Identifizierung von Biomarkersignaturen zur Diskriminierung der Pneumokokken-Pneumonie von der Staphylokokken-Pneumonie

Bacterial pneumonia is still a major cause of morbidity and mortality worldwide. One of the reasons for this may be the lack of accurate diagnostic tests that results in delayed identification of the causative agent and subsequent delay in initiating appropriate therapy. In this regard, the objective of this thesis was the identification of host biomarkers which could discriminate between pneumococcal pneumonia and staphylococcal pneumonia in experimental murine infection models using transcriptomics, metabolomics and lipidomics. A genome-wide gene expression profile was determined in the lungs and blood of mice intranasally infected with S. pneumoniae or with S. aureus using RNA-Sequencing. PCA identified the transcriptional signature for staphylococcal infection including the expression of Arg1, Defb3, Cxcl3, Ccr3, Cycs and Ear6 in lung tissue and various small nucleolar RNAs and mitochondrially encoded RNA genes as well as Nrxn3 in peripheral blood. S. pneumonia-specific transcriptional signature in the lungs and in peripheral blood comprises the expression of IFN-induced genes. Metabolic profiling was performed in lung tissue and plasma of infected mice using a targeted metabolomics approach. ROC curve analysis identified 18 metabolites with an AUC of 1 in lung tissue. Predictive models were built to identify optimal combinations of lung and blood metabolites for classifying samples as belonging to either S. pneumoniae or S. aureus infection. In plasma samples a optimal combination of 25 metabolites including 3 acetylcarnitines (C3, C8, C3-DC C4-OH), 1 phosphatidylcholine (PC), 4 lysoPCs, 5 amino acids (Ile, Leu, Met, Tyr, Val), 1 biogenic amine and 1 sphingolipid with the highest average importances was predicted to discriminate S. pneumoniae from S. aureus infection. ROC curve analysis of single plasma lipids that could discriminate between S. pneumoniae and S. aureus lung infection provided a list of 14 lipids. On the other hand, a combination of 25 lipids including 5 lysoPCs, 4 lysophosphatidylethanolamine, 3 phosphatidylinositols, 1 TAG, 1 PCs and 1 cholesterol ester with the highest average importances was predicted as optimal to discriminate S. pneumoniae from S. aureus infection. Overall, this study shows the utility of multi-omics data to identify signatures that can be used to differentiate between S. pneumoniae and S. aureus. Further studies with human samples will be needed to validate the identified pathogen-specific signatures.Bakterielle Pneumonie ist eine der Hauptursachen für Morbidität und Mortalität weltweit. Einer der Gründe liegt im Fehlen genauer diagnostischer Tests, die zu einer verzögerten Identifizierung des verursachenden Erregers und Initiierung einer nicht-geeigneten Therapie führen. Ziel dieser Arbeit war die Identifizierung von Wirts-Biomarkern, die Pneumokokken-Pneumonie von Staphylokokken-Pneumonie in einem experimentellen Maus-Infektionsmodell unter Verwendung von Transkriptomik, Metabolomik und Lipidomik unterscheiden können. Zu diesem Zweck wurde das transkriptionale Profil im Lungengewebe und peripheren Blut von S. pneumoniae und S. aureus infizierten Mäusen mittels RNA-Sequenzierung bestimmt. Mittels Hauptkomponentenanalyse identifizierte die Faktorladung der ersten Hauptkomponente die transkriptionale Staphylokokken Signatur mit der Expression von Arg1, Defb3, CXCL3, CCR3, Cycs und Ear6 in Lungengewebe und verschiedene kleine nukleoläre RNAs und mitochondrial kodierte RNA-Gene sowie Nrxn3 im peripheren Blut. Im Gegensatz dazu umfasst die S. pneumoniae-spezifische transkriptionale Signatur in der Lunge und im peripheren Blut Interferon induzierte Gene. Die Grenzwertoptimierungskurvenanalyse (ROC) identifizierte 18 Lungenmetabolite mit einer Fläche unter der ROC-Kurve (AUC) von 1. Mit Hilfe multivariater statistischer Methoden wurden verschiedene Vorhersagemodelle generiert, um die optimale Kombination von Metaboliten für die Klassifizierung nach Infektionserregern (S. pneumoniae oder S. aureus) zu identifizieren. Das beste Vorhersagemodell für Lungengewebe mit 5 Metaboliten und einer AUC von 1 setzte sich zusammen aus 3 Carnitinen (C2, C3, C4), Histamin und die Summe von Hexosen (H1). Für Plasmaproben bestand das optimale Vorhersagemodell aus 25 Metaboliten, darunter 3 Acylcarnitine (C3, C8, C3-DC C4-OH), 4 lysoPCs, 5 Aminosäuren (Ile, Leu, Met, Tyr und Val), 1 PC, 1 biogenes Amine und 1 Sphingolipid mit der höchsten Gewichtigkeit für das Modell. Die ROC-Kurvenanalyse zwischen S. pneumoniae und S. aureus infizierten Plasmaproben identifizierte 14 Lipide. Das optimale Vorhersagemodell bestand aus 25 Metaboliten, darunter 5 lysoPCs, 4 lysoPE, 3 Phosphatidylinositols, 1 TAG, 1 PCs und 1 Cholesterinester mit der höchsten Gewichtigkeit für das Modell. Die aktuelle Studie liefert zusätzliche Hinweise darauf, dass die transkriptionale und metabolische Signatur bei einer Infektion zur Differenzierung zwischen S. pneumoniae und S. aureus verwendet werden kann

