What was old is new again: using the host response to diagnose infectious disease

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.

Novel Secreted Peptides From Rhizopus arrhizus var. delemar With Immunomodulatory Effects That Enhance Fungal Pathogenesis

Secreted fungal peptides are known to influence the interactions between the pathogen and host innate immunity. The aim of this study is to screen and evaluate secreted peptides from the fungus Rhizopus arrhizus var. delemar for their immunomodulatory activity. By using mass spectrometry and immuno-informatics analysis, we identified three secreted peptides CesT (S16), Colicin (S17), and Ca2+/calmodulin-dependent protein kinase/ligand (CAMK/CAMKL; S27). Culturing peripheral blood-derived monocytic macrophages (PBMMs) in the presence of S16 or S17 caused cell clumping, while culturing them with S27 resulted in the formation of spindle-shaped cells. S27-treated PBMMs showed cell cycle arrest at G0 phase and exhibited alternatively activated macrophage phenotype with pronounced reduction in scavenger receptors CD163 and CD206. Homology prediction indicated that IL-4/IL-13 is the immunomodulatory target of S27. Confirming this prediction, S27 initiated macrophage activation through phosphorylation of STAT-6; STAT-6 inhibition reversed the activity of S27 and reduced the formation of spindle-shaped PBMMs. Lastly, S27 treatment of PBMMs was associated with altered expression of key iron regulatory genes including hepcidin, ferroportin, transferrin receptor 1, and ferritin in a pattern consistent with increased cellular iron release; a condition known to enhance Rhizopus infection. Collectively, R. arrhizus var. delemar secretes peptides with immunomodulatory activities that support fungal pathogenesis. Targeting the IL-4/IL-13R/STAT-6 axis is a potential therapeutic approach to enhance the PBMM-mediated fungal phagocytosis. This represents a potential new approach to overcome lethal mucormycosis.

Review of Liquid Chromatography-Mass Spectrometry-Based Proteomic Analyses of Body Fluids to Diagnose Infectious Diseases

Rapid and precise diagnostic methods are required to control emerging infectious diseases effectively. Human body fluids are attractive clinical samples for discovering diagnostic targets because they reflect the clinical statuses of patients and most of them can be obtained with minimally invasive sampling processes. Body fluids are good reservoirs for infectious parasites, bacteria, and viruses. Therefore, recent clinical proteomics methods have focused on body fluids when aiming to discover human- or pathogen-originated diagnostic markers. Cutting-edge liquid chromatography-mass spectrometry (LC-MS)-based proteomics has been applied in this regard; it is considered one of the most sensitive and specific proteomics approaches. Here, the clinical characteristics of each body fluid, recent tandem mass spectroscopy (MS/MS) data-acquisition methods, and applications of body fluids for proteomics regarding infectious diseases (including the coronavirus disease of 2019 [COVID-19]), are summarized and discussed.

A new paradigm for leprosy diagnosis based on host gene expression

Transcriptional profiling is a powerful tool to investigate and detect human diseases. In this study, we used bulk RNA-sequencing (RNA-Seq) to compare the transcriptomes in skin lesions of leprosy patients or controls affected by other dermal conditions such as granuloma annulare, a confounder for paucibacillary leprosy. We identified five genes capable of accurately distinguishing multibacillary and paucibacillary leprosy from other skin conditions. Indoleamine 2,3-dioxygenase 1 (IDO1) expression alone was highly discriminatory, followed by TLR10, BLK, CD38, and SLAMF7, whereas the HS3ST2 and CD40LG mRNA separated multi- and paucibacillary leprosy. Finally, from the main differentially expressed genes (DEG) and enriched pathways, we conclude that paucibacillary disease is characterized by epithelioid transformation and granuloma formation, with an exacerbated cellular immune response, while multibacillary leprosy features epithelial-mesenchymal transition with phagocytic and lipid biogenesis patterns in the skin. These findings will help catalyze the development of better diagnostic tools and potential host-based therapeutic interventions. Finally, our data may help elucidate host-pathogen interplay driving disease clinical manifestations.

