Fat regulatory mechanisms of pine nut oil based on protein interaction network analysis

BACKGROUND

Pine nut oil (PNO), a standardized and well-defined extract of Pinus koraiensis (Korean pine), has beneficial effects on wound healing, inflammatory diseases, and cancer. However, the explanation for the mechanism by which PNO reduces body fat remains uncertain. We performed a protein-protein interaction network (PPIN) analysis to explore the genes associated with pinolenic acid using the MEDILINE database from PubChem and PubMed. It was concluded through the PPIN analysis that PNO was involved in a neutral lipid biosynthetic process.

PURPOSE

This study evaluated the effects of PNO predicted by the network analysis of fat accumulation in chronic obesity mouse models established by feeding a high fat diet (HFD) to C57BL/6J mice and explored potential mechanisms.

METHODS

HFD mice were fed only HFD or HFD with PNO at 822 and 1644 mg/kg. After an oral administration of 7 weeks, several body weight and body fat-related parameters were examined, including the following: adipose weight, adipocyte size, serum lipid profiles, adipocyte expression of PPAR-γ, sterol regulatory element binding protein (SREBP)-1c, lipoprotein lipase (LPL) and leptin.

RESULTS

We showed that oral administration of PNO to HFD mice reduces body fat weight, fat in tissue, white adipose tissue weight, and adipocyte size. The serum cholesterol was improved in the HFD mice treated with PNO. Additionally, PNO has significantly attenuated the HFD-induced changes in the adipose tissue expression of PPAR-γ, SREBP-1c, LPL, and leptin.

CONCLUSIONS

The findings from this study based on the PPIN analysis suggest that PNO has potential as drug to reduce body fat through fat regulatory mechanisms by PPAR-γ and SREBP-1c.

Prediction of a Cell-Class-Specific Mouse Mesoconnectome Using Gene Expression Data

Reconstructing brain connectivity at sufficient resolution for computational models designed to study the biophysical mechanisms underlying cognitive processes is extremely challenging. For such a purpose, a mesoconnectome that includes laminar and cell-class specificity would be a major step forward. We analyzed the ability of gene expression patterns to predict cell-class and layer-specific projection patterns and assessed the functional annotations of the most predictive groups of genes. To achieve our goal we used publicly available volumetric gene expression and connectivity data and we trained computational models to learn and predict cell-class and layer-specific axonal projections using gene expression data. Predictions were done in two ways, namely predicting projection strengths using the expression of individual genes and using the co-expression of genes organized in spatial modules, as well as predicting binary forms of projection. For predicting the strength of projections, we found that ridge (L2-regularized) regression had the highest cross-validated accuracy with a median r² score of 0.54 which corresponded for binarized predictions to a median area under the ROC value of 0.89. Next, we identified 200 spatial gene modules using a dictionary learning and sparse coding approach. We found that these modules yielded predictions of comparable accuracy, with a median r² score of 0.51. Finally, a gene ontology enrichment analysis of the most predictive gene groups resulted in significant annotations related to postsynaptic function. Taken together, we have demonstrated a prediction workflow that can be used to perform multimodal data integration to improve the accuracy of the predicted mesoconnectome and support other neuroscience use cases.

Genetic and metabolic signatures of Salmonella enterica subsp. enterica associated with animal sources at the pangenomic scale

BACKGROUND

Salmonella enterica subsp. enterica is a public health issue related to food safety, and its adaptation to animal sources remains poorly described at the pangenome scale. Firstly, serovars presenting potential mono- and multi-animal sources were selected from a curated and synthetized subset of Enterobase. The corresponding sequencing reads were downloaded from the European Nucleotide Archive (ENA) providing a balanced dataset of 440 Salmonella genomes in terms of serovars and sources (i). Secondly, the coregenome variants and accessory genes were detected (ii). Thirdly, single nucleotide polymorphisms and small insertions/deletions from the coregenome, as well as the accessory genes were associated to animal sources based on a microbial Genome Wide Association Study (GWAS) integrating an advanced correction of the population structure (iii). Lastly, a Gene Ontology Enrichment Analysis (GOEA) was applied to emphasize metabolic pathways mainly impacted by the pangenomic mutations associated to animal sources (iv).

RESULTS

Based on a genome dataset including Salmonella serovars from mono- and multi-animal sources (i), 19,130 accessory genes and 178,351 coregenome variants were identified (ii). Among these pangenomic mutations, 52 genomic signatures (iii) and 9 over-enriched metabolic signatures (iv) were associated to avian, bovine, swine and fish sources by GWAS and GOEA, respectively.

