SLKB: synthetic lethality knowledge base.

Emerging CRISPR-Cas9 technology permits synthetic lethality (SL) screening of large number of gene pairs from gene combination double knockout (CDKO) experiments. However, the poor integration and annotation of CDKO SL data in current SL databases limit their utility, and diverse methods of calculating SL scores prohibit their comparison. To overcome these shortcomings, we have developed SL knowledge base (SLKB) that incorporates data of 11 CDKO experiments in 22 cell lines, 16,059 SL gene pairs and 264,424 non-SL gene pairs. Additionally, within SLKB, we have implemented five SL calculation methods: median score with and without background control normalization (Median-B/NB), sgRNA-derived score (sgRNA-B/NB), Horlbeck score, GEMINI score and MAGeCK score. The five scores have demonstrated a mere 1.21% overlap among their top 10% SL gene pairs, reflecting high diversity. Users can browse SL networks and assess the impact of scoring methods using Venn diagrams. The SL network generated from all data in SLKB shows a greater likelihood of SL gene pair connectivity with other SL gene pairs than non-SL pairs. Comparison of SL networks between two cell lines demonstrated greater likelihood to share SL hub genes than SL gene pairs. SLKB website and pipeline can be freely accessed at https://slkb.osubmi.org and https://slkb.docs.osubmi.org/, respectively.

Genetic regulation of human brain proteome reveals proteins implicated in psychiatric disorders.

Psychiatric disorders are highly heritable yet polygenic, potentially involving hundreds of risk genes. Genome-wide association studies have identified hundreds of genomic susceptibility loci with susceptibility to psychiatric disorders; however, the contribution of these loci to the underlying psychopathology and etiology remains elusive. Here we generated deep human brain proteomics data by quantifying 11,608 proteins across 268 subjects using 11-plex tandem mass tag coupled with two-dimensional liquid chromatography-tandem mass spectrometry. Our analysis revealed 788 cis-acting protein quantitative trait loci associated with the expression of 883 proteins at a genome-wide false discovery rate <5%. In contrast to expression at the transcript level and complex diseases that are found to be mainly influenced by noncoding variants, we found protein expression level tends to be regulated by non-synonymous variants. We also provided evidence of 76 shared regulatory signals between gene expression and protein abundance. Mediation analysis revealed that for most (88%) of the colocalized genes, the expression levels of their corresponding proteins are regulated by cis-pQTLs via gene transcription. Using summary data-based Mendelian randomization analysis, we identified 4 proteins and 19 genes that are causally associated with schizophrenia. We further integrated multiple omics data with network analysis to prioritize candidate genes for schizophrenia risk loci. Collectively, our findings underscore the potential of proteome-wide linkage analysis in gaining mechanistic insights into the pathogenesis of psychiatric disorders.

Gut fungi of black-necked cranes (Grus nigricollis) respond to dietary changes during wintering.

BACKGROUND: Migratory birds exhibit heterogeneity in foraging strategies during wintering to cope with environmental and migratory pressures, and gut bacteria respond to changes in host diet. However, less is known about the dynamics of diet and gut fungi during the wintering period in black-necked cranes (Grus nigricollis). RESULTS: In this work, we performed amplicon sequencing of the trnL-P6 loop and ITS1 regions to characterize the dietary composition and gut fungal composition of black-necked cranes during wintering. Results indicated that during the wintering period, the plant-based diet of black-necked cranes mainly consisted of families Poaceae, Solanaceae, and Polygonaceae. Among them, the abundance of Solanaceae, Polygonaceae, Fabaceae, and Caryophyllaceae was significantly higher in the late wintering period, which also led to a more even consumption of various food types by black-necked cranes during this period. The diversity of gut fungal communities and the abundance of core fungi were more conserved during the wintering period, primarily dominated by Ascomycota and Basidiomycota. LEfSe analysis (P < 0.05, LDA > 2) found that Pyxidiophora, Pseudopeziza, Sporormiella, Geotrichum, and Papiliotrema were significantly enriched in early winter, Ramularia and Dendryphion were significantly enriched in mid-winter, Barnettozyma was significantly abundant in late winter, and Pleuroascus was significantly abundant in late winter. Finally, mantel test revealed a significant correlation between winter diet and gut fungal. CONCLUSIONS: This study revealed the dynamic changes in the food composition and gut fungal community of black-necked cranes during wintering in Dashanbao. In the late wintering period, their response to environmental and migratory pressures was to broaden their diet, increase the intake of non-preferred foods, and promote a more balanced consumption ratio of various foods. Balanced food composition played an important role in stabilizing the structure of the gut fungal community. While gut fungal effectively enhanced the host's food utilization rate, they may also faced potential risks of introducing pathogenic fungi. Additionally, we recongnized the limitations of fecal testing in studying the composition of animal gut fungal, as it cannot effectively distinguished between fungal taxa from food or soil inadvertently ingested and intestines. Future research on functions such as cultivation and metagenomics may further elucidate the role of fungi in the gut ecosystem.

