Proteomics dataset of lysolecithin-induced demyelinated lesions in corpus callosum of Lewis rats, treated with Vagus nerve stimulation or sham treatment.

This article presents a comprehensive proteomics dataset from a lysolecithin (LPC)-induced demyelination model in the corpus callosum of female Lewis rats. The LPC model, widely used in preclinical studies of toxic demyelination, serves as a valuable tool for investigating processes of demyelination and remyelination, as well as for testing potential remyelination therapies for diseases like Multiple Sclerosis. In this study, rats received either Vagus Nerve Stimulation (VNS) or a sham treatment.

A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data.

Eukaryotic gene regulation is a combinatorial, dynamic, and quantitative process that plays a vital role in development and disease and can be modeled at a systems level in gene regulatory networks (GRNs). The wealth of multi-omics data measured on the same samples and even on the same cells has lifted the field of GRN inference to the next stage. Combinations of (single-cell) transcriptomics and chromatin accessibility allow the prediction of fine-grained regulatory programs that go beyond mere correlation of transcription factor and target gene expression, with enhancer GRNs (eGRNs) modeling molecular interactions between transcription factors, regulatory elements, and target genes.

Vagus nerve stimulation enhances remyelination and decreases innate neuroinflammation in lysolecithin-induced demyelination.

Background: Current treatments for Multiple Sclerosis (MS) poorly address chronic innate neuroinflammation nor do they offer effective remyelination. The vagus nerve has a strong regulatory role in inflammation and Vagus Nerve Stimulation (VNS) has potential to affect both neuroinflammation and remyelination in MS.

Objective: This study investigated the effects of VNS on demyelination and innate neuroinflammation in a validated MS rodent model.

Evaluation of single-sample network inference methods for precision oncology.

A major challenge in precision oncology is to detect targetable cancer vulnerabilities in individual patients. Modeling high-throughput omics data in biological networks allows identifying key molecules and processes of tumorigenesis. Traditionally, network inference methods rely on many samples to contain sufficient information for learning, resulting in aggregate networks.

HTSplotter: An end-to-end data processing, analysis and visualisation tool for chemical and genetic in vitro perturbation screening.

In biomedical research, high-throughput screening is often applied as it comes with automatization, higher-efficiency, and more and faster results. High-throughput screening experiments encompass drug, drug combination, genetic perturbagen or a combination of genetic and chemical perturbagen screens. These experiments are conducted in real-time assays over time or in an endpoint assay.

Molecular systems biology approaches to investigate mechanisms of gut-brain communication in neurological diseases.

Background: Whilst the incidence of neurological diseases is increasing worldwide, treatment remains mostly limited to symptom management. The gut-brain axis, which encompasses the communication routes between microbiota, gut and brain, has emerged as a crucial area of investigation for identifying new preventive and therapeutic targets in neurological disease.

Methods: Due to the inter-organ, systemic nature of the gut-brain axis, together with the multitude of biomolecules and microbial species involved, molecular systems biology approaches are required to accurately investigate the mechanisms of gut-brain communication.

Opportunities and challenges in interpretable deep learning for drug sensitivity prediction of cancer cells.

In precision oncology, therapy stratification is done based on the patients' tumor molecular profile. Modeling and prediction of the drug response for a given tumor molecular type will further improve therapeutic decision-making for cancer patients. Indeed, deep learning methods hold great potential for drug sensitivity prediction, but a major problem is that these models are black box algorithms and do not clarify the mechanisms of action.

Whole transcriptome profiling of liquid biopsies from tumour xenografted mouse models enables specific monitoring of tumour-derived extracellular RNA.

While cell-free DNA (cfDNA) is widely being investigated, free circulating RNA (extracellular RNA, exRNA) has the potential to improve cancer therapy response monitoring and detection due to its dynamic nature. However, it remains unclear in which blood subcompartment tumour-derived exRNAs primarily reside. We developed a host-xenograft deconvolution framework, exRNAxeno, with mapping strategies to either a combined human-mouse reference genome or both species genomes in parallel, applicable to exRNA sequencing data from liquid biopsies of human xenograft mouse models.

SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs clustering framework to analyze integrated multi-edge networks.

Background: Representing the complex interplay between different types of biomolecules across different omics layers in multi-omics networks bears great potential to gain a deep mechanistic understanding of gene regulation and disease. However, multi-omics networks easily grow into giant hairball structures that hamper biological interpretation. Module detection methods can decompose these networks into smaller interpretable modules.

RRM2 enhances MYCN-driven neuroblastoma formation and acts as a synergistic target with CHK1 inhibition.

High-risk neuroblastoma, a pediatric tumor originating from the sympathetic nervous system, has a low mutation load but highly recurrent somatic DNA copy number variants. Previously, segmental gains and/or amplifications allowed identification of drivers for neuroblastoma development. Using this approach, combined with gene dosage impact on expression and survival, we identified ribonucleotide reductase subunit M2 (RRM2) as a candidate dependency factor further supported by growth inhibition upon in vitro knockdown and accelerated tumor formation in a neuroblastoma zebrafish model coexpressing human RRM2 with MYCN.

Nearby transposable elements impact plant stress gene regulatory networks: a meta-analysis in A. thaliana and S. lycopersicum.

Background: Transposable elements (TE) make up a large portion of many plant genomes and are playing innovative roles in genome evolution. Several TEs can contribute to gene regulation by influencing expression of nearby genes as stress-responsive regulatory motifs. To delineate TE-mediated plant stress regulatory networks, we took a 2-step computational approach consisting of identifying TEs in the proximity of stress-responsive genes, followed by searching for cis-regulatory motifs in these TE sequences and linking them to known regulatory factors.

