Protocol for the systematic review of age and sex in preclinical models of age-correlated diseases.

The translation of animal-based biomedical research into clinical research is often inadequate. Maximizing translation should be central to animal research on human diseases, guiding researchers in study design and animal model selection. However, practical considerations often drive the choice of animal model, which may not always reflect key patient characteristics, such as sex and age, impacting the disease's course.

Assessment of the effect of tricaine (MS-222)-induced anesthesia on brain-wide neuronal activity of zebrafish () larvae.

Fast and effective anesthesia is the key for refining many invasive procedures in fish and gaining reliable data. For fish as for all vertebrates, it is also required by European law to reduce pain, suffering, and distress to the unavoidable minimum in husbandry and experiments. The most often used substance to induce anesthesia in zebrafish is tricaine (MS-222).

A microphysiological system for studying human bone biology under simultaneous control of oxygen tension and mechanical loading.

Throughout life, continuous remodelling is part of human bone biology and depends on the simultaneous action of physicochemical parameters such as oxygen tension and varying mechanical load. Thus, suitable model systems are needed, which allow concomitant modulation of these factors to recapitulate bone formation. Here, we report on the development of a first microphysiological system (MPS) that enables perfusion, environment-independent regulation of the oxygen tension as well as precise quantification and control of mechanical load.

GPER1 links estrogens to centrosome amplification and chromosomal instability in human colon cells.

The role of the alternate G protein-coupled estrogen receptor 1 (GPER1) in colorectal cancer (CRC) development and progression is unclear, not least because of conflicting clinical and experimental evidence for pro- and anti-tumorigenic activities. Here, we show that low concentrations of the estrogenic GPER1 ligands, 17β-estradiol, bisphenol A, and diethylstilbestrol cause the generation of lagging chromosomes in normal colon and CRC cell lines, which manifest in whole chromosomal instability and aneuploidy. Mechanistically, (xeno)estrogens triggered centrosome amplification by inducing centriole overduplication that leads to transient multipolar mitotic spindles, chromosome alignment defects, and mitotic laggards.

Analgesic treatment with buprenorphine should be adapted to the mouse strain.

Buprenorphine is a commonly used opioid to treat moderate to severe pain in mice. Although strain differences regarding basal pain sensitivity and the analgesic effect of other opioids have been described for mice, the data for buprenorphine is incomplete. Hence, we investigated basal pain sensitivity and the analgesic effect of buprenorphine (0.

Simple changes of individual studies can improve the reproducibility of the biomedical scientific process as a whole.

We developed a new probabilistic model to assess the impact of recommendations rectifying the reproducibility crisis (by publishing both positive and 'negative' results and increasing statistical power) on competing objectives, such as discovering causal relationships, avoiding publishing false positive results, and reducing resource consumption. In contrast to recent publications our model quantifies the impact of each single suggestion not only for an individual study but especially their relation and consequences for the overall scientific process. We can prove that higher-powered experiments can save resources in the overall research process without generating excess false positives.

Rethinking 3R strategies: Digging deeper into AnimalTestInfo promotes transparency in in vivo biomedical research.

In the European Union (EU), animal welfare is seen as a matter of great importance. However, with respect to animal experimentation, European citizens feel quite uninformed. The European Directive 2010/63/EU for the protection of laboratory animals aims for greater transparency and requires that a comprehensible, nontechnical summary (NTS) of each authorised research project involving animals is published by the respective Member State.

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research.

Recent studies that compared transcriptomic datasets of human diseases with datasets from mouse models using traditional gene-to-gene comparison techniques resulted in contradictory conclusions regarding the relevance of animal models for translational research. A major reason for the discrepancies between different gene expression analyses is the arbitrary filtering of differentially expressed genes. Furthermore, the comparison of single genes between different species and platforms often is limited by technical variance, leading to misinterpretation of the con/discordance between data from human and animal models.

Defining the optimal animal model for translational research using gene set enrichment analysis.

The mouse is the main model organism used to study the functions of human genes because most biological processes in the mouse are highly conserved in humans. Recent reports that compared identical transcriptomic datasets of human inflammatory diseases with datasets from mouse models using traditional gene-to-gene comparison techniques resulted in contradictory conclusions regarding the relevance of animal models for translational research. To reduce susceptibility to biased interpretation, all genes of interest for the biological question under investigation should be considered.

The DNT-EST: a predictive embryonic stem cell-based assay for developmental neurotoxicity testing in vitro.

As the developing brain is exquisitely vulnerable to chemical disturbances, testing for developmental neurotoxicity of a substance is an important aspect of characterizing its tissue specific toxicity. Mouse embryonic stem cells (mESCs) can be differentiated toward a neural phenotype, and this can be used as a model for early brain development. We developed a new in vitro assay using mESCs to predict adverse effects of chemicals and other compounds on neural development - the so-called DNT-EST.

Conducting molecular biomarker discovery studies in plants.

Molecular biomarkers are molecules whose concentrations in a biological system inform about the current phenotypical state and, more importantly, may also be predictive of future phenotypic trait endpoints. The identification of biomarkers has gained much attention in targeted plant breeding since technologies have become available that measure many molecules across different levels of molecular organization and at decreasing costs. In this chapter, we outline the general strategy and workflow of conducting biomarker discovery studies.

Network analysis of enzyme activities and metabolite levels and their relationship to biomass in a large panel of Arabidopsis accessions.

