The application potential of machine learning and genomics for understanding natural product diversity, chemistry, and therapeutic translatability.

Covering: up to the end of 2020. The machine learning field can be defined as the study and application of algorithms that perform classification and prediction tasks through pattern recognition instead of explicitly defined rules. Among other areas, machine learning has excelled in natural language processing. As such methods have excelled at understanding written languages (e.g. English), they are also being applied to biological problems to better understand the "genomic language". In this review we focus on recent advances in applying machine learning to natural products and genomics, and how those advances are improving our understanding of natural product biology, chemistry, and drug discovery. We discuss machine learning applications in genome mining (identifying biosynthetic signatures in genomic data), predictions of what structures will be created from those genomic signatures, and the types of activity we might expect from those molecules. We further explore the application of these approaches to data derived from complex microbiomes, with a focus on the human microbiome. We also review challenges in leveraging machine learning approaches in the field, and how the availability of other "omics" data layers provides value. Finally, we provide insights into the challenges associated with interpreting machine learning models and the underlying biology and promises of applying machine learning to natural product drug discovery. We believe that the application of machine learning methods to natural product research is poised to accelerate the identification of new molecular entities that may be used to treat a variety of disease indications.

A deep learning genome-mining strategy for biosynthetic gene cluster prediction.

Natural products represent a rich reservoir of small molecule drug candidates utilized as antimicrobial drugs, anticancer therapies, and immunomodulatory agents. These molecules are microbial secondary metabolites synthesized by co-localized genes termed Biosynthetic Gene Clusters (BGCs). The increase in full microbial genomes and similar resources has led to development of BGC prediction algorithms, although their precision and ability to identify novel BGC classes could be improved. Here we present a deep learning strategy (DeepBGC) that offers reduced false positive rates in BGC identification and an improved ability to extrapolate and identify novel BGC classes compared to existing machine-learning tools. We supplemented this with random forest classifiers that accurately predicted BGC product classes and potential chemical activity. Application of DeepBGC to bacterial genomes uncovered previously undetectable putative BGCs that may code for natural products with novel biologic activities. The improved accuracy and classification ability of DeepBGC represents a major addition to in-silico BGC identification.

HPV, tumour metabolism and novel target identification in head and neck squamous cell carcinoma.

BACKGROUND: Metabolic changes in tumour cells are used in clinical imaging and may provide potential therapeutic targets. Human papillomavirus (HPV) status is important in classifying head and neck cancers (HNSCC), identifying a distinct clinical phenotype; metabolic differences between these HNSCC subtypes remain poorly understood. METHODS: We used RNA sequencing to classify the metabolic expression profiles of HPV+ve and HPV-ve HNSCC, performed a meta-analysis on FDG-PET imaging characteristics and correlated results with in vitro extracellular flux analysis of HPV-ve and HPV+ve HNSCC cell lines. The monocarboxylic acid transporter-1 (MCT1) was identified as a potential metabolic target and tested in functional assays. RESULTS: Specific metabolic profiles were associated with HPV status, not limited to carbohydrate metabolism. There was dominance of all energy pathways in HPV-negative disease, with elevated expression of genes associated with glycolysis and oxidative phosphorylation. In vitro analysis confirmed comparative increased rates of oxidative phosphorylation and glycolysis in HPV-negative cell lines. PET SUV(max) scores however were unable to reliably differentiate between HPV-positive and HPV-negative tumours. MCT1 expression was significantly increased in HPV-negative tumours, and inhibition suppressed tumour cell invasion, colony formation and promoted radiosensitivity. CONCLUSION: HPV-positive and negative HNSCC have different metabolic profiles which may have potential therapeutic applications.

Immunological biomarkers as indicators for outcome after discontinuation of nucleos(t)ide analogue therapy in patients with HBeAg-negative chronic hepatitis B.

