NR2C2 of macrophages promotes inflammation via NF-κB in LPS-induced orchitis in mice.

In brief: Bacterial infection can induce testicular inflammation and damage male fertility. This paper reveals the role of nuclear receptor subfamily 2 group C member 2 (NR2C2) in macrophage cells in orchitis caused by bacterial endotoxin lipopolysaccharide (LPS) infection. Abstract: Bacterial infection and induced inflammation are important causes of male infertility. Here, we described the characteristics of expression and the regulatory role of NR2C2 in testicular inflammatory injury induced by infection with the bacterial endotoxin LPS. We found that NR2C2 was highly expressed in the testes and the expression of NR2C2 was upregulated in testicular macrophages in the LPS-induced mouse orchitis model in vivo. In primary testicular macrophages and RAW264.7 cells in vitro, RNA interference with the Nr2c2 gene downregulated the expression of inflammatory factors such as IL-1ß and IL-6. In addition, the knockdown of NR2C2 in macrophages alleviated the inhibitory effect of the inflammatory supernatant secreted by the macrophages on the proliferation of spermatogonia GC-1 SPG cells. Mechanistically, NR2C2 activated NF-κB signaling by binding with DR elements in the promotor of the Nfκb gene and promoted the development of inflammation. These data are the first to confirm that during LPS-induced bacterial infection, NR2C2 plays a proinflammatory role by activating IL-1ß and IL-6 via the NF-κB pathway in macrophages, consequently inhibiting the proliferation of spermatogonia and damaging the quality of sperm. Our findings reveal the important role of NR2C2 in testicular inflammatory injury induced via LPS and provide a new potential target and a molecular basis for the treatment of male infertility caused by bacterial infection.

Pancancer survival prediction using a deep learning architecture with multimodal representation and integration.

Motivation: Use of multi-omics data carrying comprehensive signals about the disease is strongly desirable for understanding and predicting disease progression, cancer particularly as a serious disease with a high mortality rate. However, recent methods currently fail to effectively utilize the multi-omics data for cancer survival prediction and thus significantly limiting the accuracy of survival prediction using omics data. Results: In this work, we constructed a deep learning model with multimodal representation and integration to predict the survival of patients using multi-omics data. We first developed an unsupervised learning part to extract high-level feature representations from omics data of different modalities. Then, we used an attention-based method to integrate feature representations, produced by the unsupervised learning part, into a single compact vector and finally we fed the vector into fully connected layers for survival prediction. We used multimodal data to train the model and predict pancancer survival, and the results show that using multimodal data can lead to higher prediction accuracy compared to using single modal data. Furthermore, we used the concordance index and the 5-fold cross-validation method for comparing our proposed method with current state-of-the-art methods and our results show that our model achieves better performance on the majority of cancer types in our testing datasets. Availability and implementation: https://github.com/ZhangqiJiang07/MultimodalSurvivalPrediction. Supplementary information: Supplementary data are available at Bioinformatics online.

Chromodomain helicase DNA-binding domain 2 maintains spermatogonial self-renewal by promoting chromatin accessibility and mRNA stability.

Chromodomain helicase DNA-binding domain 2 (CHD2) is a chromatin remodeling factor involved in many developmental processes. However, its role in male germ cell development has not been elucidated. Here, we confirm that CHD2 expression is enriched in the male germline. In a heterozygous knockout mouse model of Chd2 (Chd2 +/-), we demonstrated that Chd2 haploinsufficiency resulted in testicular developmental delay, an increased rate of abnormal sperm, and impaired fertility in mice. In vitro experiments in mouse spermatogonia showed that CHD2 knockdown inhibits spermatogonial self-renewal. Mechanistically, CHD2 maintains the enrichment of H3K4me3 in the Ccnb1 and Ccnd2 promotors, consequently promoting the transcription of Ccnb1 and Ccnd2. In addition, CHD2 interacts with the cleavage stimulation factor CSTF3 and upregulates the expression of OCT4 and PLZF by improving mRNA stability. This is the first study to reveal the role and mechanism of CHD2 in maintaining spermatogonial self-renewal.

microRNA-196a-5p inhibits testicular germ cell tumor progression via NR6A1/E-cadherin axis.

