Biomarker-based classification of bacterial and fungal whole-blood infections in a genome-wide expression study

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.

Deciphering the molecular and cellular atlas of immune cells in septic patients with different bacterial infections

BACKGROUND

Sepsis is a life-threatening organ dysfunction caused by abnormal immune responses to various, predominantly bacterial, infections. Different bacterial infections lead to substantial variation in disease manifestation and therapeutic strategies. However, the underlying cellular heterogeneity and mechanisms involved remain poorly understood.

METHODS

Multiple bulk transcriptome datasets from septic patients with 12 types of bacterial infections were integrated to identify signature genes for each infection. Signature genes were mapped onto an integrated large single-cell RNA (scRNA) dataset from septic patients, to identify subsets of cells associated with different sepsis types, and multiple omics datasets were combined to reveal the underlying molecular mechanisms. In addition, an scRNA dataset and spatial transcriptome data were used to identify signaling pathways in sepsis-related cells. Finally, molecular screening, optimization, and de novo design were conducted to identify potential targeted drugs and compounds.

RESULTS

We elucidated the cellular heterogeneity among septic patients with different bacterial infections. In Escherichia coli (E. coli) sepsis, 19 signature genes involved in epigenetic regulation and metabolism were identified, of which DRAM1 was demonstrated to promote autophagy and glycolysis in response to E. coli infection. DRAM1 upregulation was confirmed in an independent sepsis cohort. Further, we showed that DRAM1 could maintain survival of a pro-inflammatory monocyte subset, C10_ULK1, which induces systemic inflammation by interacting with other cell subsets via resistin and integrin signaling pathways in blood and kidney tissue, respectively. Finally, retapamulin was identified and optimized as a potential drug for treatment of E. coli sepsis targeting the signature gene, DRAM1, and inhibiting E. coli protein synthesis. Several other targeted drugs were also identified in other types of sepsis, including nystatin targeting C1QA in Neisseria sepsis and dalfopristin targeting CTSD in Streptococcus viridans sepsis.

CONCLUSION

Our study provides a comprehensive overview of the cellular heterogeneity and underlying mechanisms in septic patients with various bacterial infections, providing insights to inform development of stratified targeted therapies for sepsis.

Pathogen-specific innate immune response patterns are distinctly affected by genetic diversity

Innate immune responses vary by pathogen and host genetics. We analyze quantitative trait loci (eQTLs) and transcriptomes of monocytes from 215 individuals stimulated by fungal, Gram-negative or Gram-positive bacterial pathogens. We identify conserved monocyte responses to bacterial pathogens and a distinct antifungal response. These include 745 response eQTLs (reQTLs) and corresponding genes with pathogen-specific effects, which we find first in samples of male donors and subsequently confirm for selected reQTLs in females. reQTLs affect predominantly upregulated genes that regulate immune response via e.g., NOD-like, C-type lectin, Toll-like and complement receptor-signaling pathways. Hence, reQTLs provide a functional explanation for individual differences in innate response patterns. Our identified reQTLs are also associated with cancer, autoimmunity, inflammatory and infectious diseases as shown by external genome-wide association studies. Thus, reQTLs help to explain interindividual variation in immune response to infection and provide candidate genes for variants associated with a range of diseases.

Fungal biotechnology: From yesterday to tomorrow

Fungi have been used to better the lives of everyday people and unravel the mysteries of higher eukaryotic organisms for decades. However, comparing progress and development stemming from fungal research to that of human, plant, and bacterial research, fungi remain largely understudied and underutilized. Recent commercial ventures have begun to gain popularity in society, providing a new surge of interest in fungi, mycelia, and potential new applications of these organisms to various aspects of research. Biotechnological advancements in fungal research cannot occur without intensive amounts of time, investments, and research tool development. In this review, we highlight past breakthroughs in fungal biotechnology, discuss requirements to advance fungal biotechnology even further, and touch on the horizon of new breakthroughs with the highest potential to positively impact both research and society.