Proteomics reveals disturbances in the immune response and energy metabolism of monocytes from patients with septic shock

Sepsis results from a dyshomeostatic response to infection, which may lead to hyper or hypoimmune states. Monocytes are central regulators of the inflammatory response, but our understanding of their role in the genesis and resolution of sepsis is still limited. Here, we report a comprehensive exploration of monocyte molecular responses in a cohort of patients with septic shock via proteomic profiling. The acute stage of septic shock was associated with an impaired inflammatory phenotype, indicated by the down-regulation of MHC class II molecules and proinflammatory cytokine pathways. Simultaneously, there was an up-regulation of glycolysis enzymes and a decrease in proteins related to the citric acid cycle and oxidative phosphorylation. On the other hand, the restoration of immunocompetence was the hallmark of recovering patients, in which an upregulation of interferon signaling pathways was a notable feature. Our results provide insights into the immunopathology of sepsis and propose that, pending future studies, immunometabolism pathway components could serve as therapeutic targets in septic patients.

Exploring plasma metabolomic changes in sepsis: a clinical matching study based on gas chromatography-mass spectrometry

Background

Sepsis is a deleterious systemic inflammatory response to infection, and despite advances in treatment, the mortality rate remains high. We hypothesized that plasma metabolism could clarify sepsis in patients complicated by organ dysfunction.

Methods

Plasma samples from 31 patients with sepsis and 23 healthy individuals of comparable age, gender, and body mass index (BMI) were collected. Plasma metabolites were detected through gas chromatography–mass spectrometry (GC–MS), and relevant metabolic pathways were predicted using the Kyoto Encyclopedia of Genes and Genomics (KEGG) pathway database. Student’s t-test was employed for statistical analysis. In addition, to explore sepsis organ dysfunction, plasma samples of sepsis patients were further analyzed by metabolomics subgroup analysis according to organ dysfunction.

Results

A total of 222 metabolites were detected, which included 124 metabolites with statistical significance between the sepsis and control groups. Among these, we found 26 were fatty acids, including 3 branched fatty acids, 10 were saturated fatty acids, and 13 were unsaturated fatty acids that were found in sepsis plasma samples but not in the controls. In addition, 158 metabolic pathways were predicted, 74 of which were significant. Further subgroup analysis identified seven metabolites in acute kidney injury (AKI), three metabolites in acute respiratory distress syndrome (ARDS), seven metabolites in sepsis-induced myocardial dysfunction (SIMD), and four metabolites in acute hepatic ischemia (AHI) that were significantly different. The results showed that the sepsis samples exhibited extensive changes in amino acids, fatty acids, and tricarboxylic acid (TCA)–cycle products. In addition, three metabolic pathways—namely, energy metabolism, amino acid metabolism, and lipid metabolism—were downregulated in sepsis patients.

Conclusions

The downregulated energy, amino acid, and lipid metabolism found in our study may serve as a novel clinical marker for the dysregulated internal environment, particularly involving energy metabolism, which results in sepsis.

Diagnostic potential of a gradient boosting-based model for detecting pediatric sepsis

Pediatric sepsis is a major cause of mortality of children worldwide. However, there is still a lack of easy-to-use predictive tools that can accurately diagnose sepsis in children. This study aimed to develop an optimal gene model for the diagnosis of pediatric sepsis using statistics and machine learning approaches. Combining gene expression profiles from a training cohort of 364 pediatric samples with a Least Absolute Shrinkage and Selection Operator analysis produced eighteen genes as diagnostic markers. With the implementation of a Gradient Boosting algorithm, a model designated PEDSEPS-GBM, that aggregated these markers was developed with optimal performance for the diagnosis of pediatric samples in the validation and two independent cohorts. Moreover, a web calculator with a user-friendly interface was established for PEDSEPS-GBM. This study presents a diagnostic model that holds great potential for the detection of pediatric sepsis, and demonstrates the biologic and clinical relevance of this model.

New insights into pediatric community-acquired pneumonia gained from untargeted metabolomics: A preliminary study

BACKGROUND

Available diagnostics often fail to distinguish viral from bacterial causes of pediatric community-acquired pneumonia (pCAP). Metabolomics, which aims at characterizing diseases based on their metabolic signatures, has been applied to expand pathophysiological understanding of many diseases. In this exploratory study, we used the untargeted metabolomic analysis to shed new light on the etiology of pCAP.

