Multilevel analysis between Physcomitrium patens and Mortierellaceae endophytes explores potential long-standing interaction among land plants and fungi.

The model moss species Physcomitrium patens has long been used for studying divergence of land plants spanning from bryophytes to angiosperms. In addition to its phylogenetic relationships, the limited number of differential tissues, and comparable morphology to the earliest embryophytes provide a system to represent basic plant architecture. Based on plant-fungal interactions today, it is hypothesized these kingdoms have a long-standing relationship, predating plant terrestrialization. Mortierellaceae have origins diverging from other land fungi paralleling bryophyte divergence, are related to arbuscular mycorrhizal fungi but are free-living, observed to interact with plants, and can be found in moss microbiomes globally. Due to their parallel origins, we assess here how two Mortierellaceae species, Linnemannia elongata and Benniella erionia, interact with P. patens in coculture. We also assess how Mollicute-related or Burkholderia-related endobacterial symbionts (MRE or BRE) of these fungi impact plant response. Coculture interactions are investigated through high-throughput phenomics, microscopy, RNA-sequencing, differential expression profiling, gene ontology enrichment, and comparisons among 99 other P. patens transcriptomic studies. Here we present new high-throughput approaches for measuring P. patens growth, identify novel expression of over 800 genes that are not expressed on traditional agar media, identify subtle interactions between P. patens and Mortierellaceae, and observe changes to plant-fungal interactions dependent on whether MRE or BRE are present. Our study provides insights into how plants and fungal partners may have interacted based on their communications observed today as well as identifying L. elongata and B. erionia as modern fungal endophytes with P. patens.

Runs of homozygosity in Sable Island feral horses reveal the genomic consequences of inbreeding and divergence from domestic breeds.

BACKGROUND: Understanding inbreeding and its impact on fitness and evolutionary potential is fundamental to species conservation and agriculture. Long stretches of homozygous genotypes, known as runs of homozygosity (ROH), result from inbreeding and their number and length can provide useful population-level information on inbreeding characteristics and locations of signatures of selection. However, the utility of ROH for conservation is limited for natural populations where baseline data and genomic tools are lacking. Comparing ROH metrics in recently feral vs. domestic populations of well understood species like the horse could provide information on the genetic health of those populations and offer insight into how such metrics compare between managed and unmanaged populations. Here we characterized ROH, inbreeding coefficients, and ROH islands in a feral horse population from Sable Island, Canada, using ~41 000 SNPs and contrasted results with those from 33 domestic breeds to assess the impacts of isolation on ROH abundance, length, distribution, and ROH islands. RESULTS: ROH number, length, and ROH-based inbreeding coefficients (FROH) in Sable Island horses were generally greater than in domestic breeds. Short runs, which typically coalesce many generations prior, were more abundant than long runs in all populations, but run length distributions indicated more recent population bottlenecks in Sable Island horses. Nine ROH islands were detected in Sable Island horses, exhibiting very little overlap with those found in domestic breeds. Gene ontology (GO) enrichment analysis for Sable Island ROH islands revealed enrichment for genes associated with 3 clusters of biological pathways largely associated with metabolism and immune function. CONCLUSIONS: This study indicates that Sable Island horses tend to be more inbred than their domestic counterparts and that most of this inbreeding is due to historical bottlenecks and founder effects rather than recent mating between close relatives. Unique ROH islands in the Sable Island population suggest adaptation to local selective pressures and/or strong genetic drift and highlight the value of this population as a reservoir of equine genetic variation. This research illustrates how ROH analyses can be applied to gain insights into the population history, genetic health, and divergence of wild or feral populations of conservation concern.

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.

Microarray-Based Gene Expression Analysis Identifies Potential Diagnostic and Prognostic Biomarkers for Waldenström Macroglobulinemia.

