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New antimycotic drugs are challenging to find, as potential target proteins may have close human orthologs. We here focus on identifying metabolic targets that are critical for fungal growth and have minimal similarity to targets among human proteins. We compare and combine here: (I) direct metabolic network modeling using elementary mode analysis and flux estimates approximations using expression data, (II) targeting metabolic genes by transcriptome analysis of condition-specific highly expressed enzymes, and (III) analysis of enzyme structure, enzyme interconnectedness (“hubs”), and identification of pathogen-specific enzymes using orthology relations. We have identified 64 targets including metabolic enzymes involved in vitamin synthesis, lipid, and amino acid biosynthesis including 18 targets validated from the literature, two validated and five currently examined in own genetic experiments, and 38 further promising novel target proteins which are non-orthologous to human proteins, involved in metabolism and are highly ranked drug targets from these pipelines.
Modulating key dynamics of plant growth and development, the effects of the plant hormone cytokinin on animal cells gained much attention recently. Most previous studies on cytokinin effects on mammalian cells have been conducted with elevated cytokinin concentration (in the μM range). However, to examine physiologically relevant dose effects of cytokinins on animal cells, we systematically analyzed the impact of kinetin in cultured cells at low and high concentrations (1nM-10μM) and examined cytotoxic and genotoxic conditions. We furthermore measured the intrinsic antioxidant activity of kinetin in a cell-free system using the Ferric Reducing Antioxidant Power assay and in cells using the dihydroethidium staining method. Monitoring viability, we looked at kinetin effects in mammalian cells such as HL60 cells, HaCaT human keratinocyte cells, NRK rat epithelial kidney cells and human peripheral lymphocytes. Kinetin manifests no antioxidant activity in the cell free system and high doses of kinetin (500 nM and higher) reduce cell viability and mediate DNA damage in vitro. In contrast, low doses (concentrations up to 100 nM) of kinetin confer protection in cells against oxidative stress. Moreover, our results show that pretreatment of the cells with kinetin significantly reduces 4-nitroquinoline 1-oxide mediated reactive oxygen species production. Also, pretreatment with kinetin retains cellular GSH levels when they are also treated with the GSH-depleting agent patulin. Our results explicitly show that low kinetin doses reduce apoptosis and protect cells from oxidative stress mediated cell death. Future studies on the interaction between cytokinins and human cellular pathway targets will be intriguing.
Lung cancer is currently the leading cause of cancer related mortality due to late diagnosis and limited treatment intervention. Non-coding RNAs are not translated into proteins and have emerged as fundamental regulators of gene expression. Recent studies reported that microRNAs and long non-coding RNAs are involved in lung cancer development and progression. Moreover, they appear as new promising non-invasive biomarkers for early lung cancer diagnosis. Here, we highlight their potential as biomarker in lung cancer and present how bioinformatics can contribute to the development of non-invasive diagnostic tools. For this, we discuss several bioinformatics algorithms and software tools for a comprehensive understanding and functional characterization of microRNAs and long non-coding RNAs.
Mining biomedical images towards valuable information retrieval in biomedical and life sciences
(2016)
Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries.
Central nervous system dysfunction is an important cause of morbidity and mortality in patients with human immunodeficiency virus type 1 (HIV-1) infection and acquired immunodeficiency virus syndrome (AIDS). Patients with AIDS are usually affected by HIV-associated encephalitis (HIVE) with viral replication limited to cells of monocyte origin. To examine the molecular mechanisms underlying HIVE-induced dementia, the GSE4755 Affymetrix data were obtained from the Gene Expression Omnibus database and the differentially expressed genes (DEGs) between the samples from AIDS patients with and without apparent features of HIVE-induced dementia were identified. In addition, protein–protein interaction networks were constructed by mapping DEGs into protein–protein interaction data to identify the pathways that these DEGs are involved in. The results revealed that the expression of 1,528 DEGs is mainly involved in the immune response, regulation of cell proliferation, cellular response to inflammation, signal transduction, and viral replication cycle. Heat-shock protein alpha, class A member 1 (HSP90AA1), and fibronectin 1 were detected as hub nodes with degree values >130. In conclusion, the results indicate that HSP90A and fibronectin 1 play important roles in HIVE pathogenesis.
Over recent years next generation sequencing (NGS) technologies evolved from costly tools used by very few, to a much more accessible and economically viable technology. Through this recently gained popularity, its use-cases expanded from research environments into clinical settings. But the technical know-how and infrastructure required to analyze the data remain an obstacle for a wider adoption of this technology, especially in smaller laboratories. We present GensearchNGS, a commercial DNAseq software suite distributed by Phenosystems SA. The focus of GensearchNGS is the optimal usage of already existing infrastructure, while keeping its use simple. This is achieved through the integration of existing tools in a comprehensive software environment, as well as custom algorithms developed with the restrictions of limited infrastructures in mind. This includes the possibility to connect multiple computers to speed up computing intensive parts of the analysis such as sequence alignments. We present a typical DNAseq workflow for NGS data analysis and the approach GensearchNGS takes to implement it. The presented workflow goes from raw data quality control to the final variant report. This includes features such as gene panels and the integration of online databases, like Ensembl for annotations or Cafe Variome for variant sharing.