Coexpression network analysis of human candida infection reveals key modules and hub genes responsible for host-pathogen interactions

Invasive fungal infections are a significant reason for morbidity and mortality among organ transplant recipients. Therefore, it is critical to investigate the host and candida niches to understand the epidemiology of fungal infections in transplantation. Candida albicans is an opportunistic fungal pathogen that causes fatal invasive mucosal infections, particularly in solid organ transplant patients. Therefore, identifying and characterizing these genes would play a vital role in understanding the complex regulation of host-pathogen interactions. Using 32 RNA-sequencing samples of human cells infected with C. albicans, we developed WGCNA coexpression networks and performed DESeq2 differential gene expression analysis to identify the genes that positively correlate with human candida infection. Using hierarchical clustering, we identified 5 distinct modules. We studied the inter- and intramodular gene network properties in the context of sample status traits and identified the highly enriched genes in the correlated modules. We identified 52 genes that were common in the most significant WGCNA turquoise module and differentially expressed genes in human endothelial cells (HUVEC) infection vs. control samples. As a validation step, we identified the differentially expressed genes from the independent Candida-infected human oral keratinocytes (OKF6) samples and validated 30 of the 52 common genes. We then performed the functional enrichment analysis using KEGG and GO. Finally, we performed protein-protein interaction (PPI) analysis using STRING and CytoHubba from 30 validated genes. We identified 8 hub genes (JUN, ATF3, VEGFA, SLC2A1, HK2, PTGS2, PFKFB3, and KLF6) that were enriched in response to hypoxia, angiogenesis, vasculogenesis, hypoxia-induced signaling, cancer, diabetes, and transplant-related disease pathways. The discovery of genes and functional pathways related to the immune system and gene coexpression and differential gene expression analyses may serve as novel diagnostic markers and potential therapeutic targets.

Mitochondrial ACOD1/IRG1 in infection and sterile inflammation

Immunometabolism is a dynamic process involving the interplay of metabolism and immune response in health and diseases. Increasing evidence suggests that impaired immunometabolism contributes to infectious and inflammatory diseases. In particular, the mitochondrial enzyme aconitate decarboxylase 1 (ACOD1, best known as immunoresponsive gene 1 [IRG1]) is upregulated under various inflammatory conditions and serves as a pivotal regulator of immunometabolism involved in itaconate production, macrophage polarization, inflammasome activation, and oxidative stress. Consequently, the activation of the ACOD1 pathway is implicated in regulating the pathogenic process of sepsis and septic shock, which are part of a clinical syndrome of life-threatening organ failure caused by a dysregulated host response to pathogen infection. In this review, we discuss the latest research advances in ACOD1 expression and function, with particular attention to how the ACOD1-itaconate pathway affects infection and sterile inflammation diseases. These new insights may give us a deeper understanding of the role of immunometabolism in innate immunity.

The network interplay of interferon and Toll-like receptor signaling pathways in the anti-Candida immune response

Fungal infections represent a major global health problem affecting over a billion people that kills more than 1.5 million annually. In this study, we employed an integrative approach to reveal the landscape of the human immune responses to Candida spp. through meta-analysis of microarray, bulk, and single-cell RNA sequencing (scRNA-seq) data for the blood transcriptome. We identified across these different studies a consistent interconnected network interplay of signaling molecules involved in both Toll-like receptor (TLR) and interferon (IFN) signaling cascades that is activated in response to different Candida species (C. albicans, C. auris, C. glabrata, C. parapsilosis, and C. tropicalis). Among these molecules are several types I IFN, indicating an overlap with antiviral immune responses. scRNA-seq data confirmed that genes commonly identified by the three transcriptomic methods show cell type-specific expression patterns in various innate and adaptive immune cells. These findings shed new light on the anti-Candida immune response, providing putative molecular pathways for therapeutic intervention.

Role of circulating microRNA-132 in allergic bronchopulmonary aspergillosis: A case-control study

