Integrated Univariate, Multivariate, and Correlation-Based Network Analyses Reveal Metabolite-Specific Effects on Bacterial Growth and Biofilm Formation in Necrotizing Soft Tissue Infections

Characteristic Metabolic Changes in Skeletal Muscle Due to Vibrio vulnificus Infection in a Wound Infection Model

Vibrio vulnificus is a bacterium that inhabits warm seawater or brackish water environments and causes foodborne diseases and wound infections. In severe cases, V. vulnificus invades the skeletal muscle tissue, where bacterial proliferation leads to septicemia and necrotizing fasciitis with high mortality. Despite this characteristic, information on metabolic changes in tissue infected with V. vulnificus is not available. Here, we elucidated the metabolic changes in V. vulnificus-infected mouse skeletal muscle using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS). Metabolome analysis revealed changes in muscle catabolites and energy metabolites during V. vulnificus infection. In particular, succinic acid accumulated but fumaric acid decreased in the infected muscle. However, the virulence factor deletion mutant revealed that changes in metabolites and bacterial proliferation were abolished in skeletal muscle infected with a multifunctional-autoprocessing repeats-in-toxin (MARTX) mutant. On the other hand, mice that were immunosuppressed via cyclophosphamide (CPA) treatment exhibited a similar level of bacterial counts and metabolites between the wild type and MARTX mutant. Therefore, our data indicate that V. vulnificus induces metabolic changes in mouse skeletal muscle and proliferates by using the MARTX toxin to evade the host immune system. This study indicates a new correlation between V. vulnificus infections and metabolic changes that lead to severe reactions or damage to host skeletal muscle. IMPORTANCE V. vulnificus causes necrotizing skin and soft tissue infections (NSSTIs) in severe cases, with high mortality and sign of rapid deterioration. Despite the severity of the infection, the dysfunction of the host metabolism in skeletal muscle triggered by V. vulnificus is poorly understood. In this study, by using a mouse wound infection model, we revealed characteristic changes in muscle catabolism and energy metabolism in skeletal muscle associated with bacterial proliferation in the infected tissues. Understanding such metabolic changes in V. vulnificus-infected tissue may provide crucial information to identify the mechanism via which V. vulnificus induces severe infections. Moreover, our metabolite data may be useful for the recognition, identification, or detection of V. vulnificus infections in clinical studies.

Architecture and matrix assembly determinants of Bordetella pertussis biofilms on primary human airway epithelium

Traditionally, whooping cough or pertussis caused by the obligate human pathogen Bordetella pertussis (Bp) is described as an acute disease with severe symptoms. However, many individuals who contract pertussis are either asymptomatic or show very mild symptoms and yet can serve as carriers and sources of bacterial transmission. Biofilms are an important survival mechanism for bacteria in human infections and disease. However, bacterial determinants that drive biofilm formation in humans are ill-defined. In the current study, we show that Bp infection of well-differentiated primary human bronchial epithelial cells leads to formation of bacterial aggregates, clusters, and highly structured biofilms which are colocalized with cilia. These findings mimic observations from pathological analyses of tissues from pertussis patients. Distinct arrangements (mono-, bi-, and tri-partite) of the polysaccharide Bps, extracellular DNA, and bacterial cells were visualized, suggesting complex heterogeneity in bacteria-matrix interactions. Analyses of mutant biofilms revealed positive roles in matrix production, cell cluster formation, and biofilm maturity for three critical Bp virulence factors: Bps, filamentous hemagglutinin, and adenylate cyclase toxin. Adherence assays identified Bps as a new Bp adhesin for primary human airway cells. Taken together, our results demonstrate the multi-factorial nature of the biofilm extracellular matrix and biofilm development process under conditions mimicking the human respiratory tract and highlight the importance of model systems resembling the natural host environment to investigate pathogenesis and potential therapeutic strategies.