Waldenström macroglobulinemia (WM), also known as lymphoplasmacytic lymphoma, is rare but a clinicopathologically distinct B-cell malignancy. This study assessed differentially expressed genes (DEGs) to identify potential WM biomarkers and uncover the underlying the molecular mechanisms of WM progression using gene expression profiles from the Gene Expression Omnibus database. DEGs were identified using the LIMMA package and their potential functions were then analyzed by using the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses and the protein-protein interaction (PPI) network analysis by using the Search Tool for the Retrieval of Interacting Genes/Proteins database. Data showed that among 1,756 DEGs, 926 were upregulated and 830 were downregulated by comparing WM BM CD19+ with normal PB CD19+ B cell samples, whereas 241 DEGs (95 upregulated and 146 downregulated) were identified by comparing WM BM CD138+ with normal BM CD138+ plasma cell samples. The DEGs were enriched in different GO terms and pathways, including the apoptotic process, cell cycle arrest, immune response, cell adhesion, mitogen-activated protein kinase signaling pathway, toll-like receptor signaling pathway, and the gonadotropin-releasing hormone signaling pathway. Hub nodes in the PPI network included CDK1, JUN, CREBBP, EP300, CAD, CDK2, and MAPK14. Bioinformatics analysis of the GSE9656 dataset identified 7 hub genes that might play an important role in WM development and progression. Some of the candidate genes and pathways may serve as promising therapeutic targets for WM.

CaMELS: In silico prediction of calmodulin binding proteins and their binding sites.

Due to Ca2+ -dependent binding and the sequence diversity of Calmodulin (CaM) binding proteins, identifying CaM interactions and binding sites in the wet-lab is tedious and costly. Therefore, computational methods for this purpose are crucial to the design of such wet-lab experiments. We present an algorithm suite called CaMELS (CalModulin intEraction Learning System) for predicting proteins that interact with CaM as well as their binding sites using sequence information alone. CaMELS offers state of the art accuracy for both CaM interaction and binding site prediction and can aid biologists in studying CaM binding proteins. For CaM interaction prediction, CaMELS uses protein sequence features coupled with a large-margin classifier. CaMELS models the binding site prediction problem using multiple instance machine learning with a custom optimization algorithm which allows more effective learning over imprecisely annotated CaM-binding sites during training. CaMELS has been extensively benchmarked using a variety of data sets, mutagenic studies, proteome-wide Gene Ontology enrichment analyses and protein structures. Our experiments indicate that CaMELS outperforms simple motif-based search and other existing methods for interaction and binding site prediction. We have also found that the whole sequence of a protein, rather than just its binding site, is important for predicting its interaction with CaM. Using the machine learning model in CaMELS, we have identified important features of protein sequences for CaM interaction prediction as well as characteristic amino acid sub-sequences and their relative position for identifying CaM binding sites. Python code for training and evaluating CaMELS together with a webserver implementation is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#camels.

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.

Identification of compound-protein interactions through the analysis of gene ontology, KEGG enrichment for proteins and molecular fragments of compounds.

Compound-protein interactions play important roles in every cell via the recognition and regulation of specific functional proteins. The correct identification of compound-protein interactions can lead to a good comprehension of this complicated system and provide useful input for the investigation of various attributes of compounds and proteins. In this study, we attempted to understand this system by extracting properties from both proteins and compounds, in which proteins were represented by gene ontology and KEGG pathway enrichment scores and compounds were represented by molecular fragments. Advanced feature selection methods, including minimum redundancy maximum relevance, incremental feature selection, and the basic machine learning algorithm random forest, were used to analyze these properties and extract core factors for the determination of actual compound-protein interactions. Compound-protein interactions reported in The Binding Databases were used as positive samples. To improve the reliability of the results, the analytic procedure was executed five times using different negative samples. Simultaneously, five optimal prediction methods based on a random forest and yielding maximum MCCs of approximately 77.55 % were constructed and may be useful tools for the prediction of compound-protein interactions. This work provides new clues to understanding the system of compound-protein interactions by analyzing extracted core features. Our results indicate that compound-protein interactions are related to biological processes involving immune, developmental and hormone-associated pathways.

Proteomic analysis of effects by x-rays and heavy ion in HeLa cells.

BACKGROUND: Carbon ion therapy may be better against cancer than the effects of a photon beam. To investigate a biological advantage of carbon ion beam over X-rays, the radioresistant cell line HeLa cells were used. Radiation-induced changes in the biological processes were investigated post-irradiation at 1 h by a clinically relevant radiation dose (2 Gy X-ray and 2 Gy carbon beam). The differential expression proteins were collected for analysing biological effects. MATERIALS AND METHODS: The radioresistant cell line Hela cells were used. In our study, the stable isotope labelling with amino acids (SILAC) method coupled with 2D-LC-LTQ Orbitrap mass spectrometry was applied to identity and quantify the differentially expressed proteins after irradiation. The Western blotting experiment was used to validate the data. RESULTS: A total of 123 and 155 significantly changed proteins were evaluated with treatment of 2 Gy carbon and X-rays after radiation 1 h, respectively. These deregulated proteins were found to be mainly involved in several kinds of metabolism processes through Gene Ontology (GO) enrichment analysis. The two groups perform different response to different types of irradiation. CONCLUSIONS: The radioresistance of the cancer cells treated with 2 Gy X-rays irradiation may be largely due to glycolysis enhancement, while the greater killing effect of 2 Gy carbon may be due to unchanged glycolysis and decreased amino acid metabolism.