Allergic bronchopulmonary aspergillosis (ABPA) is an allergic respiratory disease. In the current study, we aimed to evaluate the roles of miRNA-21 and miRNA132 as biomarkers in the diagnosis of ABPA. A total of 30 controls, 30 allergic asthmatic patients, 30 severe asthma with fungal sensitization (SAFS) patients, and 30 ABPA patients were included. Real-time polymerase chain reaction was used to quantify the level of miRNAs expression. The expression level of miRNA-21 was significantly higher in allergic asthmatic, SAFS, and ABPA patients in comparison with controls (p < 0.001). However, no significant difference was detected in the expression level of miRNA-21 among the different patient groups (p > 0.05). The ABPA patients had significantly higher levels of miRNA-132 expression compared to controls, allergic asthmatic patients, and SAFS patients (p < 0.001), but there was a non-significant difference between controls and allergic asthmatic patients (p = 0.09). At a cut-off of 1.52, the sensitivity of miRNA-132 expression was 93.3% and the specificity was 100% different ABPA from healthy controls. At a cut-off of 6.5, miRNA-132 expression was found to reliably differentiate between ABPA and SAFS, with a sensitivity of 86.7% and a specificity of 80%. In ABPA patients, miRNA-132 expression positively correlation with the levels of serum IL-5 (r = 0.91, p < 0.001). miRNA-132 has a role in ABPA detection and distinguishing ABPA from allergic asthma and SAFS. These preliminary data from case-control study need further studies to confirm its finding.

In vitro infection models to study fungal-host interactions

Fungal infections (mycoses) affect over a billion people per year. Approximately, two million of these infections are life-threatening, especially for patients with a compromised immune system. Fungi of the genera Aspergillus, Candida, Histoplasma and Cryptococcus are opportunistic pathogens that contribute to a substantial number of mycoses. To optimize the diagnosis and treatment of mycoses, we need to understand the complex fungal–host interplay during pathogenesis, the fungal attributes causing virulence and how the host resists infection via immunological defenses. In vitro models can be used to mimic fungal infections of various tissues and organs and the corresponding immune responses at near-physiological conditions. Furthermore, models can include fungal interactions with the host–microbiota to mimic the in vivo situation on skin and mucosal surfaces. This article reviews currently used in vitro models of fungal infections ranging from cell monolayers to microfluidic 3D organ-on-chip (OOC) platforms. We also discuss how OOC models can expand the toolbox for investigating interactions of fungi and their human hosts in the future.

Survival Strategies of Pathogenic Candida Species in Human Blood Show Independent and Specific Adaptations

To ensure their survival, pathogens have to adapt immediately to new environments in their hosts, for example, during the transition from the gut to the bloodstream. Here, we investigated the basis of this adaptation in a group of fungal species which are among the most common causes of hospital-acquired infections, the Candida species. On the basis of a human whole-blood infection model, we studied which genes and processes are active over the course of an infection in both the host and four different Candida pathogens. Remarkably, we found that, while the human host response during the early phase of infection is predominantly uniform, the pathogens pursue largely individual strategies and each one regulates genes involved in largely disparate processes in the blood. Our results reveal that C. albicans, C. glabrata, C. parapsilosis, and C. tropicalis all have developed individual strategies for survival in the host. This indicates that their pathogenicity in humans has evolved several times independently and that genes which are central for survival in the host for one species may be irrelevant in another.

Only four species, Candida albicans, C. glabrata, C. parapsilosis, and C. tropicalis, together account for about 90% of all Candida bloodstream infections and are among the most common causes of invasive fungal infections of humans. However, virulence potential varies among these species, and the phylogenetic tree reveals that their pathogenicity may have emerged several times independently during evolution. We therefore tested these four species in a human whole-blood infection model to determine, via comprehensive dual-species RNA-sequencing analyses, which fungal infection strategies are conserved and which are recent evolutionary developments. The ex vivo infection progressed from initial immune cell interactions to nearly complete killing of all fungal cells. During the course of infection, we characterized important parameters of pathogen-host interactions, such as fungal survival, types of interacting immune cells, and cytokine release. On the transcriptional level, we obtained a predominantly uniform and species-independent human response governed by a strong upregulation of proinflammatory processes, which was downregulated at later time points after most of the fungal cells were killed. In stark contrast, we observed that the different fungal species pursued predominantly individual strategies and showed significantly different global transcriptome patterns. Among other findings, our functional analyses revealed that the fungal species relied on different metabolic pathways and virulence factors to survive the host-imposed stress. These data show that adaptation of Candida species as a response to the host is not a phylogenetic trait, but rather has likely evolved independently as a prerequisite to cause human infections.