Prevalence of biofilms in acute infections challenges a longstanding paradigm

The significance of bacterial biofilm formation in chronic bacterial lung infections has long been recognized [1]. Likewise, chronic biofilm formation on medical devices is well accepted as a nidus for recurrent bacteremia [2,3]. Even though the prevailing paradigm relies on the dominance of planktonic bacteria in acute endobronchial infections, our understanding of the bacterial organization during acute infection is, so far, limited - virtually absent. However, by comparing similar clinical samples, we have recently demonstrated massive bacterial biofilm formation during acute lung infections resembling the immense bacterial biofilm formation during chronic lung infections. These findings pose major challenges to the basic paradigm of chronic infections being dominated by biofilm forming bacteria while acute infections are dominated by planktonic bacteria. As opposed to the similar high amount of bacterial biofilm found in chronic and acute lung infections, we found that the fast bacterial growth in acute lung infections differed from the slow bacterial growth in chronic lung infections. By highlighting these new findings, we review modes of improved treatment of biofilm infections and the relevance of bacterial growth rates for other bacterial biofilm infections than human lung infections.

Analysis of Metabolite and Lipid Association Networks Reveals Molecular Mechanisms Associated with 3-Month Mortality and Poor Functional Outcomes in Patients with Acute Ischemic Stroke after Thrombolytic Treatment with Recombinant Tissue Plasminogen Activator

Here, we present an integrated multivariate, univariate, network reconstruction and differential analysis of metabolite–metabolite and metabolite–lipid association networks built from an array of 18 serum metabolites and 110 lipids identified and quantified through nuclear magnetic resonance spectroscopy in a cohort of 248 patients, of which 22 died and 82 developed a poor functional outcome within 3 months from acute ischemic stroke (AIS) treated with intravenous recombinant tissue plasminogen activator. We explored differences in metabolite and lipid connectivity of patients who did not develop a poor outcome and who survived the ischemic stroke from the related opposite conditions. We report statistically significant differences in the connectivity patterns of both low- and high-molecular-weight metabolites, implying underlying variations in the metabolic pathway involving leucine, glycine, glutamine, tyrosine, phenylalanine, citric, lactic, and acetic acids, ketone bodies, and different lipids, thus characterizing patients’ outcomes. Our results evidence the promising and powerful role of the metabolite–metabolite and metabolite–lipid association networks in investigating molecular mechanisms underlying AIS patient’s outcome.

Discriminatory plasma biomarkers predict specific clinical phenotypes of necrotizing soft-tissue infections

BACKGROUNDNecrotizing soft-tissue infections (NSTIs) are rapidly progressing infections frequently complicated by septic shock and associated with high mortality. Early diagnosis is critical for patient outcome, but challenging due to vague initial symptoms. Here, we identified predictive biomarkers for NSTI clinical phenotypes and outcomes using a prospective multicenter NSTI patient cohort.METHODSLuminex multiplex assays were used to assess 36 soluble factors in plasma from NSTI patients with positive microbiological cultures (n = 251 and n = 60 in the discovery and validation cohorts, respectively). Control groups for comparative analyses included surgical controls (n = 20), non-NSTI controls (i.e., suspected NSTI with no necrosis detected upon exploratory surgery, n = 20), and sepsis patients (n = 24).RESULTSThrombomodulin was identified as a unique biomarker for detection of NSTI (AUC, 0.95). A distinct profile discriminating mono- (type II) versus polymicrobial (type I) NSTI types was identified based on differential expression of IL-2, IL-10, IL-22, CXCL10, Fas-ligand, and MMP9 (AUC >0.7). While each NSTI type displayed a distinct array of biomarkers predicting septic shock, granulocyte CSF (G-CSF), S100A8, and IL-6 were shared by both types (AUC >0.78). Finally, differential connectivity analysis revealed distinctive networks associated with specific clinical phenotypes.CONCLUSIONSThis study identifies predictive biomarkers for NSTI clinical phenotypes of potential value for diagnostic, prognostic, and therapeutic approaches in NSTIs.TRIAL REGISTRATIONClinicalTrials.gov NCT01790698.FUNDINGCenter for Innovative Medicine (CIMED); Region Stockholm; Swedish Research Council; European Union; Vinnova; Innovation Fund Denmark; Research Council of Norway; Netherlands Organisation for Health Research and Development; DLR Federal Ministry of Education and Research; and Swedish Children's Cancer Foundation.