The biosynthesis of storage reserves and auxin is coordinated by a hierarchical regulatory network in maize endosperm.

Grain filling in maize (Zea mays) is intricately linked to cell development, involving the regulation of genes responsible for the biosynthesis of storage reserves (starch, proteins, and lipids) and phytohormones. However, the regulatory network coordinating these biological functions remains unclear. In this study, we identified 1744 high-confidence target genes co-regulated by the transcription factors (TFs) ZmNAC128 and ZmNAC130 (ZmNAC128/130) through chromatin immunoprecipitation sequencing coupled with RNA-seq analysis in the zmnac128/130 loss-of-function mutants. We further constructed a hierarchical regulatory network using DNA affinity purification sequencing analysis of downstream TFs regulated by ZmNAC128/130. In addition to target genes involved in the biosynthesis of starch and zeins, we discovered novel target genes of ZmNAC128/130 involved in the biosynthesis of lipids and indole-3-acetic acid (IAA). Consistently, the number of oil bodies, as well as the contents of triacylglycerol, and IAA were significantly reduced in zmnac128/130. The hierarchical regulatory network centered by ZmNAC128/130 revealed a significant overlap between the direct target genes of ZmNAC128/130 and their downstream TFs, particularly in regulating the biosynthesis of storage reserves and IAA. Our results indicated that the biosynthesis of storage reserves and IAA is coordinated by a multi-TFs hierarchical regulatory network in maize endosperm.

Three Undescribed Protoilludane-type Sesquiterpene Aryl Esters from Armillaria gallica.

Three previously undescribed protoilludane-type sesquiterpene aryl esters, armillanals A-C (1-3), along with seven known ones (4-10) were obtained from Armillaria gallica Marxm. & Romagn. Compounds 1 and 2 were a rare class of sesquiterpenes featuring the Δ2(3) and Δ12(13)-protoilludane skeleton. Their structures were established by extensive spectroscopic methods. Based on electronic circular dichroism (ECD) calculations, the absolute configurations of three new compounds (1-3) were determined. The anti-inflammatory activity of compounds 1-10 was screened and compound 3 could dose-dependently decrease the level of lactate dehydrogenase, showing IC50 value of 4.525 µM.

At-Home COVID-19 Rapid Antigen Test Down to 0.03 pg mL-1 of Nucleocapsid Protein.

Rapid and ultra-sensitive detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is critical for early screening and management of COVID-19. Currently, the real-time reverse transcription polymerase chain reaction (rRT-PCR) is the primary laboratory method for diagnosing SARS-CoV-2. It is not suitable for at-home COVID-19 diagnostic test due to the long operating time, specific equipment, and professional procedures. Here an all-printed photonic crystal (PC) microarray with portable device for at-home COVID-19 rapid antigen test is reported. The fluorescence-enhanced effect of PC amplifies the fluorescence intensity of the labeled probe, achieving detection of nucleocapsid (N-) protein down to 0.03 pg mL-1 . A portable fluorescence intensity measurement instrument gives the result (negative or positive) by the color of the indicator within 5 s after inserting the reacted PC microarray test card. The N protein in inactivated virus samples (with cycle threshold values of 26.6-40.0) can be detected. The PC microarray provides a general and easy-to-use method for the timely monitoring and eventual control of the global coronavirus pandemic.

Artificial intelligence and machine learning methods in predicting anti-cancer drug combination effects.

