Host Biomarkers Research Articles

Diagnostic, prognostic, and therapeutic potentials of gut microbiome profiling in human schistosomiasis: A comprehensive systematic review.

Several studies have highlighted alteration in the gut microbiome associated with the onset and progression of diseases. Recognizing the potential of gut microbiota as biomarkers, this systematic review seeks to synthesize current data on the intricate relationship between the host gut microbiome profiles and their usefulness for the development of diagnostic, prognostic and therapeutic approaches to control human schistosomiasis. A systematic literature review was carried out by searching for relevant studies published until date, that is May 2024, using Medline, Embase, Global Health, Web of Science, and Global Index Medicus databases. The keywords used to select articles were "Gut microbiome", "Gut Microbiota", "Schistosomiasis", "Bilharziasis ", and "Human". Extracted data were analysed qualitatively from the selected articles. Of the 885 articles retrieved and screened, only 13 (1.47%) met the inclusion criteria and were included in this review. Of the included studies, 6 (46.2%) explored alterations of gut microbiome in schistosome-infected patients, 4 (30.7%) in patients with liver pathologies, and 3 (23.1%) in patients treated with praziquantel. Bacteria from the genera Bacteroides, Faecalibacterium, Blautia and Megasphaera were associated with S. japonicum and S. haematobium infection in school-aged children, whereas infection with S. mansoni rather associated with Klebsiella and Enterobacter. The gut microbiota signature in patient with schistosomiasis-induced liver pathology was reported only for S. japonicum, and the genus Prevotella appeared as a non-invasive biomarker of S. japonicum-associated liver fibrosis. For S. mansoni-infected school-aged children, it further appeared that the treatment outcome following praziquantel administration associated with the abundance in the gut microbiome of bacteria from the classes Fusobacteriales, Rickettsiales and Neisseriales. The host gut microbiome appears to be a valuable, non-invasive, but still poorly utilized, source of host biomarkers potentially informative for better diagnosing, prognosing and treating schistosomiasis. Further studies are therefore needed to comprehensively define such gut microbial biomarkers of human schistosomiasis and catalyse the informed development of gut microbiome-based tools of schistosomiasis control.

P-1015. Cellular Composition of Bronchoalveolar Lavage Fluid in Patients with Hematologic Malignancies and Invasive Pulmonary Aspergillosis: Significant Associations with Peripheral Blood Cell Counts

Abstract Background Bronchoalveolar lavage (BAL) is frequently performed to diagnose invasive pulmonary aspergillosis (IPA) in patients (pts) with hematologic malignancies (HM). Given the critical role of the pulmonary immune environment for fungal control, BAL might be leveraged for diagnostic and prognostic immune biomarkers in IPA pts. However, data on the concordance of cell counts in peripheral blood (PB) and BAL fluid (BALF) of IPA pts is lacking.Figure 1.Heatmap summarizing Spearman’s rank correlation coefficients between peripheral blood white blood cell counts and differentials and those from bronchoalveolar lavage fluid at diagnosis of invasive pulmonary aspergillosis. * p<0.05, ** p<0.01, *** p<0.001. Methods We retrospectively reviewed 63 culture-positive proven/probable IPA cases in adult pts who underwent bronchoscopy at MD Anderson Cancer Center in 2011–2022. BALF cell counts, microbiology data, PB white blood cell (WBC) differentials on the day of bronchoscopy, and IPA outcomes were analyzed.Figure 2.Comparison of bronchoalveolar lavage fluid cell composition according to neutropenia status (A) and 42-day outcomes (B) in invasive aspergillosis patients with hematologic malignancies. Mann-Whitney U test. ** p<0.01, *** p<0.001. Results The median BALF WBC count at IPA diagnosis was 129/µL (interquartile range [IQR] 54–272/µL) and median neutrophil percentage was 11% (IQR 2–46%). PB total WBC counts significantly correlated with BALF WBC (r=0.55, p< 0.001), neutrophil (r=0.59, p< 0.001), and lymphocyte (r=0.48, p< 0.001) counts. Likewise, PB WBC differentials, i.e., absolute neutrophil and lymphocyte counts, significantly correlated with those in BALF (Fig. 1). Neutropenic pts (< 500/µL) had lower median BALF WBC (66 vs. 140/µL, p=0.005) and neutrophil counts (1 vs. 41/µL, p< 0.001) than non-neutropenic pts (Fig. 2A). BALF neutrophil counts were significantly higher in pts with bacterial coinfections than in those without coinfection (143 vs. 8/µL, p=0.005), while both neutrophil (37 vs. 5/µL, p=0.005) and lymphocyte (20 vs. 8/µL, p=0.021) counts were higher in pts with respiratory viral coinfection. Notably, cellular composition of BALF was not significantly associated with BALF galactomannan positivity or 42-day mortality after IPA diagnosis (Fig. 2B). Conclusion Unlike the well-known association between PB cytopenias and IPA outcome, severity of IPA was not significantly influenced by quantitative BAL cell composition, underscoring the unmet need for reliable qualitative/functional BAL biomarkers to prognosticate fungal control in HM pts. Both PB cytopenias and coinfections significantly affected immune cell mobilization into BALF. Thus, clinical context will be critical for normalization and validation of BAL-based host biomarkers. Disclosures Sebastian Wurster, MD, MSc, Astellas Pharma: Grant/Research Support|Gilead Sciences: Grant/Research Support Dimitrios P. Kontoyiannis, MD, AbbVie: Advisor/Consultant|Astellas Pharma: Advisor/Consultant|Astellas Pharma: Grant/Research Support|Astellas Pharma: Honoraria|Cidara Therapeutics: Advisor/Consultant|Gilead Sciences: Advisor/Consultant|Gilead Sciences: Grant/Research Support|Gilead Sciences: Honoraria|Knight: Advisor/Consultant|Merck: Advisor/Consultant|Scynexis: Advisor/Consultant

