Investigation of Long Noncoding RNA-NRAV and Long Noncoding RNA-Lethe Expression in Crimean-Congo Hemorrhagic Fever.

Crimean-Congo hemorrhagic fever (CCHF) is a zoonotic infectious disease caused by the CCHF virus, a member of the Bunyavirales order and the Orthonairoviridae family. The exact pathogenesis is not fully understood. Long noncoding RNAs (lncRNAs) are RNAs that are shown to play a role in various pathological processes of viral diseases.

Investigation of Association Between Expression of DYX1C1, KIAA0319, and ROBO1 Genes and Specific Learning Disorder in Children and Adolescents.

Specific learning disorder (SLD) is prevalent worldwide and is a complex disorder with variable symptoms and significant differences among individuals. Epigenetic markers may alter susceptibility to neurodevelopmental disorders (NDDs). Aberrant expression of protein-coding (mRNA) genes in this pathology shows that the detection of epigenetic molecular biomarkers is of increasing importance in the diagnosis and treatment of individuals with SLD.

Relationship of Toll-Like Receptor 7, 9, and 10 Polymorphisms and the Severity of Coronavirus Disease 2019.

Coronavirus disease 2019 (COVID-19) is a pandemic that is still affecting people and has caused many deaths. Toll-like receptors (TLRs) have an important role in the binding of disease agents to the host cell, disease susceptibility and severity, and host disease resistance. In this study, we investigated the frequencies of TLR7 (C.

Role of lncRNAs in Remodeling of the Coronary Artery Plaques in Patients with Atherosclerosis.

Introduction: Cardiovascular diseases (CVDs) are the leading cause of death worldwide according to World Health Organization (WHO) data. Atherosclerosis is considered as a chronic inflammatory disease that develops in response to damage to the vascular intima-media layer in most cases. In recent years, epigenetic events have emerged as important players in the development and progression of CVDs.

Phenomics approaches to understand genetic networks and gene function in yeast.

Over the past decade, major efforts have been made to systematically survey the characteristics or phenotypes associated with genetic variation in a variety of model systems. These so-called phenomics projects involve the measurement of 'phenomes', or the set of phenotypic information that describes an organism or cell, in various genetic contexts or states, and in response to external factors, such as environmental signals. Our understanding of the phenome of an organism depends on the availability of reagents that enable systematic evaluation of the spectrum of possible phenotypic variation and the types of measurements that can be taken.

Systematic genetics and single-cell imaging reveal widespread morphological pleiotropy and cell-to-cell variability.

Our ability to understand the genotype-to-phenotype relationship is hindered by the lack of detailed understanding of phenotypes at a single-cell level. To systematically assess cell-to-cell phenotypic variability, we combined automated yeast genetics, high-content screening and neural network-based image analysis of single cells, focussing on genes that influence the architecture of four subcellular compartments of the endocytic pathway as a model system. Our unbiased assessment of the morphology of these compartments-endocytic patch, actin patch, late endosome and vacuole-identified 17 distinct mutant phenotypes associated with ~1,600 genes (~30% of all yeast genes).

Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig.

The type and genomic context of cancer mutations depend on their causes. These causes have been characterized using signatures that represent mutation types that co-occur in the same tumours. However, it remains unclear how mutation processes change during cancer evolution due to the lack of reliable methods to reconstruct evolutionary trajectories of mutational signature activity.

Regulation of microRNAs in coronary atherosclerotic plaque.

Identification of microRNAs (miRNAs) associated with atherosclerosis may unravel novel therapeutic targets and biomarkers. We studied miRNAs differentially expressed between coronary atherosclerotic plaques (CAP) and healthy arteries. Paired CAP and internal mammary arteries (IMA) were collected from 14 coronary artery disease patients.

Characterizing ABC-Transporter Substrate-Likeness Using a Clean-Slate Genetic Background.

Mutations in ATP Binding Cassette (ABC)-transporter genes can have major effects on the bioavailability and toxicity of the drugs that are ABC-transporter substrates. Consequently, methods to predict if a drug is an ABC-transporter substrate are useful for drug development. Such methods traditionally relied on literature curated collections of ABC-transporter dependent membrane transfer assays.

Identification of potential microRNA markers related to Crimean-Congo hemorrhagic fever disease.

Crimean-Congo hemorrhagic fever (CCHF) is a tick-borne disease caused by the arbovirus Crimean-Congo hemorrhagic fever virus (CCHFV). The CCHFV has a single-stranded RNA genome of negative sense. MicroRNAs (miRNAs) are key players in virus-host interactions and viral pathogenesis.

Machine learning and computer vision approaches for phenotypic profiling.

With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach.

Chemogenomics and orthology-based design of antibiotic combination therapies.

Combination antibiotic therapies are being increasingly used in the clinic to enhance potency and counter drug resistance. However, the large search space of candidate drugs and dosage regimes makes the identification of effective combinations highly challenging. Here, we present a computational approach called INDIGO, which uses chemogenomics data to predict antibiotic combinations that interact synergistically or antagonistically in inhibiting bacterial growth.