Sirtuin 6 protects against podocyte injury by blocking the renin-angiotensin system by inhibiting the Wnt1/ß-catenin pathway.

Sirtuins (Sirts) are a family of nicotinamide adenine dinucleotide-dependent protein deacetylases that share diverse cellular functions. Increasing evidence shows that Sirts play a critical role in podocyte injury, which is a major determinant of proteinuria-associated renal disease. Membranous nephropathy (MN) is a typical glomerular disease in which podocyte damage mediates proteinuria development. In this study we investigated the molecular mechanisms underlying the regulatory roles of Sirt in podocyte injury in MN patients, rats with cationic bovine serum albumin (CBSA)-induced MN and zymosan activation serum (ZAS)-stimulated podocytes. Compared with healthy controls, MN patients showed significant reduction in intrarenal Sirt1 and Sirt6 protein expression. In CBSA-induced MN rats, significant reduction in intrarenal Sirt1, Sirt3 and Sirt6 protein expression was observed. However, only significant decrease in Sirt6 protein expression was found in ZAS-stimulated podocytes. MN patients showed significantly upregulated protein expression of Wnt1 and ß-catenin and renin-angiotensin system (RAS) components in glomeruli. CBSA-induced MN rats exhibited significantly upregulated protein expression of intrarenal Wnt1 and ß-catenin and their downstream gene products as well as RAS components. Similar results were observed in ZAS-stimulated podocytes. In ZAS-stimulated podocytes, treatment with a specific Sirt6 activator UBCS039 preserved the protein expression of podocin, nephrin and podocalyxin, accompanied by significant inhibition of the protein expression of ß-catenin and its downstream gene products, including Snail1 and Twist; treatment with a ß-catenin inhibitor ICG-001 significantly preserved the expression of podocyte-specific proteins and inhibited the upregulation of downstream ß-catenin gene products accompanied by significant suppression of the protein expression of RAS components. Thus, we demonstrate that Sirt6 ameliorates podocyte injury by blocking RAS signalling via the Wnt1/ß-catenin pathway. Sirt6 is a specific therapeutic target for the treatment of podocyte damage-associated renal disease.

SBSA: an online service for somatic binding sequence annotation.

Efficient annotation of alterations in binding sequences of molecular regulators can help identify novel candidates for mechanisms study and offer original therapeutic hypotheses. In this work, we developed Somatic Binding Sequence Annotator (SBSA) as a full-capacity online tool to annotate altered binding motifs/sequences, addressing diverse types of genomic variants and molecular regulators. The genomic variants can be somatic mutation, single nucleotide polymorphism, RNA editing, etc. The binding motifs/sequences involve transcription factors (TFs), RNA-binding proteins, miRNA seeds, miRNA-mRNA 3'-UTR binding target, or can be any custom motifs/sequences. Compared to similar tools, SBSA is the first to support miRNA seeds and miRNA-mRNA 3'-UTR binding target, and it unprecedentedly implements a personalized genome approach that accommodates joint adjacent variants. SBSA is empowered to support an indefinite species, including preloaded reference genomes for SARS-Cov-2 and 25 other common organisms. We demonstrated SBSA by annotating multi-omics data from over 30,890 human subjects. Of the millions of somatic binding sequences identified, many are with known severe biological repercussions, such as the somatic mutation in TERT promoter region which causes a gained binding sequence for E26 transformation-specific factor (ETS1). We further validated the function of this TERT mutation using experimental data in cancer cells. Availability:http://innovebioinfo.com/Annotation/SBSA/SBSA.php.

Traditional Chinese medicine improved diabetic kidney disease through targeting gut microbiota.