METHODS

Liquid chromatography coupled with mass spectrometry was used to quantify the metabolite content of urine samples collected from children hospitalized for CAP of pneumococcal or viral etiology, ascertained using a conservative algorithm combining microbiological and biochemical data.

RESULTS

Fifty-nine children with CAP were enrolled over 16 months. Pneumococcal and viral cases were distinguished by means of a multivariate model based on 93 metabolites, 20 of which were identified and considered as putative biomarkers. Among these, six metabolites belonged to the adrenal steroid synthesis and degradation pathway.

CONCLUSIONS

This preliminary study suggests that viral and pneumococcal pneumonia differently affect the systemic metabolome, with a stronger disruption of the adrenal steroid pathway in pneumococcal pneumonia. This finding may lead to the discovery of novel diagnostic biomarkers and bring us closer to personalized therapy for pCAP.

The use of host factors in microbial forensics

Advances have been made in the forensic analysis of microbes and toxins. An underdeveloped and underutilized area in microbial forensics is how the host interacts with microorganisms in a way that provides unique signatures for forensic use. For forensic purposes, an immediate goal is to distinguish a potential victim and innocent person from a perpetrator, and to distinguish between a naturally acquired or intentional infection. Principal methods that are sufficiently developed are characterization of the humoral immune response to microbial antigens including vaccine-induced immunity and detection of antibiotics that may be present in a possible perpetrator. This chapter presents central elements of the host response in a simplified fashion and describes a representative example, which, in the appropriate context, has a high potential of providing evidence that may aid an investigation to distinguish a perpetrator from a victim. This chapter also presents information about the immune system so that the interested reader can have a fuller understanding of the immune response in general.

Prognostic Potential of Alternative Splicing Markers in Endometrial Cancer

Alternative splicing (AS), an important post-transcriptional regulatory mechanism that regulates the translation of mRNA isoforms and generates protein diversity, has been widely demonstrated to be associated with oncogenic processes. In this study, we systematically analyzed genome-wide AS patterns to explore the prognostic implications of AS in endometrial cancer (EC). A total of 2,324 AS events were identified as being associated with the overall survival of EC patients, and eleven of these events were further selected using a random forest algorithm. With the implementation of a generalized, boosted regression model, a prognostic AS model that aggregated these eleven markers was ultimately established with high performance for risk stratification in EC patients. Functional analysis of these eleven AS markers revealed various potential signaling pathways implicated in the progression of EC. Splicing network analysis demonstrated the notable correlation between the expression of splicing factors and AS markers in EC and further determined eight candidate splicing factors that could be therapeutic targets for EC. Taken together, the results of this study present the utility of AS profiling in identifying biomarkers for the prognosis of EC and provide comprehensive insight into the molecular mechanisms involved in EC processes.

Characterization of the plasma proteome of nonhuman primates during Ebola virus disease or melioidosis: a host response comparison

Background

In-depth examination of the plasma proteomic response to infection with a wide variety of pathogens can assist in the development of new diagnostic paradigms, while providing insight into the interdependent pathogenic processes which encompass a host’s immunological and physiological responses. Ebola virus (EBOV) causes a highly lethal infection termed Ebola virus disease (EVD) in primates and humans. The Gram negative non-spore forming bacillus Burkholderia pseudomallei (Bp) causes melioidosis in primates and humans, characterized by severe pneumonia with high mortality. We sought to examine the host response to infection with these two bio-threat pathogens using established animal models to provide information on the feasibility of pre-symptomatic diagnosis, since the induction of host molecular signaling networks can occur before clinical presentation and pathogen detection.

Methods

Herein we report the quantitative proteomic analysis of plasma collected at various times of disease progression from 10 EBOV-infected and 5 Bp-infected nonhuman primates (NHP). Our strategy employed high resolution LC–MS/MS and a peptide-tagging approach for relative protein quantitation. In each infection type, for all proteins with > 1.3 fold abundance change at any post-infection time point, a direct comparison was made with levels obtained from plasma collected daily from 5 naïve rhesus macaques, to determine the fold changes that were significant, and establish the natural variability of abundance for endogenous plasma proteins.

