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.

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.

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.

First gene-ontology enrichment analysis based on bacterial coregenome variants: insights into adaptations of Salmonella serovars to mammalian- and avian-hosts.

BACKGROUND: Many of the bacterial genomic studies exploring evolution processes of the host adaptation focus on the accessory genome describing how the gains and losses of genes can explain the colonization of new habitats. Consequently, we developed a new approach focusing on the coregenome in order to describe the host adaptation of Salmonella serovars. METHODS: In the present work, we propose bioinformatic tools allowing (i) robust phylogenetic inference based on SNPs and recombination events, (ii) identification of fixed SNPs and InDels distinguishing homoplastic and non-homoplastic coregenome variants, and (iii) gene-ontology enrichment analyses to describe metabolic processes involved in adaptation of Salmonella enterica subsp. enterica to mammalian- (S. Dublin), multi- (S. Enteritidis), and avian- (S. Pullorum and S. Gallinarum) hosts. RESULTS: The 'VARCall' workflow produced a robust phylogenetic inference confirming that the monophyletic clade S. Dublin diverged from the polyphyletic clade S. Enteritidis which includes the divergent clades S. Pullorum and S. Gallinarum (i). The scripts 'phyloFixedVar' and 'FixedVar' detected non-synonymous and non-homoplastic fixed variants supporting the phylogenetic reconstruction (ii). The scripts 'GetGOxML' and 'EveryGO' identified representative metabolic pathways related to host adaptation using the first gene-ontology enrichment analysis based on bacterial coregenome variants (iii). CONCLUSIONS: We propose in the present manuscript a new coregenome approach coupling identification of fixed SNPs and InDels with regards to inferred phylogenetic clades, and gene-ontology enrichment analysis in order to describe the adaptation of Salmonella serovars Dublin (i.e. mammalian-hosts), Enteritidis (i.e. multi-hosts), Pullorum (i.e. avian-hosts) and Gallinarum (i.e. avian-hosts) at the coregenome scale. All these polyvalent Bioinformatic tools can be applied on other bacterial genus without additional developments.

Integrated gene expression profiling and chromatin immunoprecipitation followed by sequencing: Analysis of the C-terminal binding protein in breast cancer.

AIM: This study explored the possible mechanisms of the transcriptional regulatory activities of C-terminal binding protein (CtBP) and the role of CtBP in the pathogenesis of breast cancer. METHODS: Microarray data of GSE36529, including three CtBP-knockdown breast cancer MCF-7 cell samples, three control knockdown samples and data of CtBP binding profile in MCF-7 cells, was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened between CtBP-knockdown MCF-7 cell samples and controls. Newly developed chromatin immunoprecipitation followed by sequencing technology was used to identify the CtBP binding regions. The direct target genes of CtBP were identified using ChIP-Array software and a regulatory network was constructed, followed by gene ontology (GO) enrichment analysis of all identified DEGs and DEGs targeted by CtBP. RESULTS: In total, 404 DEGs were identified in CtBP-knockdown MCF-7 cell samples. These DEGs were enriched in different GO terms, such as cellular response to stress and cell cycle, endoplasmic reticulum and nucleotide binding. Additionally, 143 DEGs were identified as potential direct targets of CtBP in the regulatory network. CtBP target genes such as hypoxia up-regulated 1, BTG family member 2 and endothelin 1 were mainly related to response to hypoxia and regulation of cell proliferation. CONCLUSIONS: Hypoxia up-regulated 1, BTG family member 2 and endothelin 1 may be associated with the progression of breast cancer through interaction with CtBP in different biological processes. CtBP may be a therapeutic target for the treatment of breast cancer.

Comparative Genome Analysis of Japanese Field-Isolated Aspergillus for Aflatoxin Productivity and Non-Productivity.