The optimal duration of treatment with nucleos(t)ide analogues (NAs) for patients with HBeAg-negative chronic hepatitis B (CHB) is unknown. The aim of this study was to identify an immune signature associated with off-treatment remission to NA therapy. We performed microarray analysis of peripheral blood mononuclear cell (PBMCs) from six patients with chronic hepatitis B who stopped NA therapy (three with off-treatment remission, three with relapse) and five patients with chronic HBV infection (previously termed 'inactive carriers') served as controls. Results were validated using qRT-PCR on a second group of 21 individuals (17 patients who stopped treatment and four controls). PBMCs from 38 patients on long-term NA treatment were analysed for potential to stop treatment. Microarray analysis indicated that patients with off-treatment remission segregated as a distinct out-group. Twenty-one genes were selected for subsequent validation. Ten of these were expressed at significantly lower levels in the patients with off-treatment remission compared to the patients with relapse and predicted remission with AUC of 0.78-0.92. IFNγ, IL-8, FASLG and CCL4 were the most significant by logistic regression. Twelve (31.6%) of 38 patients on long-term NA therapy had expression levels of all these four genes below cut-off values and hence were candidates for stopping treatment. Our data suggest that patients with HBeAg-negative CHB who remain in off-treatment remission 3 years after NA cessation have a distinct immune signature and that PBMC RNA levels of IFNγ, IL-8, FASLG and CCL4 may serve as potential biomarkers for stopping NA therapy.

Multitissue Transcriptomics Delineates the Diversity of Airway T Cell Functions in Asthma.

Asthma arises from the complex interplay of inflammatory pathways in diverse cell types and tissues. We sought to undertake a comprehensive transcriptomic assessment of the epithelium and airway T cells that remain understudied in asthma and investigate interactions between multiple cells and tissues. Epithelial brushings and flow-sorted CD3+ T cells from sputum and BAL were obtained from healthy subjects (n = 19) and patients with asthma (mild, moderate, and severe asthma; n = 46). Gene expression was assessed using Affymetrix HT HG-U133+ PM GeneChips, and results were validated by real-time quantitative PCR. In the epithelium, IL-13 response genes (POSTN, SERPINB2, and CLCA1), mast cell mediators (CPA3 and TPSAB1), inducible nitric oxide synthase, and cystatins (CST1, CST2, and CST4) were upregulated in mild asthma, but, except for cystatins, were suppressed by corticosteroids in moderate asthma. In severe asthma-with predominantly neutrophilic phenotype-several distinct processes were upregulated, including neutrophilia (TCN1 and MMP9), mucins, and oxidative stress responses. The majority of the disease signature was evident in sputum T cells in severe asthma, where 267 genes were differentially regulated compared with health, highlighting compartmentalization of inflammation. This signature included IL-17-inducible chemokines (CXCL1, CXCL2, CXCL3, IL8, and CSF3) and chemoattractants for neutrophils (IL8, CCL3, and LGALS3), T cells, and monocytes. A protein interaction network in severe asthma highlighted signatures of responses to bacterial infections across tissues (CEACAM5, CD14, and TLR2), including Toll-like receptor signaling. In conclusion, the activation of innate immune pathways in the airways suggests that activated T cells may be driving neutrophilic inflammation and steroid-insensitive IL-17 response in severe asthma.

Transcriptomic Analysis Implicates the p53 Signaling Pathway in the Establishment of HIV-1 Latency in Central Memory CD4 T Cells in an In Vitro Model.