Drug combinations have exhibited promising therapeutic effects in treating cancer patients with less toxicity and adverse side effects. However, it is infeasible to experimentally screen the enormous search space of all possible drug combinations. Therefore, developing computational models to efficiently and accurately identify potential anti-cancer synergistic drug combinations has attracted a lot of attention from the scientific community. Hypothesis-driven explicit mathematical methods or network pharmacology models have been popular in the last decade and have been comprehensively reviewed in previous surveys. With the surge of artificial intelligence and greater availability of large-scale datasets, machine learning especially deep learning methods are gaining popularity in the field of computational models for anti-cancer drug synergy prediction. Machine learning-based methods can be derived without strong assumptions about underlying mechanisms and have achieved state-of-the-art prediction performances, promoting much greater growth of the field. Here, we present a structured overview of available large-scale databases and machine learning especially deep learning methods in computational predictive models for anti-cancer drug synergy prediction. We provide a unified framework for machine learning models and detail existing model architectures as well as their contributions and limitations, shedding light into the future design of computational models. Besides, unbiased experiments are conducted to provide in-depth comparisons between reviewed papers in terms of their prediction performance.

CEDA: integrating gene expression data with CRISPR-pooled screen data identifies essential genes with higher expression.

MOTIVATION: Clustered regularly interspaced short palindromic repeats (CRISPR)-based genetic perturbation screen is a powerful tool to probe gene function. However, experimental noises, especially for the lowly expressed genes, need to be accounted for to maintain proper control of false positive rate. METHODS: We develop a statistical method, named CRISPR screen with Expression Data Analysis (CEDA), to integrate gene expression profiles and CRISPR screen data for identifying essential genes. CEDA stratifies genes based on expression level and adopts a three-component mixture model for the log-fold change of single-guide RNAs (sgRNAs). Empirical Bayesian prior and expectation-maximization algorithm are used for parameter estimation and false discovery rate inference. RESULTS: Taking advantage of gene expression data, CEDA identifies essential genes with higher expression. Compared to existing methods, CEDA shows comparable reliability but higher sensitivity in detecting essential genes with moderate sgRNA fold change. Therefore, using the same CRISPR data, CEDA generates an additional hit gene list. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DGCyTOF: Deep learning with graphic cluster visualization to predict cell types of single cell mass cytometry data.

Single-cell mass cytometry, also known as cytometry by time of flight (CyTOF) is a powerful high-throughput technology that allows analysis of up to 50 protein markers per cell for the quantification and classification of single cells. Traditional manual gating utilized to identify new cell populations has been inadequate, inefficient, unreliable, and difficult to use, and no algorithms to identify both calibration and new cell populations has been well established. A deep learning with graphic cluster (DGCyTOF) visualization is developed as a new integrated embedding visualization approach in identifying canonical and new cell types. The DGCyTOF combines deep-learning classification and hierarchical stable-clustering methods to sequentially build a tri-layer construct for known cell types and the identification of new cell types. First, deep classification learning is constructed to distinguish calibration cell populations from all cells by softmax classification assignment under a probability threshold, and graph embedding clustering is then used to identify new cell populations sequentially. In the middle of two-layer, cell labels are automatically adjusted between new and unknown cell populations via a feedback loop using an iteration calibration system to reduce the rate of error in the identification of cell types, and a 3-dimensional (3D) visualization platform is finally developed to display the cell clusters with all cell-population types annotated. Utilizing two benchmark CyTOF databases comprising up to 43 million cells, we compared accuracy and speed in the identification of cell types among DGCyTOF, DeepCyTOF, and other technologies including dimension reduction with clustering, including Principal Component Analysis (PCA), Factor Analysis (FA), Independent Component Analysis (ICA), Isometric Feature Mapping (Isomap), t-distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) with k-means clustering and Gaussian mixture clustering. We observed the DGCyTOF represents a robust complete learning system with high accuracy, speed and visualization by eight measurement criteria. The DGCyTOF displayed F-scores of 0.9921 for CyTOF1 and 0.9992 for CyTOF2 datasets, whereas those scores were only 0.507 and 0.529 for the t-SNE+k-means; 0.565 and 0.59, for UMAP+ k-means. Comparison of DGCyTOF with t-SNE and UMAP visualization in accuracy demonstrated its approximately 35% superiority in predicting cell types. In addition, observation of cell-population distribution was more intuitive in the 3D visualization in DGCyTOF than t-SNE and UMAP visualization. The DGCyTOF model can automatically assign known labels to single cells with high accuracy using deep-learning classification assembling with traditional graph-clustering and dimension-reduction strategies. Guided by a calibration system, the model seeks optimal accuracy balance among calibration cell populations and unknown cell types, yielding a complete and robust learning system that is highly accurate in the identification of cell populations compared to results using other methods in the analysis of single-cell CyTOF data. Application of the DGCyTOF method to identify cell populations could be extended to the analysis of single-cell RNASeq data and other omics data.