ACOD1 in immunometabolism and disease

Immunometabolism plays a fundamental role in health and diseases and involves multiple genes and signals. Aconitate decarboxylase 1 (ACOD1; also known as IRG1) is emerging as a regulator of immunometabolism in inflammation and infection. Upregulation of ACOD1 expression occurs in activated immune cells (e.g., macrophages and monocytes) in response to pathogen infection (e.g., bacteria and viruses), pathogen-associated molecular pattern molecules (e.g., LPS), cytokines (e.g., TNF and IFNs), and damage-associated molecular patterns (e.g., monosodium urate). Mechanistically, several immune receptors (e.g., TLRs and IFNAR), adapter proteins (e.g., MYD88), ubiquitin ligases (e.g., A20), and transcription factors (e.g., NF-κB, IRFs, and STATs) form complex signal transduction networks to control ACOD1 expression in a context-dependent manner. Functionally, ACOD1 mediates itaconate production, oxidative stress, and antigen processing and plays dual roles in immunity and diseases. On the one hand, activation of the ACOD1 pathway may limit pathogen infection and promote embryo implantation. On the other hand, abnormal ACOD1 expression can lead to tumor progression, neurodegenerative disease, and immune paralysis. Further understanding of the function and regulation of ACOD1 is important for the application of ACOD1-based therapeutic strategies in disease.

Two Gene Set Variation Index as Biomarker of Bacterial and Fungal Sepsis

Background

The incidence of sepsis has been increasing in recent years. The molecular mechanism of different pathogenic sepsis remains elusive, and biomarkers of sepsis against different pathogens are still lacking.

Methods

The microarray data of bacterial sepsis, fungal sepsis, and mock-treated samples were applied to perform differentially expressed gene (DEG) analysis to identify a bacterial sepsis-specific gene set and a fungal sepsis-specific gene set. Functional enrichment analysis was used to explore the body's response to bacterial sepsis and fungal sepsis. Gene set variation analysis (GSVA) was used to score individual samples against the two pathogen-specific gene sets, and each sample gets a GSVA index. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic value of sepsis. An independent data set was used to validate the bacterial sepsis-specific GSVA index.

Results

The genes differentially expressed only in bacterial sepsis and the genes differentially expressed only in fungal sepsis were significantly involved in different biological processes (BPs) and pathways. This indicated that the body's responses to fungal sepsis and bacterial sepsis are varied. Twenty-two genes were identified as bacterial sepsis-specific genes and upregulated in bacterial sepsis, and 23 genes were identified as fungal sepsis-specific genes and upregulated in fungal sepsis. ROC curve analysis showed that both of the two pathogen sepsis-specific GSVA indexes may be a reliable biomarker for corresponding pathogen-induced sepsis (AUC = 1.000), while the mRNA of CALCA (also known as PCT) have a poor diagnostic value with AUC = 0.512 in bacterial sepsis and AUC = 0.705 in fungi sepsis. In addition, the AUC of the bacterial sepsis-specific GSVA index in the independent data set was 0.762.

Conclusion

We proposed a bacterial sepsis-specific gene set and a fungal sepsis-specific gene set; the bacterial sepsis GSVA index may be a reliable biomarker for bacterial sepsis.

Dynamic Interplay of Host and Pathogens in an Avian Whole-Blood Model

Microbial survival in blood is an essential step toward the development of disseminated diseases and blood stream infections. For poultry, however, little is known about the interactions of host cells and pathogens in blood. We established an ex vivo chicken whole-blood infection assay as a tool to analyze interactions between host cells and three model pathogens, Escherichia coli, Staphylococcus aureus, and Candida albicans. Following a systems biology approach, we complemented the experimental measurements with functional and quantitative immune characteristics by virtual infection modeling. All three pathogens were killed in whole blood, but each to a different extent and with different kinetics. Monocytes, and to a lesser extent heterophils, associated with pathogens. Both association with host cells and transcriptional activation of genes encoding immune-associated functions differed depending on both the pathogen and the genetic background of the chickens. Our results provide first insights into quantitative interactions of three model pathogens with different immune cell populations in avian blood, demonstrating a broad spectrum of different characteristics during the immune response that depends on the pathogen and the chicken line.