The search for an HIV-1 cure has been greatly hindered by the presence of a viral reservoir that persists despite antiretroviral therapy (ART). Studies of HIV-1 latency in vivo are also complicated by the low proportion of latently infected cells in HIV-1 infected individuals. A number of models of HIV-1 latency have been developed to examine the signaling pathways and viral determinants of latency and reactivation. A primary cell model of HIV-1 latency, which incorporates the generation of primary central memory CD4 T cells (TCM), full-length virus infection (HIVNL4-3) and ART to suppress virus replication, was used to investigate the establishment of HIV latency using RNA-Seq. Initially, an investigation of host and viral gene expression in the resting and activated states of this model indicated that the resting condition was reflective of a latent state. Then, a comparison of the host transcriptome between the uninfected and latently infected conditions of this model identified 826 differentially expressed genes, many of which were related to p53 signaling. Inhibition of the transcriptional activity of p53 by pifithrin-α during HIV-1 infection reduced the ability of HIV-1 to be reactivated from its latent state by an unknown mechanism. In conclusion, this model may be used to screen latency reversing agents utilized in shock and kill approaches to cure HIV, to search for cellular markers of latency, and to understand the mechanisms by which HIV-1 establishes latency.

Quantitative Non-canonical Amino Acid Tagging (QuaNCAT) Proteomics Identifies Distinct Patterns of Protein Synthesis Rapidly Induced by Hypertrophic Agents in Cardiomyocytes, Revealing New Aspects of Metabolic Remodeling.

Cardiomyocytes undergo growth and remodeling in response to specific pathological or physiological conditions. In the former, myocardial growth is a risk factor for cardiac failure and faster protein synthesis is a major factor driving cardiomyocyte growth. Our goal was to quantify the rapid effects of different pro-hypertrophic stimuli on the synthesis of specific proteins in ARVC and to determine whether such effects are caused by alterations on mRNA abundance or the translation of specific mRNAs. Cardiomyocytes have very low rates of protein synthesis, posing a challenging problem in terms of studying changes in the synthesis of specific proteins, which also applies to other nondividing primary cells. To study the rates of accumulation of specific proteins in these cells, we developed an optimized version of the Quantitative Noncanonical Amino acid Tagging LC/MS proteomic method to label and selectively enrich newly synthesized proteins in these primary cells while eliminating the suppressive effects of pre-existing and highly abundant nonisotope-tagged polypeptides. Our data revealed that a classical pathologic (phenylephrine; PE) and the recently identified insulin stimulus that also contributes to the development of pathological cardiac hypertrophy (insulin), both increased the synthesis of proteins involved in, e.g. glycolysis, the Krebs cycle and beta-oxidation, and sarcomeric components. However, insulin increased synthesis of many metabolic enzymes to a greater extent than PE. Using a novel validation method, we confirmed that synthesis of selected candidates is indeed up-regulated by PE and insulin. Synthesis of all proteins studied was up-regulated by signaling through mammalian target of rapamycin complex 1 without changes in their mRNA levels, showing the key importance of translational control in the rapid effects of hypertrophic stimuli. Expression of PKM2 was up-regulated in rat hearts following TAC. This isoform possesses specific regulatory properties, so this finding indicates it may be involved in metabolic remodeling and also serve as a novel candidate biomarker. Levels of translation factor eEF1 also increased during TAC, likely contributing to faster cell mass accumulation. Interestingly those two candidates were not up-regulated in pregnancy or exercise induced CH, indicating PKM2 and eEF1 were pathological CH specific markers. We anticipate that the methodologies described here will be valuable for other researchers studying protein synthesis in primary cells.

IFN-γ Influences Epithelial Antiviral Responses via Histone Methylation of the RIG-I Promoter.

