Multi-omics-based identification of atopic dermatitis target genes and their potential associations with metabolites and miRNAs.

Atopic dermatitis (AD), or atopic eczema, is one of the most common inflammatory skin diseases with up to 10% prevalence in adults, and approximately 15-20% in children in industrialized countries. As a result, there is an unmet need for faster, safer, and effective treatments for AD. AD pathogenesis represents a complex interplay between multiple factors, such as environmental factors or stimuli, genetic factors, immune dysfunctions. However, although multi-omics label studies have been very useful in understanding the pathophysiological mechanisms of AD and its clinical manifestations, there have been very few studies that integrate different labels of omics data. Here, we attempted to integrate gene expression and metabolomics datasets from multiple different publicly available AD cohort datasets and conduct an integrated systems-level AD analysis. We used four different GEO transcriptome data sets and, by applying an elastic net machine learning algorithm, identified robust hub genes that can be used as signatures, for example, H2AFX, MCM7, ESR1 and SF3A2. Moreover, we investigated potential associations of those genes by applying a pathway-based approach over metabolomics and miRNA datasets. Our results revealed potential novel associations between fatty acids and peroxisomal lipid metabolism pathways, as well as with several microRNAs

Multi-omics-based identification of atopic dermatitis target genes and their potential associations with metabolites and miRNAs.

Atopic dermatitis (AD), or atopic eczema, is one of the most common inflammatory skin diseases with up to 10% prevalence in adults, and approximately 15-20% in children in industrialized countries. As a result, there is an unmet need for faster, safer, and effective treatments for AD. AD pathogenesis represents a complex interplay between multiple factors, such as environmental factors or stimuli, genetic factors, immune dysfunctions. However, although multi-omics label studies have been very useful in understanding the pathophysiological mechanisms of AD and its clinical manifestations, there have been very few studies that integrate different labels of omics data. Here, we attempted to integrate gene expression and metabolomics datasets from multiple different publicly available AD cohort datasets and conduct an integrated systems-level AD analysis. We used four different GEO transcriptome data sets and, by applying an elastic net machine learning algorithm, identified robust hub genes that can be used as signatures, for example, H2AFX, MCM7, ESR1 and SF3A2. Moreover, we investigated potential associations of those genes by applying a pathway-based approach over metabolomics and miRNA datasets. Our results revealed potential novel associations between fatty acids and peroxisomal lipid metabolism pathways, as well as with several microRNAs

Immune infiltration and prognostic and diagnostic use of LGALS4 in colon adenocarcinoma and bladder urothelial carcinoma.

Colon adenocarcinoma (COAD) is a common tumor of the gastrointestinal tract with a high mortality rate. Current research has identified many genes associated with immune infiltration that play a vital role in the development of COAD. In this study, we analysed the prognostic and diagnostic features of such immune-related genes in the context of colonic adenocarcinoma (COAD). We analysed 17 overlapping gene expression profiles of COAD and healthy samples obtained from TCGA-COAD and public single-cell sequencing resources, to identify potential therapeutic COAD targets. We evaluated the abundance of immune infiltration with those genes using the TIMER (Tumor Immune Estimation Resource) deconvolution method. Subsequently, we developed predictive and survival models to assess the prognostic value of these genes. The LGALS4 (Galectin-4) gene was found to be significantly (P<0.05) downregulated in COAD and bladder urothelial carcinoma (BLCA) compared to healthy samples. We identified LGALS4 as a prognostic and diagnostic marker for multiple cancer types, including COAD and BLCA. Our analysis reveals a series of novel candidate drug targets, as well as candidate molecular markers, that may explain the pathogenesis of COAD and BLCA. LGALS4 gene is associated with multiple cancer types and is a possible prognostic, as well as diagnostic, marker of COAD and BLCA

Silk fibroin nanoparticles support in vitro sustained antibiotic release and osteogenesis on titanium surface

Increasing amounts of metal-based implants are used for orthopedic or dental surgeries throughout the world. Still several implant-related problems such as inflammation, loosening and bacterial infection are prevalent. These problems stem from the immediate microbial contamination and failure of initial osteoblast adhesion. Additionally, bacterial infections can cause serious and life-threatening conditions such as osteomyelitis. Here, antibiotic (gentamicin)-loaded silk proteinfibroin (non-mulberry silkworm, Antheraea mylitta) nanoparticles are fabricated and deposited over the titanium surface to achieve sustained drug release in vitro and to alter the surface nano-roughness. Based on the altered surface topography, chemistry and antibacterial activity, we conclude that the nanoparticle-deposited surfaces are superior for osteoblast adhesion, proliferation and differentiation in comparison to bare Ti. This method can be utilized as a cost-effective approach in implant modification