Analyses of blood-derived host biomarkers for pulmonary tuberculosis diagnosis in human immunodeficiency virus co-infected individuals in sub-Saharan Africa: a systematic review and meta-analysis

ObjectiveOur objective was to conduct a review of host blood-derived biomarkers as potential diagnostic targets for pulmonary TB and as alternative tests to identify active tuberculosis in HIV co-infected individuals.MethodsA systematic review and meta-analysis of host blood-derived biomarkers with potential for diagnosis of active tuberculosis in HIV co-infected individuals was conducted. Cochrane Library, Embase, MEDLINE, PubMed and Web of Science databases were searched up to 7 November 2023. A hierarchical summary receiver operating characteristic (HSROC) model was used to evaluate the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic odds ratio (DOR) of the following potential biomarkers: C-reactive protein (CRP), Interferon gamma induced protein-10 (IP-10), Neopterin, IGRA, Kynurenine to tryptophan (K/T) ratio and use of different panels of combined biomarkers; including 5 biomarker panel (IL-6, INF-y, MIG, CRP, and IL-18), 4 biomarker panel (IL-6, IL-21, INF-y, IL-1a), 6 biomarker panel (APO-ACIII, CXCL1, CXCL9, CCL8, CCL-1, and CD56), and 9 biomarker panel (Alpha-2-macroglobulin, fibrinogen, CRP, MMP-a, transthyretin, complement factor H, INF-y, IP-10, and TNF-α).ResultsTwenty-three studies were included. The pooled sensitivity of CRP, IP-10, Neopterin, combined biomarker signatures, IGRA and K/T ratio were 77% (60–88), 79% (72 - 84), 82% (43–96), 78% (64–88), 71% (65–76), 95% (90–98), respectively and the pooled specificity were 90% (80–96), 82% (59–93), 42% (22–66), 85% (73–92), 33% (18–54), and 95% (82–99), respectively.ConclusionCRP, IP-10, K/T ratio and the panels of multiple combined biomarkers that include the following cytokines, chemokines, and acute phase proteins IL-6, INF-y, MIG, CRP, IL-18, IL-21, IL-1a, APO-ACIII, CXCL1, CXCL9, CCL8, CCL-1, CD56, Alpha-2-macroglobulin, fibrinogen, MMP-a, transthyretin, complement factor H, IP-10, and TNF-α are potential blood biomarkers that can aid TB diagnosis in HIV co-infected individuals.

Multi-omic Biomarkers Distinguish Rheumatoid Arthritis in Discordant Monozygotic Twins.

