved in inflammasome pathways, such as, absent in melanoma or interferon-inducible protein AIM2, caspases, IL1B, and NLR family domain containing transcripts (Figure 4). Inflammasome components are part of the larger molecular network of pattern-recognition receptors (PRRs), including toll-like receptors and cytosolic surveillance pathway molecules, many of which were also upregulated (Supplementary Figure 2). PRRs recognize diverse pathogen-associated molecular pattern (PAMPs) molecules found in variety of microorganisms, including Cryptococcus species [15, 16]. Late C-IRIS also exhibited the up-regulation of transcripts involved in the inflammasome signaling pathway, such as CASP5 and AIM2. However, late C-IRIS events were primarily characterized by up-regulation of CD2, CD3, CD8, CD302 (expressed on T and B cells), and CD160, killer cell lectin- like receptors (expressed on NK cells), and also IFNG, granzymes and other mediators of inflammation, as compared to W00. In addition, we compared gene expression at the 12-week time point in the C-IRIS group with corresponding gene expression samples at late C-IRIS events, aiming to identify gene expression pattern that precedes late C-IRIS. The increased expression of transcripts encoding CCL-, CCR-, CXCR-type chemokines and chemokine receptors, and adhesion molecules such as integrins, selectins and claudins at week 12 of observation (most proximal time point to late C-IRIS diagnosis in corresponding patient). The fold change expression values for these molecules can be found in Supplementary table 2. These findings suggest that systemic upregulation of transcripts involved in inflammatory immune cell migration occur in peripheral blood prior to the development of late C-IRIS. We analyzed the enrichment of canonical pathways using IPA software, for early and late C- IRIS subgroups. The top 5 canonical pathways with altered expression at the time of early C-IRIS (Figure 5A) represent the activation of recognition of bacteria and viruses through cytosolic pattern recognition receptors. All five pathways encode components of innate immunity and could be considered as drivers of early C-IRIS. The late C-IRIS is driven by molecular pathways that included Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript transcripts involved in T- and B- cell signaling, natural killer cell signaling and apoptosis (Figure 5B). In addition, we utilized IPA causal network analysis to detect upstream regulatory molecules, affecting gene expression during C-IRIS events. This analysis predicted pathogen-associated molecular patterns (PAMPs) molecules were upstream regulators of early C-IRIS (shown in supplementary figure 2), consistent with our finding that transcripts encoding TLR/inflammasome pathways were upregulated. Conversely, IFNG, IL2 and IL21 were predicted to be upstream regulators of late C-IRIS gene expression. Thus, early and late C-IRIS events appeared to be due to over-exuberant inflammatory responses that are driven by different signaling cascades for early versus late C-IRIS events. Biomarkers of Early and Late C-IRIS The next step was to identify transcripts that may be predictive biomarkers of C-IRIS events, utilizing two independent computational algorithms available in JMP Pro13: the predictor screening (based on random bootstrap forest algorithm, RBSF) and partial least square (PLS) probability modeling. The random bootstrap forest algorithm was performed on all 34668 probe IDs expressed on microarrays. The top 200 high rank importance predictors whose expression distinguished C-IRIS from other subgroups (is listed in Supplementary table 3.2. Among them was the inhibitor of differentiation (ID4), bone barrow stromal antigen (BST1), and IL32, suggesting a strong contribution of abnormal differentiation and proliferation processes into C-IRIS onset. One interesting transcript, BEX1 (brain-expressed X-linked like family) that was steadily upregulated in C-IRIS group at all time points, could represent a predictive peripheral blood biomarker of Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript neuroinflammation. The majority of identified biomarkers were poorly annotated, highlighting that we were only beginning to unravel the complex nature of immune reconstitution. Aiming to identify immune biomarkers of C-IRIS events, we exported from IPA software all annotated immune genes that are significantly changed, at any time point of observation post ART. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript This export generated 1700 transcripts, which were analyzed with partial least square and RBSF algorithms. PLS analysis was performed on the following groups of samples: early C-IRIS, late C- IRIS and no C-IRIS, and also on all subgroups, including all time points within each group. A quantitative estimation of the discriminatory power of each predictor in PLS is provided by means of VIP scores (listed in Supplementary table 3.3, 3.4, 3.5). Subgroup analysis revealed that most of the top ranked biomarkers were specific for either early or late C-IRIS, and anti-correlate with each other: for example, differential expression of transcripts encoding of CCL8, CASP12, DDX58, TNF, positively correlated with early C-IRIS events (VIP>0.8, R>0.2), but negatively correlated with late C-IRIS (VIP>0.8, R>-0.2). The differential expression of transcripts encoding CLECs (C-type lectins), and SIGLECs (sialic acid binding immunoglobulin-like lectins) positively predicted early C- IRIS, but not with late C-IRIS. A particularly high association of co-expression of IFNG, complement pathway component C1QC and IL27 were uniquely observed in late C-IRIS. Interestingly, elevated expression of transcripts that reflect neuronal apoptotic processes or neurological deterioration, such as galectins, ephrin receptors, and kallikreins were significantly upregulated in both early and late C- IRIS events (p<0.01). Low baseline expression of interferon-response genes, TLR 2,7, DEFA3, and CD69, predicted subsequent early and late C-IRIS, after ART commencement. The correlation coefficients for least square mean, and a corresponding statistical VIP (values ranking descriptors) for 13 transcripts that identified by both algorithms can be found in Supplementary table 3.1. All of these transcripts represent novel biomarkers of C-IRIS. Biomarker validation We used real-time quantitative PCR to validate the differential expression of mRNA for 15 selected genes as described in the methods section, and the results are shown in supplementary figures 4 A, B, C, D. The 4A depicts the expression profiles for biomarkers of both C-IRIS events, which were persistently elevated in both C-IRIS subgroups over 12 weeks of observation: AIM2, BEX1, C1QB Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript (>1.5 fold in expression). The CASP5, DDX58 and TNF mRNA levels were upregulated >2 fold in early C-IRIS patients, and subsequently downregulated by week 12, although not to the level of control mRNAs (figure 4B). Compared to the baseline, the late C-IRIS patients exhibited significantly higher levels of IFNG, IL27 and KLRB1 transcripts after 12 weeks of observation, but had very low expression of these transcripts at baseline, as compared to control samples (see figure 4C). We validated several biomarkers that constitutes favorable immune recovery: the antiviral defense transcripts were upregulated at baseline in the control group (IFI44, IRF7, MX1), and had distinct downregulation profiles over 12 weeks of observation, as compared to C-IRIS group (see figure 4D). At baseline, the levels of antimicrobial defense mRNAs (DEFA3, CLEC5A, CD69) were higher in the C-IRIS group when compared to the control group (figure 4E). The levels of these transcripts subsequently decreased even more, in the control group, but not in the early C-IRIS group. Overall, the expression profiles for these transcripts were similar for the real-time quantitative PCR results and the microarray results. Discussion C-IRIS has emerged as an important complication of ART in HIV-CM co-infection. Utilizing longitudinal data analysis of samples from a well-characterized prospective cohort of HIV-CM co- infected patients initiating ART, who experienced C-IRIS compared to CD4+ T cell-matched controls [5], we showed that C-IRIS subjects exhibited significantly lower expression of transcripts encoding antiviral defense proteins and upregulation of antimicrobial defense genes prior to ART commencement. Poor cellular antiviral and inflammatory responses in cerebrospinal fluid (CSF) have Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript been suggested to be associated with poor CM outcomes and predispose patients to IRIS [5, 6, 17, 18]. Low cerebrospinal fluid levels of IFNG has been previously shown to be associated with poorer CSF clearance of cryptococci, and adjunctive IFNG treatment has been shown in two studies to improve Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript rate of CSF clearance [19, 20]. We have previously shown that IFNG secretion in whole blood stimulated with cryptococcal mannoprotein is reduced in C-IRIS prior to ART initiation [21]. Indeed, here, the predictor screening algorithms identified the low expression of the components of antiviral defense pathway such as interferon inducible genes, as predictive