Heat-stress-induced ROS in maize silks cause late pollen tube growth arrest and sterility.

The reproductive phase of plants is highly sensitive to ambient temperature stresses. To investigate sensitivity of female reproductive organs in grass crops during the pollination phase, we exposed the elongated stigma (silk) of maize to ambient environment at the silking stage. Moderate heat stress causes cell death of silk hair cells but did not affect early pollen tube growth inside the silk.

Network analyses predict major regulators of resistance to early blight disease complex in tomato.

Background: Early blight and brown leaf spot are often cited as the most problematic pathogens of tomato in many agricultural regions. Their causal agents are Alternaria spp., a genus of Ascomycota containing numerous necrotrophic pathogens.

Ablation of Vitamin D Signaling in Cardiomyocytes Leads to Functional Impairment and Stimulation of Pro-Inflammatory and Pro-Fibrotic Gene Regulatory Networks in a Left Ventricular Hypertrophy Model in Mice.

The association between vitamin D deficiency and cardiovascular disease remains a controversial issue. This study aimed to further elucidate the role of vitamin D signaling in the development of left ventricular (LV) hypertrophy and dysfunction. To ablate the vitamin D receptor (VDR) specifically in cardiomyocytes, VDR mice were crossed with Mlcv2-Cre mice.

Drugst.One - a plug-and-play solution for online systems medicine and network-based drug repurposing.

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities.

Single-Cell Multi-Omics Map of Cell Type-Specific Mechanistic Drivers of Multiple Sclerosis Lesions.

Background And Objectives: In progressive multiple sclerosis (MS), compartmentalized inflammation plays a pivotal role in the complex pathology of tissue damage. The interplay between epigenetic regulation, transcriptional modifications, and location-specific alterations within white matter (WM) lesions at the single-cell level remains underexplored.

Methods: We examined intracellular and intercellular pathways in the MS brain WM using a novel dataset obtained by integrated single-cell multi-omics techniques from 3 active lesions, 3 chronic active lesions, 3 remyelinating lesions, and 3 control WM of 6 patients with progressive MS and 3 non-neurologic controls.

Inference of differential key regulatory networks and mechanistic drug repurposing candidates from scRNA-seq data with SCANet.

Motivation: The reconstruction of small key regulatory networks that explain the differences in the development of cell (sub)types from single-cell RNA sequencing is a yet unresolved computational problem.

Results: To this end, we have developed SCANet, an all-in-one package for single-cell profiling that covers the whole differential mechanotyping workflow, from inference of trait/cell-type-specific gene co-expression modules, driver gene detection, and transcriptional gene regulatory network reconstruction to mechanistic drug repurposing candidate prediction. To illustrate the power of SCANet, we examined data from two studies.

Laminarin-triggered defence responses are geographically dependent in natural populations of Solanum chilense.

Natural plant populations are polymorphic and show intraspecific variation in resistance properties against pathogens. The activation of the underlying defence responses can depend on variation in perception of pathogen-associated molecular patterns or elicitors. To dissect such variation, we evaluated the responses induced by laminarin (a glucan, representing an elicitor from oomycetes) in the wild tomato species Solanum chilense and correlated this to observed infection frequencies of Phytophthora infestans.

reconstruction and identification of transcriptional gene regulatory network modules differentiating single-cell clusters.

Single-cell RNA sequencing (scRNA-seq) technology provides an unprecedented opportunity to understand gene functions and interactions at single-cell resolution. While computational tools for scRNA-seq data analysis to decipher differential gene expression profiles and differential pathway expression exist, we still lack methods to learn differential regulatory disease mechanisms directly from the single-cell data. Here, we provide a new methodology, named DiNiro, to unravel such mechanisms and report them as small, easily interpretable transcriptional regulatory network modules.

Enabling single-cell trajectory network enrichment.

Single-cell sequencing (scRNA-seq) technologies allow the investigation of cellular differentiation processes with unprecedented resolution. Although powerful software packages for scRNA-seq data analysis exist, systems biology-based tools for trajectory analysis are rare and typically difficult to handle. This hampers biological exploration and prevents researchers from gaining deeper insights into the molecular control of developmental processes.

Exploring the SARS-CoV-2 virus-host-drug interactome for drug repurposing.

Coronavirus Disease-2019 (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Various studies exist about the molecular mechanisms of viral infection. However, such information is spread across many publications and it is very time-consuming to integrate, and exploit.

Deep Splicing Code: Classifying Alternative Splicing Events Using Deep Learning.

Alternative splicing (AS) is the process of combining different parts of the pre-mRNA to produce diverse transcripts and eventually different protein products from a single gene. In computational biology field, researchers try to understand AS behavior and regulation using computational models known as "Splicing Codes". The final goal of these algorithms is to make an in-silico prediction of AS outcome from genomic sequence.

DeePromoter: Robust Promoter Predictor Using Deep Learning.

The promoter region is located near the transcription start sites and regulates transcription initiation of the gene by controlling the binding of RNA polymerase. Thus, promoter region recognition is an important area of interest in the field of bioinformatics. Numerous tools for promoter prediction were proposed.