Natural genetic diversity provides a powerful resource to investigate how networks respond to multiple simultaneous changes. In this work, we profile maximum catalytic activities of 37 enzymes from central metabolism and generate a matrix to investigate species-wide connectivity between metabolites, enzymes, and biomass. Most enzyme activities change in a highly coordinated manner, especially those in the Calvin-Benson cycle.

Discovering plant metabolic biomarkers for phenotype prediction using an untargeted approach.

Biomarkers are used to predict phenotypical properties before these features become apparent and, therefore, are valuable tools for both fundamental and applied research. Diagnostic biomarkers have been discovered in medicine many decades ago and are now commonly applied. While this is routine in the field of medicine, it is of surprise that in agriculture this approach has never been investigated.

Prediction of hybrid biomass in Arabidopsis thaliana by selected parental SNP and metabolic markers.

A recombinant inbred line (RIL) population, derived from two Arabidopsis thaliana accessions, and the corresponding testcrosses with these two original accessions were used for the development and validation of machine learning models to predict the biomass of hybrids. Genetic and metabolic information of the RILs served as predictors. Feature selection reduced the number of variables (genetic and metabolic markers) in the models by more than 80% without impairing the predictive power.

Starch as a major integrator in the regulation of plant growth.

Rising demand for food and bioenergy makes it imperative to breed for increased crop yield. Vegetative plant growth could be driven by resource acquisition or developmental programs. Metabolite profiling in 94 Arabidopsis accessions revealed that biomass correlates negatively with many metabolites, especially starch.

QTL analysis of early stage heterosis for biomass in Arabidopsis.

The main objective of this study was to identify genomic regions involved in biomass heterosis using QTL, generation means, and mode-of-inheritance classification analyses. In a modified North Carolina Design III we backcrossed 429 recombinant inbred line and 140 introgression line populations to the two parental accessions, C24 and Col-0, whose F (1) hybrid exhibited 44% heterosis for biomass. Mid-parent heterosis in the RILs ranged from -31 to 99% for dry weight and from -58 to 143% for leaf area.

Identification of heterotic metabolite QTL in Arabidopsis thaliana RIL and IL populations.

Two mapping populations of a cross between the Arabidopsis thaliana accessions Col-0 and C24 were cultivated and analyzed with respect to the levels of 181 metabolites to elucidate the biological phenomenon of heterosis at the metabolic level. The relative mid-parent heterosis in the F(1) hybrids was <20% for most metabolic traits. The first mapping population consisting of 369 recombinant inbred lines (RILs) and their test cross progeny with both parents allowed us to determine the position and effect of 147 quantitative trait loci (QTL) for metabolite absolute mid-parent heterosis (aMPH).

Improved heterosis prediction by combining information on DNA- and metabolic markers.

Background: Hybrids represent a cornerstone in the success story of breeding programs. The fundamental principle underlying this success is the phenomenon of hybrid vigour, or heterosis. It describes an advantage of the offspring as compared to the two parental lines with respect to parameters such as growth and resistance against abiotic or biotic stress.

Towards systems biology of heterosis: a hypothesis about molecular network structure applied for the Arabidopsis metabolome.

We propose a network structure-based model for heterosis, and investigate it relying on metabolite profiles from Arabidopsis. A simple feed-forward two-layer network model (the Steinbuch matrix) is used in our conceptual approach. It allows for directly relating structural network properties with biological function.

A rapid approach for phenotype-screening and database independent detection of cSNP/protein polymorphism using mass accuracy precursor alignment.

The dynamics of a proteome can only be addressed with large-scale, high-throughput methods. To cope with the inherent complexity, techniques based on targeted quantification using proteotypic peptides are arising. This is an essential systems biology approach; however, for the exploratory discovery of unexpected markers, nontargeted detection of proteins, and protein modifications is indispensable.

Mode of inheritance of primary metabolic traits in tomato.

To evaluate components of fruit metabolic composition, we have previously metabolically phenotyped tomato (Solanum lycopersicum) introgression lines containing segmental substitutions of wild species chromosome in the genetic background of a cultivated variety. Here, we studied the hereditability of the fruit metabolome by analyzing an additional year's harvest and evaluating the metabolite profiles of lines heterozygous for the introgression (ILHs), allowing the evaluation of putative quantitative trait locus (QTL) mode of inheritance. These studies revealed that most of the metabolic QTL (174 of 332) were dominantly inherited, with relatively high proportions of additively (61 of 332) or recessively (80 of 332) inherited QTL and a negligible number displaying the characteristics of overdominant inheritance.

Metabolite profile analysis: from raw data to regression and classification.

Successful metabolic profile analysis will aid in the fundamental understanding of physiology. Here, we present a possible analysis workflow. Initially, the procedure to transform raw data into a data matrix containing relative metabolite levels for each sample is described.

Identification of metabolic and biomass QTL in Arabidopsis thaliana in a parallel analysis of RIL and IL populations.

Plant growth and development are tightly linked to primary metabolism and are subject to natural variation. In order to obtain an insight into the genetic factors controlling biomass and primary metabolism and to determine their relationships, two Arabidopsis thaliana populations [429 recombinant inbred lines (RIL) and 97 introgression lines (IL), derived from accessions Col-0 and C24] were analyzed with respect to biomass and metabolic composition using a mass spectrometry-based metabolic profiling approach. Six and 157 quantitative trait loci (QTL) were identified for biomass and metabolic content, respectively.