biomarkers of subsequent C-IRIS. Thus, the paucity of anti-viral gene expressions in peripheral blood at the time of ART initiation could have predictive value for identifying those at risk for C-IRIS. Patients who exhibit such signatures also display activation of granulocyte- dependent pro-inflammatory responses (through upregulation of CLECs and DEFAs) and may require longer antifungal therapy before initiation of ART to achieve clearance and reduce risk of C-IRIS [22]. We have previously shown that HIV-CM co-infected patients who were able to achieve cryptococcal culture negativity in the CSF prior to ART initiation compared to those with residual cryptococcal positive cultures were associated with reduced neurological deterioration, C-IRIS and cryptococcal relapse [5]. Others have shown that patients who initiate ART early compared to late in HIV-CM co-infection have increased mortality [23, 24] and increased C-IRIS incidence [25]. Thus, an initial effective immune response, characterized by adequate production of interferons and interferon-response genes, may lead to better antigen clearance and improved outcomes. The course of immune recovery on ART in our control group is similar to our previously demonstrated results in advanced stage HIV-infected patients without opportunistic infections [14]. Both groups showed maintenance of innate host defenses in an activated state through IFN I/II-STAT pathway prior to ART initiation, which subsequently declined on ART [14]. While both studies showed that IFN I/II pathway were down-regulated early in the controls, the IFNG pathway remains inert in the C-IRIS group post-ART. Our results suggest that the differences in longitudinal transcriptomic profiles of those with or without C-IRIS gives insight into what constitutes an effective response that leads to immune recovery on ART, as opposed to a pathological response that leads to C-IRIS. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Moreover, the C-IRIS group exhibited abnormal longitudinal pro-inflammatory changes in gene expression profiles that were evident in the blood weeks after ART initiation and transition to a fluconazole maintenance therapy, as discussed below. There was a significant modification in gene expression signatures and differences in identified biomarkers seen in late C-IRIS events versus early C-IRIS, suggesting that early and late C-IRIS have distinct molecular phenotypes. The AIM2, C1QB and BEX1 were identified as common biomarkers for both early and late C-IRIS events, and thus warrant future investigation. Early C-IRIS was associated with upregulation in innate responses and late C-IRIS had more pronounced signature of adaptive immune activation. Unlike in immunocompetent patients [26], in immunocompromised patients the innate immune system is the primary branch that elicits immune response to combat opportunistic infections. Over-activation of it through pattern recognition receptor signaling sets up a cycle of local and systemic inflammation [27]. In our study, TLR and inflammasome components (including AIM2, CASP5 and NLR families) identified as biomarkers of early C-IRIS events represent a response toward ongoing viral replication and cryptococcal antigens. Recent data suggests the inflammasome pathway drives CD4+ T-cell depletion in HIV-1 infection [28] and delays immune reconstitution. Thus, inflammasome activation may contribute to the neurological deterioration symptoms in C-IRIS patients, which has been also proposed for tuberculosis meningitis IRIS (TBM-IRIS) [29-32], and represent a peripheral blood biomarker of brain inflammation for both CM and TBM-IRIS. Other identified biomarker transcripts that reflected innate immune responses specifically toward cryptococcal antigen (e.g. CLECs, SIGLECs, dectins) should also be studied in depth to understand the pathogenesis of immune reconstitution in C-IRIS patients. The findings in late C-IRIS such as the activation of components of complement and the inflammasome, along with IFNG, IL27, KLRB1 and other transcripts expressed in T, B and NK cells suggest an impaired restoration of adaptive immunity. Poor recovery of adaptive immunity and impaired communication between innate and adaptive immune branches due to lack of negative Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript regulatory feedback responses, leads to an inability to resolve inflammation [33]. The onset of late C- IRIS is preceded by excessive expression of chemokines and integrins in peripheral blood, and thus, can be predicted through monitoring the aberrant kinetics of immune reconstitution in patients at risk. In this study we identified number of biomarker transcripts that encode intracellular molecules expressed in various circulating immune cells. We hypothesize that transcripts (AIM2, C1QB and BEX1) identified by two predictor algorithms will compose a bundle biomarker signature of neuroinflammation during complex, antigen-driven process of immune reconstitution. One limitation of this study is that we did not systematically look for other opportunistic infections that might be present in these patients (such as tuberculosis, toxoplasmosis, cytomegalovirus, etc.), as these unknown factors may have skewed our results. Further in-depth study and interpretation of the biological processes associated with the biomarkers identified through statistical modeling in our study will enhance the robustness of our findings [34]. In conclusion, the information presented in this report provides an insight into the molecular drivers of C-IRIS pathogenesis and could in the future be applied to developing diagnostic tests that guide targeted immunomodulatory treatments. Acknowledgement This work is supported by a UMN departmental start-up fund to I.A.S. and National Institutes of Health grant AI072068 to P.R.B, and C.C.C is supported by an Australian NHMRC Early Career Fellowship (1092160). We acknowledge the patients and research investigators of the parent C-IRIS study, including the clinical and laboratory staff at King Edward VIII hospital and HIV Pathogenesis Programme, Durban, South Africa. Our special thanks to Professor Thumbi Ndung’u for providing the facilities for samples storage. We acknowledge the University of Minnesota Supercomputing Institute for providing the access to Ingenuity Pathway Assistant, and University Biomedical Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Genomics facility for hybridizing and scanning microarrays. We thank Tawa Alabi, Ashley Johnson and Calandra Sagarsky for experimental assistance. Author contributions I.V.S and C.C.C contributed equally to this work; C.C.C directed the clinical study; I.V.S directed the genomics study and data analysis; S.S. performed experiments; I.V.S., C.C.C., P.R.B and M.A.F wrote and edited the manuscript. The authors declare no conflict of interest. Abbreviations: IRF7, Interferon regulatory factor; IFI, interferon alpha inducible protein; IFIT, interferon induced protein with tetratricopeptide repeats; IFNG, interferon gamma, RSAD2, radical S-adenosyl methionine domain containing 2; OAS, 2'-5'-oligoadenylate synthetases; DHX58, DExH-box helicase 58 (LGP2); DDX58, DExD/H-box helicase 58 (RIG1); C1QB, C1QC, components of complement; AIM2, Absent In Melanoma 2; CASP, caspases; IL, interleukin; TLR, Toll-like receptors; NOD2, Nucleotide-binding oligomerization domain-containing protein 2; NLRP, NLR (nucleotide-binding oligomerization domain (NOD), leucine-rich repeat (LRR)-containing protein) family pyrin domain containing; KLR, killer cell lectin like receptor; CCL, C-C motif chemokine ligand; CXCR, C-X-C motif chemokine receptor; ITGA, integrin subunit alpha; CLDN, claudin; SELL, selectin L; TREM1, triggering receptor expressed on myeloid cells 1; PAMP, pathogen-associated molecular patterns; ID3,4, inhibitor of DNA binding 3,4; ADAR, adenosine deaminase, RNA specific; BTS1, bone marrow stromal cell antigen 1; BEX1, brain-expressed X- linked like family; SIGLEC, sialic acid binding immunoglobulin like lectins; ID4, Inhibitor of differentiation 4; CLEC, C-type lectin domain family members; LGALS13, galectin 13; EPH A/B, ephrin receptors, KLK1, kallikrein 1; RSAD2, Radical S-Adenosyl Methionine Domain Containing 2; TNF, tumor necrosis factor; VIP, variable importance for the projection score. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript References 1. Rajasingham R, Rhein J, Klammer K, et al. Epidemiology of meningitis in an HIV-infected Ugandan cohort. The American journal of tropical medicine and hygiene 2015; 92(2): 274-9. 2. Rajasingham R, Smith RM, Park BJ, et al. Global burden of disease of HIV-associated cryptococcal meningitis: an updated analysis. Lancet Infect Dis 2017; 17(8): 873-81. 3. Haddow LJ, Colebunders R, Meintjes G, et al. Cryptococcal immune reconstitution inflammatory syndrome in HIV-1-infected individuals: proposed clinical case definitions. Lancet Infect Dis 2010; 10(11): 791-802. 4. Muller M, Wandel S, Colebunders R, Attia S, Furrer H, Egger M. Immune reconstitution inflammatory syndrome in patients starting antiretroviral therapy for HIV infection: a systematic review and meta-analysis. Lancet Infect Dis 2010; 10(4): 251-61. 5. Chang CC, Dorasamy AA, Gosnell BI, et al. Clinical and mycological predictors of cryptococcosis-associated immune reconstitution inflammatory syndrome. AIDS 2013; 27(13): 2089-99. 