Aspergillus flavus produces aflatoxin, a carcinogenic fungal toxin that poses a threat to the agricultural and food industries. There is a concern that the distribution of aflatoxin-producing A. flavus is expanding in Japan due to climate change, and it is necessary to understand what types of strains inhabit. In this study, we sequenced the genomes of four Aspergillus strains isolated from agricultural fields in the Ibaraki prefecture of Japan and identified their genetic variants. Phylogenetic analysis based on single-nucleotide variants revealed that the two aflatoxin-producing strains were closely related to A. flavus NRRL3357, whereas the two non-producing strains were closely related to the RIB40 strain of Aspergillus oryzae, a fungus widely used in the Japanese fermentation industry. A detailed analysis of the variants in the aflatoxin biosynthetic gene cluster showed that the two aflatoxin-producing strains belonged to different morphotype lineages. RT-qPCR results indicated that the expression of aflatoxin biosynthetic genes was consistent with aflatoxin production in the two aflatoxin-producing strains, whereas the two non-producing strains expressed most of the aflatoxin biosynthetic genes, unlike common knowledge in A. oryzae, suggesting that the lack of aflatoxin production was attributed to genes outside of the aflatoxin biosynthetic gene cluster in these strains.

Therapeutic, Clinicopathological, and Molecular Correlates of PRKACA Expression in Gastrointestinal Cancers.

BACKGROUND/OBJECTIVES: PRKACA alterations have clear diagnostic and biological roles in the fibrolamellar variant of hepatocellular carcinoma and a potential predictive role in that cancer type. However, the roles of PRKACA have not been comprehensively examined in gastric and colorectal cancers (GC and CRC). This study, therefore, sought to investigate the roles of PRKACA expression in GC and CRC. METHODS: The clinico-genomic data of 441 GC and 629 CRC cases were analyzed for therapeutic, clinicopathological, and biological correlates using appropriate bioinformatics and statistical tools. Furthermore, the deregulation of PRKACA expression in GC and CRC was investigated using correlative and regression analyses. RESULTS: The results showed that PRKACA expression subsets were enriched for gene targets of chemotherapeutics, tyrosine kinase, and ß-adrenergic inhibitors. Moreover, high PRKACA expression was associated with adverse clinicopathological and genomic features of GC and CRC. Gene Ontology Enrichment Analysis also showed that PRKACA-high subsets of the GI cancers were enriched for the biological and molecular functions that are associated with cell motility, invasion, and metastasis but not cell proliferation. Finally, multiple regression analyses identified multiple methylation loci, transcription factors, miRNA species, and PRKACA copy number changes that deregulated PRKACA expression in GC and CRC. CONCLUSIONS: This study has identified potential predictive and clinicopathological roles for PRKACA expression in GI cancers and has added to the growing body of knowledge on the deregulation of PRKACA in cancer.

miRinGO: Prediction of Biological Processes Indirectly Targeted by Human microRNAs.

MicroRNAs (miRNAs) are small non-coding RNAs that are known for their role in the post-transcriptional regulation of target genes. Typically, their functions are predicted by first identifying their target genes and then finding biological processes enriched in these targets. Current tools for miRNA functional analysis use only genes with physical binding sites as their targets and exclude other genes that are indirectly targeted transcriptionally through transcription factors. Here, we introduce a method to predict gene ontology (GO) annotations indirectly targeted by microRNAs. The proposed method resulted in better performance in predicting known miRNA-GO term associations compared to the canonical approach. To facilitate miRNA GO enrichment analysis, we developed an R Shiny application, miRinGO, that is freely available online at GitHub.

Deep learning-based morphological feature analysis and the prognostic association study in colon adenocarcinoma histopathological images.

