Domestic dog breeds are characterized by an unrivaled diversity of morphologic traits and breed-associated behaviors resulting from human selective pressures. To identify the genetic underpinnings of such traits, we analyze 722 canine whole genome sequences (WGS), documenting over 91 million single nucleotide and small indels, creating a large catalog of genomic variation for a companion animal species. We undertake both selective sweep analyses and genome wide association studies (GWAS) inclusive of over 144 modern breeds, 54 wild canids and a hundred village dogs. Our results identify variants of strong impact associated with 16 phenotypes, including body weight variation which, when combined with existing data, explain greater than 90% of body size variation in dogs. We thus demonstrate that GWAS and selection scans performed with WGS are powerful complementary methods for expanding the utility of companion animal systems for the study of mammalian growth and biology.

/pdf/whole-genome-sequencing-of-canids-reveals-genomic-regions-3g4e2jfbwf.pdf

Whole genome sequencing of canids reveals genomic regions under selection and variants influencing morphology

Motivation The molecular mechanism of Alzheimer's disease (AD) has not been clearly revealed and there is no clinically reliable genetic risk factor. Therefore, diagnosis of AD has been mostly performed by analyzing brain images such as magnetic resonance imaging and neuropsychological tests. Identifying the molecular-level mechanism of AD has been lacking data owing to the difficulty of sampling in the posterior brains of normal and AD patients; however, recent studies have produced and analyzed large-scale omics data for brain areas such as prefrontal cortex. Therefore, it is necessary to develop AD diagnosis or prediction methods based on these data. Results This paper proposed a deep learning-based model that can predict AD using large-scale gene expression and DNA methylation data. The most challenging problem in constructing a model to diagnose AD based on the multi-omics dataset is how to integrate different omics data and how to deal with high-dimensional and low-sample-size data. To solve this problem, we proposed a novel but simple approach to reduce the number of features based on a differentially expressed gene and a differentially methylated position in the multi-omics dataset. Moreover, we developed a deep neural network-based prediction model that improves performance compared to that of conventional machine learning algorithms. The feature selection method and the prediction model presented in this paper outperformed conventional machine learning algorithms, which utilize typical dimension reduction methods. In addition, we demonstrated that integrating gene expression and DNA methylation data could improve the prediction accuracy. Availability https://github.com/ChihyunPark/DNN_for_ADprediction .

Prediction of Alzheimer's disease based on deep neural network by integrating gene expression and DNA methylation dataset

Abstract Chemotherapy elicits tumor immune evasion with poorly characterized mechanisms. Here, we demonstrate that chemotherapy markedly enhances the expression levels of CD47 in osteosarcoma tissues, which are positively associated with patient mortality. We reveal that macrophages in response to chemotherapy secrete interleukin-18, which in turn upregulates expression of L-amino acid transporter 2 (LAT2) in tumor cells for substantially enhanced uptakes of leucine and glutamine, two potent stimulators of mTORC1. The increased levels of leucine and enhanced glutaminolysis activate mTORC1 and subsequent c-Myc-mediated transcription of CD47. Depletion of LAT2 or treatment of tumor cells with a LAT inhibitor downregulates CD47 with enhanced macrophage infiltration and phagocytosis of tumor cells, and sensitizes osteosarcoma to doxorubicin treatment in mice. These findings unveil a mutual regulation between macrophage and tumor cells that plays a critical role in tumor immune evasion and underscore the potential to intervene with the LAT2-mediated amino acid uptake for improving cancer therapies. 

/pdf/metabolic-control-of-cd47-expression-through-lat2-mediated-1yjr3txs.pdf

Metabolic control of CD47 expression through LAT2-mediated amino acid uptake promotes tumor immune evasion

Osteosarcoma is a common malignant bone tumor with a propensity for drug resistance, recurrence, and metastasis. A growing number of studies have elucidated the dual role of pyroptosis in the development of cancer, which is a gasdermin-regulated novel inflammatory programmed cell death. However, the interaction between pyroptosis and the overall survival (OS) of osteosarcoma patients is poorly understood. This study aimed to construct a prognostic model based on pyroptosis-related genes to provide new insights into the prognosis of osteosarcoma patients. We identified 46 differentially expressed pyroptosis-associated genes between osteosarcoma tissues and normal control tissues. A total of six risk genes affecting the prognosis of osteosarcoma patients were screened to form a pyroptosis-related signature by univariate and LASSO regression analysis and verified using GSE21257 as a validation cohort. Combined with other clinical characteristics, including age, gender, and metastatic status, we found that the pyroptosis-related signature score, which we named “PRS-score,” was an independent prognostic factor for patients with osteosarcoma and that a low PRS-score indicated better OS and a lower risk of metastasis. The result of ssGSEA and ESTIMATE algorithms showed that a lower PRS-score indicated higher immune scores, higher levels of tumor infiltration by immune cells, more active immune function, and lower tumor purity. In summary, we developed and validated a pyroptosis-related signature for predicting the prognosis of osteosarcoma, which may contribute to early diagnosis and immunotherapy of osteosarcoma.