6. Boulware DR, Bonham SC, Meya DB, et al. Paucity of initial cerebrospinal fluid inflammation in cryptococcal meningitis is associated with subsequent immune reconstitution inflammatory syndrome. J Infect Dis 2010; 202(6): 962-70. 7. Meya DB, Manabe YC, Boulware DR, Janoff EN. The immunopathogenesis of cryptococcal immune reconstitution inflammatory syndrome: understanding a conundrum. Current opinion in infectious diseases 2016; 29(1): 10-22. 8. Nelson AM, Manabe YC, Lucas SB. Immune Reconstitution Inflammatory Syndrome (IRIS): What pathologists should know. Semin Diagn Pathol 2017; 34(4): 340-51. 9. Williamson PR, Jarvis JN, Panackal AA, et al. Cryptococcal meningitis: epidemiology, immunology, diagnosis and therapy. Nat Rev Neurol 2017; 13(1): 13-24. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript 10. Boulware DR, Meya DB, Bergemann TL, et al. Clinical features and serum biomarkers in HIV immune reconstitution inflammatory syndrome after cryptococcal meningitis: a prospective cohort study. PLoS Med 2010; 7(12): e1000384. 11. Jarvis JN, Casazza JP, Stone HH, et al. The phenotype of the Cryptococcus-specific CD4+ memory T-cell response is associated with disease severity and outcome in HIV- associated cryptococcal meningitis. J Infect Dis 2013; 207(12): 1817-28. 12. Vlasova IA, McNabb J, Raghavan A, et al. Coordinate stabilization of growth-regulatory transcripts in T cell malignancies. Genomics 2005; 86(2): 159-71. 13. Vlasova IA, Tahoe NM, Fan D, et al. Conserved GU-rich elements mediate mRNA decay by binding to CUG-binding protein 1. Molecular Cell 2008; 29(2): 263-70. 14. Boulware DR, Meya DB, Bergemann TL, et al. Antiretroviral therapy down-regulates innate antiviral response genes in patients with AIDS in sub-saharan Africa. Journal of acquired immune deficiency syndromes 2010; 55(4): 428-38. 15. Ellerbroek PM, Walenkamp AM, Hoepelman AI, Coenjaerts FE. Effects of the capsular polysaccharides of Cryptococcus neoformans on phagocyte migration and inflammatory mediators. Curr Med Chem 2004; 11(2): 253-66. 16. Hole C, Wormley FL, Jr. Innate host defenses against Cryptococcus neoformans. Journal of microbiology (Seoul, Korea) 2016; 54(3): 202-11. 17. Boulware DR, von Hohenberg M, Rolfes MA, et al. Human Immune Response Varies by the Degree of Relative Cryptococcal Antigen Shedding. Open forum infectious diseases 2016; 3(1): ofv194. 18. Scriven JE, Graham LM, Schutz C, et al. A Glucuronoxylomannan-Associated Immune Signature, Characterized by Monocyte Deactivation and an Increased Interleukin 10 Level, Is a Predictor of Death in Cryptococcal Meningitis. J Infect Dis 2016; 213(11): 1725-34. 19. Pappas PG, Bustamante B, Ticona E, et al. Recombinant interferon- gamma 1b as adjunctive therapy for AIDS-related acute cryptococcal meningitis. J Infect Dis 2004; 189(12): 2185-91. 20. Jarvis JN, Meintjes G, Rebe K, et al. Adjunctive interferon-gamma immunotherapy for the treatment of HIV-associated cryptococcal meningitis: a randomized controlled trial. Aids 2012; 26(9): 1105-13. 21. Chang CC, Lim A, Omarjee S, et al. Cryptococcosis-IRIS is associated with lower cryptococcus-specific IFN-gamma responses before antiretroviral therapy but not higher T-cell responses during therapy. J Infect Dis 2013; 208(6): 898-906. 22. Dyal J, Akampurira A, Rhein J, et al. Reproducibility of CSF quantitative culture methods for estimating rate of clearance in cryptococcal meningitis. Medical mycology 2016; 54(4): 361-9. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript 23. Makadzange AT, Ndhlovu CE, Takarinda K, et al. Early versus delayed initiation of antiretroviral therapy for concurrent HIV infection and cryptococcal meningitis in sub- saharan Africa. Clin Infect Dis 2010; 50(11): 1532-8. 24. Boulware DR, Meya DB, Muzoora C, et al. Timing of antiretroviral therapy after diagnosis of cryptococcal meningitis. N Engl J Med 2014; 370(26): 2487-98. 25. Bisson GP, Nthobatsong R, Thakur R, et al. The use of HAART is associated with decreased risk of death during initial treatment of cryptococcal meningitis in adults in Botswana. J Acquir Immune Defic Syndr 2008; 49(2): 227-9. 26. Panackal AA, Wuest SC, Lin YC, et al. Paradoxical Immune Responses in Non-HIV Cryptococcal Meningitis. PLoS Pathog 2015; 11(5): e1004884. 27. Lai RP, Meintjes G, Wilkinson KA, et al. HIV-tuberculosis-associated immune reconstitution inflammatory syndrome is characterized by Toll-like receptor and inflammasome signalling. Nat Commun 2015; 6: 8451. 28. Doitsh G, Galloway NL, Geng X, et al. Cell death by pyroptosis drives CD4 T-cell depletion in HIV-1 infection. Nature 2014; 505(7484): 509-14. 29. Marais S, Lai RP, Wilkinson KA, Meintjes G, O'Garra A, Wilkinson RJ. Inflammasome activation underlies central nervous system deterioration in HIV-associated tuberculosis. J Infect Dis 2016. 30. Oliver BG, Elliott JH, Price P, et al. Mediators of innate and adaptive immune responses differentially affect immune restoration disease associated with Mycobacterium tuberculosis in HIV patients beginning antiretroviral therapy. J Infect Dis 2010; 202(11): 1728-37. 31. Tan HY, Yong YK, Andrade BB, et al. Plasma interleukin-18 levels are a biomarker of innate immune responses that predict and characterize tuberculosis-associated immune reconstitution inflammatory syndrome. AIDS (London, England) 2015; 29(4): 421-31. 32. Tan HY, Yong YK, Shankar EM, et al. Aberrant Inflammasome Activation Characterizes Tuberculosis-Associated Immune Reconstitution Inflammatory Syndrome. J Immunol 2016; 196(10): 4052-63. 33. Antonelli LR, Mahnke Y, Hodge JN, et al. Elevated frequencies of highly activated CD4+ T cells in HIV+ patients developing immune reconstitution inflammatory syndrome. Blood 2010; 116(19): 3818-27. 34. Selvan LD, Sreenivasamurthy SK, Kumar S, et al. Characterization of host response to Cryptococcus neoformans through quantitative proteomic analysis of cryptococcal meningitis co-infected with HIV. Mol Biosyst 2015; 11(9): 2529-40. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Figures Figure 1. Study protocol, modified from [5]. Microarray blood collections were scheduled for W00 (pre-ART initiation), W04 and W12 during the 24-week follow-up post ART. Additional microarray samples were collected at time of the C-IRIS event – early (within 12 weeks) and late (between 12-24 weeks) of ART initiation. Figure 2. The low anti-viral gene expressions at the time of ART initiation in C-IRIS group. C- IRIS group exhibited significantly lower expression of genes associated with innate immune responses: specifically, those that involve activation of antiviral defense (for example, interferon inducible genes (IFI), and other, connected with orange lines), or genes encoding enzymes, that directly restrict or inhibit viral replication (for example, oligoadenylate synthase (OAS) or helicases (DHX)). IFIT family genes are predominantly induced by type I and type III interferons and are regulated by the pattern recognition and the JAK-STAT signaling pathway. OAS genes encode a synthase family that are induced by interferons and catalyze the 2',5'-oligomers of adenosine in order Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript to bind and activate RNase L. The IFIT and OAS families of genes are one of many interferon- stimulated genes that play a significant role in the inhibition of cellular protein synthesis in infected cells and is particularly important in cellular resistance to viral infections. Transcripts (nodes), colored in blue were down-regulated (>1.5 fold) with the intensity of blue reflecting the degree of down-regulation. Fold change for these transcripts’ expression are shown in Table 1. The orange line represents activation effect of the encoded protein. The blue line represents inactivation effect of the encoded protein. Transcripts were identified through functional analysis using Ingenuity Pathway Assistant software (IPA). Figure 3. Identification of C-IRIS features through unsupervised learning algorithms. A. Principal component analysis shows distinct patterns in RNA expression between C-IRIS patients and controls. Using all 34668 expressed probe identifiers and 162 individual arrays, three principal components (PC) account for 33% total variance in gene expression, (PC#1 17.2%, PC#2 11.4%, PC#3 4.4% respectively). Samples were color-coded by study subgroups (see legend). Ellipsoids are drawn around samples that represent 2 standard deviations, for early (black) and late (pink) C-IRIS events. B. Hierarchical clustering analysis of 1700 probe IDs representing immune genes that were differentially expressed between early and late C-IRIS events. Hierarchical clustering is built based on normalized log transformed gene expression values. Heatmap coloring is based on expression levels: yellow, high level of expression, blue, low level of expression. Both rows and columns are hierarchically clustered according to the Pearson’s average linkage dissimilarity method in Partek GS. Top grey bar represents early C-IRIS, and pink bar represents late C-IRIS (individual cases). Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Figure 4. Transcriptomic signature of early C-IRIS events (< week 12 of ART initiation). The inflammasome pathway is shown. Transcripts, colored in yellow are upregulated >1.5 fold in early C-IRIS samples, with the intensity of yellow reflecting the degree of upregulation (p<0.05). Fold changes of upregulation (early C-IRIS versus W00 in IRIS group samples) are shown below the nodes. Nodes in grey are significantly differently expressed (p<0.05), with less than 1.5 fold change expression. The intensity of color reflects the degree of changes. Figure 5. Top 5 canonical pathways that were over-represented in up-or down- regulated transcripts in A) early and B) late C-IRIS subgroups. The top 5 canonical pathways (left axis) are displayed as stacked bar charts with percentage (top axis) of processes enriched by upregulated genes (yellow), by down-regulated genes (blue) and those without a detectable change (white). The orange line graph represents the log-transformed p values (- log(p-value) calculated by Fisher’s exact test (bottom axis) for the association between IPA- annotated genes within the pathway (assumed 100%), and the number of genes found in the uploaded dataset (numbers on the top of the bar). A. The top 5 canonical pathways with altered expression at the time of early C-IRIS: 1. Role of pattern recognition of bacteria and viruses (137); 2. Activation of interferon regulatory factors (IRF) by cytosolic pattern recognition receptors (63); 3. TREM1 (the triggering receptor expressed on myeloid cells 1) signaling (75); 4. Inflammasome (20); 5. Toll- like receptor signaling (76). There was a minimal overlap in the transcripts between pathways, varying between 0-5 molecules. B. The top 5 canonical pathways with altered expression at the time of late C-IRIS: 1. Th1 and Th2 Activation (185); 2. Natural killer cell signaling (122); 3. iCOS- iCOSL (Inducible costimulator (ICOS), and its ligand, ICOS ligand (ICOSL)) signaling (123); 4. Calcium induced T lymphocyte apoptosis (66); 5. Altered T and B signaling in inflammation (90). There was a minor overlap in the transcripts between pathways, varying between 3 to 11 molecules. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Table 1. Pre-ART gene expression in C-IRIS versus control groups. Gene Fold symbol, p-value, change biofunction FDR Antiviral response IFI44L 2.2E-16 -4.5 IFI44 5.2E-16 -3.6 IFIT1 5.5E-11 -3.0 IFIT2 1.9E-21 -3.2 IFIT3 1.1E-13 -2.7 IFIT5 2.1E-08 -1.5 ISG15 3.0E-10 -3.3 OAS1 2.1E-17 -2.4 OAS2 2.7E-18 -2.6 OAS3 2.4E-17 -3.0 MX1 1.1E-16 -3.0 DDX58 1.6E-09 -1.6 TLR7 4.4E-27 -2.8 DHX58 1.8E-15 -2.5 Antimicrobial response OLFM4 1.2E-28 8.6 CD177 2.8E-17 4.1 DEFA 6.7E-07 2.6 DEFA1B 4.8E-10 3.5 DEFA3 2.5E-09 3.1 DEFA4 9.4E-14 3.8 DEFA5 1.0E-22 1.6 DEFB106B 7.1E-18 1.5 CLEC5A 8.1E-21 3.3 CLEC4D 3.8E-11 2.0 CLEC1B 2.2E-10 2.2 CLECL1 1.6E-13 1.7 SIGLEC14 9.1E-03 2.1 Apoptosis MMP9 9.9E-10 3.0 MMP8 7.3E-28 6.9 MPO 1.7E-20 2.9 C1QTNF5 1.6E-25 2.4 ARG1 1.7E-17 3.7 LCN2 1.9E-24 5.8 Transcript symbol, abbreviated transcript name. Positive values, up-regulated, negative values, down-regulated between two baselines (week 00). Biofunction, as defined in the GO database. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Figure 1. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Figure 2. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Figure 3. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Figure 4. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Figure 5. 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Transcriptomic predictors of paradoxical cryptococcosis-associated immune reconstitution inflammatory syndrome

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Oxford University Press
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© The Author(s) 2018. Published by Oxford University Press on behalf of Infectious Diseases Society of America.
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2328-8957
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10.1093/ofid/ofy157
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Abstract

Background. Paradoxical cryptococcosis-associated immune reconstitution inflammatory syndrome (C-IRIS) affects ~25% of HIV-infected patients with cryptococcal meningitis (CM) after they commence antiretroviral therapy (ART) resulting in significant morbidity and mortality. Genomic studies in cryptococcal meningitis and C-IRIS are rarely performed. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Methods. We assessed whole blood transcriptomic profiles in 54 HIV-infected subjects with CM who developed C-IRIS (27) and compared to control subjects (27) who did not experience neurological deterioration over 24 weeks after ART initiation. Samples were analyzed by whole genome microarrays. Results. The predictor screening algorithms identified the low expression of the components of interferon-driven antiviral defense pathways such as interferon inducible genes, and higher expression of transcripts that encode granulocyte-dependent pro-inflammatory response molecules as predictive biomarkers of subsequent C-IRIS. Subjects who developed early C-IRIS (occurred within 12 weeks of ART initiation) were characterized by upregulation of biomarker transcripts involved in innate immunity such as the inflammasome pathway; while those with late C-IRIS events (after 12 weeks of ART) were characterized by abnormal upregulation of transcripts expressed in T, B, and NK cells, such as IFNG, IL27, KLRB1 and others. The AIM2, BEX1 and C1QB were identified as novel biomarkers for both early and late C-IRIS events. Conclusion. An inability to mount an effective interferon-driven antiviral immune response, accompanied by systemic granulocyte proinflammatory signature, pre-ART, predisposes patients to the development of C-IRIS. Although early and late C-IRIS have seemingly similar clinical manifestations, they have different molecular phenotypes (as categorized by bioinformatics analysis) and are driven by different inflammatory signaling cascades. Background Cryptococcal meningitis (CM) is responsible for 60% of meningitis-associated hospitalizations and deaths in persons with HIV in resource limited settings [1, 2]. Moreover, ~25% of patients with CM who begin antifungal treatment and antiretroviral therapy (ART) experience neurological deterioration characterized by headaches, seizures, or confusion caused by paradoxical Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript cryptococcosis-associated immune reconstitution inflammatory syndrome (C-IRIS) [3-5]. The risk factors associated with C-IRIS include an advanced state of immunosuppression (CD4+ T cell count less than 100 cells/uL) and high cryptococcal fungal burden at ART initiation [5, 6]. The clinical manifestations of C-IRIS usually begin within the first four weeks of ART initiation; however, late presentation beyond 12 weeks has also been reported [4, 7]. The pathogenesis of C-IRIS is still poorly understood. In the setting of immune reconstitution after initiation of effective ART, an exaggerated and dysregulated immune response toward cryptococcal antigens is hypothesized to drive the development of C-IRIS [8, 9]. C-IRIS is associated with activation of multiple immune/inflammatory pathways, but few biomarkers have been reported to define C-IRIS [5, 10, 11]. There are no laboratory tests available in routine clinical practice to confirm C-IRIS, which makes diagnosis and treatment difficult. Thus, identifying a comprehensive transcriptomic signature of C-IRIS is an important step to understanding the pathogenesis of C-IRIS and to identify diagnostic and prognostic biomarkers. In this study, we performed genome-wide transcriptomic analyses using microarrays to characterize ART-induced changes in gene expression in peripheral blood of HIV-infected individuals with CM who did or did not developed C-IRIS, in order to understand the immune dysregulation that causes C-IRIS and identify novel diagnostic and prognostic molecular biomarkers for C-IRIS. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Methods Study setting and sample collection This study utilized de-identified pre-existing samples obtained from the C-IRIS study performed in Durban, South Africa, which followed 130 HIV-infected subjects with confirmed CM for 24 weeks and determined which subjects developed C-IRIS and which did not [5]. Recruitment, follow-up of patients and the criteria for C-IRIS diagnosis have previously been published (see Figure 1) [5]. Ethics approval for the parent study was obtained from University of KwaZulu-Natal (BF053/09, Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Monash University (2009001224, 2016-1197), University of Western Australia (RA/4/1/2541) and University of Minnesota (1007E86393). For the current study, whole blood (2.5mL) was collected into PAXgene tubes (QIAGEN Inc) from 27 subjects who developed C-IRIS in the 6-months following ART commencement and from 27 CD4+ T cell count matched control subjects who did not develop neurological deterioration. Whole blood samples were collected at the time of ART initiation (W00), at week 4 and 12 (W4, W12) post ART commencement and at time of C-IRIS event (Figure 1). We categorized “early C-IRIS” events (21 samples) as those occurring within 12 weeks of ART and those occurring after 12 weeks as “late C-IRIS” (6 samples). RNA extraction and microarray analyses Total RNA was extracted from whole blood using PAXgene Blood RNA kits (QIAGEN Inc) according to the manufacturer’s protocol. The RNA was submitted to University of Minnesota Biomedical Genomics facility for quality controls, assessed by Agilent 2100Bioanalyzer (Agilent Technologies). One ug of total RNA (with the integrity numbers >6.0) was used to prepare amplified and biotinylated antisense complementary RNA targets using the Illumina TotalPrep RNA amplification kit. Seven hundred ng of labeled cRNA were hybridized to human-HT-12v4 BeadChip arrays (Illumina Inc), which contained 47,400 hybridization probes. The arrays were then washed, blocked, stained and scanned on BeadStation500. The microarray data from this study has been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series ticket number #18800677, and available on 01.10.2019. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Microarray signal normalization and statistical data analysis Signal intensity values from the scans were generated using Illumina BeadStudio v2 software. Raw microarray data were background subtracted, and log2 transformed (thus a 1.5-fold change cut offs were chosen for downstream analysis). A stringent filter was implemented to select only those transcripts whose probe sets had at least one present detection above the background detection call, and missing intensity probes values were excluded. Datasets were normalized using the quintile inter- array normalization method and exported into a Partek Genomic Suite v6.6 for batch effect removal and statistical analysis (Partek Inc). Batch effect was removed on random effects, such as date of microarray scan and the position of bead channels within microarray chips. Normalized gene expression values were considered for statistical analysis. Principal component analysis (PCA) visualized specimen-specific gene expression as PC correlations between groups (C-IRIS vs control) and subgroups (time points within each group and IRIS event). In the heat map visualizations of these data sets, the clustering was performed using both Euclidean and Pearson uncentred distance metrics and average linkage based algorithms to demonstrate how cases clustered based on RNA expression. Samples and differentially expressed probe sets were then ordered using hierarchical clustering such that groups and time points (within each group), could be distinguished according to similarities or differences in gene expression patterns. The list of differentially expressed genes was obtained from analysis of variance with restricted maximum likelihood, ANOVA-REML. Significantly changed probe intensities were filtered based on a minimum fold change threshold of 1.5 for up or down regulation, in both groups at any time point. Values of P were corrected for multiple testing using the Benjamini & Hochberg false discovery rate (FDR). Biomarkers search Predictor screening (based on Bootstrap Forest platform) was performed with standard parameters using JMP13 Pro (SAS Institute Inc.). Predictor screening allows simultaneous analysis of all Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript variables for model validation within two groups (IRIS versus no IRIS), or within an analysis of 8 subgroups (control week 0,4,12; and IRIS week 0,4,12, early C-IRIS and late C-IRIS). For C-IRIS biomarker identification, the Nonlinear Iterative Partial Least Squares (NIPALS) algorithm with leave Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript one out cross validation method, available in JMP13 Pro was used, which was carried out on 1700 transcripts. Variable Importance in the Projection (VIP) scores were extracted to reveal relations between predictors and outcome and plotted against coefficient of regression. Predictors and responses were scaled to have a mean of 0 and a standard deviation of 1 (by dividing each column by its standard deviation). To consider the transcript as a potential biomarker, the filters were set as such: the VIP score was >0.8 and the regression correlation coefficient was >0.2 in at least one of the studied subgroups. Model coefficients with their respective ranked VIP contributions are presented in Supplementary Table 3. The algorithm is available upon request. Functional and Pathway Analysis Ingenuity Pathways Analysis, IPA (Ingenuity Systems Inc) software was used to define biological networks, functional analyses of molecular interactions, and upstream regulators for the differentially expressed genes. IPA workflow comprised core, functional and canonical pathway analyses, and was used as a reference data set. Both direct and indirect molecular relationships were included in the analysis settings and the significance of relationships 1700 immune gene transcripts was indicated with z-score and Fisher’s exact test p-values <0.05. qPCR validation assay Quantitative real time PCR was performed in triplicate specimens for each transcript of interest. Relative concentrations were calculated against internally built standard curves. cDNA was synthesized from 200 ng total cellular RNA using Strata Script TM III reverse transcriptase Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript (Stratagene) using transcript-specific primers as described previously [12, 13]. The 600 ng of the pool of total RNA (per plate) was used to create for upper standard, which was titrated 1:3 to generate four point curve. PCR amplifications were then performed using the SYBR® Green PCR Kit (Bio-Rad Laboratories) according to manufacturer’s instructions. Transcript-specific oligonucleotide primers Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript were custom designed and synthesized by IDT Inc. The PCR amplification was done using a Mx3000 Stratagen™ thermocycler and data was analyzed using MxPro v4.01™ software. Relative concentrations of each amplified transcript were normalized to the relative levels of the hypoxanthine phosphoribosyltransferase (HPRT) transcript synthesized from the same specimen. The plots were derived as follows: concentrations of control samples at baseline (W00) were set as 1. The relative fold change expressions for control and C-IRIS groups were plotted longitudinally over 0, 4, 12-week time points; early/later C-IRIS events were correspondingly assigned to W03/W16. Results Subjects enrolled into this study underwent approximately 2 weeks of induction anti-fungal therapy with Amphotericin B and subsequently started ART. Figure 1 shows the time line of antifungal therapy, ART, C-IRIS events, and sample collection. Comparative demographic details of the original cohort of 130 participants, and current sub study are found in Chang et al. [5], where the C-IRIS group and the control group without neurological deterioration showed no significant difference in age, gender, baseline HIV viral load or serum cryptococcal antigen titers. At the time of ART initiation, transcriptome profiles differed between C-IRIS and control groups We hypothesized that HIV-infected patients with CM who developed C-IRIS would show a transcriptomic signature of immune deactivation at the time of ART initiation. ANOVA-REML analysis, using criteria described in the methods, identified over 1000 differentially expressed Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript transcripts between the C-IRIS and the control groups at the time of ART initiation. C-IRIS samples exhibited decreased expression of transcripts associated with antiviral responses (Figure 2), specifically those that lead to downregulation of viral replication including interferon induced protein 44 (IFI44), and interferon induced proteins with tetratricopeptide repeats (IFIT1-5). In addition, the Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript DExH and DExD/H-Box helicases (DDX58, DHX58), and transcripts encoding enzymes that directly restrict or inhibit viral replication (e.g. oligoadenylate synthases OIS1-3) were also downregulated in C-IRIS group. This peripheral blood transcriptomic signature in the C-IRIS group reflected a paucity of expression of genes that play a significant role in cellular resistance to viral infections. In contrast, the expression of transcripts encoding markers of activated granulocytes, tissue infiltration and destruction were significantly higher expressed in subjects who subsequently developed C-IRIS (e.g. matrix metallopeptidases, or myeloperoxidases, see table 1). Transcripts encoding C-type lectin domain family members (CLEC 5A,1B,4D) and the family of small antimicrobial cytotoxic peptides defensins (DEFA1,3,4,5) also exhibited significantly increased expression in the group at baseline. Taken into account that defensins are more abundant in 2+ neutrophil granules, and CLECs play a role in MHCI Ca -dependent pathogen recognition, this baseline transcriptomic signature suggests an activation of granulocyte-mediated inflammation, which may put patients at risk for the development of C-IRIS. ART-associated down-regulation of antiviral pro-inflammatory responses in the control group is seen over 12 weeks Longitudinal data analysis in the control group identified 588 and 1,412 differentially abundant transcripts at week 4 and 12, compared to W00 (pre-ART commencement). We detected longitudinal upregulation of transcripts such as IL7R, numerous cluster differentiation and HLA molecules during 12 weeks on ART (see Supplementary table 1). Such longitudinal increases in expression are a reflection of the effective immune recovery in lymphocyte populations and improvement in the antigen-presentation function in the control group. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Functional analysis of down-regulated transcripts indicated progressive decline of two predominant pathways: type I/II IFN–STAT signaling and NF-kB signaling (see Supplementary Figure 1). Activation of these pro-inflammatory genes is often associated with cell death, thus the reduction of Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript expression of these transcripts is beneficial for the effective immune reconstitution on ART [14]. On the contrary, the abundance of some interferon I/II-STAT and NF-kB signaling components had increased in the C-IRIS group at week 4 post-ART, suggesting a switch to aberrant immune activation as presented below. Altered blood transcriptomic profiles in subjects who developed C-IRIS We hypothesized that HIV-infected patients with CM show a transcriptomic signature of aberrant immune activation in the blood during C-IRIS event. We performed principal component analysis (PCA) to visualize the differences in gene expression levels in the entire dataset. Figure 3A depicts distinct separation between C-IRIS and control samples (PCA1). The PCA2 segregated samples collected at scheduled visits (week 0, 4, and 12) longitudinally within C-IRIS and control groups, and at time of C-IRIS event. Moreover, PCA3 clustered together samples from IRIS events that occurred within first 12 weeks on ART (early C-IRIS), and separately from C-IRIS events that occurred after 12 weeks (late C-IRIS). This segregation of transcriptomic profiles suggests that although early and late C-IRIS events have seemingly similar clinical manifestations, they differ significantly in transcriptomic signature. Early C-IRIS events were characterized by upregulation of innate immune response genes while late C-IRIS events showed deregulation of genes in adaptive immune response pathways. Using Partek Genomics Suite software, we obtained a similarity matrix based on the hierarchical clustering (as described in methods) to identify differences in transcript expression at early versus late C-IRIS events (Figure 3B, and supplementary Figure 3). ANOVA-REML analysis identified 539 and Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript 1143 transcripts that were differentially expressed at the time of early or late C-IRIS events, respectively, as compared to W00. Early C-IRIS events were characterized by up-regulation of numerous transcripts involved in innate immune responses: for example, upregulation of transcripts Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript involved in inflammasome pathways, such as, absent in melanoma or interferon-inducible protein AIM2, caspases, IL1B, and NLR family domain containing transcripts (Figure 4). Inflammasome components are part of the larger molecular network of pattern-recognition receptors (PRRs), including toll-like receptors and cytosolic surveillance pathway molecules, many of which were also upregulated (Supplementary Figure 2). PRRs recognize diverse pathogen-associated molecular pattern (PAMPs) molecules found in variety of microorganisms, including Cryptococcus species [15, 16]. Late C-IRIS also exhibited the up-regulation of transcripts involved in the inflammasome signaling pathway, such as CASP5 and AIM2. However, late C-IRIS events were primarily characterized by up-regulation of CD2, CD3, CD8, CD302 (expressed on T and B cells), and CD160, killer cell lectin- like receptors (expressed on NK cells), and also IFNG, granzymes and other mediators of inflammation, as compared to W00. In addition, we compared gene expression at the 12-week time point in the C-IRIS group with corresponding gene expression samples at late C-IRIS events, aiming to identify gene expression pattern that precedes late C-IRIS. The increased expression of transcripts encoding CCL-, CCR-, CXCR-type chemokines and chemokine receptors, and adhesion molecules such as integrins, selectins and claudins at week 12 of observation (most proximal time point to late C-IRIS diagnosis in corresponding patient). The fold change expression values for these molecules can be found in Supplementary table 2. These findings suggest that systemic upregulation of transcripts involved in inflammatory immune cell migration occur in peripheral blood prior to the development of late C-IRIS. We analyzed the enrichment of canonical pathways using IPA software, for early and late C- IRIS subgroups. The top 5 canonical pathways with altered expression at the time of early C-IRIS (Figure 5A) represent the activation of recognition of bacteria and viruses through cytosolic pattern recognition receptors. All five pathways encode components of innate immunity and could be considered as drivers of early C-IRIS. The late C-IRIS is driven by molecular pathways that included Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript transcripts involved in T- and B- cell signaling, natural killer cell signaling and apoptosis (Figure 5B). In addition, we utilized IPA causal network analysis to detect upstream regulatory molecules, affecting gene expression during C-IRIS events. This analysis predicted pathogen-associated molecular patterns (PAMPs) molecules were upstream regulators of early C-IRIS (shown in supplementary figure 2), consistent with our finding that transcripts encoding TLR/inflammasome pathways were upregulated. Conversely, IFNG, IL2 and IL21 were predicted to be upstream regulators of late C-IRIS gene expression. Thus, early and late C-IRIS events appeared to be due to over-exuberant inflammatory responses that are driven by different signaling cascades for early versus late C-IRIS events. Biomarkers of Early and Late C-IRIS The next step was to identify transcripts that may be predictive biomarkers of C-IRIS events, utilizing two independent computational algorithms available in JMP Pro13: the predictor screening (based on random bootstrap forest algorithm, RBSF) and partial least square (PLS) probability modeling. The random bootstrap forest algorithm was performed on all 34668 probe IDs expressed on microarrays. The top 200 high rank importance predictors whose expression distinguished C-IRIS from other subgroups (is listed in Supplementary table 3.2. Among them was the inhibitor of differentiation (ID4), bone barrow stromal antigen (BST1), and IL32, suggesting a strong contribution of abnormal differentiation and proliferation processes into C-IRIS onset. One interesting transcript, BEX1 (brain-expressed X-linked like family) that was steadily upregulated in C-IRIS group at all time points, could represent a predictive peripheral blood biomarker of Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript neuroinflammation. The majority of identified biomarkers were poorly annotated, highlighting that we were only beginning to unravel the complex nature of immune reconstitution. Aiming to identify immune biomarkers of C-IRIS events, we exported from IPA software all annotated immune genes that are significantly changed, at any time point of observation post ART. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript This export generated 1700 transcripts, which were analyzed with partial least square and RBSF algorithms. PLS analysis was performed on the following groups of samples: early C-IRIS, late C- IRIS and no C-IRIS, and also on all subgroups, including all time points within each group. A quantitative estimation of the discriminatory power of each predictor in PLS is provided by means of VIP scores (listed in Supplementary table 3.3, 3.4, 3.5). Subgroup analysis revealed that most of the top ranked biomarkers were specific for either early or late C-IRIS, and anti-correlate with each other: for example, differential expression of transcripts encoding of CCL8, CASP12, DDX58, TNF, positively correlated with early C-IRIS events (VIP>0.8, R>0.2), but negatively correlated with late C-IRIS (VIP>0.8, R>-0.2). The differential expression of transcripts encoding CLECs (C-type lectins), and SIGLECs (sialic acid binding immunoglobulin-like lectins) positively predicted early C- IRIS, but not with late C-IRIS. A particularly high association of co-expression of IFNG, complement pathway component C1QC and IL27 were uniquely observed in late C-IRIS. Interestingly, elevated expression of transcripts that reflect neuronal apoptotic processes or neurological deterioration, such as galectins, ephrin receptors, and kallikreins were significantly upregulated in both early and late C- IRIS events (p<0.01). Low baseline expression of interferon-response genes, TLR 2,7, DEFA3, and CD69, predicted subsequent early and late C-IRIS, after ART commencement. The correlation coefficients for least square mean, and a corresponding statistical VIP (values ranking descriptors) for 13 transcripts that identified by both algorithms can be found in Supplementary table 3.1. All of these transcripts represent novel biomarkers of C-IRIS. Biomarker validation We used real-time quantitative PCR to validate the differential expression of mRNA for 15 selected genes as described in the methods section, and the results are shown in supplementary figures 4 A, B, C, D. The 4A depicts the expression profiles for biomarkers of both C-IRIS events, which were