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Early detection and accurate monitoring of chronic kidney disease (CKD) could improve care and retard progression to end-stage renal disease. Here, using untargeted metabolomics in 2155 participants including patients with stage 1–5 CKD and healthy controls, we identify five metabolites, including 5-methoxytryptophan (5-MTP), whose levels strongly correlate with clinical markers of kidney disease. 5-MTP levels decrease with progression of CKD, and in mouse kidneys after unilateral ureteral obstruction (UUO). Treatment with 5-MTP ameliorates renal interstitial fibrosis, inhibits IκB/NF-κB signaling, and enhances Keap1/Nrf2 signaling in mice with UUO or ischemia/reperfusion injury, as well as in cultured human kidney cells. Overexpression of tryptophan hydroxylase-1 (TPH-1), an enzyme involved in 5-MTP synthesis, reduces renal injury by attenuating renal inflammation and fibrosis, whereas TPH-1 deficiency exacerbates renal injury and fibrosis by activating NF-κB and inhibiting Nrf2 pathways. Together, our results suggest that TPH-1 may serve as a target in the treatment of CKD. Accurate monitoring of chronic kidney disease (CKD) progression is essential for efficient disease management. Here Chen et al. identify five serum metabolites in patients with stage 1–5 CKD whose levels associate with disease progression, and find that 5-methoxytryptophan and its regulatory enzyme TPH-1 exert anti-fibrotic effects in mouse models of kidney injury.

Heat maps and clustering are used frequently in expression analysis studies for data visualization and quality control. Simple clustering and heat maps can be produced from the “heatmap” function in R. However, the “heatmap” function lacks certain functionalities and customizability, preventing it from generating advanced heat maps and dendrograms. To tackle the limitations of the “heatmap” function, we have developed an R package “heatmap3” which significantly improves the original “heatmap” function by adding several more powerful and convenient features. The “heatmap3” package allows users to produce highly customizable state of the art heat maps and dendrograms. The “heatmap3” package is developed based on the “heatmap” function in R, and it is completely compatible with it. The new features of “heatmap3” include highly customizable legends and side annotation, a wider range of color selections, new labeling features which allow users to define multiple layers of phenotype variables, and automatically conducted association tests based on the phenotypes provided. Additional features such as different agglomeration methods for estimating distance between two samples are also added for clustering.

The sustainable utilization of solid waste is crucial for environmental protection. This work investigates the fabrication of foamed ceramics from Cr slag and municipal solid waste incineration (MSWI) fly ash, focusing on the effects of three inhibitors—NH2SO3H, ZnO·TiO2, and (NH4)2HPO4—on material properties and Cr leaching behavior. Experimental analysis, chemical thermodynamic calculations, and material characterization were all employed. Results show that the prepared foamed ceramics meet the JG/T 511-2017 standard for building materials, exhibiting excellent physical properties but significant Cr leaching. Among the inhibitors, (NH4)2HPO4 with a molar ratio of n(P)/n(Cr) = 1 shows the best performance, achieving a bulk density of 205 kg/m3, compressive strength of 0.850 MPa, Cr leaching concentration of 188 μg/L, and a 70.0% of Cr leaching inhibition rate. The improvement is attributed to the AlPO4 formation that enhancing the strength, and Ca2P2O7 that stabilizing Cr during sintering. This work provides a feasible method for the safe resource utilization of Cr-containing waste.

Solar-to-H 2 O 2 photocatalytic conversion has attracted increasing attention since H 2 O 2 is a vital oxidizing reagent and the solar energy is inexhaustible and sustainable. Though it has been widely recognized that the photocatalytic performance for H 2 O 2 generation could be enhanced through P incorporating into g-C 3 N 4 framework, its intrinsic reason is still ambiguous. In the present work, internal electronic field (IEF) was ascertained to be constructed in the framework of P doped g-C 3 N 4 (PDCN), and its intensity could be feasibly adjusted through changing the P doping amount. Particularly, PDCN-10, as the optimum photocatalyst synthesized when the P doping amount was 10%, possessed an IEF intensity of 3.1 times than the pristine g-C 3 N 4 , leading to 10.0-folds higher of H 2 O 2 yield. The present research for the first time discloses the intrinsic reason for the promoted photocatalytic performance for H 2 O 2 generation over P doped g-C 3 N 4 , thereby providing a new insight for the design of photocatalyst with satisfactory performance via constructing IEF. Grapic Abstract