MiR-30c-5p mediates inflammatory responses and promotes microglia survival by targeting eIF2α during Cryptococcus neoformans infection

Cryptococcosis is a disease predominantly caused by Cryptococcus neoformans in China and C. neoformans is the main form that causes cryptococcal meningitis. In this study, we examined the influence of MiR-30c-5p during Cryptococcus neoformans infection. microRNAs were extracted from Cerebrospinal fluid and sera of patients. To identify pathogenic microRNAs, RNASeq were performed. The results were confirmed with quantitative real-time PCR (qRT-PCR), transient transfection of siRNAs or microRNA mimics into cultured BV2 cell, flow cytometry, immunoblotting, luciferase assay and immunohistochemistry. In this study we found that miR-30c expression was downregulated and that inflammation, apoptosis, and autophagy were activated. The overexpression of miR-30c-5p significantly inhibited inflammation and autophagic activity and decreased apoptosis, and treatment with sieIF2α resulted in a significant decrease in inflammation, apoptosis. In addition, clinical samples of cerebrospinal fluid and serum of patients with cryptococcal meningitis who have undergone standard antifungal treatment showed that the expression of miR-30c-5p was increased while that of eIF2α was decreased, which was in accordance with the in vitro experiments. These studies demonstrated that miRNA-30c-5p can inhibit inflammatory, apoptotic, and autophagic activity through the eIF2α/ATF4 pathway, and it is thus a potential target for the diagnosis, treatment, and detection of cryptococcal meningitis.

Identification of Potential Transcriptional Biomarkers Differently Expressed in Both S. aureus- and E. coli-Induced Sepsis via Integrated Analysis

Sepsis is a critical, complex medical condition, and the major causative pathogens of sepsis are both Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). Genome-wide studies identify differentially expressed genes for sepsis. However, the results for the identification of DEGs are inconsistent or discrepant among different studies because of heterogeneity of specimen sources, various data processing methods, or different backgrounds of the samples. To identify potential transcriptional biomarkers that are differently expressed in S. aureus- and E. coli-induced sepsis, we have analyzed four microarray datasets from GEO database and integrated results with bioinformatics tools. 42 and 54 DEGs were identified in both S. aureus and E. coli samples from any three different arrays, respectively. Hierarchical clustering revealed dramatic differences between control and sepsis samples. GO functional annotations suggested that DEGs in the S. aureus group were mainly involved in the responses of both defense and immune regulation, but DEGs in the E. coli group were mainly related to the regulation of endopeptidase activity involved in the apoptotic signaling pathway. Although KEGG showed inflammatory bowel disease in the E. coli group, the KEGG pathway analysis showed that these DEGs were mainly involved in the tumor necrosis factor signaling pathway, fructose metabolism, and mannose metabolism in both S. aureus- and E. coli-induced sepsis. Eight common genes were identified between sepsis patients with either S. aureus or E. coli infection and controls in this study. All the candidate genes were further validated to be differentially expressed by an ex-vivo human blood model, and the relative expression of these genes was performed by qPCR. The qPCR results suggest that GK and PFKFB3 might contribute to the progression of S. aureus-induced sepsis, and CEACAM1, TNFAIP6, PSTPIP2, SOCS3, and IL18RAP might be closely linked with E. coli-induced sepsis. These results provide new viewpoints for the pathogenesis of both sepsis and pathogen identification.

The host response as a tool for infectious disease diagnosis and management

INTRODUCTION

A century of advances in infectious disease diagnosis and treatment changed the face of medicine. However, challenges continue to develop including multi-drug resistance, globalization that increases pandemic risks, and high mortality from severe infections. These challenges can be mitigated through improved diagnostics, and over the past decade, there has been a particular focus on the host response. Since this article was originally published in 2015, there have been significant developments in the field of host response diagnostics, warranting this updated review. Areas Covered: This review begins by discussing developments in single biomarkers and pauci-analyte biomarker panels. It then delves into 'omics, an area where there has been truly exciting progress. Specifically, progress has been made in sepsis diagnosis and prognosis; differentiating viral, bacterial, and fungal pathogen classes; pre-symptomatic diagnosis; and understanding disease-specific diagnostic challenges in tuberculosis, Lyme disease, and Ebola. Expert Commentary: As 'omics have become faster, more precise, and less expensive, the door has been opened for academic, industry, and government efforts to develop host-based infectious disease classifiers. While there are still obstacles to overcome, the chasm separating these scientific advances from the patient's bedside is shrinking.