Results

A total of 41 plasma proteins displayed significant alterations in abundance during EBOV infection, and 28 proteins had altered levels during Bp infection, when compared to naïve NHPs. Many major acute phase proteins quantitated displayed similar fold-changes between the two infection types but exhibited different temporal dynamics. Proteins related to the clotting cascade, immune signaling and complement system exhibited significant differential abundance during infection with EBOV or Bp, indicating a specificity of the response.

Conclusions

These results advance our understanding of the global plasma proteomic response to EBOV and Bp infection in relevant primate models for human disease and provide insight into potential innate immune response differences between viral and bacterial infections.

Electronic supplementary material

The online version of this article (10.1186/s12014-019-9227-3) contains supplementary material, which is available to authorized users.

Multiplexed enrichment and genomic profiling of peripheral blood cells reveal subset-specific immune signatures

Specialized immune cell subsets are involved in autoimmune disease, cancer immunity, and infectious disease through a diverse range of functions mediated by overlapping pathways and signals. However, subset-specific responses may not be detectable in analyses of whole blood samples, and no efficient approach for profiling cell subsets at high throughput from small samples is available. We present a low-input microfluidic system for sorting immune cells into subsets and profiling their gene expression. We validate the system's technical performance against standard subset isolation and library construction protocols and demonstrate the importance of subset-specific profiling through in vitro stimulation experiments. We show the ability of this integrated platform to identify subset-specific disease signatures by profiling four immune cell subsets in blood from patients with systemic lupus erythematosus (SLE) and matched control subjects. The platform has the potential to make multiplexed subset-specific analysis routine in many research laboratories and clinical settings.

RNA Sequencing (RNA-Seq) Reveals Extremely Low Levels of Reticulocyte-Derived Globin Gene Transcripts in Peripheral Blood From Horses (Equus caballus) and Cattle (Bos taurus)

RNA-seq has emerged as an important technology for measuring gene expression in peripheral blood samples collected from humans and other vertebrate species. In particular, transcriptomics analyses of whole blood can be used to study immunobiology and develop novel biomarkers of infectious disease. However, an obstacle to these methods in many mammalian species is the presence of reticulocyte-derived globin mRNAs in large quantities, which can complicate RNA-seq library sequencing and impede detection of other mRNA transcripts. A range of supplementary procedures for targeted depletion of globin transcripts have, therefore, been developed to alleviate this problem. Here, we use comparative analyses of RNA-seq data sets generated from human, porcine, equine, and bovine peripheral blood to systematically assess the impact of globin mRNA on routine transcriptome profiling of whole blood in cattle and horses. The results of these analyses demonstrate that total RNA isolated from equine and bovine peripheral blood contains very low levels of globin mRNA transcripts, thereby negating the need for globin depletion and greatly simplifying blood-based transcriptomic studies in these two domestic species.

Incipient and Subclinical Tuberculosis: a Clinical Review of Early Stages and Progression of Infection

Tuberculosis (TB) is the leading infectious cause of mortality worldwide, due in part to a limited understanding of its clinical pathogenic spectrum of infection and disease. Historically, scientific research, diagnostic testing, and drug treatment have focused on addressing one of two disease states: latent TB infection or active TB disease. Recent research has clearly demonstrated that human TB infection, from latent infection to active disease, exists within a continuous spectrum of metabolic bacterial activity and antagonistic immunological responses. This revised understanding leads us to propose two additional clinical states: incipient and subclinical TB. The recognition of incipient and subclinical TB, which helps divide latent and active TB along the clinical disease spectrum, provides opportunities for the development of diagnostic and therapeutic interventions to prevent progression to active TB disease and transmission of TB bacilli. In this report, we review the current understanding of the pathogenesis, immunology, clinical epidemiology, diagnosis, treatment, and prevention of both incipient and subclinical TB, two emerging clinical states of an ancient bacterium.

Early Diagnosis of Sepsis: Is an Integrated Omics Approach the Way Forward?