CONTEXT: Diabetic kidney disease (DKD) affects nearly 40% of diabetic patients, often leading to end-stage renal disease that requires renal replacement therapies, such as dialysis and transplantation. The gut microbiota, an integral aspect of human evolution, plays a crucial role in this condition. Traditional Chinese medicine (TCM) has shown promising outcomes in ameliorating DKD by addressing the gut microbiota. OBJECTIVE: This review elucidates the modifications in gut microbiota observed in DKD and explores the impact of TCM interventions on correcting microbial dysregulation. METHODS: We searched relevant articles from databases including Web of Science, PubMed, ScienceDirect, Wiley, and Springer Nature. The following keywords were used: diabetic kidney disease, diabetic nephropathy, gut microbiota, natural product, TCM, Chinese herbal medicine, and Chinese medicinal herbs. Rigorous criteria were applied to identify high-quality studies on TCM interventions against DKD. RESULTS: Dysregulation of the gut microbiota, including Lactobacillus, Streptococcus, and Clostridium, has been observed in individuals with DKD. Key indicators of microbial dysregulation include increased uremic solutes and decreased short-chain fatty acids. Various TCM therapies, such as formulas, tablets, granules, capsules, and decoctions, exhibit unique advantages in regulating the disordered microbiota to treat DKD. CONCLUSION: This review highlights the importance of targeting the gut-kidney axis to regulate microbial disorders, their metabolites, and associated signaling pathways in DKD. The Qing-Re-Xiao-Zheng formula, the Shenyan Kangfu tablet, the Huangkui capsule, and the Bekhogainsam decoction are potential candidates to address the gut-kidney axis. TCM interventions offer a significant therapeutic approach by targeting microbial dysregulation in patients with DKD.

Integrative phosphatidylcholine metabolism through phospholipase A2 in rats with chronic kidney disease.

Dysregulation in lipid metabolism is the leading cause of chronic kidney disease (CKD) and also the important risk factors for high morbidity and mortality. Although lipid abnormalities were identified in CKD, integral metabolic pathways for specific individual lipid species remain to be clarified. We conducted ultra-high-performance liquid chromatography-high-definition mass spectrometry-based lipidomics and identified plasma lipid species and therapeutic effects of Rheum officinale in CKD rats. Adenine-induced CKD rats were administered Rheum officinale. Urine, blood and kidney tissues were collected for analyses. We showed that exogenous adenine consumption led to declining kidney function in rats. Compared with control rats, a panel of differential plasma lipid species in CKD rats was identified in both positive and negative ion modes. Among the 50 lipid species, phosphatidylcholine (PC), lysophosphatidylcholine (LysoPC) and lysophosphatidic acid (LysoPA) accounted for the largest number of identified metabolites. We revealed that six PCs had integral metabolic pathways, in which PC was hydrolysed into LysoPC, and then converted to LysoPA, which was associated with increased cytosolic phospholipase A2 protein expression in CKD rats. The lower levels of six PCs and their corresponding metabolites could discriminate CKD rats from control rats. Receiver operating characteristic curves showed that each individual lipid species had high values of area under curve, sensitivity and specificity. Administration of Rheum officinale significantly improved impaired kidney function and aberrant PC metabolism in CKD rats. Taken together, this study demonstrates that CKD leads to PC metabolism disorders and that the dysregulation of PC metabolism is involved in CKD pathology.

Cancer-specific expression quantitative loci are affected by expression dysregulation.