Colorectal cancer (CRC) is now the third most common malignancy to cause mortality worldwide, and its prognosis is of great importance. Recent CRC prognostic prediction studies mainly focused on biomarkers, radiometric images, and end-to-end deep learning methods, while only a few works paid attention to exploring the relationship between the quantitative morphological features of patients' tissue slides and their prognosis. However, existing few works in this area suffered from the drawback of choosing the cells randomly from the whole slides, which contain the non-tumor region that lakes information about prognosis. In addition, the existing works, which tried to demonstrate their biological interpretability using patients' transcriptome data, failed to show the biological meaning closely related to cancer. In this study, we proposed and evaluated a prognostic model using morphological features of cells in the tumor region. The features were first extracted by the software CellProfiler from the tumor region selected by Eff-Unet deep learning model. Features from different regions were then averaged for each patient as their representative, and the Lasso-Cox model was used to select the prognosis-related features. The prognostic prediction model was at last constructed using the selected prognosis-related features and was evaluated through KM estimate and cross-validation. In terms of biological meaning, Gene Ontology (GO) enrichment analysis of the expressed genes that correlated with the prognostically significant features was performed to show the biological interpretability of our model.With the help of tumor segmentation, our model achieved better statistical significance and better biological interpretability compared to the results without tumor segmentation. Statistically, the Kaplan Meier (KM) estimate of our model showed that the model using features in the tumor region has a higher C-index, a lower p-value, and a better performance on cross-validation than the model without tumor segmentation. In addition, revealing the pathway of the immune escape and the spread of the tumor, the model with tumor segmentation demonstrated a biological meaning much more related to cancer immunobiology than the model without tumor segmentation. Our prognostic prediction model using quantitive morphological features from tumor regions was almost as good as the TNM tumor staging system as they had a close C-index, and our model can be combined with the TNM tumor stage system to make a better prognostic prediction. And to the best of our knowledge, the biological mechanisms in our study were the most relevant to the immune mechanism of cancer compared to the previous studies.

Five hub genes contributing to the oncogenesis and trastuzumab-resistance in gastric cancer.

BACKGROUND: Monoclonal antibodies, as the targeted therapeutic strategies, provide huge clinical benefits for tumor patients. However, after undergoing several times treatment, patients developed drug resistance which is a major bottleneck in clinical cancer therapy. In this study, we aimed to explore the potential molecular mechanism of trastuzumab-resistant and cancer progression, and identify valuable diagnosis biomarkers for gastric cancer. MATERIALS AND METHODS: Gene expression profiles and RNA-sequencing dataset of gastric cancer were acquired from Gene Expression Omnibus (GEO) dataset and The Cancer Genome Atlas (TCGA) dataset, respectively. The Differently expressed genes (DEGs) were screened by R programing language, and Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were adopted separately to analyze the function and pathway of DEGs. Subsequently, Search Tool for the Retrieval of Interacting (STRING) and Cytoscape was performed to establish the protein-protein interaction (PPI) network and to screen hub genes. The Receiver Operating Characteristic (ROC) curves were used to evaluate the diagnostic values of the hub genes. RESULTS: Integrated analysis of TCGA-STAD (Stomach adenocarcinoma) and GEO databases identified 310 common DEGs. GSEA, GO and KEGG enrichment analysis revealed several crucial enriched oncological signatures and trastuzumab-resistant signaling pathways, which may help to explain the potential modulating mechanisms of trastuzumab-resistant. Based on the PPI network, 10 hub genes were screened and five genes (GNGT1, KRT7, KRT16, SOX9, TIMP1) were identified with good performance in the diagnosis of gastric cancer by ROC analysis. Furthermore, Kaplan-Meier analysis and log-rank test suggested that upregulation of KRT16 was correlated with overall survival in gastric cancer. CONCLUSION: Overall, our study identified five hub genes that may play a critical role in promoting trastuzumab-resistant in gastric cancer, and would be a promising diagnostic and therapeutic biomarker for trastuzumab-resistant gastric cancer.

Filtering the rejection set while preserving false discovery rate control.

Scientific hypotheses in a variety of applications have domain-specific structures, such as the tree structure of the International Classification of Diseases (ICD), the directed acyclic graph structure of the Gene Ontology (GO), or the spatial structure in genome-wide association studies. In the context of multiple testing, the resulting relationships among hypotheses can create redundancies among rejections that hinder interpretability. This leads to the practice of filtering rejection sets obtained from multiple testing procedures, which may in turn invalidate their inferential guarantees. We propose Focused BH, a simple, flexible, and principled methodology to adjust for the application of any pre-specified filter. We prove that Focused BH controls the false discovery rate under various conditions, including when the filter satisfies an intuitive monotonicity property and the p-values are positively dependent. We demonstrate in simulations that Focused BH performs well across a variety of settings, and illustrate this method's practical utility via analyses of real datasets based on ICD and GO.

Bioinformatics analysis of ferroptosis in spinal cord injury.

