The good, the bad and the tick

How tick-borne pathogens (TBPs) could help us understand cancer? The diversity of pathogens transmitted by ticks is higher than that of any other known arthropod vector and includes protozoa (e.g., Babesia spp. and Theileria spp.), bacteria (e.g., intracellular Rickettsia spp. and extracellular Borrelia spp.), viruses (e.g., Tick-borne encephalitis (TBE) and Crimean-Congo hemorrhagic fever (CCHF) virus), helminths (e.g., Cercopithifilaria) and, although less known, fungi (e.g., Dermatophilus) (Otranto et al., 2013; Brites-Neto et al., 2015; de la Fuente et al., 2017). TBPs have complex life cycles that involve vertebrate hosts and the ticks. Intracellular TBP infection triggers cellular and molecular responses that change host cell physiology in fundamental ways. Within vertebrate host cells, the apicomplexan parasites Theileria parva and Theileria annulata activate molecular pathways that result in increased production of reactive oxygen species (ROS), cell immortalization, cancer and host death. In contrast, infection by the rickettsia Anaplasma phagocytophilum inhibits apoptosis, block the production of ROS and results in a self-limiting infection that rarely is lethal for the host. Theileria spp. and A. phagocytophilum modulates host cell response by inducing transcriptional reprogramming of their vertebrate host cells, leukocytes. Transcriptional reprogramming is induced by pathogen-encoded effector proteins that modify host epigenetic pathways that affect not only gene transcription but also protein levels

A side-effect free method for identifying cancer drug targets

Identifying efective drug targets, with little or no side efects, remains an ever challenging task. A potential pitfall of failing to uncover the correct drug targets, due to side efect of pleiotropic genes, might lead the potential drugs to be illicit and withdrawn. Simplifying disease complexity, for the investigation of the mechanistic aspects and identifcation of efective drug targets, have been done through several approaches of protein interactome analysis. Of these, centrality measures have always gained importance in identifying candidate drug targets. Here, we put forward an integrated method of analysing a complex network of cancer and depict the importance of k-core, functional connectivity and centrality (KFC) for identifying efective drug targets. Essentially, we have extracted the proteins involved in the pathways leading to cancer from the pathway databases which enlist real experimental datasets. The interactions between these proteins were mapped to build an interactome. Integrative analyses of the interactome enabled us to unearth plausible reasons for drugs being rendered withdrawn, thereby giving future scope to pharmaceutical industries to potentially avoid them (e.g. ESR1, HDAC2, F2, PLG, PPARA, RXRA, etc). Based upon our KFC criteria, we have shortlisted ten proteins (GRB2, FYN, PIK3R1, CBL, JAK2, LCK, LYN, SYK, JAK1 and SOCS3) as efective candidates for drug development

Application of Weighted Gene Co-expression Network Analysis for Data from Paired Design

Investigating how genes jointly affect complex human diseases is important, yet challenging. The network approach (e.g., weighted gene co-expression network analysis (WGCNA)) is a powerful tool. However, genomic data usually contain substantial batch effects, which could mask true genomic signals. Paired design is a powerful tool that can reduce batch effects. However, it is currently unclear how to appropriately apply WGCNA to genomic data from paired design. In this paper, we modified the current WGCNA pipeline to analyse high-throughput genomic data from paired design. We illustrated the modified WGCNA pipeline by analysing the miRNA dataset provided by Shiah et al. (2014), which contains forty oral squamous cell carcinoma (OSCC) specimens and their matched non-tumourous epithelial counterparts. OSCC is the sixth most common cancer worldwide. The modified WGCNA pipeline identified two sets of novel miRNAs associated with OSCC, in addition to the existing miRNAs reported by Shiah et al. (2014). Thus, this work will be of great interest to readers of various scientific disciplines, in particular, genetic and genomic scientists as well as medical scientists working on cancer

