Probabilistic pathway-based multimodal factor analysis.

MOTIVATION: Multimodal profiling strategies promise to produce more informative insights into biomedical cohorts via the integration of the information each modality contributes. To perform this integration, however, the development of novel analytical strategies is needed. Multimodal profiling strategies often come at the expense of lower sample numbers, which can challenge methods to uncover shared signals across a cohort. Thus, factor analysis approaches are commonly used for the analysis of high-dimensional data in molecular biology, however, they typically do not yield representations that are directly interpretable, whereas many research questions often center around the analysis of pathways associated with specific observations. RESULTS: We develop PathFA, a novel approach for multimodal factor analysis over the space of pathways. PathFA produces integrative and interpretable views across multimodal profiling technologies, which allow for the derivation of concrete hypotheses. PathFA combines a pathway-learning approach with integrative multimodal capability under a Bayesian procedure that is efficient, hyper-parameter free, and able to automatically infer observation noise from the data. We demonstrate strong performance on small sample sizes within our simulation framework and on matched proteomics and transcriptomics profiles from real tumor samples taken from the Swiss Tumor Profiler consortium. On a subcohort of melanoma patients, PathFA recovers pathway activity that has been independently associated with poor outcome. We further demonstrate the ability of this approach to identify pathways associated with the presence of specific cell-types as well as tumor heterogeneity. Our results show that we capture known biology, making it well suited for analyzing multimodal sample cohorts. AVAILABILITY AND IMPLEMENTATION: The tool is implemented in python and available at https://github.com/ratschlab/path-fa.

Adverse Outcome Pathway-Driven Analysis of Liver Steatosis in Vitro: A Case Study with Cyproconazole.

Adverse outcome pathways (AOPs) describe causal relationships between molecular perturbation and adverse cellular effects and are being increasingly adopted for linking in vitro mechanistic toxicology to in vivo data from regulatory toxicity studies. In this work, a case study was performed by developing a bioassay toolbox to assess key events in the recently proposed AOP for chemically induced liver steatosis. The toolbox is comprised of in vitro assays to measure nuclear receptor activation, gene and protein expression, lipid accumulation, mitochondrial respiration, and formation of fatty liver cells. Assay evaluation was performed in human HepaRG hepatocarcinoma cells exposed to the model compound cyproconazole, a fungicide inducing steatosis in rodents. Cyproconazole dose-dependently activated RARα and PXR, two molecular initiating events in the steatosis AOP. Moreover, cyproconazole provoked a disruption of mitochondrial functions and induced triglyceride accumulation and the formation of fatty liver cells as described in the AOP. Gene and protein expression analysis, however, showed expression changes different from those proposed in the AOP, thus suggesting that the current version of the AOP might not fully reflect the complex mechanisms linking nuclear receptor activation and liver steatosis. Our study shows that cyproconazole induces steatosis in human liver cells in vitro and demonstrates the utility of systems-based approaches in the mechanistic assessment of molecular and cellular key events in an AOP. AOP-driven in vitro testing as demonstrated can further improve existing AOPs, provide insight regarding molecular mechanisms of toxicity, and inform predictive risk assessment.

Protein tyrosine phosphatase 1B restrains mammary alveologenesis and secretory differentiation.

Tyrosine phosphorylation plays a fundamental role in mammary gland development. However, the role of specific tyrosine phosphatases in controlling mammary cell fate remains ill defined. We have identified protein tyrosine phosphatase 1B (PTP1B) as an essential regulator of alveologenesis and lactogenesis. PTP1B depletion increased the number of luminal mammary progenitors in nulliparous mice, leading to enhanced alveoli formation upon pregnancy. Mechanistically, Ptp1b deletion enhanced the expression of progesterone receptor and phosphorylation of Stat5, two key regulators of alveologenesis. Furthermore, glands from Ptp1b knockout mice exhibited increased expression of milk proteins during pregnancy due to enhanced Stat5 activation. These findings reveal that PTP1B constrains the number of mammary progenitors and thus prevents inappropriate onset of alveologenesis in early pregnancy. Moreover, PTP1B restrains the expression of milk proteins during pregnancy and thus prevents premature lactogenesis. Our work has implications for breast tumorigenesis because Ptp1b deletion has been shown to prevent or delay the onset of mammary tumors.

Elucidation of host-virus surfaceome interactions using spatial proteotyping.

The cellular surfaceome and its residing extracellularly exposed proteins are involved in a multitude of molecular signaling processes across the viral infection cycle. Successful viral propagation, including viral entry, immune evasion, virion release and viral spread rely on dynamic molecular interactions with the surfaceome. Decoding of these viral-host surfaceome interactions using advanced technologies enabled the discovery of fundamental new functional insights into cellular and viral biology. In this review, we highlight recently developed experimental strategies, with a focus on spatial proteotyping technologies, aiding in the rational design of theranostic strategies to combat viral infections.

The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support.

The application and integration of molecular profiling technologies create novel opportunities for personalized medicine. Here, we introduce the Tumor Profiler Study, an observational trial combining a prospective diagnostic approach to assess the relevance of in-depth tumor profiling to support clinical decision-making with an exploratory approach to improve the biological understanding of the disease.

An adverse outcome pathway-based approach to assess steatotic mixture effects of hepatotoxic pesticides in vitro.

Exposure to complex chemical mixtures requires a tiered strategy for efficient mixture risk assessment. As a part of the EuroMix project we developed an adverse outcome pathway (AOP)-based assay toolbox to investigate the combined effects of the liver steatosis-inducing compounds imazalil, thiacloprid, and clothianidin in human HepaRG hepatocarcinoma cells. Compound-specific relative potency factors were determined using a benchmark dose approach. Equipotent mixtures were tested for nuclear receptor activation, gene and protein expression, and triglyceride accumulation, according to the molecular initiating events and key events proposed in the steatosis AOP. All three compounds affected the activity of nuclear receptors, but not key genes/proteins as proposed. Triglyceride accumulation was observed with three different methods. Mixture effects were in agreement with the assumption of dose additivity for all the combinations and endpoints tested. Compound-specific RPFs remained similar over the different endpoints studied downstream the AOP. Therefore, it might be possible to reduce testing to a smaller battery of key tests. The results demonstrate the suitability of our in vitro assay toolbox, integrated within an AOP framework and combined with the RPF approach, for the analysis of steatotic effects of chemical mixtures. However, mRNA results suggest that the steatosis AOP still needs improvement.