Testicular germ cell tumors (TGCTs) are a diverse group of neoplasms that are derived from dysfunctional fetal germ cells and can also present in extragonadal sites. The genetic drivers underlying malignant transformation of TGCTs have not been fully elucidated so far. The aim of the present study is to clarify the functional role and regulatory mechanism of miR-196a-5p in TGCTs. We demonstrated that miR-196a-5p was downregulated in TGCTs. It can inhibit the proliferation, migration, and invasion of testicular tumor cell lines including NT-2 and NCCIT through targeting the NR6A1 gene, which we proved its role in promotion of cell proliferation and repression of cellular junction and aggregation. Mechanistically, NR6A1 inhibited E-cadherin through binding with DR0 sites in the CDH1 gene promoter and recruiting methyltransferases Dnmt1. Further, NR6A1 promoted neuronal marker protein MAP2 expression in RA-induced neurodifferentiation of NT-2 cells and testicular tumor xenografts. Clinical histopathologically, NR6A1 was positively correlated with MAP2, and negatively correlated with E-cadherin in TGCTs. These findings revealed that the miR-196a-5p represses cell proliferation, migration, invasion, and tumor neurogenesis by inhibition of NR6A1/E-cadherin signaling axis, which may be a potential target for diagnosis and therapy of TGCTs.

MetFID: artificial neural network-based compound fingerprint prediction for metabolite annotation.

INTRODUCTION: Metabolite annotation is a critical and challenging step in mass spectrometry-based metabolomic profiling. In a typical untargeted MS/MS-based metabolomic study, experimental MS/MS spectra are matched against those in spectral libraries for metabolite annotation. Yet, existing spectral libraries comprise merely a marginal percentage of known compounds. OBJECTIVE: The objective is to develop a method that helps rank putative metabolite IDs for analytes whose reference MS/MS spectra are not present in spectral libraries. METHODS: We introduce MetFID, which uses an artificial neural network (ANN) trained for predicting molecular fingerprints based on experimental MS/MS data. To narrow the search space, MetFID retrieves candidates from metabolite databases using molecular formula or m/z value of the precursor ions of the analytes. The candidate whose fingerprint is most analogous to the predicted fingerprint is used for metabolite annotation. A comprehensive evaluation was performed by training MetFID using MS/MS spectra from the MoNA repository and NIST library and by testing with structure-disjoint MS/MS spectra from the NIST library, the CASMI 2016 dataset, and in-house MS/MS data from a cancer biomarker discovery study. RESULTS: We observed that training separate models for distinct ranges of collision energies enhanced model performance compared to a single model that covers a wide range of collision energies. Using MetaboQuest to retrieve candidates, MetFID prioritized the correct putative ID in the first place rank for about 50% of the testing cases. Through the independent testing dataset, we demonstrated that MetFID has the potential to improve the accuracy of ranking putative metabolite IDs by more than 5% compared to other tools such as ChemDistiller, CSI:FingerID, and MetFrag. CONCLUSION: MetFID offers a promising opportunity to enhance the accuracy of metabolite annotation by using ANN for molecular fingerprint prediction.

MOTA: Network-Based Multi-Omic Data Integration for Biomarker Discovery.

The recent advancement of omic technologies provides researchers with the possibility to search for disease-associated biomarkers at the system level. The integrative analysis of data from a large number of molecules involved at various layers of the biological system offers a great opportunity to rank disease biomarker candidates. In this paper, we propose MOTA, a network-based method that uses data acquired at multiple layers to rank candidate disease biomarkers. The networks constructed by MOTA allow users to investigate the biological significance of the top-ranked biomarker candidates. We evaluated the performance of MOTA in ranking disease-associated molecules from three sets of multi-omic data representing three cohorts of hepatocellular carcinoma (HCC) cases and controls with liver cirrhosis. The results demonstrate that MOTA allows the identification of more top-ranked metabolite biomarker candidates that are shared by two different cohorts compared to traditional statistical methods. Moreover, the mRNA candidates top-ranked by MOTA comprise more cancer driver genes compared to those ranked by traditional differential expression methods.

Metabolomic Analysis of Liver Tissues for Characterization of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) causes more than half a million annual deaths worldwide. Understanding the mechanisms contributing to HCC development is highly desirable for improved surveillance, diagnosis, and treatment. Liver tissue metabolomics has the potential to reflect the physiological changes behind HCC development. Also, it allows identification of biomarker candidates for future evaluation in biofluids and investigation of racial disparities in HCC. Tumor and nontumor tissues from 40 patients were analyzed by both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) platforms to increase the metabolome coverage. The levels of the metabolites extracted from solid liver tissue of the HCC area and adjacent non-HCC area were compared. Among the analytes detected by GC-MS and LC-MS with significant alterations, 18 were selected based on biological relevance and confirmed metabolite identification. These metabolites belong to TCA cycle, glycolysis, purines, and lipid metabolism and have been previously reported in liver metabolomic studies where high correlation with HCC progression is implied. We demonstrated that metabolites related to HCC pathogenesis can be identified through liver tissue metabolomic analysis. Additionally, this study has enabled us to identify race-specific metabolites associated with HCC.