A protocol for processing exome genotyping array data that proceeds by targeting the exome plus rare SNPs and provides a feasible, cheaper alternative to exome sequencing when analyzing data from large genome-wide association studies. With the rise of high-throughput sequencing technology, traditional genotyping arrays are gradually being replaced by sequencing technology. Against this trend, Illumina has introduced an exome genotyping array that provides an alternative approach to sequencing, especially suited to large-scale genome-wide association studies (GWASs). The exome genotyping array targets the exome plus rare single-nucleotide polymorphisms (SNPs), a feature that makes it substantially more challenging to process than previous genotyping arrays that targeted common SNPs. Researchers have struggled to generate a reliable protocol for processing exome genotyping array data. The Vanderbilt Epidemiology Center, in cooperation with Vanderbilt Technologies for Advanced Genomics Analysis and Research Design (VANGARD), has developed a thorough exome chip–processing protocol. The protocol was developed during the processing of several large exome genotyping array-based studies, which included over 60,000 participants combined. The protocol described herein contains detailed clustering techniques and robust quality control procedures, and it can benefit future exome genotyping array–based GWASs.

Background One of the most important and often neglected components of a successful RNA sequencing (RNA-Seq) experiment is sample size estimation. A few negative binomial model-based methods have been developed to estimate sample size based on the parameters of a single gene. However, thousands of genes are quantified and tested for differential expression simultaneously in RNA-Seq experiments. Thus, additional issues should be carefully addressed, including the false discovery rate for multiple statistic tests, widely distributed read counts and dispersions for different genes. Results To solve these issues, we developed a sample size and power estimation method named RnaSeqSampleSize, based on the distributions of gene average read counts and dispersions estimated from real RNA-seq data. Datasets from previous, similar experiments such as the Cancer Genome Atlas (TCGA) can be used as a point of reference. Read counts and their dispersions were estimated from the reference’s distribution; using that information, we estimated and summarized the power and sample size. RnaSeqSampleSize is implemented in R language and can be installed from Bioconductor website. A user friendly web graphic interface is provided at https://cqs.app.vumc.org/shiny/RnaSeqSampleSize/ . Conclusions RnaSeqSampleSize provides a convenient and powerful way for power and sample size estimation for an RNAseq experiment. It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization.

Background The spectrum of nonalcoholic fatty liver disease (NAFLD) describes disease conditions deteriorating from nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH) to cirrhosis (CIR) to hepatocellular carcinoma (HCC). From a molecular and biochemical perspective, our understanding of the etiology of this disease is limited by the broad spectrum of disease presentations, the lack of a thorough understanding of the factors contributing to disease susceptibility, and ethical concerns related to repeat sampling of the liver. To better understand the factors associated with disease progression, we investigated by next-generation RNA sequencing the altered expression of microRNAs (miRNAs) in liver biopsies of class III obese subjects (body mass index ≥40 kg/m 2 ) biopsied at the time of elective bariatric surgery. Methods Clinical characteristics and unbiased RNA expression profiles for 233 miRs, 313 transfer RNAs (tRNAs), and 392 miscellaneous small RNAs (snoRNAs, snRNAs, rRNAs) were compared among 36 liver biopsy specimens stratified by disease severity. Results The abundances of 3 miRNAs that were found to be differentially regulated (miR-301a-3p and miR-34a-5p increased and miR-375 decreased) with disease progression were validated by RT-PCR. No tRNAs or miscellaneous RNAs were found to be associated with disease severity. Similar patterns of increased miR-301a and decreased miR-375 expression were observed in 134 hepatocellular carcinoma (HCC) samples deposited in The Cancer Genome Atlas (TCGA). Conclusions Our analytical results suggest that NAFLD severity is associated with a specific pattern of altered hepatic microRNA expression that may drive the hallmark of this disorder: altered lipid and carbohydrate metabolism. The three identified miRNAs can potentially be used as biomarkers to access the severity of NAFLD. The persistence of this miRNA expression pattern in an external validation cohort of HCC samples suggests that specific microRNA expression patterns may permit and/or sustain NAFLD development to HCC.