The asthmatic lung is prone to respiratory viral infections that exacerbate the symptoms of the underlying disease. Recent work has suggested that a deficient T-helper cell type 1 response in early life may lead to these aberrant antiviral responses. To study the development of long-term dysregulation of innate responses, which is a hallmark of asthma, we investigated whether the inflammatory environment of the airway epithelium can modulate antiviral gene expression via epigenetic mechanisms. We primed AALEB cells, a human bronchial epithelial cell line, with IFN-γ and IL-13, and subsequently infected the cells with respiratory syncytial virus (RSV). We then analyzed the expression of innate antiviral genes and their epigenetic markers. Priming epithelial cells with IFN-γ reduced the RSV viral load. Microarray analysis identified that IFN-γ priming enhanced retinoic acid-inducible gene (RIG)-I mRNA expression, and this expression correlated with epigenetic changes at the RIG-I promoter that influenced its transcription. Using chromatin immunoprecipitation, we observed a reduction of trimethylated histone 3 lysine 9 at the RIG-I promoter. Addition of inhibitor BIX-01294 to this model indicated an involvement of lysine methyltransferase G9a in RIG-I epigenetic regulation. These data suggest that prior exposure to IFN-γ may leave an epigenetic mark on the chromatin that enhances airway cells' ability to resist infection, possibly via epigenetic upregulation of RIG-I. These observations provide further evidence for a crucial role of IFN-γ in the development of mature antiviral responses within a model of respiratory infection. Further clinical validation is required to determine whether this effect in early life leads to changes in antiviral responses associated with asthma.

Structural and Functional Changes of the Invariant NKT Clonal Repertoire in Early Rheumatoid Arthritis.

Invariant NKT cells (iNKT) are potent immunoregulatory T cells that recognize CD1d via a semi-invariant TCR (iNKT-TCR). Despite the knowledge of a defective iNKT pool in several autoimmune conditions, including rheumatoid arthritis (RA), a clear understanding of the intrinsic mechanisms, including qualitative and structural changes of the human iNKT repertoire at the earlier stages of autoimmune disease, is lacking. In this study, we compared the structure and function of the iNKT repertoire in early RA patients with age- and gender-matched controls. We analyzed the phenotype and function of the ex vivo iNKT repertoire as well as CD1d Ag presentation, combined with analyses of a large panel of ex vivo sorted iNKT clones. We show that circulating iNKTs were reduced in early RA, and their frequency was inversely correlated to disease activity score 28. Proliferative iNKT responses were defective in early RA, independent of CD1d function. Functional iNKT alterations were associated with a skewed iNKT-TCR repertoire with a selective reduction of high-affinity iNKT clones in early RA. Furthermore, high-affinity iNKTs in early RA exhibited an altered functional Th profile with Th1- or Th2-like phenotype, in treatment-naive and treated patients, respectively, compared with Th0-like Th profiles exhibited by high-affinity iNKTs in controls. To our knowledge, this is the first study to provide a mechanism for the intrinsic qualitative defects of the circulating iNKT clonal repertoire in early RA, demonstrating defects of iNKTs bearing high-affinity TCRs. These defects may contribute to immune dysregulation, and our findings could be exploited for future therapeutic intervention.

Enhancing the Biological Relevance of Machine Learning Classifiers for Reverse Vaccinology.

Reverse vaccinology (RV) is a bioinformatics approach that can predict antigens with protective potential from the protein coding genomes of bacterial pathogens for subunit vaccine design. RV has become firmly established following the development of the BEXSERO® vaccine against Neisseria meningitidis serogroup B. RV studies have begun to incorporate machine learning (ML) techniques to distinguish bacterial protective antigens (BPAs) from non-BPAs. This research contributes significantly to the RV field by using permutation analysis to demonstrate that a signal for protective antigens can be curated from published data. Furthermore, the effects of the following on an ML approach to RV were also assessed: nested cross-validation, balancing selection of non-BPAs for subcellular localization, increasing the training data, and incorporating greater numbers of protein annotation tools for feature generation. These enhancements yielded a support vector machine (SVM) classifier that could discriminate BPAs (n = 200) from non-BPAs (n = 200) with an area under the curve (AUC) of 0.787. In addition, hierarchical clustering of BPAs revealed that intracellular BPAs clustered separately from extracellular BPAs. However, no immediate benefit was derived when training SVM classifiers on data sets exclusively containing intra- or extracellular BPAs. In conclusion, this work demonstrates that ML classifiers have great utility in RV approaches and will lead to new subunit vaccines in the future.