Although genetic factors have been identified in the pathogenesis of rheumatoid arthritis (RA), the concordance rate in monozygotic (MZ) twins is low, suggesting that other features contribute to disease development. Further, the relative contribution of such non-genetic elements in identical twins have not been characterized. Here, we aimed to measure differentiating host and microbial biomarkers of RA by studying MZ twins discordant for disease using a multi-omics approach. Eight pairs of MZ twins discordant for RA (n=16) were enrolled. Gut microbiome was assessed using shotgun metagenomic sequencing. Autoantibodies, cytokines, and other plasma proteins were measured in both plasma and feces. Levels of short and medium-chain fatty acids from serum and feces were quantified using gas chromatography mass spectrometry (GC-MS). While overall microbiome diversity and composition did not significantly differ between twins, we observed a decrease in Blautia faecis in affected twins. Affected twins had higher concentrations of both fecal and plasma citrullinated and non-citrullinated autoantibodies, as well as significantly lower concentrations of fecal butyrate and propionate. Multi-omics biomarkers differentiate MZ twins discordant for RA. Blautia faecis , which is associated with reduced inflammatory cytokine expression, was decreased in RA twins. Similarly, short-chain fatty acids, known to have immune modulatory effects, were decreased in affected twins, suggesting further bi-directional interactions between inflammation at the gut barrier and disease state. If confirmed in other cohorts, exhaustive multi-omics approaches may improve our understanding of RA pathogenesis and potentially contribute to novel diagnostics and co-adjuvant therapies.

Whole Blood vs Serum-Derived Exosomes for Host and Pathogen-Specific Tuberculosis Biomarker Identification: RNA-Seq-Based Machine-Learning Approach

Whole Blood vs Serum-Derived Exosomes for Host and Pathogen-Specific Tuberculosis Biomarker Identification: RNA-Seq-Based Machine-Learning Approach

Combined urine proteomics and metabolomics analysis for the diagnosis of pulmonary tuberculosis

BackgroundTuberculosis (TB) diagnostic monitoring is paramount to clinical decision-making and the host biomarkers appears to play a significant role. The currently available diagnostic technology for TB detection is inadequate. In the present study, we aimed to identify biomarkers for diagnosis of pulmonary tuberculosis (PTB) using urinary metabolomic and proteomic analysis. Methods: In the study, urine from 40 PTB, 40 lung cancer (LCA), 40 community-acquired pneumonia (CAP) patients and 40 healthy controls (HC) was collected. Biomarker panels were selected based on random forest (RF) analysis. Results: A total of 3,868 proteins and 1,272 annotated metabolic features were detected using pairwise comparisons. Using AUC ≥ 0.80 as a cutoff value, we picked up five protein biomarkers for PTB diagnosis. The five-protein panel yielded an AUC for PTB/HC, PTB/CAP and PTB/LCA of 0.9840, 0.9680 and 0.9310, respectively. Additionally, five metabolism biomarkers were selected for differential diagnosis purpose. By employment of the five-metabolism panel, we could differentiate PTB/HC at an AUC of 0.9940, PTB/CAP of 0.8920, and PTB/LCA of 0.8570. Conclusion: Our data demonstrate that metabolomic and proteomic analysis can identify a novel urine biomarker panel to diagnose PTB with high sensitivity and specificity. The receiver operating characteristic curve analysis showed that it is possible to perform non-invasive clinical diagnoses of PTB through these urine biomarkers.

Unraveling potential gene biomarkers for dengue infection through RNA sequencing

Dengue virus hijacks host cell mechanisms and immune responses in order to replicate efficiently. The interaction between the host and the virus affects the host's gene expression, which remains largely unexplored. This pilot study aimed to profile the host transcriptome as a potential strategy for identifying specific biomarkers for dengue prediction and detection. High-throughput RNA sequencing (RNA-seq) was employed to generate host transcriptome profiles in 16 dengue patients and 10 healthy controls. Differentially expressed genes (DEGs) were identified in patients with severe dengue and those with dengue with warning signs compared to healthy individuals. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to elucidate the functions of upregulated and downregulated genes. Compared to healthy controls, 6466 genes were significantly differentially expressed (p < 0.05) in the dengue with warning signs group and 3082 genes in the severe dengue group, with over half being upregulated. The major KEGG pathways implicated included transport and catabolism (14.4%–16.3%), signal transduction (6.6%–7.3%), global and overview maps (6.7%–7.1%), viral diseases (4.6%–4.8%), and the immune system (4.4%–4.6%). Several genes exhibited consistent and significant upregulation across all dengue patients, regardless of severity: Interferon alpha inducible protein 27 (IFI27), Potassium Channel Tetramerization Domain Containing 14 (KCTD14), Syndecan 1 (SDC1), DCC netrin 1 receptor (DCC), Ubiquitin C-terminal hydrolase L1 (UCHL1), Marginal zone B and B1 cell-specific protein (MZB1), Nestin (NES), C–C motif chemokine ligand 2 (CCL2), TNF receptor superfamily member 17 (TNFSF17), and TNF receptor superfamily member 13B (TNFRSF13B). Further analysis revealed potential biomarkers for severe dengue prediction, including TNF superfamily member 15 (TNFSF15), Plasminogen Activator Inhibitor-2 (SERPINB2), motif chemokine ligand 7 (CCL7), aconitate decarboxylase 1 (ACOD1), Metallothionein 1G (MT1G), and Myosin Light Chain Kinase (MYLK2), which were expressed 3.5 times, 2.9 times, 2.3 times, 2.1 times, 1.7 times, and 1.4 times greater, respectively, than dengue patients exhibiting warning signs. The identification of these host biomarkers through RNA-sequencing holds promising implications and potential to augment existing dengue detection algorithms, contributing significantly to improved diagnostic and prognostic capabilities.