Expression quantitative trait loci (eQTLs) have been touted as the missing piece that can bridge the gap between genetic variants and phenotypes. Over the past decade, we have witnessed a sharp rise of effort in the identification and application of eQTLs. The successful application of eQTLs relies heavily on their reproducibility. The current eQTL databases such as Genotype-Tissue Expression (GTEx) were populated primarily with eQTLs deriving from germline single nucleotide polymorphisms and normal tissue gene expression. The novel scenarios that employ eQTL models for prediction purposes often involve disease phenotypes characterized by altered gene expressions. To evaluate eQTL reproducibility across diverse data sources and the effect of disease-specific gene expression alteration on eQTL identification, we conducted an eQTL study using 5178 samples from The Cancer Genome Atlas (TCGA). We found that the reproducibility of eQTLs between normal and tumor tissues was low in terms of the number of shared eQTLs. However, among the shared eQTLs, the effect directions were generally concordant. This suggests that the source of the gene expression (normal or tumor tissue) has a strong effect on the detectable eQTLs and the effect direction of the eQTLs. Additional analyses demonstrated good directional concordance of eQTLs between GTEx and TCGA. Furthermore, we found that multi-tissue eQTLs may exert opposite effects across multiple tissue types. In summary, our results suggest that eQTL prediction models need to carefully address tissue and disease dependency of eQTLs. Tissue-disease-specific eQTL databases can afford more accurate prediction models for future studies.

MutEx: a multifaceted gateway for exploring integrative pan-cancer genomic data.

Somatic mutation and gene expression dysregulation are considered two major tumorigenesis factors. While independent investigations of either factor pervade, studies of associations between somatic mutations and gene expression changes have been sporadic and nonsystematic. Utilizing genomic data collected from 11 315 subjects of 33 distinct cancer types, we constructed MutEx, a pan-cancer integrative genomic database. This database records the relationships among gene expression, somatic mutation and survival data for cancer patients. MutEx can be used to swiftly explore the relationship between these genomic/clinic features within and across cancer types and, more importantly, search for corroborating evidence for hypothesis inception. Our database also incorporated Gene Ontology and several pathway databases to enhance functional annotation, and elastic net and a gene expression composite score to aid in survival analysis. To demonstrate the usability of MutEx, we provide several application examples, including top somatic mutations associated with the most extensive expression dysregulation in breast cancer, differential mutational burden downstream of DNA mismatch repair gene mutations and composite gene expression score-based survival difference in breast cancer. MutEx can be accessed at http://www.innovebioinfo.com/Databases/Mutationdb_About.php.

Intrarenal 1-methoxypyrene, an aryl hydrocarbon receptor agonist, mediates progressive tubulointerstitial fibrosis in mice.

Recent studies have shown that endogenous metabolites act via aryl hydrocarbon receptor (AhR) signalling pathway in tubulointerstitial fibrosis (TIF) pathogenesis. However, the mechanisms underlying endogenous metabolite-mediated AhR activation are poorly characterised. In this study, we conducted untargeted metabolomics analysis to identify the significantly altered intrarenal metabolites in a mouse model of unilateral ureteral obstruction (UUO). We found that the levels of the metabolite 1-methoxypyrene (MP) and the mRNA expression of AhR and its target genes CYP1A1, CYP1A2, CYP1B1 and COX-2 were progressively increased in the obstructed kidney at Weeks 1, 2 and 3. Furthermore, these changes were positively correlated with progressive TIF in UUO mice. In NRK-52E, RAW 264.7 and NRK-49F cells, MP dose-dependently upregulated the mRNA expression of AhR and its four target genes and the protein expression of nuclear AhR, accompanied by the upregulated protein expression of collagen I, α-SMA and fibronectin, as well as downregulated E-cadherin expression. Consistently, oral administration of MP in mice progressively enhanced AhR activity and upregulated profibrotic protein expression in the kidneys; these effects were partially inhibited by AhR knockdown in MP-treated mice and cell lines. In addition, we screened and identified erythro-guaiacylglycerol-ß-ferulic acid ether (GFA), which was isolated from Semen plantaginis, as a new AhR antagonist. GFA significantly attenuated TIF in MP-treated NRK-52E cells and mice by partially antagonising AhR activity. Our results suggest that MP activates AhR signalling, thus mediating TIF through epithelial-mesenchymal transition and macrophage-myofibroblast transition. MP is a crucial metabolite that contributes to TIF via AhR signalling pathway.

Gut microbiota-derived tryptophan metabolism mediates renal fibrosis by aryl hydrocarbon receptor signaling activation.