A Novel Pyroptosis-Related Signature for Predicting Prognosis and Indicating Immune Microenvironment Features in Osteosarcoma

Obesity is a global health concern with limited treatment options due to side effects and limited efficacy. Bioactive peptides (BAPs) derived from food proteins have shown promise as safe and effective anti-obesity agents by regulating adipocyte differentiation through various signaling pathways. BAPs derived from milk, eggs, fish, and marine sources have been extensively studied for their anti-obesity effects in vitro and in vivo. These peptides can alter gut hormones and appetite to regulate energy balance while decreasing adipogenesis and increasing adipocyte apoptosis. This paper provides a comprehensive overview of the studies investigating the potency of BAPs in managing obesity. It focuses on their ability to modulate adipogenesis and regulate appetite and lipid metabolism. Key findings and Conclusions: BAPs derived from various food sources such as milk, eggs, fish, and marine byproducts have the potential to act as anti-obesity agents. These peptides have been shown to be able to regulate adipogenesis, lipid metabolism, and appetite control through different mechanisms, making them promising candidates for developing functional foods or nutraceuticals for obesity prevention and management. This paper also highlights advantages of using BAPs over conventional anti-obesity drugs, such as their safety, natural origin, and lower risk of adverse effects. Further research and development in this field can lead to the identification and utilization of specific BAPs with optimized efficacy and minimal side effects, ultimately contributing to the development of novel anti-obesity therapies. 

Bioactive peptides from food proteins as potential anti-obesity agents: Mechanisms of action and future perspectives

Osteoarthritis (OA) is a common type of arthritis, which may cause pain and disability. Alterations in gene expression and DNA methylation have been proven to be associated with the development of OA. The aim of the present study was to identify potential therapeutic targets and associated processes for OA via the combined analysis of gene expression and DNA methylation datasets. The gene expression and DNA methylation profiles were obtained from the Gene Expression Omnibus, and differentially expressed genes (DEGs) and differentially methylated sites (DMSs) were identified in the present study, using R programming software. The enriched functions of DEGs and DMSs were obtained via the Database for Annotation, Visualization and Integrated Discovery. Finally, cross analysis of DEGs and DMSs was performed to identify genes that exhibited differential expression and methylation simultaneously. The protein‑protein interaction (PPI) network of overlaps between DEGs and DMSs was obtained using the Human Protein Reference Database; the topological properties of PPI network overlaps were additionally obtained. Hub genes in the PPI network were further confirmed via reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR). The results of the present study revealed that the majority of DEGs and DMSs were upregulated and hypomethylated in patients with OA, respectively. DEGs and DMSs were primarily involved in inflammatory, immune and gene expression regulation‑associated processes and pathways. Cross analysis revealed 30 genes that exhibited differential expression and methylation in OA simultaneously. Topological analysis of the PPI network revealed that numerous genes, including G protein subunit α1 (GNAI1), runt related transcription factor 2 (RUNX2) and integrin subunit β2 (ITGB2), may be involved in the development of OA. Additionally, RT‑qPCR analysis of GNAI1, RUNX2 and ITGB2 provided further confirmation. Numerous known and novel therapeutic targets were obtained via network analysis. The results of the present study may be beneficial for the diagnosis and treatment of OA.

/pdf/combined-bioinformatics-analysis-reveals-gene-expression-and-omet39c0sh.pdf

Combined bioinformatics analysis reveals gene expression and DNA methylation patterns in osteoarthritis.

Background: Hepatocellular carcinoma (HCC) refers to the malignant tumor associated with a high mortality rate. This work focused on identifying a robust tumor glycolysis-immune-related gene signature to facilitate the prognosis prediction of HCC cases. Methods: This work adopted t-SNE algorithms for predicting glycolysis status in accordance with The Cancer Genome Atlas (TCGA)-derived cohort transcriptome profiles. In addition, the Cox regression model was utilized together with LASSO to identify prognosis-related genes (PRGs). In addition, the results were externally validated with the International Cancer Genome Consortium (ICGC) cohort. Results: Accordingly, the glycolysis-immune-related gene signature, which consisted of seven genes, PSRC1, CHORDC1, KPNA2, CDCA8, G6PD, NEIL3, and EZH2, was constructed based on TCGA-HCC patients. Under a range of circumstances, low-risk patients had extended overall survival (OS) compared with high-risk patients. Additionally, the developed gene signature acted as the independent factor, which was significantly associated with clinical stage, grade, portal vein invasion, and intrahepatic vein invasion among HCC cases. In addition, as revealed by the receiver operating characteristic (ROC) curve, the model showed high efficiency. Moreover, the different glycolysis and immune statuses between the two groups were further revealed by functional analysis. Conclusion: Our as-constructed prognosis prediction model contributes to HCC risk stratification.