Single cell spatial profiling of FFPE splenic tissue from a humanized mouse model of HIV infection

BackgroundLatency remains a major obstacle to finding a cure for HIV despite the availability of antiretroviral therapy. Due to virus dormancy, limited biomarkers are available to identify latent HIV-infected cells. Profiling of individual HIV-infected cells is needed to explore potential latency biomarkers and to study the mechanisms of persistence that maintain the HIV reservoir.MethodsSingle cell spatial transcriptomic characterization using the CosMx Spatial Molecular Imager platform was conducted to analyze HIV-infected cells in formalin-fixed paraffin-embedded sections of splenic tissue surgically obtained from an HIV-infected humanized mouse model. Regulation of over a thousand human genes was quantified in both viremic and aviremic specimens. In addition, in situ hybridization and immunohistochemistry were performed in parallel to identify HIV viral RNA- and p24-containing cells, respectively. Finally, initial findings from CosMx gene profiling were confirmed by isolating RNA from CD4 + T cells obtained from a person living with HIV on antiretroviral therapy following either PMA/Ionomycin or DMSO treatment. RNA was quantified using qPCR for a panel of targeted human host genes.ResultsSupervised cell typing revealed that most of the HIV-infected cells in the mouse spleen sections were differentiated CD4 + T cells. A significantly higher number of infected cells, 2781 (1.61%) in comparison to 112 (0.06%), and total HIV transcripts per infected cell were observed in viremic samples compared to aviremic samples, respectively, which was consistent with the data obtained from ISH and IHC. Notably, the expression of 55 genes was different in infected cells within tissue from aviremic animals compared to viremic. In particular, both spleen tyrosine kinase (SYK) and CXCL17, were expressed approximately 100-fold higher. This data was further evaluated against bulk RNA isolated from HIV-infected human primary CD4 + T cells. A nearly 6-fold higher expression of SYK mRNA was observed in DMSO-treated CD4 + T cells compared to those stimulated with PMA/Ionomycin.ConclusionThis study found that the CosMx SMI platform is valuable for assessing HIV infection and providing insights into host biomarkers associated with HIV reservoirs. Higher relative expression of the SYK gene in aviremic-infected cells from the humanized mouse HIV model was consistent with levels found in CD4 + T cells of aviremic donors.

Progress on nonculture based diagnostic tests for invasive mould infection.

This review describes the current status of diagnosing invasive mould disease and Pneumocystis pneumonia using nonconventional diagnostics methods. There has been significant development in the range of nonculture mycological tests. Lateral flow tests (LFTs) for diagnosing aspergillosis complement galactomannan ELISA testing, and LFTs for other fungal diseases are in development. Rapid and low through-put B-D-Glucan assays increase access to testing and there has been significant progress in the standardization/development of molecular tests. Despite this, no single perfect test exists and combining tests (e.g., antigen and molecular testing) is likely required for the optimal diagnosis of most fungal diseases. Based on established clinical performance few mycological tests can be used alone for optimal diagnosis of fungal disease (FD) and combining tests, including classical approaches is the preferred route for confirming and excluding disease. Next-generation sequencing will likely play an increasing role in how we diagnose disease, but optimization, standardization and validation of the entire molecular process is needed and we must consider how host biomarkers can stratify risk. Given the burden of FD in low- and medium-income countries, improved access to novel but more so existing diagnostic testing is critical along with simplification of testing processes.

Vaginal Bacteria and Proinflammatory Host Immune Mediators as Biomarkers of Human Immunodeficiency Virus Acquisition Risk Among African Women.