The gut microbiota has a crucial effect on regulating the intestinal mucosal immunity and maintaining intestinal homeostasis both in health and in disease state. Many effects are mediated by gut microbiota-derived metabolites and tryptophan, an essential aromatic amino acid, is considered important among many metabolites in the crosstalk between gut microbiota and the host. Kynurenine, serotonin, and indole derivatives are derived from the three major tryptophan metabolism pathways modulated by gut microbiota directly or indirectly. Aryl hydrocarbon receptor (AHR) is a cytoplasmic ligand-activated transcription factor involved in multiple cellular processes. Tryptophan metabolites as ligands can activate AHR signaling in various diseases such as inflammation, oxidative stress injury, cancer, aging-related diseases, cardiovascular diseases (CVD), and chronic kidney diseases (CKD). Accumulated uremic toxins in the body fluids of CKD patients activate AHR and affect disease progression. In this review, we will elucidate the relationship between gut microbiota-derived uremic toxins by tryptophan metabolism and AHR activation in CKD and its complications. This review will provide therapeutic avenues for targeting CKD and concurrently present challenges and opportunities for designing new therapeutic strategies against renal fibrosis.

EditPredict: Prediction of RNA editable sites with convolutional neural network.

RNA editing exerts critical impacts on numerous biological processes. While millions of RNA editings have been identified in humans, much more are expected to be discovered. In this work, we constructed Convolutional Neural Network (CNN) models to predict human RNA editing events in both Alu regions and non-Alu regions. With a validation dataset resulting from CRISPR/Cas9 knockout of the ADAR1 enzyme, the validation accuracies reached 99.5% and 93.6% for Alu and non-Alu regions, respectively. We ported our CNN models in a web service named EditPredict. EditPredict not only works on reference genome sequences but can also take into consideration single nucleotide variants in personal genomes. In addition to the human genome, EditPredict tackles other model organisms including bumblebee, fruitfly, mouse, and squid genomes. EditPredict can be used stand-alone to predict novel RNA editing and it can be used to assist in filtering for candidate RNA editing detected from RNA-Seq data.

AnnoGen: annotating genome-wide pragmatic features.

MOTIVATION: Genome annotation is an important step for all in-depth bioinformatics analysis. It is imperative to augment quantity and diversity of genome-wide annotation data for the latest reference genome to promote its adoption by ongoing and future impactful studies. RESULTS: We developed a python toolkit AnnoGen, which at the first time, allows the annotation of three pragmatic genomic features for the GRCh38 genome in enormous base-wise quantities. The three features are chemical binding Energy, sequence information Entropy and Homology Score. The Homology Score is an exceptional feature that captures the genome-wide homology through single-base-offset tiling windows of 100 continual nucleotide bases. AnnoGen is capable of annotating the proprietary pragmatic features for variable user-interested genomic regions and optionally comparing two parallel sets of genomic regions. AnnoGen is characterized with simple utility modes and succinct HTML report of informative statistical tables and plots. AVAILABILITY AND IMPLEMENTATION: https://github.com/shengqh/annogen.

Deciphering the cellular mechanisms underlying fibrosis-associated diseases and therapeutic avenues.

Fibrosis is the excessive deposition of extracellular matrix components, which results in disruption of tissue architecture and loss of organ function. Fibrosis leads to high morbidity and mortality worldwide, mainly due to the lack of effective therapeutic strategies against fibrosis. It is generally accepted that fibrosis occurs during an aberrant wound healing process and shares a common pathogenesis across different organs such as the heart, liver, kidney, and lung. A better understanding of the fibrosis-related cellular and molecular mechanisms will be helpful for development of targeted drug therapies. Extensive studies revealed that numerous mediators contributed to fibrogenesis, suggesting that targeting these mediators may be an effective therapeutic strategy for antifibrosis. In this review, we describe a number of mediators involved in tissue fibrosis, including aryl hydrocarbon receptor, Yes-associated protein, cannabinoid receptors, angiopoietin-like protein 2, high mobility group box 1, angiotensin-converting enzyme 2, sphingosine 1-phosphate receptor-1, SH2 domain-containing phosphatase-2, and long non-coding RNAs, with the goal that drugs targeting these important mediators might exhibit a beneficial effect on antifibrosis. In addition, these mediators show profibrotic effects on multiple tissues, suggesting that targeting these mediators will exert antifibrotic effects on different organs. Furthermore, we present a variety of compounds that exhibit therapeutic effects against fibrosis. This review suggests therapeutic avenues for targeting organ fibrosis and concurrently identifies challenges and opportunities for designing new therapeutic strategies against fibrosis.