CONCLUSIONS

Our results suggest that the genetic and metabolic determinants of Salmonella adaptation to animal sources may have been driven by the natural feeding environment of the animal, distinct livestock diets modified by human, environmental stimuli, physiological properties of the animal itself, and work habits for health protection of livestock.

Machine learning with the TCGA-HNSC dataset: improving usability by addressing inconsistency, sparsity, and high-dimensionality

Background

In the era of precision oncology and publicly available datasets, the amount of information available for each patient case has dramatically increased. From clinical variables and PET-CT radiomics measures to DNA-variant and RNA expression profiles, such a wide variety of data presents a multitude of challenges. Large clinical datasets are subject to sparsely and/or inconsistently populated fields. Corresponding sequencing profiles can suffer from the problem of high-dimensionality, where making useful inferences can be difficult without correspondingly large numbers of instances. In this paper we report a novel deployment of machine learning techniques to handle data sparsity and high dimensionality, while evaluating potential biomarkers in the form of unsupervised transformations of RNA data. We apply preprocessing, MICE imputation, and sparse principal component analysis (SPCA) to improve the usability of more than 500 patient cases from the TCGA-HNSC dataset for enhancing future oncological decision support for Head and Neck Squamous Cell Carcinoma (HNSCC).

Results

Imputation was shown to improve prognostic ability of sparse clinical treatment variables. SPCA transformation of RNA expression variables reduced runtime for RNA-based models, though changes to classifier performance were not significant. Gene ontology enrichment analysis of gene sets associated with individual sparse principal components (SPCs) are also reported, showing that both high- and low-importance SPCs were associated with cell death pathways, though the high-importance gene sets were found to be associated with a wider variety of cancer-related biological processes.

Conclusions

MICE imputation allowed us to impute missing values for clinically informative features, improving their overall importance for predicting two-year recurrence-free survival by incorporating variance from other clinical variables. Dimensionality reduction of RNA expression profiles via SPCA reduced both computation cost and model training/evaluation time without affecting classifier performance, allowing researchers to obtain experimental results much more quickly. SPCA simultaneously provided a convenient avenue for consideration of biological context via gene ontology enrichment analysis.

Impact of angiogenic activation and inhibition on miRNA profiles of human retinal endothelial cells

BACKGROUND

Human retinal microvascular endothelial cells (HRMVECs) are involved in the pathogenesis of retinopathy of prematurity. In this study, the microRNA (miRNA) expression profiles of HRMVECs were investigated under resting conditions, angiogenic stimulation (VEGF treatment) and anti-VEGF treatment.

MATERIALS AND METHODS

The miRNA profiles of HRMVECs under resting and angiogenic conditions (VEGF treatment), as well as after addition of aflibercept, bevacizumab or ranibizumab were evaluated by analyzing the transcriptome of small non-coding RNAs. Differentially expressed miRNAs were validated using qPCR and classified using Gene Ontology enrichment analysis.

RESULTS

Ten miRNAs were found to be significantly changed more than 2-fold. Seven of these miRNAs were changed between resting conditions and angiogenic stimulation. Four of these miRNAs (miR-139-5p/-3p and miR-335-5p/-3p) were validated by qPCR in independent experiments and were found to be associated with angiogenesis and cell migration in Gene Ontology analysis. In addition, analysis of the most abundant miRNAs in the HRMVEC miRNome (representing at least 1% of the miRNome) was conducted and identified miR-21-5p, miR-29a-3p, miR-100-5p and miR-126-5p/-3p to be differently expressed by at least 15% between resting conditions and angiogenic conditions. These miRNAs were found to be associated with apoptotic signaling, regulation of kinase activity, intracellular signal transduction, cell surface receptor signaling and positive regulation of cell differentiation in Gene Ontology analysis. No differentially regulated miRNAs between angiogenic stimulation and angiogenic stimulation plus anti-VEGF treatment were identified.

CONCLUSION

In this study we characterized the miRNA profile of HRMVECs under resting, angiogenic and anti-angiogenic conditions and identified several miRNAs of potential pathophysiologic importance for angioproliferative retinal diseases. Our results have implications for possible miRNA-targeted angiomodulatory approaches in diseases like diabetic retinopathy or retinopathy of prematurity.