From DNA Copy Number Gains and Tumor Dependencies to Novel Therapeutic Targets for High-Risk Neuroblastoma.

Neuroblastoma is a pediatric tumor arising from the sympatho-adrenal lineage and a worldwide leading cause of childhood cancer-related deaths. About half of high-risk patients die from the disease while survivors suffer from multiple therapy-related side-effects. While neuroblastomas present with a low mutational burden, focal and large segmental DNA copy number aberrations are highly recurrent and associated with poor survival.

Function, dynamics and evolution of network motif modules in integrated gene regulatory networks of worm and plant.

Gene regulatory networks (GRNs) consist of different molecular interactions that closely work together to establish proper gene expression in time and space. Especially in higher eukaryotes, many questions remain on how these interactions collectively coordinate gene regulation. We study high quality GRNs consisting of undirected protein-protein, genetic and homologous interactions, and directed protein-DNA, regulatory and miRNA-mRNA interactions in the worm Caenorhabditis elegans and the plant Arabidopsis thaliana.

Arabidopsis ensemble reverse-engineered gene regulatory network discloses interconnected transcription factors in oxidative stress.

The abiotic stress response in plants is complex and tightly controlled by gene regulation. We present an abiotic stress gene regulatory network of 200,014 interactions for 11,938 target genes by integrating four complementary reverse-engineering solutions through average rank aggregation on an Arabidopsis thaliana microarray expression compendium. This ensemble performed the most robustly in benchmarking and greatly expands upon the availability of interactions currently reported.

Reciprocal responses in the interaction between Arabidopsis and the cell-content-feeding chelicerate herbivore spider mite.

Most molecular-genetic studies of plant defense responses to arthropod herbivores have focused on insects. However, plant-feeding mites are also pests of diverse plants, and mites induce different patterns of damage to plant tissues than do well-studied insects (e.g.

The membrane-bound NAC transcription factor ANAC013 functions in mitochondrial retrograde regulation of the oxidative stress response in Arabidopsis.

Upon disturbance of their function by stress, mitochondria can signal to the nucleus to steer the expression of responsive genes. This mitochondria-to-nucleus communication is often referred to as mitochondrial retrograde regulation (MRR). Although reactive oxygen species and calcium are likely candidate signaling molecules for MRR, the protein signaling components in plants remain largely unknown.

Identification of cis-regulatory elements specific for different types of reactive oxygen species in Arabidopsis thaliana.

The type of reactive oxygen species (ROS) is a major factor that determines the specificity of biological responses. These responses may be elicited by activation of transcription factors that recognize ROS-specific cis-regulatory elements in target genes. In search for Arabidopsis promoter motifs specific for particular types of ROS, genome-wide microarray expression profiles for 283 abiotic stress-related conditions were subjected to cluster analysis to identify gene groups induced by singlet oxygen, superoxide radicals, and H(2)O(2).

Transcription regulatory networks in Caenorhabditis elegans inferred through reverse-engineering of gene expression profiles constitute biological hypotheses for metazoan development.

Differential gene expression governs the development, function and pathology of multicellular organisms. Transcription regulatory networks study differential gene expression at a systems level by mapping the interactions between regulatory proteins and target genes. While microarray transcription profiles are the most abundant data for gene expression, it remains challenging to correctly infer the underlying transcription regulatory networks.

Matrix and Steiner-triple-system smart pooling assays for high-performance transcription regulatory network mapping.

Yeast one-hybrid (Y1H) assays provide a gene-centered method for the identification of interactions between gene promoters and regulatory transcription factors (TFs). To date, Y1H assays have involved library screens that are relatively expensive and laborious. We present two Y1H strategies that allow immediate prey identification: matrix assays that use an array of 755 individual Caenorhabditis elegans TFs, and smart-pool assays that use TF multiplexing.

Transcription factor modularity in a gene-centered C. elegans core neuronal protein-DNA interaction network.

Transcription regulatory networks play a pivotal role in the development, function, and pathology of metazoan organisms. Such networks are comprised of protein-DNA interactions between transcription factors (TFs) and their target genes. An important question pertains to how the architecture of such networks relates to network functionality.

Gateway-compatible yeast one-hybrid screens.

INTRODUCTIONProtein-DNA interactions (PDIs) between transcription factors (TFs) and their target genes form the backbone of transcription regulatory networks. Such PDIs can be identified with either a TF or a gene as a starting point. The Gateway-compatible yeast one-hybrid (Y1H) system provides a high-throughput, gene-centered method for the identification of interactions between a "DNA bait" (e.

In vitro intestinal transport and antihypertensive activity of ACE inhibitory pea and whey digests.

Angiotensin I converting enzyme (ACE) inhibitory peptides cause an antihypertensive effect if they reach the systemic circulation. This was investigated for the high ACE inhibitory activity present in peas and whey in vitro gastrointestinal digests. The samples retained high ACE inhibitory activity when incubated in Caco-2 homogenates or rat intestinal acetone powder, both sources of small intestine peptidases.

Bioavailability of angiotensin I converting enzyme inhibitory peptides.

Hypertension or high blood pressure is a significant health problem worldwide. Bioactive peptides that inhibit angiotensin I converting enzyme (ACE) in the cardiovascular system can contribute to the prevention and treatment of hypertension. These ACE inhibitory peptides are derived from many food proteins, especially milk proteins.