Few investigations have assessed contributions of both vaginal bacteria and proinflammatory immune mediators to human immunodeficiency virus (HIV) acquisition risk in a prospective cohort. We conducted a nested case-control study of African women who participated in a randomized placebo-controlled trial of daily oral versus vaginal tenofovir-based preexposure prophylaxis for HIV infection. Vaginal concentrations of 23 bacterial taxa and 16 immune mediators were measured. Relationships between individual bacterial concentrations or immune mediators and HIV risk were analyzed using generalized estimating equations in a multivariable model. Factor analysis assessed relationships between combinations of bacterial taxa, immune mediators, and HIV acquisition risk. We identified 177 HIV pre-seroconversion visits from 150 women who acquired HIV and 531 visits from 436 women who remained HIV uninfected. Fourteen bacterial taxa and 6 proinflammatory cytokines and chemokines were individually associated with greater HIV risk after adjusting for confounders. Women with all 14 taxa versus <14 taxa (adjusted odds ratio [aOR], 4.45 [95% confidence interval {CI}, 2.20-8.98]; P < .001) or all 6 immune mediators versus <6 mediators (aOR, 1.77 [95% CI, 1.24-2.52]; P < .001) had greater risk for HIV acquisition. Factor analysis demonstrated that a bacterial factor comprised of 14 high-risk bacterial taxa (aOR, 1.57 [95% CI, 1.27-1.93]; P < 0.001) and the interferon gamma-induced protein 10 (highest quartile: aOR, 3.19 [95% CI, 1.32-7.72]; P = 0.002) contributed to the highest HIV risk. Bacterial and host biomarkers for predicting HIV acquisition risk identify women at greatest risk for HIV infection and can focus prevention efforts.

Association of Dengue Severity with Host Biomarkers

Association of Dengue Severity with Host Biomarkers

Host urinary biomarkers in HIV positive and HIV negative patients with tubercular uveitis and other uveitic diseases

PurposeTo determine if host urinary biomarker profiles could distinguish between tubercular uveitis (TBU) and other uveitic diseases (OUD) in patients with and without HIV infection. MethodsConcentrations of 29 different host biomarkers were measured in urine samples using the Luminex platform. Data were analyzed to describe differences between patients diagnosed with and without TBU and with and without HIV co-infection. ResultsOne-hundred-and-eighteen urine samples were collected and 39% participants were diagnosed as TBU+. Mean age TBU+ was 39.3±13.6 years with 45.7% males. Anterior and panuveitis and unilateral involvement were most common. 32.6% were TBU+HIV+ (median CD4+=215) while 40.2% were OUD+HIV+ (median CD4+=234). Only sVEGF3 was decreased in TBU+ versus OUD+ (p=0.03), regardless of HIV status. Some biomarkers were significantly raised in HIV+ TBU+ compared to HIV- TBU+: sIL-6Rα, CD30, sRAGE , sTNFR I&-II, IP-10, MIP-1β, sEGFR and Ferritin. HIV+ OUD+ had increased sVEGFR3, CD30, sIL-6Rα, IP-10, sTNFR I&-II, Ferritin and Haptoglobin compared to HIV- OUD+. VEGF-A (p = 0.04) was decreased in HIV+ OUD+ versus HIV- OUD+. ConclusionDecreased urinary concentrations of VEGFR3 were observed in TBU+ compared to TBU-. HIV+ individuals demonstrated increased concentrations of multiple urinary analytes when compared to HIV- patients with uveitis.

Application of a Machine Learning-Based Classification Approach for Developing Host Protein Diagnostic Models for Infectious Disease

In recent years, infectious disease diagnosis has increasingly turned to host-centered approaches as a complement to pathogen-directed ones. The former, however, typically requires the interpretation of complex multiple biomarker datasets to arrive at an informative diagnostic outcome. This report describes a machine learning (ML)-based classification workflow that is intended as a template for researchers seeking to apply ML approaches for developing host-based infectious disease biomarker classifiers. As an example, we built a classification model that could accurately distinguish between three disease etiology classes: bacterial, viral, and normal in human sera using host protein biomarkers of known diagnostic utility. After collecting protein data from known disease samples, we trained a series of increasingly complex Auto-ML models until arriving at an optimized classifier that could differentiate viral, bacterial, and non-disease samples. Even when limited to a relatively small training set size, the model had robust diagnostic characteristics and performed well when faced with a blinded sample set. We present here a flexible approach for applying an Auto-ML-based workflow for the identification of host biomarker classifiers with diagnostic utility for infectious disease, and which can readily be adapted for multiple biomarker classes and disease states.