Celiac disease biomarkers identified by transcriptome analysis of small intestinal biopsies

Establishing a celiac disease (CD) diagnosis can be difficult, such as when CD-specific antibody levels are just above cutoff or when small intestinal biopsies show low-grade injuries. To investigate the biological pathways involved in CD and select potential biomarkers to aid in CD diagnosis, RNA sequencing of duodenal biopsies from subjects with either confirmed Active CD (n = 20) or without any signs of CD (n = 20) was performed. Gene enrichment and pathway analysis highlighted contexts, such as immune response, microbial infection, phagocytosis, intestinal barrier function, metabolism, and transportation. Twenty-nine potential CD biomarkers were selected based on differential expression and biological context. The biomarkers were validated by real-time polymerase chain reaction of eight RNA sequencing study subjects, and further investigated using an independent study group (n = 43) consisting of subjects not affected by CD, with a clear diagnosis of CD on either a gluten-containing or a gluten-free diet, or with low-grade intestinal injury. Selected biomarkers were able to classify subjects with clear CD/non-CD status, and a subset of the biomarkers (CXCL10, GBP5, IFI27, IFNG, and UBD) showed differential expression in biopsies from subjects with no or low-grade intestinal injury that received a CD diagnosis based on biopsies taken at a later time point. A large number of pathways are involved in CD pathogenesis, and gene expression is affected in CD mucosa already in low-grade intestinal injuries. RNA sequencing of low-grade intestinal injuries might discover pathways and biomarkers involved in early stages of CD pathogenesis.

Leptin regulates the pro-inflammatory response in human epidermal keratinocytes

The role of leptin in cutaneous wound healing process has been suggested in genetically obese mouse studies. However, the molecular and cellular effects of leptin on human epidermal keratinocytes are still unclear. In this study, the whole-genome-scale microarray analysis was performed to elucidate the effect of leptin on epidermal keratinocyte functions. In the leptin-treated normal human keratinocytes (NHKs), we identified the 151 upregulated and 53 downregulated differentially expressed genes (DEGs). The gene ontology (GO) enrichment analysis with the leptin-induced DEGs suggests that leptin regulates NHKs to promote pro-inflammatory responses, extracellular matrix organization, and angiogenesis. Among the DEGs, the protein expression of IL-8, MMP-1, fibronectin, and S100A7, which play roles in which is important in the regulation of cutaneous inflammation, was confirmed in the leptin-treated NHKs. The upregulation of the leptin-induced proteins is mainly regulated by the STAT3 signaling pathway in NHKs. Among the downregulated DEGs, the protein expression of nucleosome assembly-associated centromere protein A (CENPA) and CENPM was confirmed in the leptin-treated NHKs. However, the expression of CENPA and CENPM was not coupled with those of other chromosome passenger complex like Aurora A kinase, INCENP, and survivin. In cell growth kinetics analysis, leptin had no significant effect on the cell growth curves of NHKs in the normal growth factor-enriched condition. Therefore, leptin-dependent downregulation of CENPA and CENPM in NHKs may not be directly associated with mitotic regulation during inflammation.

Integrated omics analysis of root-preferred genes across diverse rice varieties including Japonica and indica cultivars

Plant root systems play essential roles in developmental processes, such as the absorption of water and inorganic nutrients, and structural support. Gene expression is affected by growth conditions and the genetic background of plants. To identify highly conserved root-preferred genes in rice across diverse growth conditions and varieties, we used two independent meta-anatomical expression profiles based on a large collection of Affymetrix and Agilent 44K microarray data sets available for public use. We then identified 684 loci with root-preferred expression, which were validated with in silico analysis using both meta-expression profiles. The expression patterns of four candidate genes were confirmed in vivo by monitoring expression of β-glucuronidase under control of the candidate-gene promoters, providing new tools to manipulate agronomic traits associated with roots. We also utilized real-time PCR to examine the root-preferential expression of 14 genes across four rice varieties, including japonica and indica cultivars. Using a database of rice genes with known functions, we identified the reported functions of 39 out of the 684 candidate genes. Sixteen genes are directly involved in root development, while the remaining are involved in processes indirectly related to root development (i.e., soil-stress tolerance or growth retardation). This indicates the importance of our candidate genes for studies on root development and function. Gene ontology enrichment analysis in the 'biological processes' category revealed that root-preferred genes in rice are closely associated with nutrient transport-related genes, indicating that the primary role of roots is the uptake of nutrients from soil. In addition, predicted protein-protein interaction analysis suggested a molecular network for root development composed of 215 interactions associated with 44 root-preferred or root development-related genes. Taken together, our data provide an important foundation for future research on root development in rice.