persistently elevated in both C-IRIS subgroups over 12 weeks of observation: AIM2, BEX1, C1QB Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript (>1.5 fold in expression). The CASP5, DDX58 and TNF mRNA levels were upregulated >2 fold in early C-IRIS patients, and subsequently downregulated by week 12, although not to the level of control mRNAs (figure 4B). Compared to the baseline, the late C-IRIS patients exhibited significantly higher levels of IFNG, IL27 and KLRB1 transcripts after 12 weeks of observation, but had very low expression of these transcripts at baseline, as compared to control samples (see figure 4C). We validated several biomarkers that constitutes favorable immune recovery: the antiviral defense transcripts were upregulated at baseline in the control group (IFI44, IRF7, MX1), and had distinct downregulation profiles over 12 weeks of observation, as compared to C-IRIS group (see figure 4D). At baseline, the levels of antimicrobial defense mRNAs (DEFA3, CLEC5A, CD69) were higher in the C-IRIS group when compared to the control group (figure 4E). The levels of these transcripts subsequently decreased even more, in the control group, but not in the early C-IRIS group. Overall, the expression profiles for these transcripts were similar for the real-time quantitative PCR results and the microarray results. Discussion C-IRIS has emerged as an important complication of ART in HIV-CM co-infection. Utilizing longitudinal data analysis of samples from a well-characterized prospective cohort of HIV-CM co- infected patients initiating ART, who experienced C-IRIS compared to CD4+ T cell-matched controls [5], we showed that C-IRIS subjects exhibited significantly lower expression of transcripts encoding antiviral defense proteins and upregulation of antimicrobial defense genes prior to ART commencement. Poor cellular antiviral and inflammatory responses in cerebrospinal fluid (CSF) have Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript been suggested to be associated with poor CM outcomes and predispose patients to IRIS [5, 6, 17, 18]. Low cerebrospinal fluid levels of IFNG has been previously shown to be associated with poorer CSF clearance of cryptococci, and adjunctive IFNG treatment has been shown in two studies to improve Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript rate of CSF clearance [19, 20]. We have previously shown that IFNG secretion in whole blood stimulated with cryptococcal mannoprotein is reduced in C-IRIS prior to ART initiation [21]. Indeed, here, the predictor screening algorithms identified the low expression of the components of antiviral defense pathway such as interferon inducible genes, as predictive biomarkers of subsequent C-IRIS. Thus, the paucity of anti-viral gene expressions in peripheral blood at the time of ART initiation could have predictive value for identifying those at risk for C-IRIS. Patients who exhibit such signatures also display activation of granulocyte- dependent pro-inflammatory responses (through upregulation of CLECs and DEFAs) and may require longer antifungal therapy before initiation of ART to achieve clearance and reduce risk of C-IRIS [22]. We have previously shown that HIV-CM co-infected patients who were able to achieve cryptococcal culture negativity in the CSF prior to ART initiation compared to those with residual cryptococcal positive cultures were associated with reduced neurological deterioration, C-IRIS and cryptococcal relapse [5]. Others have shown that patients who initiate ART early compared to late in HIV-CM co-infection have increased mortality [23, 24] and increased C-IRIS incidence [25]. Thus, an initial effective immune response, characterized by adequate production of interferons and interferon-response genes, may lead to better antigen clearance and improved outcomes. The course of immune recovery on ART in our control group is similar to our previously demonstrated results in advanced stage HIV-infected patients without opportunistic infections [14]. Both groups showed maintenance of innate host defenses in an activated state through IFN I/II-STAT pathway prior to ART initiation, which subsequently declined on ART [14]. While both studies showed that IFN I/II pathway were down-regulated early in the controls, the IFNG pathway remains inert in the C-IRIS group post-ART. Our results suggest that the differences in longitudinal transcriptomic profiles of those with or without C-IRIS gives insight into what constitutes an effective response that leads to immune recovery on ART, as opposed to a pathological response that leads to C-IRIS. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Moreover, the C-IRIS group exhibited abnormal longitudinal pro-inflammatory changes in gene expression profiles that were evident in the blood weeks after ART initiation and transition to a fluconazole maintenance therapy, as discussed below. There was a significant modification in gene expression signatures and differences in identified biomarkers seen in late C-IRIS events versus early C-IRIS, suggesting that early and late C-IRIS have distinct molecular phenotypes. The AIM2, C1QB and BEX1 were identified as common biomarkers for both early and late C-IRIS events, and thus warrant future investigation. Early C-IRIS was associated with upregulation in innate responses and late C-IRIS had more pronounced signature of adaptive immune activation. Unlike in immunocompetent patients [26], in immunocompromised patients the innate immune system is the primary branch that elicits immune response to combat opportunistic infections. Over-activation of it through pattern recognition receptor signaling sets up a cycle of local and systemic inflammation [27]. In our study, TLR and inflammasome components (including AIM2, CASP5 and NLR families) identified as biomarkers of early C-IRIS events represent a response toward ongoing viral replication and cryptococcal antigens. Recent data suggests the inflammasome pathway drives CD4+ T-cell depletion in HIV-1 infection [28] and delays immune reconstitution. Thus, inflammasome activation may contribute to the neurological deterioration symptoms in C-IRIS patients, which has been also proposed for tuberculosis meningitis IRIS (TBM-IRIS) [29-32], and represent a peripheral blood biomarker of brain inflammation for both CM and TBM-IRIS. Other identified biomarker transcripts that reflected innate immune responses specifically toward cryptococcal antigen (e.g. CLECs, SIGLECs, dectins) should also be studied in depth to understand the pathogenesis of immune reconstitution in C-IRIS patients. The findings in late C-IRIS such as the activation of components of complement and the inflammasome, along with IFNG, IL27, KLRB1 and other transcripts expressed in T, B and NK cells suggest an impaired restoration of adaptive immunity. Poor recovery of adaptive immunity and impaired communication between innate and adaptive immune branches due to lack of negative Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript regulatory feedback responses, leads to an inability to resolve inflammation [33]. The onset of late C- IRIS is preceded by excessive expression of chemokines and integrins in peripheral blood, and thus, can be predicted through monitoring the aberrant kinetics of immune reconstitution in patients at risk. In this study we identified number of biomarker transcripts that encode intracellular molecules expressed in various circulating immune cells. We hypothesize that transcripts (AIM2, C1QB and BEX1) identified by two predictor algorithms will compose a bundle biomarker signature of neuroinflammation during complex, antigen-driven process of immune reconstitution. One limitation of this study is that we did not systematically look for other opportunistic infections that might be present in these patients (such as tuberculosis, toxoplasmosis, cytomegalovirus, etc.), as these unknown factors may have skewed our results. Further in-depth study and interpretation of the biological processes associated with the biomarkers identified through statistical modeling in our study will enhance the robustness of our findings [34]. In conclusion, the information presented in this report provides an insight into the molecular drivers of C-IRIS pathogenesis and could in the future be applied to developing diagnostic tests that guide targeted immunomodulatory treatments. Acknowledgement This work is supported by a UMN departmental start-up fund to I.A.S. and National Institutes of Health grant AI072068 to P.R.B, and C.C.C is supported by an Australian NHMRC Early Career Fellowship (1092160). We acknowledge the patients and research investigators of the parent C-IRIS study, including the clinical and laboratory staff at King Edward VIII hospital and HIV Pathogenesis Programme, Durban, South Africa. Our special thanks to Professor Thumbi Ndung’u for providing the facilities for samples storage. We acknowledge the University of Minnesota Supercomputing Institute for providing the access to Ingenuity Pathway Assistant, and University Biomedical Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Genomics facility for hybridizing and scanning microarrays. We thank Tawa Alabi, Ashley Johnson and Calandra Sagarsky for experimental assistance. Author contributions I.V.S and C.C.C contributed equally to this work; C.C.C