Nikola S. Mueller

Phenome-transcriptome correlation unravels anxiety and depression related pathways

The identification of pathways pertinent to human diseases is critical for gaining a better understanding of their pathophysiology. Pathway knowledge in turn can provide disease marker information required for diagnosis, drug development and improved patient treatment. Psychiatric disorders including anxiety and depression are complex diseases and are caused by a combination of multiple genetic and environmental factors affecting certain brain circuits. Here we used a systems biology approach to identify molecular pathways that affect anxiety- and depression-like phenotypes. For this purpose we screened pathways for stable enrichment in a great number of publicly available transcriptome data from the Gene Expression Omnibus related to anxiety- and depression-like phenotypes. In case of anxiety our analysis implicate a dysregulation of carbohydrate metabolism, tight junction and the phosphatidylinositol signaling system, whereas for depression gap junction, gonadotropin-releasing hormone signaling and ubiquitin-mediated proteolysis pathways are affected. Furthermore, both anxiety and depression show a dysregulation of VEGF signaling, long term potentiation and the glycolysis pathway. Molecular entities that are part of the identified pathways can serve as biomarkers and potential therapeutic targets for diagnosis and treatment of depression and anxiety disorders.

Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data

Motivation: High-dimensional single-cell snapshot data are becoming widespread in the systems biology community, as a mean to understand biological processes at the cellular level. However, as temporal information is lost with such data, mathematical models have been limited to capture only static features of the underlying cellular mechanisms. Results: Here, we present a modular framework which allows to recover the temporal behaviour from single-cell snapshot data and reverse engineer the dynamics of gene expression. The framework combines a dimensionality reduction method with a cell time-ordering algorithm to generate pseudo time-series observations. These are in turn used to learn transcriptional ODE models and do model selection on structural network features. We apply it on synthetic data and then on real hematopoietic stem cells data, to reconstruct gene expression dynamics during differentiation pathways and infer the structure of a key gene regulatory network. Availability and implementation: C++ and Matlab code available at https://www.helmholtz-muenchen.de/fileadmin/ICB/software/inferenceSnapshot.zip. Contact: fabian.theis@helmholtz-muenchen.de Supplementary information: Supplementary data are available at Bioinformatics online.

QuantiSpec - Quantitative mass spectrometry data analysis of 15N-metabolically labeled proteins

For relative protein quantitation by mass spectrometry we metabolically labeled E. coli bacteria with (15)N-enriched diets. Proteins extracted from (15)N-labeled and unlabeled E. coli bacteria were mixed, separated by two-dimensional gel electrophoresis and enzymatically digested. The resulting tryptic peptides were analyzed by MALDI mass spectrometry. For the relative protein quantitation we developed fully automated software, QuantiSpec (Quantitative Mass Spectrometry Analysis Software), which uses data from MALDI TOF mass spectrometry and the Mascot database search engine. QuantiSpec detects natural as well as partially or fully labeled peptide isotope distributions. For each identified peptide the (15)N incorporation rate is determined by comparing the experimental to a set of theoretical isotope patterns based on the peptide sequence. Relative quantitation is accomplished by calculating the signal intensity ratios for each (14)N/(15)N peptide pair.

miTALOS v2: Analyzing Tissue Specific microRNA Function

MicroRNAs are involved in almost all biological processes and have emerged as regulators of signaling pathways. We show that miRNA target genes and pathway genes are not uniformly expressed across human tissues. To capture tissue specific effects, we developed a novel methodology for tissue specific pathway analysis of miRNAs. We incorporated the most recent and highest quality miRNA targeting data (TargetScan and StarBase), RNA-seq based gene expression data (EBI Expression Atlas) and multiple new pathway data sources to increase the biological relevance of the predicted miRNA-pathway associations. We identified new potential roles of miR-199a-3p, miR-199b-3p and the miR-200 family in hepatocellular carcinoma, involving the regulation of metastasis through MAPK and Wnt signaling. Also, an association of miR-571 and Notch signaling in liver fibrosis was proposed. To facilitate data update and future extensions of our tool, we developed a flexible database backend using the graph database neo4j. The new backend as well as the novel methodology were included in the updated miTALOS v2, a tool that provides insights into tissue specific miRNA regulation of biological pathways. miTALOS v2 is available at http://mips.helmholtz-muenchen.de/mitalos.