Invasive candidiasis: future directions in non-culture based diagnosis

INTRODUCTION

Delayed initial antifungal therapy is associated with high mortality rates caused by invasive candida infections, since accurate detection of the opportunistic pathogenic yeast and its identification display a diagnostic challenge. diagnosis of candida infections relies on time-consuming methods such as blood cultures, serologic and histopathologic examination. to allow for fast detection and characterization of invasive candidiasis, there is a need to improve diagnostic tools. trends in diagnostics switch to non-culture-based methods, which allow specified diagnosis within significantly shorter periods of time in order to provide early and appropriate antifungal treatment. Areas covered: within this review comprise novel pathogen- and host-related testing methods, e.g. multiplex-PCR analyses, T2 magnetic resonance, fungus-specific DNA microarrays, microRNA characterization or analyses of IL-17 as biomarker for early detection of invasive candidiasis. Expert commentary: Early recognition and diagnosis of fungal infections is a key issue for improved patient management. As shown in this review, a broad range of novel molecular based tests for the detection and identification of Candida species is available. However, several assays are in-house assays and lack standardization, clinical validation as well as data on sensitivity and specificity. This underscores the need for the development of faster and more accurate diagnostic tests.

Fungal biomarker discovery by integration of classifiers

Background

The human immune system is responsible for protecting the host from infection. However, in immunocompromised individuals the risk of infection increases substantially with possible drastic consequences. In extreme, systemic infection can lead to sepsis which is responsible for innumerous deaths worldwide. Amongst its causes are infections by bacteria and fungi. To increase survival, it is mandatory to identify the type of infection rapidly. Discriminating between fungal and bacterial pathogens is key to determine if antifungals or antibiotics should be administered, respectively. For this, in situ experiments have been performed to determine regulation mechanisms of the human immune system to identify biomarkers. However, these studies led to heterogeneous results either due different laboratory settings, pathogen strains, cell types and tissues, as well as the time of sample extraction, to name a few.

Methods

To generate a gene signature capable of discriminating between fungal and bacterial infected samples, we employed Mixed Integer Linear Programming (MILP) based classifiers on several datasets comprised of the above mentioned pathogens.

Results

When combining the classifiers by a joint optimization we could increase the consistency of the biomarker gene list independently of the experimental setup. An increase in pairwise overlap (the number of genes that overlap in each cross-validation) of 43% was obtained by this approach when compared to that of single classifiers. The refined gene list was composed of 19 genes and ranked according to consistency in expression (up- or down-regulated) and most of them were linked either directly or indirectly to the ERK-MAPK signalling pathway, which has been shown to play a key role in the immune response to infection. Testing of the identified 12 genes on an unseen dataset yielded an average accuracy of 83%.

Conclusions

In conclusion, our method allowed the combination of independent classifiers and increased consistency and reliability of the generated gene signatures.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-4006-x) contains supplementary material, which is available to authorized users.

Targeted antimicrobial therapy in the microbiome era

Even though the oral microbiome is one of the most complex sites on the body it is an excellent model for narrow‐spectrum antimicrobial therapy. Current research indicates that disruption of the microbiome leads to a dysbiotic environment allowing for the overgrowth of pathogenic species and the onset of oral diseases. The gram‐negative colonizer, Porphyromonas gingivalis has long been considered a key player in the initiation of periodontitis and Streptococcus mutans has been linked to dental caries. With antibiotic research still on the decline, new strategies are greatly needed to combat infectious diseases. By targeting key pathogens, it may be possible to treat oral infections while allowing for the recolonization of the beneficial, healthy flora. In this review, we examine unique strategies to specifically target periodontal pathogens and address what is needed for the success of these approaches in the microbiome era.