Non-canonical RNA-DNA differences and other human genomic features are enriched within very short tandem repeats.

Very short tandem repeats bear substantial genetic, evolutional, and pathological significance in genome analyses. Here, we compiled a census of tandem mono-nucleotide/di-nucleotide/tri-nucleotide repeats (MNRs/DNRs/TNRs) in GRCh38, which we term "polytracts" in general. Of the human genome, 144.4 million nucleotides (4.7%) are occupied by polytracts, and 0.47 million single nucleotides are identified as polytract hinges, i.e., break-points of tandem polytracts. Preliminary exploration of the census suggested polytract hinge sites and boundaries of AAC polytracts may bear a higher mapping error rate than other polytract regions. Further, we revealed landscapes of polytract enrichment with respect to nearly a hundred genomic features. We found MNRs, DNRs, and TNRs displayed noticeable difference in terms of locational enrichment for miscellaneous genomic features, especially RNA editing events. Non-canonical and C-to-U RNA-editing events are enriched inside and/or adjacent to MNRs, while all categories of RNA-editing events are under-represented in DNRs. A-to-I RNA-editing events are generally under-represented in polytracts. The selective enrichment of non-canonical RNA-editing events within MNR adjacency provides a negative evidence against their authenticity. To enable similar locational enrichment analyses in relation to polytracts, we developed a software Polytrap which can handle 11 reference genomes. Additionally, we compiled polytracts of four model organisms into a Track Hub which can be integrated into USCS Genome Browser as an official track for convenient visualization of polytracts.

Small molecule inhibitors of epithelial-mesenchymal transition for the treatment of cancer and fibrosis.

Tissue fibrosis and cancer both lead to high morbidity and mortality worldwide; thus, effective therapeutic strategies are urgently needed. Because drug resistance has been widely reported in fibrotic tissue and cancer, developing a strategy to discover novel targets for targeted drug intervention is necessary for the effective treatment of fibrosis and cancer. Although many factors lead to fibrosis and cancer, pathophysiological analysis has demonstrated that tissue fibrosis and cancer share a common process of epithelial-mesenchymal transition (EMT). EMT is associated with many mediators, including transcription factors (Snail, zinc-finger E-box-binding protein and signal transducer and activator of transcription 3), signaling pathways (transforming growth factor-ß1, RAC-α serine/threonine-protein kinase, Wnt, nuclear factor-kappa B, peroxisome proliferator-activated receptor, Notch, and RAS), RNA-binding proteins (ESRP1 and ESRP2) and microRNAs. Therefore, drugs targeting EMT may be a promising therapy against both fibrosis and tumors. A large number of compounds that are synthesized or derived from natural products and their derivatives suppress the EMT by targeting these mediators in fibrosis and cancer. By targeting EMT, these compounds exhibited anticancer effects in multiple cancer types, and some of them also showed antifibrotic effects. Therefore, drugs targeting EMT not only have both antifibrotic and anticancer effects but also exert effective therapeutic effects on multiorgan fibrosis and cancer, which provides effective therapy against fibrosis and cancer. Taken together, the results highlighted in this review provide new concepts for discovering new antifibrotic and antitumor drugs.