Development and verification of the glycolysis-associated and immune-related prognosis signature for hepatocellular carcinoma

Osteosarcoma is a common bone tumor which is affected by E2, the most representative estrogen. Gene regulation function of E2 is highly dependent on estrogen receptor. The purpose of this study was to explore the gene regulation patterns of E2 through estrogen receptor α (ESR1) in osteosarcoma based on the combined analysis of ChIP-seq and gene microarray. All of the datasets were downloaded from the Gene Expression Omnibus (GEO). Differential expression genes (DEGs) in E2 treated U2OS cells expressing ESR1 (U2OS-ERα) compared with those treated with vehicle were obtained based on R programming software. ESR1-specific binding sites (peaks) in E2 treated U2OS cells were identified through MACS. Overlaps between DEGs and ESR1 target genes which contained peaks in promoters were considered as reliable E2-mediated genes through ESR1 in osteosarcoma. Moreover, we conducted miRNA-Gene regulation analysis for those genes through miRWalk database to identify potential therapeutic targets for the genes. Functional enrichment analysis of DEGs indicated their potential involvement in cancer, and cell activity-related processes. Fifteen overlaps were identified between DEGs and target genes of ESR1, of which 12 were found to be regulated by miRNA. Several known estrogen response genes and novel genes were obtained in this study and they might provide potential therapeutic targets for osteosarcoma.

/pdf/combined-analysis-of-chip-seq-and-gene-microarray-datasets-3dve4ogsai.pdf

Combined analysis of ChIP-seq and gene microarray datasets identify the E2-mediated genes in ERα-dependent manner in osteosarcoma

Background: Osteosarcoma is a common malignancy of bone with inferior survival outcome. Autophagy can exert multifactorial influence on tumorigenesis and tumor progression. However, the specific function of genes related to autophagy in the prognosis of osteosarcoma patients remains unclear. Herein, we aimed to explore the association of genes related to autophagy with the survival outcome of osteosarcoma patients. Methods: The autophagy-associated genes that were related to the prognosis of osteosarcoma were optimized by LASSO Cox regression analysis. The survival of osteosarcoma patients was forecasted by multivariate Cox regression analysis. The immune infiltration status of 22 immune cell types in osteosarcoma patients with high and low risk scores was compared by using the CIBERSORT tool. Results: The risk score model constructed according to 14 autophagy-related genes (ATG4A, BAK1, BNIP3, CALCOCO2, CCL2, DAPK1, EGFR, FAS, GRID2, ITGA3, MYC, RAB33B, USP10, and WIPI1) could effectively predict the prognosis of patients with osteosarcoma. A nomogram model was established based on risk score and metastasis. Conclusion: Autophagy-related genes were identified as pivotal prognostic signatures, which could guide the clinical decision making in the treatment of osteosarcoma.

/pdf/prognostic-signature-of-osteosarcoma-based-on-14-autophagy-470n88fyc0.pdf

Prognostic Signature of Osteosarcoma Based on 14 Autophagy-Related Genes.

Obesity is an increasingly severe public health problem, which brings huge social and economic burdens. Increased body adiposity in obesity is not only tightly associated with type 2 diabetes, but also significantly increases the risks of other chronic diseases including cardiovascular diseases, fatty liver diseases and cancers. Adipogenesis describes the process of the differentiation and maturation of adipocytes, which accumulate in distributed adipose tissue at various sites in the body. The major functions of white adipocytes are to store energy as fat during periods when energy intake exceeds expenditure and to mobilize this stored fuel when energy expenditure exceeds intake. Brown/beige adipocytes contribute to non-shivering thermogenesis upon cold exposure and adrenergic stimulation, and thereby promote energy consumption. The imbalance of energy intake and expenditure causes obesity. Recent interest in epigenetics and signaling pathways has utilized small molecule tools aimed at modifying obesity-specific gene expression. In this review, we discuss compounds with adipogenesis-related signaling pathways and epigenetic modulating properties that have been identified as potential therapeutic agents which cast some light on the future treatment of obesity.

/pdf/the-potential-to-fight-obesity-with-adipogenesis-modulating-2rvgdtbn.pdf

The Potential to Fight Obesity with Adipogenesis Modulating Compounds

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