Outlier detection at the transcriptome-proteome interface.

BACKGROUND: In high-throughput experimental biology, it is widely acknowledged that while expression levels measured at the levels of transcriptome and the corresponding proteome do not, in general, correlate well, messenger RNA levels are used as convenient proxies for protein levels. Our interest is in developing data-driven computational models that can bridge the gap between these two levels of measurement at which different mechanisms of regulation may act on different molecular species causing any observed lack of correlations. To this end, we build data-driven predictors of protein levels using mRNA levels and known proxies of translation efficiencies as covariates. Previous work showed that in such a setting, outliers with respect to the model are reliable candidates for post-translational regulation. RESULTS: Here, we present and compare two novel formulations of deriving a protein concentration predictor from which outliers may be extracted in a systematic manner. The first approach, outlier rejecting regression, allows explicit specification of a certain fraction of the data as outliers. In a regression setting, this is a non-convex optimization problem which we solve by deriving a difference of convex functions algorithm (DCA). With post-translationally regulated proteins, one expects their concentrations to be affected primarily by disruption of protein stability. Our second algorithm exploits this observation by minimizing an asymmetric loss using quantile regression and extracts outlier proteins whose measured concentrations are lower than what a genome-wide regression would predict. We validate the two approaches on a dataset of yeast transcriptome and proteome. Functional annotation check on detected outliers demonstrate that the methods are able to identify post-translationally regulated genes with high statistical confidence.

Sex-specific vitamin D effects on blood coagulation among overweight adults.

BACKGROUND: Overweight adults are at increased risk for cardiovascular disease and vitamin D deficiency, whereas an important feature to vitamin D physiology is its sex dependence. The aim of this study was to examine whether vitamin D status improvement exerts a sexually dimorphic effect on serum proteins associated with cardiovascular risk among overweight adults. MATERIALS AND METHODS: Unprocessed serum from age- and BMI-matched men (n = 26) and premenopausal women (n = 24) with vitamin D deficiency and after they achieved sufficiency through a 12-month nutritional intervention was analysed using our previously published depletion-free quantitative proteomics method. Key findings were verified with ELISA. Differentially expressed proteins were subjected to in silico bioinformatics assessment using principal component analysis, hierarchical clustering and Metacore™ pathway analysis. All mass spectrometry proteomic data are available via ProteomeXchange (identifier: PXD003663). RESULTS: A total of 282 proteins were differentially expressed after the intervention between men and women (P-value ≤ 0·05), in which the blood coagulation pathway was significantly enriched. In agreement with the proteomics findings, ELISA measurements showed vitamin K-dependent protein C, von Willebrand factor, fibrinogen gamma chain and multimerin-1 proteins, of relevance to blood coagulation, to be differentially affected (P-value ≤ 0·05) between sexes after vitamin D status correction. CONCLUSIONS: This study identified novel protein-level molecular indicators on the sexually dimorphic effect of vitamin D status correction associated with blood coagulation among overweight adults. These sex-mediated vitamin D effects should be factored in the design and interpretation of vitamin D observational and interventional studies testing cardiometabolic outcomes.

Acute psychological stress induces short-term variable immune response.