Ferroptosis plays a key role in aggravating the progression of spinal cord injury (SCI), but the specific mechanism remains unknown. In this study, we constructed a rat model of T10 SCI using a modified Allen method. We identified 48, 44, and 27 ferroptosis genes that were differentially expressed at 1, 3, and 7 days after SCI induction. Compared with the sham group and other SCI subgroups, the subgroup at 1 day after SCI showed increased expression of the ferroptosis marker acyl-CoA synthetase long-chain family member 4 and the oxidative stress marker malondialdehyde in the injured spinal cord while glutathione in the injured spinal cord was lower. These findings with our bioinformatics results suggested that 1 day after SCI was the important period of ferroptosis progression. Bioinformatics analysis identified the following top ten hub ferroptosis genes in the subgroup at 1 day after SCI: STAT3, JUN, TLR4, ATF3, HMOX1, MAPK1, MAPK9, PTGS2, VEGFA, and RELA. Real-time polymerase chain reaction on rat spinal cord tissue confirmed that STAT3, JUN, TLR4, ATF3, HMOX1, PTGS2, and RELA mRNA levels were up-regulated and VEGFA, MAPK1 and MAPK9 mRNA levels were down-regulated. Ten potential compounds were predicted using the DSigDB database as potential drugs or molecules targeting ferroptosis to repair SCI. We also constructed a ferroptosis-related mRNA-miRNA-lncRNA network in SCI that included 66 lncRNAs, 10 miRNAs, and 12 genes. Our results help further the understanding of the mechanism underlying ferroptosis in SCI.

Single-Cell RNA Sequencing Reveals Smooth Muscle Cells Heterogeneity in Experimental Aortic Dissection.

Purpose: This study aims to illustrate the cellular landscape in the aorta of experimental aortic dissection (AD) and elaborate on the smooth muscle cells (SMCs) heterogeneity and functions among various cell types. Methods: Male Apolipoprotein deficient (ApoE-/-) mice at 28 weeks of age were infused with Ang II (2,500 ng/kg/min) to induce AD. Aortas from euthanized mice were harvested after 7 days for 10×Genomics single-cell RNA sequencing (scRNA-seq), followed by the identification of cell types and differentially expressed genes (DEGs). Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was conducted. Results: AD was successfully induced in ApoE-/- mice. scRNA-seq identified 15 cell clusters and nine cell types, including non-immune cells (endothelials, fibroblasts, and SMCs) and immune cells (B cells, natural killer T cell, macrophages, dendritic cells, neutrophils, and mast cells). The relative numbers of SMCs were remarkably changed, and seven core DEGs (ACTA2,IL6,CTGF,BGN,ITGA8,THBS1, and CDH5) were identified in SMCs. Moreover, we found SMCs can differentiate into 8 different subtypes through single-cell trajectory analysis. Conclusion: scRNA-seq technology can successfully identify unique cell composition in experimental AD. To our knowledge, this is the first study that provided the complete cellular landscape in AD tissues from mice, seven core DEGs and eight subtypes of SMCs were identified, and the SMCs have evolution from matrix type to inflammatory type.

A pilot study to show that asymptomatic sexually transmitted infections alter the foreskin epithelial proteome.

There is limited data on the role of asymptomatic STIs (aSTIs) on the risk of human immunodeficiency virus (HIV) acquisition in the male genital tract (MGT). The impact of foreskin removal on lowering HIV acquisition is well described, but molecular events leading to HIV acquisition are unclear. Here, in this pilot study, we show that asymptomatic urethral infection with Chlamydia trachomatis (CT) significantly impacts the foreskin proteome composition. We developed and optimized a shotgun liquid chromatography coupled tandem mass spectrometry (MS)-based proteomics approach and utilized this on foreskins collected at medical male circumcision (MMC) from 16 aSTI+ men and 10 age-matched STI- controls. We used a novel bioinformatic metaproteomic pipeline to detect differentially expressed (DE) proteins. Gene enrichment ontology analysis revealed proteins associated with inflammatory and immune activation function in both inner and outer foreskin from men with an aSTI. Neutrophil activation/degranulation and viral-evasion proteins were significantly enriched in foreskins from men with aSTI, whereas homotypic cell-cell adhesion proteins were enriched in foreskin tissue from men without an aSTI. Collectively, our data show that asymptomatic urethral sexually transmitted infections result in profound alterations in epithelial tissue that are associated with depletion of barrier integrity and immune activation.