DSCN: Double-target selection guided by CRISPR screening and network.

Cancer is a complex disease with usually multiple disease mechanisms. Target combination is a better strategy than a single target in developing cancer therapies. However, target combinations are generally more difficult to be predicted. Current CRISPR-cas9 technology enables genome-wide screening for potential targets, but only a handful of genes have been screend as target combinations. Thus, an effective computational approach for selecting candidate target combinations is highly desirable. Selected target combinations also need to be translational between cell lines and cancer patients. We have therefore developed DSCN (double-target selection guided by CRISPR screening and network), a method that matches expression levels in patients and gene essentialities in cell lines through spectral-clustered protein-protein interaction (PPI) network. In DSCN, a sub-sampling approach is developed to model first-target knockdown and its impact on the PPI network, and it also facilitates the selection of a second target. Our analysis first demonstrated a high correlation of the DSCN sub-sampling-based gene knockdown model and its predicted differential gene expressions using observed gene expression in 22 pancreatic cell lines before and after MAP2K1 and MAP2K2 inhibition (R2 = 0.75). In DSCN algorithm, various scoring schemes were evaluated. The 'diffusion-path' method showed the most significant statistical power of differentialting known synthetic lethal (SL) versus non-SL gene pairs (P = 0.001) in pancreatic cancer. The superior performance of DSCN over existing network-based algorithms, such as OptiCon and VIPER, in the selection of target combinations is attributable to its ability to calculate combinations for any gene pairs, whereas other approaches focus on the combinations among optimized regulators in the network. DSCN's computational speed is also at least ten times fast than that of other methods. Finally, in applying DSCN to predict target combinations and drug combinations for individual samples (DSCNi), DSCNi showed high correlation between target combinations predicted and real synergistic combinations (P = 1e-5) in pancreatic cell lines. In summary, DSCN is a highly effective computational method for the selection of target combinations.

Inhibition of enhancer of zeste homolog 2 (EZH2) overcomes enzalutamide resistance in castration-resistant prostate cancer.

Enzalutamide, approved by the United States Food and Drug Administration in 2018 for the management of metastatic castration-resistant prostate cancer (CRPC), is an androgen receptor (AR) inhibitor. It blocks androgen binding to the AR, AR nuclear translocation, and AR-mediated DNA binding. Unfortunately, a considerable proportion of tumors eventually develop resistance during the treatment. The molecular mechanisms underlying enzalutamide resistance are not completely understood. Enhancer of zeste homolog 2 (EZH2), the catalytic subunit of polycomb repressor complex 2, has been proposed as a prognostic marker for prostate cancer (PCa). With the goal to test whether EZH2 also plays a critical role in acquisition of enzalutamide resistance in CRPC, here we examined whether EZH2 inhibition/depletion enhances the efficacy of enzalutamide in enzalutamide-resistant PCa cells. We show that combining the EZH2 inhibitor GSK126 with enzalutamide synergistically inhibits cell proliferation and colony formation and promotes apoptosis in enzalutamide-resistant PCa cells. EZH2 depletion also overcomes enzalutamide resistance in both cultured cells and xenograft tumors. Mechanistically, we found that EZH2 directly binds to the promoter of prostate-specific antigen and inhibits its expression in enzalutamide-resistant PCa cells. In agreement, bioinformatics analysis of clinical RNA sequencing data involving GSEA indicated a strong correlation between AR and EZH2 gene expression during PCa progression. Our study provides critical insights into the mechanisms underlying enzalutamide resistance, which may offer new approaches to enhance the efficacy of enzalutamide in CRPC.

NOTCH signaling is activated in and contributes to resistance in enzalutamide-resistant prostate cancer cells.

