Exploring secretory proteome and cytokine kinetic of human peripheral blood mononuclear cells exposed to methicillin-resistant Staphylococcus aureus biofilms and planktonic bacteria.

Staphylococcus aureus is a highly successful pathogen infecting various body parts and forming biofilms on natural and artificial surfaces resulting in difficult-to-treat and chronic infections. We investigated the secreted cytokines and proteomes of isolated peripheral blood mononuclear cells (PBMCs) from healthy volunteers exposed to methicillin-resistant S. aureus (MRSA) biofilms or planktonic bacteria. Additionally, the cytokine profiles in sera from patients with community-acquired pneumonia (CAP) caused by S. aureus were investigated. The aim was to gain insights into the immune response involved and differentiate between the planktonic and sessile MRSA forms. We identified 321 and 298 targets that were significantly differently expressed in PBMCs when exposed to planktonic or biofilm-embedded bacteria, respectively. PBMCs exposed to planktonic MRSA cells secreted increased levels of TNF-α, while IL-18 was elevated when exposed to the biofilm. The machine-learning analyses of the cytokine profiles obtained for the in vitro PBMCs and CAP sera distinguished between the two types of bacteria forms based on cytokines IL-18, IL12, and IL-17, and with a lower importance IL-6. Particularly, IL-18 which has not been correlated with S. aureus biofilms so far might represent a suitable marker for monitoring chronification during MRSA infection to individualize the therapy, but this hypothesis must be proved in clinical trials.

PITX1 Is a Regulator of TERT Expression in Prostate Cancer with Prognostic Power.

The current risk stratification in prostate cancer (PCa) is frequently insufficient to adequately predict disease development and outcome. One hallmark of cancer is telomere maintenance. For telomere maintenance, PCa cells exclusively employ telomerase, making it essential for this cancer entity. However, TERT, the catalytic protein component of the reverse transcriptase telomerase, itself does not suit as a prognostic marker for prostate cancer as it is rather low expressed. We investigated if, instead of TERT, transcription factors regulating TERT may suit as prognostic markers. To identify transcription factors regulating TERT, we developed and applied a new gene regulatory modeling strategy to a comprehensive transcriptome dataset of 445 primary PCa. Six transcription factors were predicted as TERT regulators, and most prominently, the developmental morphogenic factor PITX1. PITX1 expression positively correlated with telomere staining intensity in PCa tumor samples. Functional assays and chromatin immune-precipitation showed that PITX1 activates TERT expression in PCa cells. Clinically, we observed that PITX1 is an excellent prognostic marker, as concluded from an analysis of more than 15,000 PCa samples. PITX1 expression in tumor samples associated with (i) increased Ki67 expression indicating increased tumor growth, (ii) a worse prognosis, and (iii) correlated with telomere length.

Reprogramming of macrophages employing gene regulatory and metabolic network models.

Upon exposure to different stimuli, resting macrophages undergo classical or alternative polarization into distinct phenotypes that can cause fatal dysfunction in a large range of diseases, such as systemic infection leading to sepsis or the generation of an immunosuppressive tumor microenvironment. Investigating gene regulatory and metabolic networks, we observed two metabolic switches during polarization. Most prominently, anaerobic glycolysis was utilized by M1-polarized macrophages, while the biosynthesis of inosine monophosphate was upregulated in M2-polarized macrophages. Moreover, we observed a switch in the urea cycle. Gene regulatory network models revealed E2F1, MYC, PPARγ and STAT6 to be the major players in the distinct signatures of these polarization events. Employing functional assays targeting these regulators, we observed the repolarization of M2-like cells into M1-like cells, as evidenced by their specific gene expression signatures and cytokine secretion profiles. The predicted regulators are essential to maintaining the M2-like phenotype and function and thus represent potential targets for the therapeutic reprogramming of immunosuppressive M2-like macrophages.

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.