Identification of a circulating long non-coding RNA signature panel in plasma as a novel biomarker for the detection of acute/early-stage HIV-1 infection

BackgroundIndividuals with acute / early HIV-1 infection are often unaware that they are infected with HIV-1 and may be involved in high-risk behavior leading to transmission of HIV-1. Identifying individuals with acute / early HIV-1 infection is critical to prevent further HIV-1 transmission, as diagnosis can lead to several effective HIV-1 prevention strategies. Identification of disease-stage specific non-viral host biomarkers would be useful as surrogate markers to accurately identify new HIV-1 infections. The goal of this study was to identify a panel of host derived plasma long non-coding RNAs (lncRNAs) that could serve as prognostic and predictive biomarkers to detect early/acute HIV-1 infection.MethodsA total of 84 lncRNAs were analyzed in sixteen plasma samples from HIV-1 infected individuals and four healthy controls using the lncRNA PCR-array. Twenty-one lncRNAs were selected and validated in 80 plasma samples from HIV-1 infected individuals [HIV-1 infected patients in the eclipse stage (n = 20), acute stage (n = 20), post-seroconversion p31 negative stage (n = 20), and post-seroconversion p31 positive stage (n = 20) of infection] and 20 healthy controls. The validation study results were used to develop a plasma lncRNA panel that was evaluated in the panel test phase to detect early/acute HIV-1 infection in 52 independent samples.ResultsWe identified a lncRNA panel (Pmodel−I) containing eight lncRNAs (DISC2, H19, IPW, KRASP1, NEAT1, PRINS, WT1-AS and ZFAS1) that could distinguish HIV-1 infection from healthy controls with high AUC 0·990 (95% CI 0.972-1.000), sensitivity (98.75%), and specificity (95%). We also found that Pmodel−II and Pmodel−III demonstrates 100% sensitivity and specificity (AUC 1·00; 95%CI:1·00–1·00) and could distinguish eclipse stage and acute stage of HIV-1 infection from healthy controls respectively. Antiretroviral treatment (ART) cumulatively restored the levels of lncRNAs to healthy controls levels.ConclusionlncRNA expression changes significantly in response to HIV-1 infection. Our findings also highlight the potential of using circulating lncRNAs to detect both the eclipse and acute stages of HIV-1 infection, which may help to shorten the window period and facilitate early detection and treatment initiation. Initiating ART treatment at this stage would significantly reduce HIV-1 transmission. The differentially expressed lncRNAs identified in this study could serve as potential prognostic and diagnostic biomarkers of HIV-1 infection, as well as new therapeutic targets.

Clinical manifestations and biomarkers to predict mortality risk in adults with invasive Streptococcus dysgalactiae subsp. equisimilis infections

PurposeThe incidence of invasive Streptococcus dysgalactiae subsp. equisimilis (iSDSE) infections is increasing in developed countries, but studies on the risk factors for death in iSDSE infections are scant. Here, we aimed to clarify risk factors and predictors of mortality in adults with iSDSE infections.MethodsA multicentre observational study of adults with iSDSE infections was conducted to investigate the effects of host factors, disease severity, biomarkers, and antibiotic regimens, and bacterial factors on 28-day mortality.ResultsThe overall mortality rate of 588 patients was 10.4%, with a significant increase in those aged ≥ 60 years. Most of the patients (97.4%) had underlying diseases. The mortality rate (70.4%) of patients with severe disease was significantly higher than that of patients with mild-to-moderate disease (4.3%; p < 0.001). The risk factors for death identified using multivariable analysis were age ≥ 60 years (hazard ratio [HR], 3.4; 95% confidence interval [CI], 1.0–11.3, p = 0.042); severe disease (HR, 15.0; 95% CI 7.7–29.2, p < 0.001); bacteraemia without primary focus (HR, 20.5; 95% CI 2.8–152.3, p = 0.003); serum creatinine ≥ 2.0 mg/dL (HR, 2.2; 95% CI 1.2–4.0, p = 0.010); serum creatine kinase ≥ 300 IU/L (HR, 2.1; 95% CI 1.1–3.8, p = 0.019); and macrolide resistance (HR, 1.8; 95% CI 1.0–3.3, p = 0.048). Treatment regimens and emm types were not associated with poor outcomes.ConclusionEvaluation of clinical manifestations and biomarkers on admission is important to predict invasive SDSE infection prognosis.