Background High throughput sequencing technology enables the both the human genome and transcriptome to be screened at the single nucleotide resolution. Tools have been developed to infer single nucleotide variants (SNVs) from both DNA and RNA sequencing data. To evaluate how much difference can be expected between DNA and RNA sequencing data, and among tissue sources, we designed a study to examine the single nucleotide difference among five sources of high throughput sequencing data generated from the same individual, including exome sequencing from blood, tumor and adjacent normal tissue, and RNAseq from tumor and adjacent normal tissue. Results Through careful quality control and analysis of the SNVs, we found little difference between DNA-DNA pairs (1%–2%). However, between DNA-RNA pairs, SNV differences ranged anywhere from 10% to 20%. Conclusions Only a small portion of these differences can be explained by RNA editing. Instead, the majority of the DNA-RNA differences should be attributed to technical errors from sequencing and post-processing of RNAseq data. Our analysis results suggest that SNV detection using RNAseq is subject to high false positive rates.

Macrophages present a spectrum of phenotypes that mediate both the pathogenesis and resolution of atherosclerotic lesions. Inflammatory macrophage phenotypes are pro-atherogenic, but the stimulatory factors that promote these phenotypes remain incompletely defined. Here we demonstrate that microbial small RNAs (msRNA) are enriched on low-density lipoprotein (LDL) and drive pro-inflammatory macrophage polarization and cytokine secretion via activation of the RNA sensor toll-like receptor 8 (TLR8). Removal of msRNA cargo during LDL re-constitution yields particles that readily promote sterol loading but fail to stimulate inflammatory activation. Competitive antagonism of TLR8 with non-targeting locked nucleic acids was found to prevent native LDL-induced macrophage polarization in vitro, and re-organize lesion macrophage phenotypes in vivo, as determined by single-cell RNA sequencing. Critically, this was associated with reduced disease burden in distinct mouse models of atherosclerosis. These results identify LDL-msRNA as instigators of atherosclerosis-associated inflammation and support alternative functions of LDL beyond cholesterol transport. Allen et al. identify a pathogenic role for microbial small RNAs enriched on low-density lipoprotein to activate TLR8 signalling, thereby promoting polarization of inflammatory macrophages and facilitating the development of atherosclerosis.

Extracellular microRNAs (miRNAs) are a new class of biomarkers for cellular phenotypes and disease, and are bioactive signals within intercellular communication networks. Previously, we reported that miRNAs are secreted from macrophage to high-density lipoproteins (HDL) and delivered to recipient cells to regulate gene expression. Despite the potential importance of HDL-miRNAs, regulation of HDL-miRNA export from cells has not been fully studied. Here, we report that pancreatic islets and beta cells abundantly export miR-375-3p to HDL and this process is inhibited by cellular mechanisms that promote insulin secretion. Small RNA sequencing and PCR approaches were used to quantify beta cell miRNA export to HDL. Strikingly, high glucose conditions were found to inhibit HDL-miR-375-3p export, which was dependent on extracellular calcium. Likewise, stimulation of cAMP was found to repress HDL-miR-375-3p export. Furthermore, we found that beta cell ATP-sensitive potassium channel (K ATP ) channels are required for HDL-miRNA export as chemical inhibition (tolbutamide) and global genetic knockout ( Abcc8 −/− ) approaches inhibited HDL-miR-375-3p export. This process is not likely associated with cholesterol flux, as gain-of-function and loss-of-function studies for cholesterol transporters failed to alter HDL-miR-375-3p export. In conclusion, results support that pancreatic beta cells export miR-375-3p to HDL and this process is inversely regulated to insulin secretion.