A modular framework for gene set analysis integrating multilevel omics data

Modern high-throughput methods allow the investigation of biological functions across multiple ‘omics’ levels. Levels include mRNA and protein expression profiling as well as additional knowledge on, for example, DNA methylation and microRNA regulation. The reason for this interest in multi-omics is that actual cellular responses to different conditions are best explained mechanistically when taking all omics levels into account. To map gene products to their biological functions, public ontologies like Gene Ontology are commonly used. Many methods have been developed to identify terms in an ontology, overrepresented within a set of genes. However, these methods are not able to appropriately deal with any combination of several data types. Here, we propose a new method to analyse integrated data across multiple omics-levels to simultaneously assess their biological meaning. We developed a model-based Bayesian method for inferring interpretable term probabilities in a modular framework. Our Multi-level ONtology Analysis (MONA) algorithm performed significantly better than conventional analyses of individual levels and yields best results even for sophisticated models including mRNA fine-tuning by microRNAs. The MONA framework is flexible enough to allow for different underlying regulatory motifs or ontologies. It is ready-to-use for applied researchers and is available as a standalone application from http://icb.helmholtz-muenchen.de/mona.

AURKA, DLGAP5, TPX2, KIF11 and CKAP5: Five specific mitosis-associated genes correlate with poor prognosis for non-small cell lung cancer patients

The growth of a tumor depends to a certain extent on an increase in mitotic events. Key steps during mitosis are the regulated assembly of the spindle apparatus and the separation of the sister chromatids. The microtubule-associated protein Aurora kinase A phosphorylates DLGAP5 in order to correctly segregate the chromatids. Its activity and recruitment to the spindle apparatus is regulated by TPX2. KIF11 and CKAP5 control the correct arrangement of the microtubules and prevent their degradation. In the present study, we investigated the role of these five molecules in non-small cell lung cancer (NSCLC). We analyzed the expression of the five genes in a large cohort of NSCLC patients (n=362) by quantitative real-time PCR. Each of the genes was highly overexpressed in the tumor tissues compared to corresponding normal lung tissue. The correlation of the expression of the individual genes depended on the histology. An increased expression of AURKA, DLGAP5, TPX2, KIF11 and CKAP5 was associated with poor overall survival (P=0.001–0.065). AURKA was a significant prognostic marker using multivariate analyses (P=0.006). Immunofluorescence studies demonstrated that the five mitosis-associated proteins co-localized with the spindle apparatus during cell division. Taken together, our data demonstrate that the expression of the mitosis-associated genes AURKA, DLGAP5, TPX2, KIF11 and CKAP5 is associated with the prognosis of NSCLC patients.

A novel molecular disease classifier for psoriasis and eczema

Novel specific therapies for psoriasis and eczema have been developed, and they mark a new era in the treatment of these complex inflammatory skin diseases. However, within their broad clinical spectrum, psoriasis and eczema phenotypes overlap making an accurate diagnosis impossible in special cases, not to speak about predicting the clinical outcome of an individual patient. Here, we present a novel robust molecular classifier (MC) consisting of NOS2 and CCL27 gene that diagnosed psoriasis and eczema with a sensitivity and specificity of >95% in a cohort of 129 patients suffering from (i) classical forms; (ii) subtypes; and (iii) clinically and histologically indistinct variants of psoriasis and eczema. NOS2 and CCL27 correlated with clinical and histological hallmarks of psoriasis and eczema in a mutually antagonistic way, thus highlighting their biological relevance. In line with this, the MC could be transferred to the level of immunofluorescence stainings for iNOS and CCL27 protein on paraffin‐embedded sections, where patients were diagnosed with sensitivity and specificity >88%. Our MC proved superiority over current gold standard methods to distinguish psoriasis and eczema and may therefore build the basis for molecular diagnosis of chronic inflammatory skin diseases required to establish personalized medicine in the field.

Integrative Analysis of MicroRNA and mRNA Data Reveals an Orchestrated Function of MicroRNAs in Skeletal Myocyte Differentiation in Response to TNF-α or IGF1