Changed expression of cytoskeleton proteins during lung injury in a mouse model of Streptococcus pneumoniae infection

Infections by Streptococcus pneumoniae are a major cause of morbidity and mortality worldwide, often causing community-acquired pneumonia, otitis media and also bacteremia and meningitis. Studies on S. pneumoniae are mainly focused on its virulence or capacity to evade the host immune system, but little is known about the injury caused in lungs during a pneumococcal infection. Herein we investigated this issue comparing the proteome profile of lungs from S. pneumoniae-infected mice with control mice by means of difference gel electrophoresis (DIGE) technology. In order to obtain reliable results three biological replicas were used, and four technical replicas were carried out in each biological replica. Proteomic comparison was performed at two time points: 24 and 48 h post infection. A total of 91 proteins were identified with different abundance. We found important changes in the protein profiles during pneumococcal infection mainly associated with regulation of vesicle-mediated transport, wound healing, and cytoskeleton organization. In conclusion, the results obtained show that the cytoskeleton of the host cell is modified in S. pneumoniae infection

In-vitro Selektion von DNA-Molekülen für Schimmelpilz-Biosensoren

Schimmelpilze in Innenräumen können bei den Bewohnern verschiedene Gesundheitsprobleme auslösen, wie beispielsweise Mykosen, Mykotoxikosen und Allergien. Standardmethoden für die Quantifizierung und Identifizierung der Pilze sind oft zeitaufwändig und nur durch qualifiziertes Personal durchführbar. Deshalb entwickeln wir unter Nutzung des SELEX-Verfahrens ssDNA – Aptamere für die Sporen von Aspergillus- und Penicillium- Stämmen, die in Biosensoren für die Detektion von Schimmelpilzen in Innenräumen eingesetzt werden sollen. Beide Stämme gehören zu den häufig in Innenräumen auftretenden Schimmelpilzen, von denen bekannt ist, daß sie verschiedene Mykotoxine freisetzen. Die Gewinnung von Aptameren erfolgt unter Verwendung einer evolutionären Methode, dem SELEX-Verfahren (Systematic Evolution of Ligands by Exponential Enrichment). Dabei werden in einem Bindungsschritt aus einem Pool verschiedenster einzelsträngiger DNA-Moleküle (ss DNA) zunächst diejenigen gewonnen, die aufgrund ihrer individuellen Struktur bevorzugt an das Zielmolekül binden. Nach ihrer Ablösung von den Zielmolekülen (intakte Sporen) werden sie mittels PCR vervielfältigt. Dabei enstehen Doppelstränge, von denen die für die Bindungsreaktion an die Zielmoleküle überflüssigen Gegenstränge abgetrennt werden müssen. Dies geschieht über die Spaltung einer Ribose-Bindung im Gegenstrang, die durch modifizierter Primer eingeführt wurde. In dem auf diese Weise entstandenen Pool sind nun besser bindende ssDNA Moleküle angereichert, die für die Bindungsreaktion der nächsten Selektionsrunde zur Verfügung stehen. Dieser Prozeß wird so lange wiederholt, bis keine weitere Verbesserung der Affinität erfolgt. Die Affinität der auf diesem Wege hergestellten Aptamere wird unter Verwendung verschiedener Methoden getestet. Dazu gehören ein Standard-Filter-Bindungs-Test, eine fluoreszenz-mikroskopische Methode, die Messung der Fluoreszenzdepolarisation und die Anwendung der Resonant Mirror Spektroskopie