Prostate cancer is the second leading cause of cancer death among men in the United States. The androgen receptor (AR) antagonist enzalutamide is a Food and Drug Administration-approved drug for treatment of patients with late-stage prostate cancer and is currently under clinical study for early-stage prostate cancer treatment. After a short positive response period, tumors will develop drug resistance. In this study using RNA-Seq and bioinformatics analyses, we observed that NOTCH signaling is a deregulated pathway in enzalutamide-resistant cells. NOTCH2 and c-MYC gene expression positively correlated with AR expression in samples from patient with hormone refractory disease in which AR expression levels correspond to those typically observed in enzalutamide resistance. Cleaved NOTCH1, HES1 (Hes family BHLH transcription factor 1), and c-MYC protein expression levels are elevated in two enzalutamide-resistant cell lines, MR49F and C4-2R, indicating NOTCH signaling activation. Moreover, inhibition of the overexpressed ADAM metallopeptidase domain 10 (ADAM10) in the resistant cells induces an exclusive reduction in cleaved NOTCH1 expression. Furthermore, exposure of enzalutamide-resistant cells to both PF-03084014 and enzalutamide increased cell death, decreased colony formation ability, and resensitized cells to enzalutamide. Knockdown of NOTCH1 in C4-2R increased enzalutamide sensitivity by decreasing cell proliferation and increasing cleaved PARP expression. In a 22RV1 xenograft model, PF-03084014 and enzalutamide decreased tumor growth through reducing cell proliferation and increasing apoptosis. These results indicate that NOTCH1 signaling may contribute to enzalutamide resistance in prostate cancer, and inhibition of NOTCH signaling can resensitize resistant cells to enzalutamide.

Inhibition of noncanonical Wnt pathway overcomes enzalutamide resistance in castration-resistant prostate cancer.

BACKGROUND: Because androgen receptor (AR) signaling is essential for prostate cancer (PCa) initiation and progression, castration is the main approach for treatment. Unfortunately, patients tend to enter a stage called castration-resistant prostate cancer (CRPC) despite the initial response to castration. For various reasons, AR signaling is reactivated in CRPC. As such, AR signaling inhibitors, such as enzalutamide, has been approved by the Food and Drug Administration to treat CRPC in the clinic. However, the limited success of these new drugs suggests an immediate unmet need to understand the underlying mechanisms for resistance so novel targets can be identified to enhance their efficacy. METHODS: An unbiased bioinformatics analysis was performed with the existing human patient dataset and RNA-seq results of in-house PCa cell lines to identify new targets to overcome enzalutamide resistance. Cell viability and growth were detected by 3-(4,5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide and colony formation assay. Cell invasion and migration were detected by transwell assay. Protein levels were detected by Western blot or immunofluorescence. RESULTS: We found that the noncanonical Wnt signaling was activated in enzalutamide-resistant PCa cells and that the activation of noncanonical Wnt signaling was correlated with AR expression and disease progression. This was validated by the elevated expression of noncanonical Wnt pathway members such as Wnt5a, RhoA, and ROCK in enzalutamide-resistant PCa cells in comparison to their enzalutamide-sensitive counterparts. And, both Y27632, an inhibitor of ROCK, and depletion of ROCK enhanced the efficacy of enzalutamide in enzalutamide-resistant PCa cells. Of significance, a combination of Y27632 and enzalutamide inhibited 22RV1-derived xenograft tumor growth synergistically. Finally, ROCK depletion plus enzalutamide treatment inhibited invasion and migration of enzalutamide-resistant PCa cells via inhibition of epithelial-mesenchymal transition. CONCLUSIONS: The noncanonical Wnt pathway is activated in enzalutamide-resistant PCa and inhibition of noncanonical Wnt pathway overcomes enzalutamide resistance and enhances its efficacy in CRPC.

Effects of ticagrelor pretreatment on electrophysiological properties of stellate ganglion neurons following myocardial infarction.

Higher sympathetic activity predisposes to malignant ventricular arrhythmias in the context of myocardial infarction (MI). This is, in part, mediated by the electrical activity of the stellate ganglion (SG). The aim of this study is to examine the effects of ticagrelor pretreatment on the electrophysiological properties of SG neurons following MI in rabbits. MI was induced by isoproterenol (ISO) of 150 mg kg-1 d-1 (twice at an interval of 24 hours). Ticagrelor pretreatment was administered at low- (10 mg kg-1 d-1) or high-dose (20 mg kg-1 d-1). Protein and RNA expression were determined by immunohistochemical analysis and real-time PCR, respectively. The activity of sodium channel current (INa), delayed rectifier potassium current (IKDR), M-type potassium current (IKM) as well as action potentials (APs) from SG neurons were measured by whole-cell patch-clamp. Intracellular calcium concentrations were measured by confocal microscopy. Compared with the control group, the MI group exhibited a greater amplitude of INa, IKDR and IKM, significantly altered activation and inactivation characteristics of INa, no significant alterations in protein or mRNA expression of sodium and M-type potassium channels, along with higher AP amplitude and frequency and intracellular calcium concentrations. Most of these abnormalities were prevented by pretreatment with low- or high-dose ticagrelor. Our data suggest that ticagrelor exerts cardioprotective effects, potentially through modulating the activity of different ion channels in SG neurons.