Exploration of Blood Lipoprotein and Lipid Fraction Profiles in Healthy Subjects through Integrated Univariate, Multivariate, and Network Analysis Reveals Association of Lipase Activity and Cholesterol Esterification with Sex and Age

In this study, we investigated blood lipoprotein and lipid fraction profiles, quantified using nuclear magnetic resonance, in a cohort of 844 healthy blood donors, integrating standard univariate and multivariate analysis with predictive modeling and network analysis. We observed a strong association of lipoprotein and lipid main fraction profiles with sex and age. Our results suggest an age-dependent remodulation of lipase lipoprotein activity in men and a change in the mechanisms controlling the ratio between esterified and non-esterified cholesterol in both men and women.

The INFECT-Project: An International and Multidisciplinary Project on Necrotizing Soft Tissue Infections

This book describes clinical and microbiologic aspects, pathogenesis, and diagnostics, related to the severe and rapidly spreading necrotizing soft tissue infections. The work has its foundation in a recently completed European Union funded FP7-project called INFECT, which during the period 2013-2018 focused on utilizing a systems medicine approach to increase our understanding of these heterogenous and complex life-threatening infections. In this chapter, the aim and scope as well as key achievements of the INFECT-project are described.

Systems Biology and Biomarkers in Necrotizing Soft Tissue Infections

In necrotizing soft tissue infection (NSTI) there is a need to identify biomarker sets that can be used for diagnosis and disease management. The INFECT study was designed to obtain such insights through the integration of patient data and results from different clinically relevant experimental models by use of systems biology approaches. This chapter describes the current state of biomarkers in NSTI and how biomarkers are categorized. We introduce the fundamentals of top-down systems biology approaches including analysis tools and we review the use of current methods and systems biology approaches to biomarker discover. Further, we discuss how different "omics" signatures (gene expression, protein, and metabolites) from NSTI patient samples can be used to identify key host and pathogen factors involved in the onset and development of infection, as well as exploring associations to disease outcomes.

Differential Network Analysis Reveals Molecular Determinants Associated with Blood Pressure and Heart Rate in Healthy Subjects

There is mounting evidence that subclinical nonpathological high blood pressure and heart rate during youth and adulthood steadily increase the risk of developing a cardiovascular disease at a later stage. For this reason, it is important to understand the mechanisms underlying the subclinical elevation of blood pressure and heart rate in healthy, relatively young individuals. In the present study, we present a network-based metabolomic study of blood plasma metabolites and lipids measured using nuclear magnetic resonance spectroscopy on 841 adult healthy blood donor volunteers, which were stratified for subclinical low and high blood pressure (systolic and diastolic) and heart rate. Our results indicate a rewiring of metabolic pathways active in high and low groups, indicating that the subjects with subclinical high blood pressure and heart rate could present latent cardiometabolic dysregulations.

Evaluation of Single Sample Network Inference Methods for Metabolomics-Based Systems Medicine

Networks and network analyses are fundamental tools of systems biology. Networks are built by inferring pair-wise relationships among biological entities from a large number of samples such that subject-specific information is lost. The possibility of constructing these sample (individual)-specific networks from single molecular profiles might offer new insights in systems and personalized medicine and as a consequence is attracting more and more research interest. In this study, we evaluated and compared LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples) and ssPCC (single sample network based on Pearson correlation) in the metabolomics context of metabolite–metabolite association networks. We illustrated and explored the characteristics of these two methods on (i) simulated data, (ii) data generated from a dynamic metabolic model to simulate real-life observed metabolite concentration profiles, and (iii) 22 metabolomic data sets and (iv) we applied single sample network inference to a study case pertaining to the investigation of necrotizing soft tissue infections to show how these methods can be applied in metabolomics. We also proposed some adaptations of the methods that can be used for data exploration. Overall, despite some limitations, we found single sample networks to be a promising tool for the analysis of metabolomics data.