An Interferon Signature Discriminates Pneumococcal From Staphylococcal Pneumonia

Streptococcus pneumoniae is the most common cause of community-acquired pneumonia (CAP). Despite the low prevalence of CAP caused by methicillin-resistant Staphylococcus aureus (MRSA), CAP patients often receive empirical antibiotic therapy providing coverage for MRSA such as vancomycin or linezolid. An early differentiation between S. pneumoniae and S. aureus pneumonia can help to reduce the use of unnecessary antibiotics. The objective of this study was to identify candidate biomarkers that can discriminate pneumococcal from staphylococcal pneumonia. A genome-wide transcriptional analysis of lung and peripheral blood performed in murine models of S. pneumoniae and S. aureus lung infection identified an interferon signature specifically associated with S. pneumoniae infection. Prediction models built using a support vector machine and Monte Carlo cross-validation, identified the combination of the interferon-induced chemokines CXCL9 and CXCL10 serum concentrations as the set of biomarkers with best sensitivity, specificity, and predictive power that enabled an accurate discrimination between S. pneumoniae and S. aureus pneumonia. The predictive performance of these biomarkers was further validated in an independent cohort of mice. This study highlights the potential of serum CXCL9 and CXCL10 biomarkers as an adjunctive diagnostic tool that could facilitate prompt and correct pathogen-targeted therapy in CAP patients

Size Matters: Problems and Advantages Associated with Highly Miniaturized Sensors

There is no doubt that the recent advances in nanotechnology have made it possible to realize a great variety of new sensors with signal transduction mechanisms utilizing physical phenomena at the nanoscale. Some examples are conductivity measurements in nanowires, deflection of cantilevers and spectroscopy of plasmonic nanoparticles. The fact that these techniques are based on the special properties of nanostructural entities provides for extreme sensor miniaturization since a single structural unit often can be used as transducer. This review discusses the advantages and problems with such small sensors, with focus on biosensing applications and label-free real-time analysis of liquid samples. Many aspects of sensor design are considered, such as thermodynamic and diffusion aspects on binding kinetics as well as multiplexing and noise issues. Still, all issues discussed are generic in the sense that the conclusions apply to practically all types of surface sensitive techniques. As a counterweight to the current research trend, it is argued that in many real world applications, better performance is achieved if the active sensor is larger than that in typical nanosensors. Although there are certain specific sensing applications where nanoscale transducers are necessary, it is argued herein that this represents a relatively rare situation. Instead, it is suggested that sensing on the microscale often offers a good compromise between utilizing some possible advantages of miniaturization while avoiding the complications. This means that ensemble measurements on multiple nanoscale sensors are preferable instead of utilizing a single transducer entity

Nitrite Biosensing via Selective Enzymes—A Long but Promising Route

The last decades have witnessed a steady increase of the social and political awareness for the need of monitoring and controlling environmental and industrial processes. In the case of nitrite ion, due to its potential toxicity for human health, the European Union has recently implemented a number of rules to restrict its level in drinking waters and food products. Although several analytical protocols have been proposed for nitrite quantification, none of them enable a reliable and quick analysis of complex samples. An alternative approach relies on the construction of biosensing devices using stable enzymes, with both high activity and specificity for nitrite. In this paper we review the current state-of-the-art in the field of electrochemical and optical biosensors using nitrite reducing enzymes as biorecognition elements and discuss the opportunities and challenges in this emerging market

A Nitrite Biosensor Based on Co-immobilization of Nitrite Reductase and Viologen-modified Chitosan on a Glassy Carbon Electrode

An electrochemical nitrite biosensor based on co-immobilization of copper- containing nitrite reductase (Cu-NiR, from Rhodopseudomonas sphaeroides forma sp. denitrificans) and viologen-modified chitosan (CHIT-V) on a glassy carbon electrode (GCE) is presented. Electron transfer (ET) between a conventional GCE and immobilized Cu-NiR was mediated by the co-immobilized CHIT-V. Redox-active viologen was covalently linked to a chitosan backbone, and the thus produced CHIT-V was co-immobilized with Cu-NiR on the GCE surface by drop-coating of hydrophilic polyurethane (HPU). The electrode responded to nitrite with a limit of detection (LOD) of 40 nM (S/N = 3). The sensitivity, linear response range, and response time (t90%) were 14.9 nA/μM, 0.04−11 μM (r2 = 0.999) and 15 s, respectively. The corresponding Lineweaver-Burk plot showed that the apparent Michaelis-Menten constant (KMapp) was 65 μM. Storage stability of the biosensor (retaining 80% of initial activity) was 65 days under ambient air and room temperature storage conditions. Reproducibility of the sensor showed a relative standard deviation (RSD) of 2.8% (n = 5) for detection of 1 μM of nitrite. An interference study showed that anions commonlyfound in water samples such as chlorate, chloride, sulfate and sulfite did not interfere with the nitrite detection. However, nitrate interfered with a relative sensitivity of 64% and this interference effect was due to the intrinsic character of the NiR employed in this study