Facing the challenges of multiscale modelling of bacterial and fungal pathogen-host interactions

Recent and rapidly evolving progress on high-throughput measurement techniques and computational performance has led to the emergence of new disciplines, such as systems medicine and translational systems biology. At the core of these disciplines lies the desire to produce multiscale models: mathematical models that integrate multiple scales of biological organization, ranging from molecular, cellular and tissue models to organ, whole-organism and population scale models. Using such models, hypotheses can systematically be tested. In this review, we present state-of-the-art multiscale modelling of bacterial and fungal infections, considering both the pathogen and host as well as their interaction. Multiscale modelling of the interactions of bacteria, especially Mycobacterium tuberculosis, with the human host is quite advanced. In contrast, models for fungal infections are still in their infancy, in particular regarding infections with the most important human pathogenic fungi, Candida albicans and Aspergillus fumigatus. We reflect on the current availability of computational approaches for multiscale modelling of host-pathogen interactions and point out current challenges. Finally, we provide an outlook for future requirements of multiscale modelling.

Specific and Novel microRNAs Are Regulated as Response to Fungal Infection in Human Dendritic Cells

Within the last two decades, the incidence of invasive fungal infections has been significantly increased. They are characterized by high mortality rates and are often caused by Candida albicans and Aspergillus fumigatus. The increasing number of infections underlines the necessity for additional anti-fungal therapies, which require extended knowledge of gene regulations during fungal infection. MicroRNAs are regulators of important cellular processes, including the immune response. By analyzing their regulation and impact on target genes, novel therapeutic and diagnostic approaches may be developed. Here, we examine the role of microRNAs in human dendritic cells during fungal infection. Dendritic cells represent the bridge between the innate and the adaptive immune systems. Therefore, analysis of gene regulation of dendritic cells is of particular significance. By applying next-generation sequencing of small RNAs, we quantify microRNA expression in monocyte-derived dendritic cells after 6 and 12 h of infection with C. albicans and A. fumigatus as well as treatment with lipopolysaccharides (LPS). We identified 26 microRNAs that are differentially regulated after infection by the fungi or LPS. Three and five of them are specific for fungal infections after 6 and 12 h, respectively. We further validated interactions of miR-132-5p and miR-212-5p with immunological relevant target genes, such as FKBP1B, KLF4, and SPN, on both RNA and protein level. Our results indicate that these microRNAs fine-tune the expression of immune-related target genes during fungal infection. Beyond that, we identified previously undiscovered microRNAs. We validated three novel microRNAs via qRT-PCR. A comparison with known microRNAs revealed possible relations with the miR-378 family and miR-1260a/b for two of them, while the third one features a unique sequence with no resemblance to known microRNAs. In summary, this study analyzes the effect of known microRNAs in dendritic cells during fungal infections and proposes novel microRNAs that could be experimentally verified.

Dissecting Candida albicans Infection from the Perspective of C. albicans Virulence and Omics Approaches on Host-Pathogen Interaction: A Review

Candida bloodstream infections remain the most frequent life-threatening fungal disease, with Candida albicans accounting for 70% to 80% of the Candida isolates recovered from infected patients. In nature, Candida species are part of the normal commensal flora in mammalian hosts. However, they can transform into pathogens once the host immune system is weakened or breached. More recently, mortality attributed to Candida infections has continued to increase due to both inherent and acquired drug resistance in Candida, the inefficacy of the available antifungal drugs, tedious diagnostic procedures, and a rising number of immunocompromised patients. Adoption of animal models, viz. minihosts, mice, and zebrafish, has brought us closer to unraveling the pathogenesis and complexity of Candida infection in human hosts, leading towards the discovery of biomarkers and identification of potential therapeutic agents. In addition, the advancement of omics technologies offers a holistic view of the Candida-host interaction in a non-targeted and non-biased manner. Hence, in this review, we seek to summarize past and present milestone findings on C. albicans virulence, adoption of animal models in the study of C. albicans infection, and the application of omics technologies in the study of Candida–host interaction. A profound understanding of the interaction between host defense and pathogenesis is imperative for better design of novel immunotherapeutic strategies in future.