Introduction Skeletal muscle cell differentiation is impaired by elevated levels of the inflammatory cytokine tumor necrosis factor-α (TNF-α) with pathological significance in chronic diseases or inherited muscle disorders. Insulin like growth factor-1 (IGF1) positively regulates muscle cell differentiation. Both, TNF-α and IGF1 affect gene and microRNA (miRNA) expression in this process. However, computational prediction of miRNA-mRNA relations is challenged by false positives and targets which might be irrelevant in the respective cellular transcriptome context. Thus, this study is focused on functional information about miRNA affected target transcripts by integrating miRNA and mRNA expression profiling data. Methodology/Principal Findings Murine skeletal myocytes PMI28 were differentiated for 24 hours with concomitant TNF-α or IGF1 treatment. Both, mRNA and miRNA expression profiling was performed. The data-driven integration of target prediction and paired mRNA/miRNA expression profiling data revealed that i) the quantity of predicted miRNA-mRNA relations was reduced, ii) miRNA targets with a function in cell cycle and axon guidance were enriched, iii) differential regulation of anti-differentiation miR-155-5p and miR-29b-3p as well as pro-differentiation miR-335-3p, miR-335-5p, miR-322-3p, and miR-322-5p seemed to be of primary importance during skeletal myoblast differentiation compared to the other miRNAs, iv) the abundance of targets and affected biological processes was miRNA specific, and v) subsets of miRNAs may collectively regulate gene expression. Conclusions Joint analysis of mRNA and miRNA profiling data increased the process-specificity and quality of predicted relations by statistically selecting miRNA-target interactions. Moreover, this study revealed miRNA-specific predominant biological implications in skeletal muscle cell differentiation and in response to TNF-α or IGF1 treatment. Furthermore, myoblast differentiation-associated miRNAs are suggested to collectively regulate gene clusters and targets associated with enriched specific gene ontology terms or pathways. Predicted miRNA functions of this study provide novel insights into defective regulation at the transcriptomic level during myocyte proliferation and differentiation due to inflammatory stimuli.

The global gene expression profile of the secondary transition during pancreatic development.

Pancreas organogenesis is a highly dynamic process where neighboring tissue interactions lead to dynamic changes in gene regulatory networks that orchestrate endocrine, exocrine, and ductal lineage formation. To understand the spatio-temporal regulatory logic we have used the Forkhead transcription factor Foxa2-Venus fusion (FVF) knock-in reporter mouse to separate the FVF(+) pancreatic epithelium from the FVF(−) surrounding tissue (mesenchyme, neurons, blood, and blood vessels) to perform a genome-wide mRNA expression profiling at embryonic days (E) 12.5-15.5. Annotating genes and molecular processes suggest that FVF marks endoderm-derived multipotent epithelial progenitors at several lineage restriction steps, when the bulk of endocrine, exocrine and ductal cells are formed during the secondary transition. In the pancreatic epithelial compartment, we identified most known endocrine and exocrine lineage determining factors and diabetes-associated genes, but also unknown genes with spatio-temporal regulated pancreatic expression. In the non-endoderm-derived compartment, we identified many well-described regulatory genes that are not yet functionally annotated in pancreas development, emphasizing that neighboring tissue interactions are still ill defined. Pancreatic expression of over 635 genes was analyzed with them RNA in situ hybridization Genepaint public database. This validated the quality of the profiling data set and identified hundreds of genes with spatially restricted expression patterns in the pancreas. Some of these genes are also targeted by pancreatic transcription factors and show active chromatin marks in human islets of Langerhans. Thus, with the highest spatio-temporal resolution of a global gene expression profile during the secondary transition, our study enables to shed light on neighboring tissue interactions, developmental timing and diabetes gene regulation.

Epigenetically Regulated Chromosome 14q32 miRNA Cluster Induces Metastasis and Predicts Poor Prognosis in Lung Adenocarcinoma Patients

Most lung cancer deaths are related to metastases, which indicates the necessity of detecting and inhibiting tumor cell dissemination. Here, we aimed to identify miRNAs involved in metastasis of lung adenocarcinoma as prognostic biomarkers and therapeutic targets. To that end, lymph node metastasis–associated miRNAs were identified in The Cancer Genome Atlas lung adenocarcinoma patient cohort (sequencing data; n = 449) and subsequently validated by qRT-PCR in an independent clinical cohort (n = 108). Overexpression of miRNAs located on chromosome 14q32 was associated with metastasis in lung adenocarcinoma patients. Importantly, Kaplan–Meier analysis and log-rank test revealed that higher expression levels of individual 14q32 miRNAs (mir-539, mir-323b, and mir-487a) associated with worse disease-free survival of never-smoker patients. Epigenetic analysis including DNA methylation microarray data and bisulfite sequencing validation demonstrated that the induction of 14q32 cluster correlated with genomic hypomethylation of the 14q32 locus. CRISPR activation technology, applied for the first time to functionally study the increase of clustered miRNA levels in a coordinated manner, showed that simultaneous overexpression of 14q32 miRNAs promoted tumor cell migratory and invasive properties. Analysis of individual miRNAs by mimic transfection further illustrated that miR-323b-3p, miR-487a-3p, and miR-539-5p significantly contributed to the invasive phenotype through the indirect regulation of different target genes. In conclusion, overexpression of 14q32 miRNAs, associated with the respective genomic hypomethylation, promotes metastasis and correlates with poor patient prognosis in lung adenocarcinoma. Implications: This study points to chromosome 14q32 miRNAs as promising targets to inhibit tumor cell dissemination and to predict patient prognosis in lung adenocarcinoma. Mol Cancer Res; 16(3); 390–402. ©2018 AACR.