Electrochemical aptasensor for human osteopontin detection using a DNA aptamer selected by SELEX

A DNA aptamer with affinity and specificity for human osteopontin (OPN), a potential breast cancer biomarker, was selected using the SELEX process, considering its homology rate and the stability of its secondary structures. This aptamer exhibited a satisfactory affinity towards OPN, showing dissociation constants lower than 2.5 nM. It was further used to develop a simple, label-free electrochemical aptasensor against OPN. The aptasensor showed good sensitivity towards OPN in standard solutions, being the square wave voltammetry (SWV), compared to the cyclic voltammetry, the most sensitive technique with detection and quantification limits of 1.4 ± 0.4 nM and 4.2 ± 1.1 nM, respectively. It showed good reproducibility and acceptable selectivity, exhibiting low signal interferences from other proteins, as thrombin, with 2.610 times lower current signals-off than for OPN. The aptasensor also successfully detected OPN in spiked synthetic human plasma. Using SWV, detection and quantification limits (1.3 ± 0.1 and 3.9 ± 0.4 nM) within the OPN plasma levels reported for patients with breast cancer (0.44.5 nM) or with metastatic or recurrent breast cancer (0.98.4 nM) were found. Moreover, preliminary assays, using a sample of human plasma, showed that the aptasensor and the standard ELISA method quantified similar OPN levels (2.2 ± 0.7 and 1.7 ± 0.1 nM, respectively). Thus, our aptasensor coupled with SWV represents a promising alternative for the detection of relevant breast cancer biomarkers.The authors acknowledge the financial support from the Strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684), and from project BioTecNorte (project number NORTE-01-0145-FEDER-000004). This work was also financially supported by Project POCI-01–0145-FEDER-006984 – Associate Laboratory LSRE-LCM and by Project UID/QUI/00616/2013 – CQ-VR both funded by FEDER - Fundo Europeu de Desenvolvimento Regional through COMPETE2020 - Programa Operacional Competitividade e Internacionalização (POCI) – and by national funds through FCT - Fundação para a Ciência e a Tecnologia, Portugal. S. Meirinho also acknowledges the research grant provided by Project UID/EQU/50020/2013.info:eu-repo/semantics/publishedVersio

Luminescent detection of DNA-binding proteins

Transcription factors play a central role in cell development, differentiation and growth in biological systems due to their ability to regulate gene expression by binding to specific DNA sequences within the nucleus. The dysregulation of transcription factor signaling has been implicated in the pathogenesis of a number of cancers, developmental disorders, inflammation and autoimmunity. There is thus a high demand for convenient high-throughput methodologies able to detect sequence-specific DNA-binding proteins and monitor their DNA-binding activities. Traditional approaches for protein detection include gel mobility shift assays, DNA footprinting and enzyme-linked immunosorbent assays (ELISAs) which tend to be tedious, time-consuming, and may necessitate the use of radiographic labeling. By contrast, luminescence technologies offer the potential for rapid, sensitive and low-cost detection that are amenable to high-throughput and real-time analysis. The discoveries of molecular beacons and aptamers have spearheaded the development of new luminescent methodologies for the detection of proteins over the last decade. We survey here recent advances in the development of luminescent detection methods for DNA-binding proteins, including those based on molecular beacons, aptamer beacons, label-free techniques and exonuclease protection

Design Strategies for Aptamer-Based Biosensors

Aptamers have been widely used as recognition elements for biosensor construction, especially in the detection of proteins or small molecule targets, and regarded as promising alternatives for antibodies in bioassay areas. In this review, we present an overview of reported design strategies for the fabrication of biosensors and classify them into four basic modes: target-induced structure switching mode, sandwich or sandwich-like mode, target-induced dissociation/displacement mode and competitive replacement mode. In view of the unprecedented advantages brought about by aptamers and smart design strategies, aptamer-based biosensors are expected to be one of the most promising devices in bioassay related applications