Background
Defence mechanisms of organisms are shaped by their lifestyle, environment and pathogen pressure. Carpenter ants are social insects which live in huge colonies comprising genetically closely related individuals in high densities within nests. This lifestyle potentially facilitates the rapid spread of pathogens between individuals. In concert with their innate immune system, social insects may apply external immune defences to manipulate the microbial community among individuals and within nests. Additionally, carpenter ants carry a mutualistic intracellular and obligate endosymbiotic bacterium, possibly maintained and regulated by the innate immune system. Thus, different selective forces could shape internal immune defences of Camponotus floridanus.
Results
The immune gene repertoire of C. floridanus was investigated by re-evaluating its genome sequence combined with a full transcriptome analysis of immune challenged and control animals using Illumina sequencing. The genome was re-annotated by mapping transcriptome reads and masking repeats. A total of 978 protein sequences were characterised further by annotating functional domains, leading to a change in their original annotation regarding function and domain composition in about 8 % of all proteins. Based on homology analysis with key components of major immune pathways of insects, the C. floridanus immune-related genes were compared to those of Drosophila melanogaster, Apis mellifera, and other hymenoptera. This analysis revealed that overall the immune system of carpenter ants comprises many components found in these insects. In addition, several C. floridanus specific genes of yet unknown functions but which are strongly induced after immune challenge were discovered. In contrast to solitary insects like Drosophila or the hymenopteran Nasonia vitripennis, the number of genes encoding pattern recognition receptors specific for bacterial peptidoglycan (PGN) and a variety of known antimicrobial peptide (AMP) genes is lower in C. floridanus. The comparative analysis of gene expression post immune-challenge in different developmental stages of C. floridanus suggests a stronger induction of immune gene expression in larvae in comparison to adults.
Conclusions
The comparison of the immune system of C. floridanus with that of other insects revealed the presence of a broad immune repertoire. However, the relatively low number of PGN recognition proteins and AMPs, the identification of Camponotus specific putative immune genes, and stage specific differences in immune gene regulation reflects Camponotus specific evolution including adaptations to its lifestyle.
Fungal microorganisms frequently lead to life-threatening infections. Within this group of pathogens, the commensal Candida albicans and the filamentous fungus Aspergillus fumigatus are by far the most important causes of invasive mycoses in Europe. A key capability for host invasion and immune response evasion are specific molecular interactions between the fungal pathogen and its human host. Experimentally validated knowledge about these crucial interactions is rare in literature and even specialized host pathogen databases mainly focus on bacterial and viral interactions whereas information on fungi is still sparse. To establish large-scale host fungi interaction networks on a systems biology scale, we develop an extended inference approach based on protein orthology and data on gene functions. Using human and yeast intraspecies networks as template, we derive a large network of pathogen host interactions (PHI). Rigorous filtering and refinement steps based on cellular localization and pathogenicity information of predicted interactors yield a primary scaffold of fungi human and fungi mouse interaction networks. Specific enrichment of known pathogenicity-relevant genes indicates the biological relevance of the predicted PHI. A detailed inspection of functionally relevant subnetworks reveals novel host fungal interaction candidates such as the Candida virulence factor PLB1 and the anti-fungal host protein APP. Our results demonstrate the applicability of interolog-based prediction methods for host fungi interactions and underline the importance of filtering and refinement steps to attain biologically more relevant interactions. This integrated network framework can serve as a basis for future analyses of high-throughput host fungi transcriptome and proteome data.
Control of genetic regulatory networks is challenging to define and quantify. Previous control centrality metrics, which aim to capture the ability of individual nodes to control the system, have been found to suffer from plausibility and applicability problems. Here we present a new approach to control centrality based on network convergence behaviour, implemented as an extension of our genetic regulatory network simulation framework Jimena (http://stefan-karl.de/jimena). We distinguish three types of network control, and show how these mathematical concepts correspond to experimentally verified node functions and signalling pathways in immunity and cell differentiation: Total control centrality quantifies the impact of node mutations and identifies potential pharmacological targets such as genes involved in oncogenesis (e.g. zinc finger protein GLI2 or bone morphogenetic proteins in chondrocytes). Dynamic control centrality describes relaying functions as observed in signalling cascades (e.g. src kinase or Jak/Stat pathways). Value control centrality measures the direct influence of the value of the node on the network (e.g. Indian hedgehog as an essential regulator of proliferation in chondrocytes). Surveying random scale-free networks and biological networks, we find that control of the network resides in few high degree driver nodes and networks can be controlled best if they are sparsely connected.
The diploid, polymorphic yeast Candida albicans is one of the most important human pathogenic fungi. C. albicans can grow, proliferate and coexist as a commensal on or within the human host for a long time. However, alterations in the host environment can render C. albicans virulent. In this review, we describe the immunological cross-talk between C. albicans and the human innate immune system. We give an overview in form of pairs of human defense strategies including immunological mechanisms as well as general stressors such as nutrient limitation, pH, fever etc. and the corresponding fungal response and evasion mechanisms. Furthermore, Computational Systems Biology approaches to model and investigate these complex interactions are highlighted with a special focus on game-theoretical methods and agent-based models. An outlook on interesting questions to be tackled by Systems Biology regarding entangled defense and evasion mechanisms is given.