MOTA: Multi-omic integrative analysis for biomarker discovery.

Recent advancement of omic technologies provides researchers with opportunities to search for disease biomarkers at the systems level. However, selection of biomarker candidates from a large number of molecules involved at various layers of the biological system is challenging. In this paper, we propose multi-omic integrative analysis (MOTA), a network-based method that uses information from multi-omic data to identify candidate disease biomarkers. We evaluated the performance of MOTA in selecting disease-associated molecules from four sets of multi-omic data representing three cohorts of hepatocellular carcinoma (HCC) cases and patients with liver cirrhosis. The results demonstrate that MOTA leads to selection of more biomarker candidates that shared by two different cohorts compared to traditional statistical methods. Also, the networks constructed by MOTA allow users to investigate biological significance of the selected biomarker candidates.

1H NMR plasma metabolomic profiling of ovarian quiescence in energy balanced postpartum dairy cows.

BACKGROUND: As the milk production of dairy cows increases, the reproductive capacity gradually declines. Ovarian quiescence has become one of the concerns of the dairy industry. OBJECTIVE: To explore the different plasma metabolite levels between estrus and anestrus in energy balanced (EB) dairy cows. METHODS: Ten estrous and 10 anestrus EB Holstein cows in early lactation were selected for the study. 1H nuclear magnetic resonance technology was used to detect plasma metabolites and screen different plasma metabolites between anestrous and estrous cows at 60-90 days postpartum using multivariate statistical analysis. RESULTS: Within an elective waiting period of 60-90 days postpartum mean plasma concentration of total estrogens was significantly higher in estrus cows as compared to anestrus cows (71.2 ± 26.0 and 42.4 ± 16.7 pg/mL, respectively). Seven plasma metabolites (isoleucine, leucine, valine, alanine, arginine, choline and phosphatecholine) demonstrated significant decreases in estrous dairy cows relative to anestrous subjects. The main pathway was leucine, isoleucine and valine biosynthesis. CONCLUSION: Anestrus in dairy cows is accompanied by alterations in amino acid, glucose and lipid metabolism based on 1H NMR analysis.

Protein profiling of plasma proteins in dairy cows with subclinical hypocalcaemia.

Subclinical hypocalcaemia (SH) is an important metabolic disease in dairy cows that has a serious impact on production performance. The objective of this study was to investigate novel aspects of pathogenesis using proteomics technology to identify proteins that are differentially expressed in diseased and healthy animals. Dairy cows were divided into an SH group (T, n = 10) and a control group (C, n = 10) based on plasma calcium concentration. A total of 398 differentially expressed proteins were identified, of which 265 proteins were overlapped in the two parallel experiments. Of these, 24 differentially expressed proteins were statistically significant. Gene Ontology analysis yielded 74 annotations, including 7 cellular component, 55 biological process and 12 molecular function categories. Bioinformatics analysis indicated that calcium regulation, immune and inflammatory response, blood coagulation and complement pathway were all related to SH. Our iTRAQ/LC-MS/MS (isobaric tags for relative and absolute quantification/liquid chromatography-mass spectrometry/mass spectrometry) approach proved highly effective for plasma protein profiling of dairy cows with SH, and the results pave the way for further studies in this area.

Differentially expressed serum proteins associated with calcium regulation and hypocalcemia in dairy cows.