Cell landscape atlas for patients with chronic thromboembolic pulmonary hypertension after pulmonary endarterectomy constructed using single-cell RNA sequencing.

This study aimed to construct an atlas of the cell landscape and comprehensively characterize the cellular repertoire of the pulmonary endarterectomized tissues of patients with chronic thromboembolic pulmonary hypertension (CTEPH). Five pulmonary endarterectomized tissues were collected. 10× Genomics single-cell RNA sequencing was performed, followed by the identification of cluster marker genes and cell types. Gene Ontology (GO) enrichment analysis was conducted. Seventeen cell clusters were characterized, corresponding to 10,518 marker genes, and then classified into eight cell types, including fibroblast/smooth muscle cell, endothelial cell, T cell/NK cell, macrophage, mast cell, cysteine rich secretory protein LCCL domain containing 2 (CRISPLD2)+ cell, cancer stem cell, and undefined. The specific marker genes of fibroblast/smooth muscle cell, endothelial cell, T cell/NK cell, macrophage, mast cell, and cancer stem cell were significantly enriched for multiple functions associated with muscle cell migration, endothelial cell migration, T cell activation, neutrophil activation, erythrocyte homeostasis, and tissue remodeling, respectively. No functions were significantly enriched for the marker gene of CRISPLD2+ cell. Our study, for the first time, provides an atlas of the cell landscape of the pulmonary endarterectomized tissues of CTEPH patients at single-cell resolution, which may serve as a valuable resource for further elucidation of disease pathophysiology.

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.

Identification of Soybean Genes Related to Soybean Seed Protein Content Based on Quantitative Trait Loci Collinearity Analysis.

Increasing the protein content of soybean seeds through a higher ratio of glycinin is important for soybean breeding and food processing; therefore, the integration of different quantitative trait loci (QTLs) is of great significance. In this study, we investigated the collinearity of seed protein QTLs. We identified 192 collinear protein QTLs that formed six hotspot regions. The two most important regions had seed protein 36-10 and seed protein 36-20 as hub nodes. We used a chromosome segment substitution line (CSSL) population for QTL validation and identified six CSSL materials with collinear QTLs. Five materials with higher protein and glycinin contents in comparison to the recurrent parent were analyzed. A total of 13 candidate genes related to seed protein from the QTL hotspot intervals were detected, 8 of which had high expression in mature soybean seeds. These results offer a new analysis method for molecular-assisted selection (MAS) and improvement of soybean product quality.

MicroRNA expression in the hippocampal CA1 region under deep hypothermic circulatory arrest.

Using deep hypothermic circulatory arrest, thoracic aorta diseases and complex heart diseases can be subjected to corrective procedures. However, mechanisms underlying brain protection during deep hypothermic circulatory arrest are unclear. After piglet models underwent 60 minutes of deep hypothermic circulatory arrest at 14°C, expression of microRNAs (miRNAs) was analyzed in the hippocampus by microarray. Subsequently, TargetScan 6.2, RNA22 v2.0, miRWalk 2.0, and miRanda were used to predict potential targets, and gene ontology enrichment analysis was carried out to identify functional pathways involved. Quantitative reverse transcription-polymerase chain reaction was conducted to verify miRNA changes. Deep hypothermic circulatory arrest altered the expression of 35 miRNAs. Twenty-two miRNAs were significantly downregulated and thirteen miRNAs were significantly upregulated in the hippocampus after deep hypothermic circulatory arrest. Six out of eight targets among the differentially expressed miRNAs were enriched for neuronal projection (cyclin dependent kinase, CDK16 and SLC1A2), central nervous system development (FOXO3, TYRO3, and SLC1A2), ion transmembrane transporter activity (ATP2B2 and SLC1A2), and interleukin-6 receptor binding (IL6R) - these are the key functional pathways involved in cerebral protection during deep hypothermic circulatory arrest. Quantitative reverse transcription-polymerase chain reaction confirmed the results of microarray analysis. Our experimental results illustrate a new role for transcriptional regulation in deep hypothermic circulatory arrest, and provide significant insight for the development of miRNAs to treat brain injuries. All procedures were approved by the Animal Care Committee of Xuanwu Hospital, Capital Medical University, China on March 1, 2017 (approval No. XW-INI-AD2017-0112).