Integration of whole-body [18F]FDG PET/MRI with non-targeted metabolomics can provide new insights on tissue-specific insulin resistance in type 2 diabetes

Alteration of various metabolites has been linked to type 2 diabetes (T2D) and insulin resistance. However, identifying significant associations between metabolites and tissue-specific phenotypes requires a multi-omics approach. In a cohort of 42 subjects with different levels of glucose tolerance (normal, prediabetes and T2D) matched for age and body mass index, we calculated associations between parameters of whole-body positron emission tomography (PET)/magnetic resonance imaging (MRI) during hyperinsulinemic euglycemic clamp and non-targeted metabolomics profiling for subcutaneous adipose tissue (SAT) and plasma. Plasma metabolomics profiling revealed that hepatic fat content was positively associated with tyrosine, and negatively associated with lysoPC(P-16:0). Visceral adipose tissue (VAT) and SAT insulin sensitivity (K_i), were positively associated with several lysophospholipids, while the opposite applied to branched-chain amino acids. The adipose tissue metabolomics revealed a positive association between non-esterified fatty acids and, VAT and liver K_i. Bile acids and carnitines in adipose tissue were inversely associated with VAT K_i. Furthermore, we detected several metabolites that were significantly higher in T2D than normal/prediabetes. In this study we present novel associations between several metabolites from SAT and plasma with the fat fraction, volume and insulin sensitivity of various tissues throughout the body, demonstrating the benefit of an integrative multi-omics approach.

On the Use of Correlation and MI as a Measure of Metabolite-Metabolite Association for Network Differential Connectivity Analysis

Metabolite differential connectivity analysis has been successful in investigating potential molecular mechanisms underlying different conditions in biological systems. Correlation and Mutual Information (MI) are two of the most common measures to quantify association and for building metabolite-metabolite association networks and to calculate differential connectivity. In this study, we investigated the performance of correlation and MI to identify significantly differentially connected metabolites. These association measures were compared on (i) 23 publicly available metabolomic data sets and 7 data sets from other fields, (ii) simulated data with known correlation structures, and (iii) data generated using a dynamic metabolic model to simulate real-life observed metabolite concentration profiles. In all cases, we found more differentially connected metabolites when using correlation indices as a measure for association than MI. We also observed that different MI estimation algorithms resulted in difference in performance when applied to data generated using a dynamic model. We concluded that there is no significant benefit in using MI as a replacement for standard Pearson's or Spearman's correlation when the application is to quantify and detect differentially connected metabolites.

Systems and Precision Medicine in Necrotizing Soft Tissue Infections

Necrotizing soft tissue infections (NSTI) are multifactorial and characterized by dysfunctional, time dependent, highly varying hyper- to hypo-inflammatory host responses contributing to disease severity. Furthermore, host-pathogen interactions are diverse and difficult to identify and characterize, due to the many different disease endotypes. There is a need for both refined bedside diagnostics as well as novel targeted treatment options to improve outcome in NSTI. In order to achieve clinically relevant results and to guide preclinical and clinical research the vast amount of fragmented clinical and experimental datasets, which often include omics data at different levels (transcriptomics, proteomics, metabolomics, etc.), need to be organized, harmonized, integrated, and analyzed taking into account the Big Data nature of these datasets. In this chapter, we address these matters from a systems perspective and yet personalized approach. The chapter provides an overview on the increasingly more frequent use of Big Data and Artificial Intelligence (AI) to aggregate and generate knowledge from burgeoning clinical and biochemical information, addresses the challenges to manage this information, and summarizes current efforts to develop robust computer-aided clinical decision support systems so to tackle the serious challenges in NSTI diagnosis, stratification, and optimized tailored therapy.