Power and sample size calculations for high-throughput sequencing-based experiments.

Power/sample size (power) analysis estimates the likelihood of successfully finding the statistical significance in a data set. There has been a growing recognition of the importance of power analysis in the proper design of experiments. Power analysis is complex, yet necessary for the success of large studies. It is important to design a study that produces statistically accurate and reliable results. Power computation methods have been well established for both microarray-based gene expression studies and genotyping microarray-based genome-wide association studies. High-throughput sequencing (HTS) has greatly enhanced our ability to conduct biomedical studies at the highest possible resolution (per nucleotide). However, the complexity of power computations is much greater for sequencing data than for the simpler genotyping array data. Research on methods of power computations for HTS-based studies has been recently conducted but is not yet well known or widely used. In this article, we describe the power computation methods that are currently available for a range of HTS-based studies, including DNA sequencing, RNA-sequencing, microbiome sequencing and chromatin immunoprecipitation sequencing. Most importantly, we review the methods of power analysis for several types of sequencing data and guide the reader to the relevant methods for each data type.

Metabolomics in renal cell carcinoma: From biomarker identification to pathomechanism insights.

Renal cell carcinoma (RCC) is a frequently diagnosed cancer with high prevalence, which is inversely associated with survival benefit. Although myriad studies have shed light on disease causality, unfortunately, thus far, RCC diagnosis is faced with numerous obstacles partly due to the insufficient knowledge of effective biomarkers, hinting deeper mechanistic understanding are urgently needed. Metabolites are recognized as final proxies for gene-environment interactions and physiological homeostasis as they reflect dynamic processes that are ongoing or have been taken place, and metabolomics may therefore offer a far more productive and cost-effective route to disease discovery, particularly within the arena for new biomarker identification. In this review, we primarily expatiate recent advances in metabolomics that may be amenable to novel biomarkers or therapeutic targets for RCC, which may expand our armaments to win more bettles against RCC.

Advancing Pan-cancer Gene Expression Survial Analysis by Inclusion of Non-coding RNA.

Non-coding RNAs occupy a significant fraction of the human genome. Their biological significance is backed up by a plethora of emerging evidence. One of the most robust approaches to demonstrate non-coding RNA's biological relevance is through their prognostic value. Using the rich gene expression data from The Cancer Genome Altas (TCGA), we designed Advanced Expression Survival Analysis (AESA), a web tool which provides several novel survival analysis approaches not offered by previous tools. In addition to the common single-gene approach, AESA computes the gene expression composite score of a set of genes for survival analysis and utilizes permutation test or cross-validation to assess the significance of log-rank statistic and the degree of over-fitting. AESA offers survival feature selection with post-selection inference and utilizes expanded TCGA clinical data including overall, disease-specific, disease-free, and progression-free survival information. Users can analyse either protein-coding or non-coding regions of the transcriptome. We demonstrated the effectiveness of AESA using several empirical examples. Our analyses showed that non-coding RNAs perform as well as messenger RNAs in predicting survival of cancer patients. These results reinforce the potential prognostic value of non-coding RNAs. AESA is developed as a module in the freely accessible analysis suite MutEx. Abbreviation: ACC: Adrenocortical Carcinoma (n = 92); BLCA: Bladder Urothelial Carcinoma (n = 412); BRCA: Breast Invasive Carcinoma (n = 1098); CESC: Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (n = 307); CHOL: Cholangiocarcinoma (n = 51); COAD: Colon Adenocarcinoma (n = 461); DLBC: Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (n = 58); ESCA: Oesophageal Carcinoma (n = 185); GBM: Glioblastoma Multiforme (n = 617); HNSC: Head and Neck Squamous Cell Carcinoma (n = 528); KICH: Kidney Chromophobe (n = 113); KIRC: Kidney Renal Clear Cell Carcinoma (n = 537); KIRP: Kidney Renal Papillary Cell Carcinoma (n = 291); LAML: Acute Myeloid Leukaemia (n = 200); LGG: Brain Lower Grade Glioma (n = 516); LIHC: Liver Hepatocellular Carcinoma (n = 377); LUAD: Lung Adenocarcinoma (n = 585); LUSC: Lung Squamous Cell Carcinoma (n = 504); MESO: Mesothelioma (n = 87); OV: Ovarian Serous Cystadenocarcinoma (n = 608) PAAD: Pancreatic Adenocarcinoma (n = 185); PCPG: Pheochromocytoma and Paraganglioma (n = 179); PRAD: Prostate Adenocarcinoma (n = 500); READ: Rectum Adenocarcinoma (n = 172); SARC: Sarcoma (n = 261); SKCM: Skin Cutaneous Melanoma (n = 470); STAD: Stomach Adenocarcinoma (n = 443); TGCT: Testicular Germ Cell Tumours (n = 150); THCA: Thyroid Carcinoma (n = 507) THYM: Thymoma (n = 124); UCEC: Uterine Corpus Endometrial Carcinoma (n = 560); UCS: Uterine Carcinosarcoma (n = 57); UVM: Uveal Melanoma (n = 80).