directed the clinical study; I.V.S directed the genomics study and data analysis; S.S. performed experiments; I.V.S., C.C.C., P.R.B and M.A.F wrote and edited the manuscript. The authors declare no conflict of interest. Abbreviations: IRF7, Interferon regulatory factor; IFI, interferon alpha inducible protein; IFIT, interferon induced protein with tetratricopeptide repeats; IFNG, interferon gamma, RSAD2, radical S-adenosyl methionine domain containing 2; OAS, 2'-5'-oligoadenylate synthetases; DHX58, DExH-box helicase 58 (LGP2); DDX58, DExD/H-box helicase 58 (RIG1); C1QB, C1QC, components of complement; AIM2, Absent In Melanoma 2; CASP, caspases; IL, interleukin; TLR, Toll-like receptors; NOD2, Nucleotide-binding oligomerization domain-containing protein 2; NLRP, NLR (nucleotide-binding oligomerization domain (NOD), leucine-rich repeat (LRR)-containing protein) family pyrin domain containing; KLR, killer cell lectin like receptor; CCL, C-C motif chemokine ligand; CXCR, C-X-C motif chemokine receptor; ITGA, integrin subunit alpha; CLDN, claudin; SELL, selectin L; TREM1, triggering receptor expressed on myeloid cells 1; PAMP, pathogen-associated molecular patterns; ID3,4, inhibitor of DNA binding 3,4; ADAR, adenosine deaminase, RNA specific; BTS1, bone marrow stromal cell antigen 1; BEX1, brain-expressed X- linked like family; SIGLEC, sialic acid binding immunoglobulin like lectins; ID4, Inhibitor of differentiation 4; CLEC, C-type lectin domain family members; LGALS13, galectin 13; EPH A/B, ephrin receptors, KLK1, kallikrein 1; RSAD2, Radical S-Adenosyl Methionine Domain Containing 2; TNF, tumor necrosis factor; VIP, variable importance for the projection score. 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AIDS (London, England) 2015; 29(4): 421-31. 32. Tan HY, Yong YK, Shankar EM, et al. Aberrant Inflammasome Activation Characterizes Tuberculosis-Associated Immune Reconstitution Inflammatory Syndrome. J Immunol 2016; 196(10): 4052-63. 33. Antonelli LR, Mahnke Y, Hodge JN, et al. Elevated frequencies of highly activated CD4+ T cells in HIV+ patients developing immune reconstitution inflammatory syndrome. Blood 2010; 116(19): 3818-27. 34. Selvan LD, Sreenivasamurthy SK, Kumar S, et al. Characterization of host response to Cryptococcus neoformans through quantitative proteomic analysis of cryptococcal meningitis co-infected with HIV. Mol Biosyst 2015; 11(9): 2529-40. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Figures Figure 1. Study protocol, modified from [5]. Microarray blood collections were scheduled for W00 (pre-ART initiation), W04 and W12 during the 24-week follow-up post ART. Additional microarray samples were collected at time of the C-IRIS event – early (within 12 weeks) and late (between 12-24 weeks) of ART initiation. Figure 2. The low anti-viral gene expressions at the time of ART initiation in C-IRIS group. C- IRIS group exhibited significantly lower expression of genes associated with innate immune responses: specifically, those that involve activation of antiviral defense (for example, interferon inducible genes (IFI), and other, connected with orange lines), or genes encoding enzymes, that directly restrict or inhibit viral replication (for example, oligoadenylate synthase (OAS) or helicases (DHX)). IFIT family genes are predominantly induced by type I and type III interferons and are regulated by the pattern recognition and the JAK-STAT signaling pathway. OAS genes encode a synthase family that are induced by interferons and catalyze the 2',5'-oligomers of adenosine in order Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript to bind and activate RNase L. The IFIT and OAS families of genes are one of many interferon- stimulated genes that play a significant role in the inhibition of cellular protein synthesis in infected cells and is particularly important in cellular resistance to viral infections. Transcripts (nodes), colored in blue were down-regulated (>1.5 fold) with the intensity of blue reflecting the degree of down-regulation. Fold change for these transcripts’ expression are shown in Table 1. The orange line represents activation effect of the encoded protein. The blue line represents inactivation effect of the encoded protein. Transcripts were identified through functional analysis using Ingenuity Pathway Assistant software (IPA). Figure 3. Identification of C-IRIS features through unsupervised learning algorithms. A. Principal component analysis shows distinct patterns in RNA expression between C-IRIS patients and controls. Using all 34668 expressed probe identifiers and 162 individual arrays, three principal components (PC) account for 33% total variance in gene expression, (PC#1 17.2%, PC#2 11.4%, PC#3 4.4% respectively). Samples were color-coded by study subgroups (see legend). Ellipsoids are drawn around samples that represent 2 standard deviations, for early (black) and late (pink) C-IRIS events. B. Hierarchical clustering analysis of 1700 probe IDs representing immune genes that were differentially expressed between early and late C-IRIS events. Hierarchical clustering is built based on normalized log transformed gene expression values. Heatmap coloring is based on expression levels: yellow, high level of expression, blue, low level of expression. Both rows and columns are hierarchically clustered according to the Pearson’s average linkage dissimilarity method in Partek GS. Top grey bar represents early C-IRIS, and pink bar represents late C-IRIS (individual cases). Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Figure 4. Transcriptomic signature of early C-IRIS events (< week 12 of ART initiation). The inflammasome pathway is shown. Transcripts, colored in yellow are upregulated >1.5 fold in early C-IRIS samples, with the intensity of yellow reflecting the degree of upregulation (p<0.05). Fold changes of upregulation (early C-IRIS versus W00 in IRIS group samples) are shown below the nodes. Nodes in grey are significantly differently expressed (p<0.05), with less than 1.5 fold change expression. The intensity of color reflects the degree of changes. Figure 5. Top 5 canonical pathways that were over-represented in up-or down- regulated transcripts in A) early and B) late C-IRIS subgroups. The top 5 canonical pathways (left axis) are displayed as stacked bar charts with percentage (top axis) of processes enriched by upregulated genes (yellow), by down-regulated genes (blue) and those without a detectable change (white). The orange line graph represents the log-transformed p values (- log(p-value) calculated by Fisher’s exact test (bottom axis) for the association between IPA- annotated genes within the pathway (assumed 100%), and the number of genes found in the uploaded dataset (numbers on the top of the bar). A. The top 5 canonical pathways with altered expression at the time of early C-IRIS: 1. Role of pattern recognition of bacteria and viruses (137); 2. Activation of interferon regulatory factors (IRF) by cytosolic pattern recognition receptors (63); 3. TREM1 (the triggering receptor expressed on myeloid cells 1) signaling (75); 4. Inflammasome (20); 5. Toll- like receptor signaling (76). There was a minimal overlap in the transcripts between pathways, varying between 0-5 molecules. B. The top 5 canonical pathways with altered expression at the time of late C-IRIS: 1. Th1 and Th2 Activation (185); 2. Natural killer cell signaling (122); 3. iCOS- iCOSL (Inducible costimulator (ICOS), and its ligand, ICOS ligand (ICOSL)) signaling (123); 4. Calcium induced T lymphocyte apoptosis (66); 5. Altered T and B signaling in inflammation (90). There was a minor overlap in the transcripts between pathways, varying between 3 to 11 molecules. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Table 1. Pre-ART gene expression in C-IRIS versus control groups. Gene Fold symbol, p-value, change biofunction FDR Antiviral response IFI44L 2.2E-16 -4.5 IFI44 5.2E-16 -3.6 IFIT1 5.5E-11 -3.0 IFIT2 1.9E-21 -3.2 IFIT3 1.1E-13 -2.7 IFIT5 2.1E-08 -1.5 ISG15 3.0E-10 -3.3 OAS1 2.1E-17 -2.4 OAS2 2.7E-18 -2.6 OAS3 2.4E-17 -3.0 MX1 1.1E-16 -3.0 DDX58 1.6E-09 -1.6 TLR7 4.4E-27 -2.8 DHX58 1.8E-15 -2.5 Antimicrobial response OLFM4 1.2E-28 8.6 CD177 2.8E-17 4.1 DEFA 6.7E-07 2.6 DEFA1B 4.8E-10 3.5 DEFA3 2.5E-09 3.1 DEFA4 9.4E-14 3.8 DEFA5 1.0E-22 1.6 DEFB106B 7.1E-18 1.5 CLEC5A 8.1E-21 3.3 CLEC4D 3.8E-11 2.0 CLEC1B 2.2E-10 2.2 CLECL1 1.6E-13 1.7 SIGLEC14 9.1E-03 2.1 Apoptosis MMP9 9.9E-10 3.0 MMP8 7.3E-28 6.9 MPO 1.7E-20 2.9 C1QTNF5 1.6E-25 2.4 ARG1 1.7E-17 3.7 LCN2 1.9E-24 5.8 Transcript symbol, abbreviated transcript name. Positive values, up-regulated, negative values, down-regulated between two baselines (week 00). Biofunction, as defined in the GO database. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Figure 1. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Figure 2. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Figure 3. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Figure 4. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript Figure 5. Downloaded from https://academic.oup.com/ofid/advance-article-abstract/doi/10.1093/ofid/ofy157/5047451 by Ed 'DeepDyve' Gillespie user on 17 July 2018 Accepted Manuscript

Journal

Open Forum Infectious Diseases – Oxford University Press

Published: Jul 2, 2018

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