Baseline and end-of-treatment host serum biomarkers predict relapse in adults with pulmonary tuberculosis

There is a need for new tools for monitoring of the response to TB treatment. Such tools may allow for tailored treatment regimens, and stratify patients initiating TB treatment into different risk groups. We evaluated combinations between previously published host biomarkers and new candidates, as tools for monitoring TB treatment response, and prediction of relapse. Serum samples were collected at multiple time points, from patients initiating TB treatment at research sites situated in South Africa (ActionTB study), Brazil and Uganda (TBRU study). Using a multiplex immunoassay platform, we evaluated the concentrations of selected host inflammatory biomarkers in sera obtained from clinically cured patients with and without subsequent relapse within 2 years of TB treatment completion. A total of 130 TB patients, 30 (23%) of whom had confirmed relapse were included in the study. The median time to relapse was 9.7 months in the ActionTB study (n=12 patients who relapsed), and 5 months (n=18 patients who relapsed) in the TBRU study. Serum concentrations of several host biomarkers changed during TB treatment with IL-6, IP-10, IL-22 and complement C3 showing potential individually, in predicting relapse. A six-marker signature comprising of TTP, BMI, sICAM-1, IL-22, IL-1β and complement C3, predicted relapse, prior to the onset of TB treatment with 89% sensitivity and 94% specificity. Furthermore, a 3-marker signature (Apo-CIII, IP-10 and sIL-6R) predicted relapse in samples collected at the end of TB treatment with sensitivity of 71% and specificity of 74%. A previously identified baseline relapse prediction signature (TTP, BMI, TNF-β, sIL-6R, IL-12p40 and IP-10) also showed potential in the current study. Serum host inflammatory biomarkers may be useful in predicting relapse in TB patients prior to the initiation of treatment. Our findings have implications for tailored patient management and require prospective evaluation in larger studies.

Correlating transcription and protein expression profiles of immune biomarkers following lipopolysaccharide exposure in lung epithelial cells.

Universal and early recognition of pathogens occurs through recognition of evolutionarily conserved pathogen associated molecular patterns (PAMPs) by innate immune receptors and the consequent secretion of cytokines and chemokines. The intrinsic complexity of innate immune signaling and associated signal transduction challenges our ability to obtain physiologically relevant, reproducible and accurate data from experimental systems. One of the reasons for the discrepancy in observed data is the choice of measurement strategy. Immune signaling is regulated by the interplay between pathogen-derived molecules with host cells resulting in cellular expression changes. However, these cellular processes are often studied by the independent assessment of either the transcriptome or the proteome. Correlation between transcription and protein analysis is lacking in a variety of studies. In order to methodically evaluate the correlation between transcription and protein expression profiles associated with innate immune signaling, we measured cytokine and chemokine levels following exposure of human cells to the PAMP lipopolysaccharide (LPS) from the Gram-negative pathogen Pseudomonas aeruginosa. Expression of 84 messenger RNA (mRNA) transcripts and 69 proteins, including 35 overlapping targets, were measured in human lung epithelial cells. We evaluated 50 biological replicates to determine reproducibility of outcomes. Following pairwise normalization, 16 mRNA transcripts and 6 proteins were significantly upregulated following LPS exposure, while only five (CCL2, CSF3, CXCL5, CXCL8/IL8, and IL6) were upregulated in both transcriptomic and proteomic analysis. This lack of correlation between transcription and protein expression data may contribute to the discrepancy in the immune profiles reported in various studies. The use of multiomic assessments to achieve a systems-level understanding of immune signaling processes can result in the identification of host biomarker profiles for a variety of infectious diseases and facilitate countermeasure design and development.

Using the Traditional Ex Vivo Whole Blood Model to Discriminate Bacteria by Their Inducible Host Responses.

Whole blood models are rapid and versatile for determining immune responses to inflammatory and infectious stimuli, but they have not been used for bacterial discrimination. Staphylococcus aureus, S. epidermidis and Escherichia coli are the most common causes of invasive disease, and rapid testing strategies utilising host responses remain elusive. Currently, immune responses can only discriminate between bacterial 'domains' (fungi, bacteria and viruses), and very few studies can use immune responses to discriminate bacteria at the species and strain level. Here, whole blood was used to investigate the relationship between host responses and bacterial strains. Results confirmed unique temporal profiles for the 10 parameters studied: IL-6, MIP-1α, MIP-3α, IL-10, resistin, phagocytosis, S100A8, S100A8/A9, C5a and TF3. Pairwise analysis confirmed that IL-6, resistin, phagocytosis, C5a and S100A8/A9 could be used in a discrimination scheme to identify to the strain level. Linear discriminant analysis (LDA) confirmed that (i) IL-6, MIP-3α and TF3 could predict genera with 95% accuracy; (ii) IL-6, phagocytosis, resistin and TF3 could predict species at 90% accuracy and (iii) phagocytosis, S100A8 and IL-10 predicted strain at 40% accuracy. These data are important because they confirm the proof of concept that host biomarker panels could be used to identify bacterial pathogens.