Asiatic acid prevents renal fibrosis in UUO rats via promoting the production of 15d-PGJ2, an endogenous ligand of PPAR-γ.

Renal fibrosis is an inevitable outcome of all kinds of progressive chronic kidney disease (CKD). Recently, asiatic acid (AA), a triterpenoid compound from Chinese medicine Centella asiatica, has been found to attenuate renal fibrosis. In the current study, we explored the mechanisms underlying antifibrotic effect of AA on UUO model. SD rats and ICR mice were subjected to unilateral ureteral occlusion (UUO) surgery. Prior the surgery, rats were administered AA (10 mg·kg-1 per day, ig) for 7 days, whereas the mice received AA (15 mg·kg-1 per day, ig) for 3 days. UUO group displayed significant degree of renal dysfunction, interstitial fibrosis, oxidative stress, and activation of the TGF-ß/Smad and Wnt/ß-catenin signaling pathway in the kidney, these pathological changes were greatly ameliorated by pretreatment with AA. In addition, we found that co-treatment with GW9662, a selective PPAR-γ antagonist (1 mg·kg-1 per day, ip) for 7 days, abolished the protective effects of AA. We further revealed that AA pretreatment did not significantly change the expression levels of PPAR-γ in the kidney, but markedly increase the plasma levels of 15d-PGJ2, an endogenous ligand of PPAR-γ. In UUO mice, pretreatment with 15d-PGJ2 (24 µg·kg-1 per day, ip, for 7 days) produced similar protective effect as AA. Moreover, AA pretreatment upregulated the expression levels of active, nuclear-localized SREBP-1 (nSREBP-1), whereas fatostatin, a specific inhibitor of SREBP-1, decreased the expression of nSREBP-1, as well as the level of 15d-PGJ2. These results provide new insight into the antifibrotic mechanism of AA and endogenous metabolites might become a new clue for investigation of drug mechanism.

Microbiome-metabolomics reveals gut microbiota associated with glycine-conjugated metabolites and polyamine metabolism in chronic kidney disease.