In spite of advances in understanding the cross-talk between the peripheral immune system and the brain, the molecular mechanisms underlying the rapid adaptation of the immune system to an acute psychological stressor remain largely unknown. Conventional approaches to classify molecular factors mediating these responses have targeted relatively few biological measurements or explored cross-sectional study designs, and therefore have restricted characterization of stress-immune interactions. This exploratory study analyzed transcriptional profiles and flow cytometric data of peripheral blood leukocytes with physiological (endocrine, autonomic) measurements collected throughout the sequence of events leading up to, during, and after short-term exposure to physical danger in humans. Immediate immunomodulation to acute psychological stress was defined as a short-term selective up-regulation of natural killer (NK) cell-associated cytotoxic and IL-12 mediated signaling genes that correlated with increased cortisol, catecholamines and NK cells into the periphery. In parallel, we observed down-regulation of innate immune toll-like receptor genes and genes of the MyD88-dependent signaling pathway. Correcting gene expression for an influx of NK cells revealed a molecular signature specific to the adrenal cortex. Subsequently, focusing analyses on discrete groups of coordinately expressed genes (modules) throughout the time-series revealed immune stress responses in modules associated to immune/defense response, response to wounding, cytokine production, TCR signaling and NK cell cytotoxicity which differed between males and females. These results offer a spring-board for future research towards improved treatment of stress-related disease including the impact of stress on cardiovascular and autoimmune disorders, and identifies an immune mechanism by which vulnerabilities to these diseases may be gender-specific.

An in-depth comparison of latent HIV-1 reactivation in multiple cell model systems and resting CD4+ T cells from aviremic patients.

The possibility of HIV-1 eradication has been limited by the existence of latently infected cellular reservoirs. Studies to examine control of HIV latency and potential reactivation have been hindered by the small numbers of latently infected cells found in vivo. Major conceptual leaps have been facilitated by the use of latently infected T cell lines and primary cells. However, notable differences exist among cell model systems. Furthermore, screening efforts in specific cell models have identified drug candidates for "anti-latency" therapy, which often fail to reactivate HIV uniformly across different models. Therefore, the activity of a given drug candidate, demonstrated in a particular cellular model, cannot reliably predict its activity in other cell model systems or in infected patient cells, tested ex vivo. This situation represents a critical knowledge gap that adversely affects our ability to identify promising treatment compounds and hinders the advancement of drug testing into relevant animal models and clinical trials. To begin to understand the biological characteristics that are inherent to each HIV-1 latency model, we compared the response properties of five primary T cell models, four J-Lat cell models and those obtained with a viral outgrowth assay using patient-derived infected cells. A panel of thirteen stimuli that are known to reactivate HIV by defined mechanisms of action was selected and tested in parallel in all models. Our results indicate that no single in vitro cell model alone is able to capture accurately the ex vivo response characteristics of latently infected T cells from patients. Most cell models demonstrated that sensitivity to HIV reactivation was skewed toward or against specific drug classes. Protein kinase C agonists and PHA reactivated latent HIV uniformly across models, although drugs in most other classes did not.

Whole serum 3D LC-nESI-FTMS quantitative proteomics reveals sexual dimorphism in the milieu intérieur of overweight and obese adults.

Linking gender-specific differences to the molecular etiology of obesity has been largely based on genomic and transcriptomic evidence lacking endophenotypic insight and is not applicable to the extracellular fluid compartments, or the milieu intérieur, of the human body. To address this need, this study profiled the whole serum proteomes of age-matched nondiabetic overweight and obese females (n = 28) and males (n = 31) using a multiplex design with pooled biological and technical replicates. To bypass basic limitations of immunodepletion-based strategies, subproteome enrichment by size-exclusion chromatography (SuPrE-SEC) followed by iTRAQ 2D-LC-nESI-FTMS analysis was used. The study resulted in the reproducible analysis of 2472 proteins (peptide FDR < 5%, q < 0.05). A total of 248 proteins exhibited significant modulation between men and women (p < 0.05) that mapped to pathways associated with ß-estradiol, lipid and prostanoid metabolism, vitamin D function, immunity/inflammation, and the complement and coagulation cascades. This novel endophenotypic signature of gender-specific differences in whole serum confirmed and expanded the results of previous physiologic and pharmacologic studies exploring sexual dimorphism at the genomic and transcriptomic level in tissues and cells. Conclusively, the multifactorial and pleiotropic nature of human obesity exhibits sexual dimorphism in the circulating proteome of importance to clinical study design.

Replication competent virus as an important source of bias in HIV latency models utilizing single round viral constructs.