Inhibition of cholesterol biosynthesis overcomes enzalutamide resistance in castration-resistant prostate cancer (CRPC).

Enzalutamide, a nonsteroidal second-generation antiandrogen, has been recently approved for the management of castration-resistant prostate cancer (CRPC). Although patients can benefit from enzalutamide at the beginning of this therapy, acquired enzalutamide resistance usually occurs within a short period. This motivated us to investigate the mechanism involved and possible approaches for overcoming enzalutamide resistance in CRPC. In the present study, we found that 3-hydroxy-3-methyl-glutaryl-CoA reductase (HMGCR), a crucial enzyme in the mevalonate pathway for sterol biosynthesis, is elevated in enzalutamide-resistant prostate cancer cell lines. HMGCR knockdown could resensitize these cells to the drug, and HMGCR overexpression conferred resistance to it, suggesting that aberrant HMGCR expression is an important enzalutamide-resistance mechanism in prostate cancer cells. Furthermore, enzalutamide-resistant prostate cancer cells were more sensitive to statins, which are HMGCR inhibitors. Of note, a combination of simvastatin and enzalutamide significantly inhibited the growth of enzalutamide-resistant prostate cancer cells in vitro and tumors in vivo Mechanistically, simvastatin decreased protein levels of the androgen receptor (AR), which was further reduced in combination with enzalutamide. We observed that the decrease in AR may occur through simvastatin-mediated inhibition of the mTOR pathway, whose activation was associated with increased HMGCR and AR expression. These results indicate that simvastatin enhances the efficacy of enzalutamide-based therapy, highlighting the therapeutic potential of statins to overcome enzalutamide resistance in CRPC.

Maize Dek33 encodes a pyrimidine reductase in riboflavin biosynthesis that is essential for oil-body formation and ABA biosynthesis during seed development.

The maize (Zea mays) defective kernel 33 (dek33) mutant produces defective and occasionally viviparous kernel phenotypes. In this study, we cloned Dek33 by positional cloning and found that it encodes a pyrimidine reductase in riboflavin biosynthesis. In dek33, a single-base mutation (G to A) in the C-terminal COG3236 domain caused a premature stop codon (TGA), producing a weak mutant allele with only a truncated form of the DEK33 protein that occurred at much lower levels that the completed WT form, and with a reduced riboflavin content. The dek33 mutation significantly affected oil-body formation and suppressed endoreduplication. It also disrupted ABA biosynthesis, resulting in lower ABA content that might be responsible for the viviparous embryo. In addition, our results indicated that the COG3236 domain is important for the protein stability of DEK33. Yeast two-hybrid experiments identified several proteins that interacted with DEK33, including RGLG2 and SnRK1, suggesting possible post-translational regulation of DEK33 stability. The interaction between DEK33 and these proteins was further confirmed by luciferase complementation image assays. This study provides a weak mutant allele that can be utilized to explore cellular responses to impaired riboflavin biosynthesis during seed development. Our findings indicate that the COG3236 domain might be an essential regulatory structure for DEK33 stability in maize.

Lipid Modulating Anti-oxidant Stress Activity of Gastrodin on Nonalcoholic Fatty Liver Disease Larval Zebrafish Model.

Non-alcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) is the most common chronic liver disease in the world. However, there are still no drugs for NAFLD/NASH in the market. Gastrodin (GAS) is a bioactive compound that is extracted from Gastrodia elata, which is used as an active compound on nervous system diseases. Recent reports showed that GAS and Gastrodia elata possess anti-oxidant activity and lipid regulating effects, which makes us curious to reveal the anti-NAFLD effect of GAS. A high cholesterol diet (HCD) was used to induce a NAFLD larval zebrafish model, and the lipid regulation and anti-oxidant effects were tested on the model. Furthermore, qRT-PCR studied the underlying mechanism of GAS. To conclude, this study revealed a lipid regulation and anti-oxidant insights of GAS on NAFLD larval zebrafish model and provided a potential therapeutic compound for NAFLD treatment.

Diversity and abundance of denitrifiers during cow manure composting.