Integrated plasma proteomics identifies tuberculosis-specific diagnostic biomarkers.

BACKGROUNDNovel biomarkers to identify infectious patients transmitting Mycobacterium tuberculosis are urgently needed to control the global tuberculosis (TB) pandemic. We hypothesized that proteins released into the plasma in active pulmonary TB are clinically useful biomarkers to distinguish TB cases from healthy individuals and patients with other respiratory infections.METHODSWe applied a highly sensitive non-depletion tandem mass spectrometry discovery approach to investigate plasma protein expression in pulmonary TB cases compared to healthy controls in South African and Peruvian cohorts. Bioinformatic analysis using linear modeling and network correlation analyses identified 118 differentially expressed proteins, significant through 3 complementary analytical pipelines. Candidate biomarkers were subsequently analyzed in 2 validation cohorts of differing ethnicity using antibody-based proximity extension assays.RESULTSTB-specific host biomarkers were confirmed. A 6-protein diagnostic panel, comprising FETUB, FCGR3B, LRG1, SELL, CD14, and ADA2, differentiated patients with pulmonary TB from healthy controls and patients with other respiratory infections with high sensitivity and specificity in both cohorts.CONCLUSIONThis biomarker panel exceeds the World Health Organization Target Product Profile specificity criteria for a triage test for TB. The new biomarkers have potential for further development as near-patient TB screening assays, thereby helping to close the case-detection gap that fuels the global pandemic.FUNDINGMedical Research Council (MRC) (MR/R001065/1, MR/S024220/1, MR/P023754/1, and MR/W025728/1); the MRC and the UK Foreign Commonwealth and Development Office; the UK National Institute for Health Research (NIHR); the Wellcome Trust (094000, 203135, and CC2112); Starter Grant for Clinical Lecturers (Academy of Medical Sciences UK); the British Infection Association; the Program for Advanced Research Capacities for AIDS in Peru at Universidad Peruana Cayetano Heredia (D43TW00976301) from the Fogarty International Center at the US NIH; the UK Technology Strategy Board/Innovate UK (101556); the Francis Crick Institute, which receives funding from UKRI-MRC (CC2112); Cancer Research UK (CC2112); and the NIHR Biomedical Research Centre of Imperial College NHS.

Host single nucleotide polymorphisms and biomarkers of neuronal damage and inflammation in people living with HIV

ObjectivesBlood-brain barrier impairment is frequent in people living with human immunodeficiency virus (PLWHIV), affecting the penetration of target cells and antiretrovirals into the central nervous system, through transporters (e.g. ABCB1), leading to neuroinflammation. This study aimed to identify variants of genes encoding transporters able to predict neuroinflammation biomarker levels. MethodsCerebrospinal fluid (CSF) and plasma samples were obtained from PLWHIV. The CSF biomarkers were quantified by commercial assays. Genetic variants were evaluated through real-time polymerase chain reaction (PCR). ResultsA total of 107 PLWHIV (163 samples) were included in the study: 79% were male, median age was 48.5 years, CD4% was 25%, and HIV-associated neurocognitive disorder (HAND) was observed in 17.8%. The ABCB1 2677G>T genetic variant showed a different allelic distribution according to the clinical group (P = 0.026). In linear regression analyses, HIV-related central nervous system disorders, ABCG2 1194+928CC genotype, log viral load, CSF-to-serum albumin ratio, β-1,42 levels, and CSF proteins were retained in the final model as factors independently associated with CSF neopterin levels; CSF proteins and integrase inhibitor use were associated with CSF tau level in the multivariate model. Phospho-tau regression analysis reported the ABCB1 2677GT/TT genotype and CSF proteins as predictors in the final model; sex, protease inhibitors, neopterin, and ABCB1 2677 GT/ TT genotype were predictors in the multivariate regression for β-1,42. ConclusionsFor the first time, pharmacogenetic and clinical features were found to be predictors of neuro-inflammation biomarkers.