The central memory T cell (TCM) model forms a unique HIV-1 latency model based on primary cells that closely resemble in vivo TCM. The virus employed in this model is based on an engineered vector incapable of replication after initial infection. We show that despite this strategy, replication competent viral particles are released into the culture medium due to recombination between overlapping sequences of the env deleted HIV genome that is co-transfected with intact env. This finding emphasizes the need for careful data analysis and interpretation if similar constructs are employed and urges for additional caution during laboratory work.

The effect of cell subset isolation method on gene expression in leukocytes.

Multiple scientific disciplines require the isolation of specific subsets of blood cells from patient samples for gene expression analysis by microarray or RNA-sequencing, preserving disease- or treatment-related signatures. However, little is known with respect to the impact of different cell isolation methods on gene expression and the effects of positive selection, negative selection, and fluorescence activated cell sorting (FACS) have not previously been assessed in parallel. To address this knowledge gap, CD4+ T cells, CD8+ T cells, B cells, and monocytes were isolated from blood samples from five independent donors using positive immunomagnetic selection, negative immunomagnetic selection, and FACS. We hypothesized that positive selection and FACS would yield higher purity but may have an impact on gene expression since both methods utilize antibodies that bind surface receptors of the cell type of interest. Moreover, FACS might upregulate stress response genes due to passage of the cells through the sorter. Microarray gene expression data were generated and subjected to unsupervised clustering and differential gene expression analysis. Surprisingly, these analyses revealed that gene expression signatures were more similar between cells isolated by negative selection and FACS compared to cells isolated by positive selection. Moreover, genes that are involved in the response to stress generally had the highest expression in cells isolated by negative or positive selection and not FACS. Thus, FACS is the recommended method for isolation of leukocyte subsets for gene expression studies since this method results in the purest subset populations and does not appear to induce a stress response.

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.

Gene expression in human fungal pathogen Coccidioides immitis changes as arthroconidia differentiate into spherules and mature.

BACKGROUND: Coccidioides immitis is a dimorphic fungus that causes disease in mammals, including human beings. It grows as a mycelium containing arthroconidia in the soil and in the host arthroconidia differentiates into a unique structure called a spherule. We used a custom open reading frame oligonucleotide microarray to compare the transcriptome of C. immitis mycelia with early (day 2) and late stage (day 8) spherules grown in vitro. All hybridizations were done in quadruplicate and stringent criteria were used to identify significantly differentially expressed genes. RESULTS: 22% of C. immitis genes were differentially expressed in either day 2 or day 8 spherules compared to mycelia, and about 12% of genes were differentially expressed comparing the two spherule time points. Oxireductases, including an extracellular superoxide dismutase, were upregulated in spherules and they may be important for defense against oxidative stress. Many signal transduction molecules, including pleckstrin domain proteins, protein kinases and transcription factors were downregulated in day 2 spherules. Several genes involved in sulfur metabolism were downregulated in day 8 spherules compared to day 2 spherules. Transcription of amylase and α (1,3) glucan synthase was upregulated in spherules; these genes have been found to be important for differentiation to yeast in Histoplasma. There were two homologs of 4-hydroxyphenylpyruvate dioxygenase (4-HPPD); transcription of one was up- and the other downregulated. We tested the effect of a 4-HPPD inhibitor, nitisinone, on mycelial and spherule growth and found that it inhibited mycelial but not spherule growth. CONCLUSIONS: Transcription of many genes was differentially expressed in the process of arthroconidia to spherule conversion and spherule maturation, as would be expected given the magnitude of the morphologic change. The transcription profile of early stage (day 2) spherules was different than late stage (day 8) endosporulating spherules. In addition, very few genes that are important for spore to yeast conversion in other dimorphic fungi are differentially expressed in C. immitis mycelia and spherules suggesting that dimorphic fungi may have evolved different mechanisms to differentiate from mycelia to tissue invasive forms.