Diversity and abundance of the denitrifying genes nirK, nirS and nosZ were investigated in cow manure compost using polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) and real-time quantitative PCR (qPCR), respectively. These three genes were detected in all the stages of the composting process. Phylogenetic analysis showed that the nirK gene was closely related to Rhizobiales, Burkholderiales, the nirS gene was closely related to Pseudomonadales and Burkholderiales, and the nosZ gene was closely related to Rhodospirillales, Rhizobiales, Pseudomonadales, and Alteromonadales. qPCR results showed that the abundance of these three genes (nirK, nirS and nosZ) reached the peak value in the late thermophilic stage of composting and abundance of the nirK gene was higher than that of the nosZ gene and the nirS gene. Redundancy analysis (RDA) showed that the diversity of the nirK and nirS genes was significantly correlated with ammonium (p<0.05), the diversity of the nosZ gene was significantly correlated with pH (p<0.05) and the abundance of the nirK nirS and nosZ genes was significantly correlated with temperature (p<0.05).

Distinct molecular pathways in ovarian endometrioid adenocarcinoma with concurrent endometriosis.

Women with endometriosis, a benign growth of endometrial tissue outside the uterine cavity, are at increased risk of specific histotypes of epithelial ovarian cancer, such as ovarian endometrioid adenocarcinoma (OEA). Women with OEA who have endometriosis at time of surgical staging demonstrate improved clinical prognosis compared to women with OEA without evidence of endometriosis. However, the molecular contributions of the endometriotic tumor microenvironment to these ovarian cancers remain poorly understood. As a starting point, we used a platform for genome-wide transcriptomic profiling to compare specimens of OEA from women with and without concurrent endometriosis and benign reproductive tract tissues, including proliferative endometrium and typical and atypical endometrioma samples (n = 20). Principle component analysis revealed distinct clustering between benign and malignant samples as well as malignant samples with and without concurrent endometriosis. Examination of gene signatures revealed that OEA with concurrent endometriosis contained a unique molecular signature compared to OEA without concurrent endometriosis, distinguished by 682 unique genes differentially expressed (fold change < or >1.5, p < 0.01). Bioinformatic analysis of these differentially expressed gene products using ingenuity pathway analysis revealed activation of NFkB signaling, an inflammatory signaling pathway constitutively active in endometriosis. DAVID functional annotation clustering further revealed enrichment in RAS signaling as both cytoskeleton organization and GTPase regulator activity relied heavily on RAS protein signal transduction. Gene set enrichment analysis highlighted immune and inflammatory nodes involved in OEA with concurrent endometriosis. These observations provide novel resources for understanding molecular subtleties potentially involved in OEA within the context of the endometriotic tumor microenvironment.

STAT3-mediated upregulation of lncRNA HOXD-AS1 as a ceRNA facilitates liver cancer metastasis by regulating SOX4.

BACKGROUND: Several of the thousands of human long noncoding RNAs (lncRNAs) have been functionally characterized, yet their potential involvement in hepatocellular carcinoma (HCC) remains poorly understood. METHODS: LncRNA-HOXD-AS1 was identified by microarray and validated by real-time PCR. The clinicopathological significance of HOXD-AS1 was analyzed by Kaplan-Meier method. Chromatin immunoprecipitation was conducted to examine the mechanism of HOXD-AS1 upregulation. The role of HOXD-AS1 in HCC cells was assessed both in vitro and in vivo. ceRNA function of HOXD-AS1 was evaluated by RNA immunoprecipitation and biotin-coupled miRNA pull down assays. RESULTS: In this study, we found that HOXD-AS1 was significantly upregulated in HCC tissues. Clinical investigation demonstrated high expression level of HOXD-AS1 was associated with poor prognosis and high tumor node metastasis stage of HCC patients, and was an independent risk factor for survival. Moreover, our results revealed that STAT3 could specifically interact with the promoter of HOXD-AS1 and activate HOXD-AS1 transcription. Knockdown of HOXD-AS1 significantly inhibited migration and invasion of HCC cells in vitro and distant lung metastasis in vivo. Additionally, HOXD-AS1 was enriched in the cytoplasm, and shared miRNA response elements with SOX4. Overexpression of HOXD-AS1 competitively bound to miR-130a-3p that prevented SOX4 from miRNA-mediated degradation, thus activated the expression of EZH2 and MMP2 and facilitated HCC metastasis. CONCLUSIONS: In summary, HOXD-AS1 is a prognostic marker for HCC patients and it may play a pro-metastatic role in hepatocarcinogenesis.