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43 Publications visible to you, out of a total of 43
Two's company: studying interspecies relationships with dual RNA-seq.

Abstract (Expand)

Organisms do not exist isolated from each other, but constantly interact. Cells can sense the presence of interaction partners by a range of receptors and, via complex regulatory networks, specifically … react by changing the expression of many of their genes. Technological advances in next-generation sequencing over the recent years now allow us to apply RNA sequencing to two species at the same time (dual RNA-seq), and thus to directly study the gene expression of two interacting species without the need to physically separate cells or RNA. In this review, we give an overview over the latest studies in interspecies interactions made possible by dual RNA-seq, ranging from pathogenic to symbiotic relationships. We summarize state-of-the-art experimental techniques, bioinformatic data analysis and data interpretation, while also highlighting potential problems and pitfalls starting from the selection of meaningful time points and number of reads to matters of rRNA depletion. A short outlook on new trends in the field of dual RNA-seq concludes this review, looking at sequencing of non-coding RNAs during host-pathogen interactions and the prediction of molecular interspecies interactions networks.

Authors: T. Wolf, P. Kammer, S. Brunke, J. Linde

Date Published: 29th Sep 2017

Publication Type: Not specified

LASSIM-A network inference toolbox for genome-wide mechanistic modeling.

Abstract (Expand)

Recent technological advancements have made time-resolved, quantitative, multi-omics data available for many model systems, which could be integrated for systems pharmacokinetic use. Here, we present … large-scale simulation modeling (LASSIM), which is a novel mathematical tool for performing large-scale inference using mechanistically defined ordinary differential equations (ODE) for gene regulatory networks (GRNs). LASSIM integrates structural knowledge about regulatory interactions and non-linear equations with multiple steady state and dynamic response expression datasets. The rationale behind LASSIM is that biological GRNs can be simplified using a limited subset of core genes that are assumed to regulate all other gene transcription events in the network. The LASSIM method is implemented as a general-purpose toolbox using the PyGMO Python package to make the most of multicore computers and high performance clusters, and is available at https://gitlab.com/Gustafsson-lab/lassim. As a method, LASSIM works in two steps, where it first infers a non-linear ODE system of the pre-specified core gene expression. Second, LASSIM in parallel optimizes the parameters that model the regulation of peripheral genes by core system genes. We showed the usefulness of this method by applying LASSIM to infer a large-scale non-linear model of naive Th2 cell differentiation, made possible by integrating Th2 specific bindings, time-series together with six public and six novel siRNA-mediated knock-down experiments. ChIP-seq showed significant overlap for all tested transcription factors. Next, we performed novel time-series measurements of total T-cells during differentiation towards Th2 and verified that our LASSIM model could monitor those data significantly better than comparable models that used the same Th2 bindings. In summary, the LASSIM toolbox opens the door to a new type of model-based data analysis that combines the strengths of reliable mechanistic models with truly systems-level data. We demonstrate the power of this approach by inferring a mechanistically motivated, genome-wide model of the Th2 transcription regulatory system, which plays an important role in several immune related diseases.

Authors: R. Magnusson, G. P. Mariotti, M. Kopsen, W. Lovfors, D. R. Gawel, R. Jornsten, J. Linde, T. E. M. Nordling, E. Nyman, S. Schulze, C. E. Nestor, H. Zhang, G. Cedersund, M. Benson, A. Tjarnberg, M. Gustafsson

Date Published: 24th Jun 2017

Publication Type: Not specified

Specific and Novel microRNAs Are Regulated as Response to Fungal Infection in Human Dendritic Cells.

Abstract (Expand)

Within the last two decades, the incidence of invasive fungal infections has been significantly increased. They are characterized by high mortality rates and are often caused by Candida albicans and … Aspergillus fumigatus. The increasing number of infections underlines the necessity for additional anti-fungal therapies, which require extended knowledge of gene regulations during fungal infection. MicroRNAs are regulators of important cellular processes, including the immune response. By analyzing their regulation and impact on target genes, novel therapeutic and diagnostic approaches may be developed. Here, we examine the role of microRNAs in human dendritic cells during fungal infection. Dendritic cells represent the bridge between the innate and the adaptive immune systems. Therefore, analysis of gene regulation of dendritic cells is of particular significance. By applying next-generation sequencing of small RNAs, we quantify microRNA expression in monocyte-derived dendritic cells after 6 and 12 h of infection with C. albicans and A. fumigatus as well as treatment with lipopolysaccharides (LPS). We identified 26 microRNAs that are differentially regulated after infection by the fungi or LPS. Three and five of them are specific for fungal infections after 6 and 12 h, respectively. We further validated interactions of miR-132-5p and miR-212-5p with immunological relevant target genes, such as FKBP1B, KLF4, and SPN, on both RNA and protein level. Our results indicate that these microRNAs fine-tune the expression of immune-related target genes during fungal infection. Beyond that, we identified previously undiscovered microRNAs. We validated three novel microRNAs via qRT-PCR. A comparison with known microRNAs revealed possible relations with the miR-378 family and miR-1260a/b for two of them, while the third one features a unique sequence with no resemblance to known microRNAs. In summary, this study analyzes the effect of known microRNAs in dendritic cells during fungal infections and proposes novel microRNAs that could be experimentally verified.

Authors: A. Dix, K. Czakai, I. Leonhardt, K. Schaferhoff, M. Bonin, R. Guthke, H. Einsele, O. Kurzai, J. Loffler, J. Linde

Date Published: 11th Mar 2017

Publication Type: Not specified

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