Krüppel-like Factor 4 modulates interleukin-6 release in human dendritic cells after in vitro stimulation with Aspergillus fumigatus and Candida albicans

Invasive fungal infections are associated with high mortality rates and are mostly caused by the opportunistic fungi Aspergillus fumigatus and Candida albicans. Immune responses against these fungi are still not fully understood. Dendritic cells (DCs) are crucial players in initiating innate and adaptive immune responses against fungal infections. The immunomodulatory effects of fungi were compared to the bacterial stimulus LPS to determine key players in the immune response to fungal infections. A genome wide study of the gene regulation of human monocyte-derived dendritic cells (DCs) confronted with A. fumigatus, C. albicans or LPS was performed and Krüppel-like factor 4 (KLF4) was identified as the only transcription factor that was down-regulated in DCs by both fungi but induced by stimulation with LPS. Downstream analysis demonstrated the influence of KLF4 on the interleukine-6 expression in human DCs. Furthermore, KLF4 regulation was shown to be dependent on pattern recognition receptor ligation. Therefore KLF4 was identified as a controlling element in the IL-6 immune response with a unique expression pattern comparing fungal and LPS stimulation.

Use of systems biology to decipher host-pathogen interaction networks and predict biomarkers

In systems biology, researchers aim to understand complex biological systems as a whole, which is often achieved by mathematical modelling and the analyses of high-throughput data. In this review, we give an overview of medical applications of systems biology approaches with special focus on host-pathogen interactions. After introducing general ideas of systems biology, we focus on (1) the detection of putative biomarkers for improved diagnosis and support of therapeutic decisions, (2) network modelling for the identification of regulatory interactions between cellular molecules to reveal putative drug targets and (3) module discovery for the detection of phenotype-specific modules in molecular interaction networks. Biomarker detection applies supervised machine learning methods utilizing high-throughput data (e.g. single nucleotide polymorphism (SNP) detection, RNA-seq, proteomics) and clinical data. We demonstrate structural analysis of molecular networks, especially by identification of disease modules as a novel strategy, and discuss possible applications to host-pathogen interactions. Pioneering work was done to predict molecular host-pathogen interactions networks based on dual RNA-seq data. However, currently this network modelling is restricted to a small number of genes. With increasing number and quality of databases and data repositories, the prediction of large-scale networks will also be feasible that can used for multidimensional diagnosis and decision support for prevention and therapy of diseases. Finally, we outline further perspective issues such as support of personalized medicine with high-throughput data and generation of multiscale host-pathogen interaction models.

Genome-Wide Expression Profiling Reveals S100B as Biomarker for Invasive Aspergillosis

Invasive aspergillosis (IA) is a devastating opportunistic infection and its treatment constitutes a considerable burden for the health care system. Immunocompromised patients are at an increased risk for IA, which is mainly caused by the species Aspergillus fumigatus. An early and reliable diagnosis is required to initiate the appropriate antifungal therapy. However, diagnostic sensitivity and accuracy still needs to be improved, which can be achieved at least partly by the definition of new biomarkers. Besides the direct detection of the pathogen by the current diagnostic methods, the analysis of the host response is a promising strategy toward this aim. Following this approach, we sought to identify new biomarkers for IA. For this purpose, we analyzed gene expression profiles of hematological patients and compared profiles of patients suffering from IA with non-IA patients. Based on microarray data, we applied a comprehensive feature selection using a random forest classifier. We identified the transcript coding for the S100 calcium-binding protein B (S100B) as a potential new biomarker for the diagnosis of IA. Considering the expression of this gene, we were able to classify samples from patients with IA with 82.3% sensitivity and 74.6% specificity. Moreover, we validated the expression of S100B in a real-time reverse transcription polymerase chain reaction (RT-PCR) assay and we also found a down-regulation of S100B in A. fumigatus stimulated DCs. An influence on the IL1B and CXCL1 downstream levels was demonstrated by this S100B knockdown. In conclusion, this study covers an effective feature selection revealing a key regulator of the human immune response during IA. S100B may represent an additional diagnostic marker that in combination with the established techniques may improve the accuracy of IA diagnosis.

An Omics Perspective on Candida Infections: Toward Next-Generation Diagnosis and Therapy

Candida species can cause severe infections associated with high morbidity and mortality. Therefore, it is essential to gain more insight into the anti-fungal host defense response. The advent of omics technology and development of advanced systems biology tools has permitted to approach this in an unbiased and quantitative manner. This review summarizes the insights gained on anti-Candida immunity from genetic-, transcriptome-, proteome-, metabolome-, microbiome-, mycobiome-, and computational systems biology studies and discusses practical aspects and future perspectives.