A simple and robust real-time qPCR method for the detection of PIK3CA mutations

PIK3CA mutations are seemingly the most common driver mutations in breast cancer with H1047R and E545K being the most common of these, accounting together for around 60% of all PIK3CA mutations and have promising therapeutic implications. Given the low sensitivity and the high cost of current genotyping methods we sought to develop fast, simple and inexpensive assays for PIK3CA H1047R and E545K mutation screening in clinical material. The methods we describe are based on a real-time PCR including a mutation specific primer combined with a non-productive oligonucleotide which inhibits wild-type amplification and a parallel internal control reaction. We demonstrate consistent detection of PIK3CA H1047R mutant DNA in genomic DNA extracted from frozen breast cancer biopsies, FFPE material or cancer cell lines with a detection sensitivity of approximately 5% mutant allele fraction and validate these results using both Sanger sequencing and deep next generation sequencing methods. The detection sensitivity for PIK3CA E545K mutation was approximately 10%. We propose these methods as simple, fast and inexpensive diagnostic tools to determine PIK3CA mutation status

Network Analysis Reveals A Signaling Regulatory Loop in the PIK3CA-mutated Breast Cancer Predicting Survival Outcome

Mutated genes are rarely common even in the same pathological type between cancer patients and as such, it has been very challenging to interpret genome sequencing data and difficult to predict clinical outcomes. PIK3CA is one of a few genes whose mutations are relatively popular in tumors. For example, more than 46.6% of luminal-A breast cancer samples have PIK3CA mutated, whereas only 35.5% of all breast cancer samples contain PIK3CA mutations. To understand the function of PIK3CA mutations in luminal A breast cancer, we applied our recently-proposed Cancer Hallmark Network Framework to investigate the network motifs in the PIK3CA-mutated luminal A tumors. We found that more than 70% of the PIK3CA-mutated luminal A tumors contain a positive regulatory loop where a master regulator (PDGF-D), a second regulator (FLT1) and an output node (SHC1) work together. Importantly, we found the luminal A breast cancer patients harboring the PIK3CA mutation and this positive regulatory loop in their tumors have significantly longer survival than those harboring PIK3CA mutation only in their tumors. These findings suggest that the underlying molecular mechanism of PIK3CA mutations in luminal A patients can participate in a positive regulatory loop, and furthermore the positive regulatory loop (PDGF-D/FLT1/SHC1) has a predictive power for the survival of the PIK3CA-mutated luminal A patients

Die Integration von Multiskalen- und Multi-Omik-Daten zur Erforschung von Wirt-Pathogen-Interaktionen am Beispiel von pathogenen Pilzen

The ongoing development and improvement of novel measurement techniques for scientific research result in a huge amount of available data coming from hetero- geneous sources. Amongst others, these sources comprise diverse temporal and spatial scales including different omics levels. The integration of such multiscale and multi-omics data enables a comprehensive understanding of the complexity and dynamics of biological systems and their processes. However, due to the biologically and methodically induced data heterogeneity, the integration process is a well-known challenge in nowadays life science. Applying several computational integration approaches, the present doctoral thesis aimed at gaining new insights into the field of infection biology regarding host- pathogen interactions. In this context, the focus was on fungal pathogens causing a variety of local and systemic infections. Based on current examples of research, on the one hand, several well-established approaches for the analysis of multiscale and multi- omics data have been presented. On the other hand, the novel ModuleDiscoverer approach was introduced to identify regulatory modules in protein-protein interac- tion networks. It has been shown that ModuleDiscoverer effectively supports the integration of multi-omics data and, in addition, allows the detection of potential key factors that cannot be detected by other classical approaches. This thesis provides deeper insights into the complex relationships and dynamics of biological systems and, thus, represents an important contribution to the investigation of host-pathogen interactions. Due to the interactions complexity and the limitations of the currently available knowledge databases as well as the bioinformatic tools, further research is necessary to gain a comprehensive understanding of the complexity of biological systems