Formononetin inhibits IgE by huPlasma/PBMCs and mast cells/basophil activation via JAK/STAT/PI3-Akt pathways.

Rationale: Food allergy is a prevalent disease in the U.S., affecting nearly 30 million people. The primary management strategy for this condition is food avoidance, as limited treatment options are available. The elevation of pathologic IgE and over-reactive mast cells/basophils is a central factor in food allergy anaphylaxis. This study aims to comprehensively evaluate the potential therapeutic mechanisms of a small molecule compound called formononetin in regulating IgE and mast cell activation. Methods: In this study, we determined the inhibitory effect of formononetin on the production of human IgE from peripheral blood mononuclear cells of food-allergic patients using ELISA. We also measured formononetin's effect on preventing mast cell degranulation in RBL-2H3 and KU812 cells using beta-hexosaminidase assay. To identify potential targets of formononetin in IgE-mediated diseases, mast cell disorders, and food allergies, we utilized computational modeling to analyze mechanistic targets of formononetin from various databases, including SEA, Swiss Target Prediction, PubChem, Gene Cards, and Mala Cards. We generated a KEGG pathway, Gene Ontology, and Compound Target Pathway Disease Network using these targets. Finally, we used qRT-PCR to measure the gene expression of selected targets in KU812 and U266 cell lines. Results: Formononetin significantly decreased IgE production in IgE-producing human myeloma cells and PBMCs from food-allergic patients in a dose-dependent manner without cytotoxicity. Formononetin decreased beta-hexosaminidase release in RBL-2H3 cells and KU812 cells. Formononetin regulates 25 targets in food allergy, 51 in IgE diseases, and 19 in mast cell diseases. KEGG pathway and gene ontology analysis of targets showed that formononetin regulated disease pathways, primary immunodeficiency, Epstein-Barr Virus, and pathways in cancer. The biological processes regulated by formononetin include B cell proliferation, differentiation, immune response, and activation processes. Compound target pathway disease network identified NFKB1, NFKBIA, STAT1, STAT3, CCND1, TP53, TYK2, and CASP8 as the top targets regulated at a high degree by formononetin. TP53, STAT3, PTPRC, IL2, and CD19 were identified as the proteins mostly targeted by formononetin. qPCR validated genes of Formononetin molecular targets of IgE regulation in U266 cells and KU812 cells. In U266 cells, formononetin was found to significantly increase the gene expression of NFKBIA, TP53, and BCL-2 while decreasing the gene expression of BTK TYK, CASP8, STAT3, CCND1, STAT1, NFKB1, IL7R. In basophils KU812 cells, formononetin significantly increased the gene expression of NFKBIA, TP53, and BCL-2 while decreasing the gene expression of BTK, TYK, CASP8, STAT3, CCND1, STAT1, NFKB1, IL7R. Conclusion: These findings comprehensively present formononetin's mechanisms in regulating IgE production in plasma cells and degranulation in mast cells.

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.

GOTermViewer: Visualization of Gene Ontology Enrichment in Multiple Differential Gene Expression Analyses.

Gene ontology phrases are a widely used set of hierarchical terms that describe the biological properties of genes. These terms are then used to annotate individual genes, making it possible to determine the likely physiological properties of groups of genes such as a list of differentially expressed genes. Consequently, their ability to predict changes in biological features and functions based on alterations in gene expression has made gene ontology terms popular in the wide range of bioinformatic fields, such as differential gene expression and evolutionary biology. However, while they make the analysis easier, it is seldom easy to convey the results in a readily understandable manner. A number of applications have been developed to visualize gene ontology (GO) term enrichment; however, these solutions tend to focus on the display of aggregated results from a single analysis, making them unsuitable for the analysis of a series of experiments such as a time course or response to different drug treatments. As multiple pair wise comparisons are becoming a common feature of RNA profiling experiments, the absence of a mechanism to easily compare them is a significant problem. Consequently, to overcome this obstacle, we have developed GOTermViewer, an application that displays GO term enrichment data as determined by GOstats such that changes in physiological response across a number of individual analyses across a time course or range of drug treatments can be visualized.