OBJECTIVE: Hypocalcemia is an important metabolic disease of dairy cows during the transition period, although the effect of hypocalcemia on biological function in dairy cows remains unknown. METHODS: In this study, proteomic, mass spectrum, bioinformatics and western blotting were employed to identify differentially expressed proteins related to serum Ca concentration. Serum samples from dairy cows were collected at three time points: 3rd days before calving (day -3), the day of calving (day 0), and 3rd days after calving (day +3). According to the Ca concentration on day 0, a total of 27 dairy cows were assigned to one of three groups (clinical, subclinical, and healthy). Samples collected on day -3 were used for discovery of differentially expressed proteins, which were separated and identified via proteomic analysis and mass spectrometry. Bioinformatics analysis was performed to determine the function of the identified proteins (gene ontology and pathway analysis). The differentially expressed proteins were verified by western blot analysis. RESULTS: There were 57 differential spots separated and eight different proteins were identified. Vitamin D-binding protein precursor (group-specific component, GC), alpha-2-macroglobulin (A2M) protein, and apolipoprotein A-IV were related to hypocalcemia by bioinformatics analysis. Due to its specific expression (up-regulated in clinical hypocalcemia and down-regulated in subclinical hypocalcemia), A2M was selected for validation. The results were consistent with those of proteomic analysis. CONCLUSION: A2M was as an early detection index for distinguishing clinical and subclinical hypocalcemia. The possible pathogenesis of clinical hypocalcemia caused by GC and apolipoprotein A-IV was speculated. The down-regulated expression of GC was a probable cause of the decrease in calcium concentration.

Metabolic profiles using (1)H-nuclear magnetic resonance spectroscopy in postpartum dairy cows with ovarian inactivity.

To understand the differences in metabolic changes between cows with ovarian inactivity and estrus cows, we selected cows at 60-90 days postpartum from an intensive dairy farm. According to clinical manifestations, B-ultrasound scan, rectal examination, 10 cows were assigned to the estrus group (A) and 10 to the ovarian inactivity group (B). All plasma samples were analyzed by (1)H-nuclear magnetic resonance spectroscopy to compare plasma metabolomic profiles between the groups. We used multivariate pattern recognition to screen for different metabolites in plasma of anestrus cows. Compared with normal estrous cows, there were abnormalities in 12 kinds of metabolites in postpartum cows with ovarian inactivity (|r|> 0.602), including an increase in acetic acid (r = -0.817), citric acid (r = -0.767), and tyrosine (r = -0.714), and a decrease in low-density lipoprotein (r = 0.820), very low-density lipoprotein (r = 0.828), lipids (r = 0.769), alanine (r = 0.816), pyruvate (r = 0.721), creatine (r = 0.801), choline (r = 0.639), phosphorylcholine (r = 0.741), and glycerophosphorylcholine (r = 0.881). These metabolites were closely related to abnormality of glucose, amino acid, lipoprotein and choline metabolism, which may disturb the normal estrus. The decrease in plasma creatine and the increase in tyrosine were new changes for ovarian inactivity of postpartum cows. The decrease in plasma creatine and choline and the increase in tyrosine and p-hydroxyphenylalanine in cows with ovarian inactivity provide new directions for research on the mechanism of ovarian inactivity in cows.

Relative Biological Effectiveness of Energetic Heavy Ions for Intestinal Tumorigenesis Shows Male Preponderance and Radiation Type and Energy Dependence in APC(1638N/+) Mice.

PURPOSE: There are uncertainties associated with the prediction of colorectal cancer (CRC) risk from highly energetic heavy ion (HZE) radiation. We undertook a comprehensive assessment of intestinal and colonic tumorigenesis induced after exposure to high linear energy transfer (high-LET) HZE radiation spanning a range of doses and LET in a CRC mouse model and compared the results with the effects of low-LET γ radiation. METHODS AND MATERIALS: Male and female APC(1638N/+) mice (n=20 mice per group) were whole-body exposed to sham-radiation, γ rays, (12)C, (28)Si, or (56)Fe radiation. For the >1 Gy HZE dose, we used γ-ray equitoxic doses calculated using relative biological effectiveness (RBE) determined previously. The mice were euthanized 150 days after irradiation, and intestinal and colon tumor frequency was scored. RESULTS: The highest number of tumors was observed after (28)Si, followed by (56)Fe and (12)C radiation, and tumorigenesis showed a male preponderance, especially after (28)Si. Analysis showed greater tumorigenesis per unit of radiation (per cGy) at lower doses, suggesting either radiation-induced elimination of target cells or tumorigenesis reaching a saturation point at higher doses. Calculation of RBE for intestinal and colon tumorigenesis showed the highest value with (28)Si, and lower doses showed greater RBE relative to higher doses. CONCLUSIONS: We have demonstrated that the RBE of heavy ion radiation-induced intestinal and colon tumorigenesis is related to ion energy, LET, gender, and peak RBE is observed at an LET of 69 keV/µm. Our study has implications for understanding risk to astronauts undertaking long duration space missions.