Dysbiosis of the gut microbiome and related metabolites in chronic kidney disease (CKD) have been intimately associated with the prevalence of cardiovascular diseases. Unfortunately, thus far, there is a paucity of sufficient knowledge of gut microbiome and related metabolites on CKD progression partly due to the severely limited investigations. Using a 5/6 nephrectomized (NX) rat model, we carried out 16S rRNA sequence and untargeted metabolomic analyses to explore the relationship between colon's microbiota and serum metabolites. Marked decline in microbial diversity and richness was accompanied by significant changes in 291 serum metabolites, which were mediated by altered enzymatic activities and dysregulations of lipids, amino acids, bile acids and polyamines metabolisms. Interestingly, CCr was directly associated with some microbial genera and polyamine metabolism. However, SBP was directly related to certain microbial genera and glycine-conjugated metabolites in CKD rats. Administration of poricoic acid A (PAA) and Poria cocos (PC) ameliorated microbial dysbiosis as well as attenuated hypertension and renal fibrosis. In addition, treatments with PAA and PC lowered serum levels of microbial-derived products including glycine-conjugated compounds and polyamine metabolites. Collectively, the present study confirmed the CKD-associated gut microbial dysbiosis and identified a novel dietary and therapeutic strategy to improve the gut microbial dysbiosis and the associated metabolomic disorders and retarded the progression of kidney disease in the rat model of CKD.

Quality and concordance of genotyping array data of 12,064 samples from 5840 cancer patients.

Genotyping arrays characterize genome-wide SNPs for a study cohort and were the primary technology behind genome wide association studies over the last decade. The Cancer Genome Atlas (TCGA) is one of the largest cancer consortium studies, and it collected genotyping data for all of its participants. Using TCGA SNP data genotyped using the Affymetrix 6.0 SNP array from 12,064 samples, we conducted a comprehensive comparisons across DNA sources (tumor tissue, normal tissue, and blood) and sample storage protocols (formalin-fixed paraffin-embedded (FFPE) vs. freshly frozen (FF)), examining genotypes, transition/transversion ratios, and mutation catalogues. During the analysis, we made important observations in relevance to the data quality issues. SNP concordance was excellent between blood and normal tissues, and slightly lower between blood and tumor tissue due to potential somatic mutations in the tumors. The observed poor SNP concordance between FFPE and FF samples suggested a batch effect. The transition/transversion ratio, a metric commonly used for quality control purpose in exome sequencing projects, appeared less applicable for genotyping array data due to the whole-genome coverage built into the array design. Moreover, there were substantially more loss of heterozygosity events than gain of heterozygosity when comparing tumors relative to normal tissues and blood. This might be a consequence of extensive copy number deletions in tumors. In summary, our thorough evaluation calls for more adequate quality control practices and provides guidelines for improved application of TCGA genotyping data.

Architectures and accuracy of artificial neural network for disease classification from omics data.

BACKGROUND: Deep learning has made tremendous successes in numerous artificial intelligence applications and is unsurprisingly penetrating into various biomedical domains. High-throughput omics data in the form of molecular profile matrices, such as transcriptomes and metabolomes, have long existed as a valuable resource for facilitating diagnosis of patient statuses/stages. It is timely imperative to compare deep learning neural networks against classical machine learning methods in the setting of matrix-formed omics data in terms of classification accuracy and robustness. RESULTS: Using 37 high throughput omics datasets, covering transcriptomes and metabolomes, we evaluated the classification power of deep learning compared to traditional machine learning methods. Representative deep learning methods, Multi-Layer Perceptrons (MLP) and Convolutional Neural Networks (CNN), were deployed and explored in seeking optimal architectures for the best classification performance. Together with five classical supervised classification methods (Linear Discriminant Analysis, Multinomial Logistic Regression, Naïve Bayes, Random Forest, Support Vector Machine), MLP and CNN were comparatively tested on the 37 datasets to predict disease stages or to discriminate diseased samples from normal samples. MLPs achieved the highest overall accuracy among all methods tested. More thorough analyses revealed that single hidden layer MLPs with ample hidden units outperformed deeper MLPs. Furthermore, MLP was one of the most robust methods against imbalanced class composition and inaccurate class labels. CONCLUSION: Our results concluded that shallow MLPs (of one or two hidden layers) with ample hidden neurons are sufficient to achieve superior and robust classification performance in exploiting numerical matrix-formed omics data for diagnosis purpose. Specific observations regarding optimal network width, class imbalance tolerance, and inaccurate labeling tolerance will inform future improvement of neural network applications on functional genomics data.