Functional enrichment analysis of mutated genes in children with hyperthyroidism.

Objective: Hyperthyroidism in Chinese children is relatively high and has been increasing in recent years, which has a significant impact on their healthy development. Hyperthyroidism is a polygenic disorder that presents greater challenges in terms of prediction and treatment than monogenic diseases. This study aims to elucidate the associated functions and gene sets of mutated genes in children with hyperthyroidism in terms of the gene ontology through GO enrichment analysis and in terms of biological signaling pathways through KEGG enrichment analysis, thereby enhancing our understanding of the expected effects of multiple mutated genes on hyperthyroidism in children. Methods: Whole-exome sequencing was performed on the DNA samples of children with hyperthyroidism. Screening for pathogenic genes related to hyperthyroidism in affected children was performed using the publicly available disease databases Malacards, MutationView, and Clinvar, and the functions and influences of the identified pathogenic genes were analyzed using statistical analysis and the gene enrichment approach. Results: Through GO enrichment analysis, it was found that the most significant gene ontology enrichment was the function "hormone activity" in terms of gene ontology molecular function. The corresponding mutated genes set that has common effects on hyperthyroidism in children included TG, CALCA, POMC, CGA, PTH, GHRL, FBN1, TRH, PRL, LEP, ADIPOQ, INS, GH1. The second most significant gene ontology enrichment was the function "response to peptide hormone" in terms of biological process. The corresponding mutated genes set that has common effects on hyperthyroidism in children included LRP6, TSC2, KANK1, COL1A1, CDKN1B, POMC, STAT1, MEN1, APC, GHRL, TSHR, GJB2, FBN1, GPT, LEP, ADIPOQ, INS, GH1. Through KEGG enrichment analysis, it was found that the most significant biological signaling pathway enrichment was the pathway "Thyroid hormone signaling pathway" function. The corresponding mutated genes set that has common effects on hyperthyroidism in children included NOTCH3, MYH7, TSC2, STAT1, MED13L, MAP2K2, SLCO1C1, SLC16A2, and THRB. The second most significant biological signaling pathway enrichment was the pathway "Hypertrophic cardiomyopathy" in terms of biological process. The corresponding mutated genes set that has common effects on hyperthyroidism in children included IGF1, CACNA1S, MYH7, IL6, TTN, CACNB2, LAMA2, and DMD. Conclusion: The mutated genes in children with hyperthyroidism were closely linked to function involved in "hormone activity" and "response to peptide hormone" in terms of the biological signaling pathway, and to the functional pathways involved in "Thyroid hormone signaling pathway" and "Hypertrophic cardiomyopathy" in terms of the biological signaling pathway.

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.

Osteoporosis and osteoarthritis: a bi-directional Mendelian randomization study.

OBJECTIVE: To investigate the causal relationship between low bone mineral density (BMD) and osteoarthritis (OA) using Mendelian randomization (MR) design. METHODS: Two-sample bi-directional MR analyses were performed using summary-level information on OA traits from UK Biobank and arcOGEN. Sensitivity analyses including MR-Egger, simple median, weighted median, MR pleiotropy residual sum, and outlier approaches were utilized in conjunction with inverse variance weighting (IVW). Gene ontology (GO) enrichment analyses and expression quantitative trait locus (eQTL) colocalization analyses were used to investigate the potential mechanism and shared genes between osteoporosis (OP) and OA. RESULTS: The IVW method revealed that genetically predicted low femoral neck BMD was significantly linked with hip (ß = 0.105, 95% CI: 0.023-0.188) and knee OA (ß = 0.117, 95% CI: 0.049-0.184), but not with other site-specific OA. Genetically predicted low lumber spine BMD was significantly associated with OA at any sites (ß = 0.048, 95% CI: 0.011-0.085), knee OA (ß = 0.101, 95% CI: 0.045-0.156), and hip OA (ß = 0.150, 95% CI: 0.077-0.224). Only hip OA was significantly linked with genetically predicted reduced total bone BMD (ß = 0.092, 95% CI: 0.010-0.174). In the reverse MR analyses, no evidence for a causal effect of OA on BMD was found. GO enrichment analysis and eQTL analysis illustrated that DDN and SMAD-3 were the most prominent co-located genes. CONCLUSIONS: These findings suggested that OP may be causally linked to an increased risk of OA, indicating that measures to raise BMD may be effective in preventing OA. More research is required to determine the underlying processes via which OP causes OA.

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.