Sepsis remains one of the leading causes of death in the USA and it is expected to get worse as the population ages. Moreover, the standard of care, which recommends aggressive treatment with appropriate antibiotics, has led to an increase in multiple drug-resistant organisms. There is a dire need for the development of new antibiotics, improved antibiotic stewardship, and therapies that treat the host response. Development of new sepsis therapeutics has been a disappointment as no drugs are currently approved to treat the various complications from sepsis. Much of the failure has been blamed on animal models that do not accurately reflect the course of the disease. However, recent improvements in metabolomic, transcriptomic, genomic, and proteomic platforms have allowed for a broad-spectrum look at molecular changes in the host response using clinical samples. Integration of these multi-omic datasets allows researchers to perform systems biology approaches to identify novel pathophysiology of the disease. In this review, we highlight what is currently known about sepsis and how integrative omics has identified new diagnostic and predictive models of sepsis as well as novel mechanisms. These changes may improve patient care as well as guide future preclinical analysis of sepsis.

Metabolomics in Sepsis and Its Impact on Public Health

Sepsis, with its often devastating consequences for patients and their families, remains a major public health concern that poses an increasing financial burden. Early resuscitation together with the elucidation of the biological pathways and pathophysiological mechanisms with the use of "-omics" technologies have started changing the clinical and research landscape in sepsis. Metabolomics (i.e., the study of the metabolome), an "-omics" technology further down in the "-omics" cascade between the genome and the phenome, could be particularly fruitful in sepsis research with the potential to alter the clinical practice. Apart from its benefit for the individual patient, metabolomics has an impact on public health that extends beyond its applications in medicine. In this review, we present recent developments in metabolomics research in sepsis, with a focus on pneumonia, and we discuss the impact of metabolomics on public health, with a focus on free/libre open source software.

Human genetic and metabolite variation reveals that methylthioadenosine is a prognostic biomarker and an inflammatory regulator in sepsis

Sepsis is a deleterious inflammatory response to infection with high mortality. Reliable sepsis biomarkers could improve diagnosis, prognosis, and treatment. Integration of human genetics, patient metabolite and cytokine measurements, and testing in a mouse model demonstrate that the methionine salvage pathway is a regulator of sepsis that can accurately predict prognosis in patients. Pathway-based genome-wide association analysis of nontyphoidal Salmonella bacteremia showed a strong enrichment for single-nucleotide polymorphisms near the components of the methionine salvage pathway. Measurement of the pathway's substrate, methylthioadenosine (MTA), in two cohorts of sepsis patients demonstrated increased plasma MTA in nonsurvivors. Plasma MTA was correlated with levels of inflammatory cytokines, indicating that elevated MTA marks a subset of patients with excessive inflammation. A machine-learning model combining MTA and other variables yielded approximately 80% accuracy (area under the curve) in predicting death. Furthermore, mice infected with Salmonella had prolonged survival when MTA was administered before infection, suggesting that manipulating MTA levels could regulate the severity of the inflammatory response. Our results demonstrate how combining genetic data, biomolecule measurements, and animal models can shape our understanding of disease and lead to new biomarkers for patient stratification and potential therapeutic targeting.

Neonatal Meningitis: Overcoming Challenges in Diagnosis, Prognosis, and Treatment with Omics

Neonatal meningitis is a devastating condition. Prognosis has not improved in decades, despite the advent of improved antimicrobial therapy and heightened index of suspicion among clinicians caring for affected infants. One in ten infants die from meningitis, and up to half of survivors develop significant lifelong complications, including seizures, impaired hearing and vision, and delayed or arrested development of such basic skills as talking and walking. At present, it is not possible to predict which infants will suffer poor outcomes. Early treatment is critical to promote more favorable outcomes, though diagnosis of meningitis in infants is technically challenging, time-intensive, and invasive. Profound neuronal injury has long been described in the setting of neonatal meningitis, as has elevated levels of many pro- and anti-inflammatory cytokines. Mechanisms of the host immune response that drive clearance of the offending organism and underlie brain injury due to meningitis are not well understood, however. In this review, we will discuss challenges in diagnosis, prognosis, and treatment of neonatal meningitis. We will highlight transcriptomic, proteomic, and metabolomic data that contribute to suggested mechanisms of inflammation and brain injury in this setting with a view toward fruitful areas for future investigation.