m6 A deposition is regulated by PRMT1-mediated arginine methylation of METTL14 in its disordered C-terminal region.

The N6-methyladenosine (m6 A) RNA modification serves crucial functions in RNA metabolism; however, the molecular mechanisms underlying the regulation of m6 A are not well understood. Here, we establish arginine methylation of METTL14, a component of the m6 A methyltransferase complex, as a novel pathway that controls m6 A deposition in mammalian cells. Specifically, protein arginine methyltransferase 1 (PRMT1) interacts with, and methylates the intrinsically disordered C terminus of METTL14, which promotes its interaction with RNA substrates, enhances its RNA methylation activity, and is crucial for its interaction with RNA polymerase II (RNAPII). Mouse embryonic stem cells (mESCs) expressing arginine methylation-deficient METTL14 exhibit significantly reduced global m6 A levels. Transcriptome-wide m6 A analysis identified 1,701 METTL14 arginine methylation-dependent m6 A sites located in 1,290 genes involved in various cellular processes, including stem cell maintenance and DNA repair. These arginine methylation-dependent m6 A sites are associated with enhanced translation of genes essential for the repair of DNA interstrand crosslinks; thus, METTL14 arginine methylation-deficient mESCs are hypersensitive to DNA crosslinking agents. Collectively, these findings reveal important aspects of m6 A regulation and new functions of arginine methylation in RNA metabolism.

Tracking pre-mRNA maturation across subcellular compartments identifies developmental gene regulation through intron retention and nuclear anchoring.

Steps of mRNA maturation are important gene regulatory events that occur in distinct cellular locations. However, transcriptomic analyses often lose information on the subcellular distribution of processed and unprocessed transcripts. We generated extensive RNA-seq data sets to track mRNA maturation across subcellular locations in mouse embryonic stem cells, neuronal progenitor cells, and postmitotic neurons. We find disparate patterns of RNA enrichment between the cytoplasmic, nucleoplasmic, and chromatin fractions, with some genes maintaining more polyadenylated RNA in chromatin than in the cytoplasm. We bioinformatically defined four regulatory groups for intron retention, including complete cotranscriptional splicing, complete intron retention in the cytoplasmic RNA, and two intron groups present in nuclear and chromatin transcripts but fully excised in cytoplasm. We found that introns switch their regulatory group between cell types, including neuronally excised introns repressed by polypyrimidine track binding protein 1 (PTBP1). Transcripts for the neuronal gamma-aminobutyric acid (GABA) B receptor, 1 (Gabbr1) are highly expressed in mESCs but are absent from the cytoplasm. Instead, incompletely spliced Gabbr1 RNA remains sequestered on chromatin, where it is bound by PTBP1, similar to certain long noncoding RNAs. Upon neuronal differentiation, Gabbr1 RNA becomes fully processed and exported for translation. Thus, splicing repression and chromatin anchoring of RNA combine to allow posttranscriptional regulation of Gabbr1 over development. For this and other genes, polyadenylated RNA abundance does not indicate functional gene expression. Our data sets provide a rich resource for analyzing many other aspects of mRNA maturation in subcellular locations and across development.

Intensive Treatment of Organic Wastewater by Three-Dimensional Electrode System within Mn-Loaded Steel Slag as Catalytic Particle Electrodes.

Developing a green, low-carbon, and circular economic system is the key to achieving carbon neutrality. This study investigated the organics removal efficiency in a three-dimensional electrode reactor (3DER) constructed from repurposed industrial solid waste, i.e., Mn-loaded steel slag, as the catalytic particle electrodes (CPE). The CPE, a micron-grade material consisting primarily of transition metals, including Fe and Mn, exhibited excellent electric conductivity, catalytic ability, and recyclability. High rhodamine B (RhB) removal efficiency in the 3DER was observed through a physical modelling experiment. The optimal operating condition was determined through a single-factor experiment in which 5.0 g·L-1 CPE and 3 mM peroxymonosulfate (PMS) were added to a 200 mL solution of 10 mM RhB under a current intensity of 0.5 A and a 1.5 to 2.0 cm distance between the 2D electrodes. When the initial pH value of the simulated solution was 3 to 9, the RhB removal rate exceeded 96% after 20 min reaction. In addition, the main reactive oxidation species in the 3DER were determined. The results illustrated that HO• and SO4•- both existed, but that the contribution of SO4•- to RhB removal was much lower than that of HO• in the 3DER. In summary, this research provides information on the potential of the 3DER for removing refractory organics from water.

Regional Variation of Splicing QTLs in Human Brain.

A major question in human genetics is how sequence variants of broadly expressed genes produce tissue- and cell type-specific molecular phenotypes. Genetic variation of alternative splicing is a prevalent source of transcriptomic and proteomic diversity in human populations. We investigated splicing quantitative trait loci (sQTLs) in 1,209 samples from 13 human brain regions, using RNA sequencing (RNA-seq) and genotype data from the Genotype-Tissue Expression (GTEx) project. Hundreds of sQTLs were identified in each brain region. Some sQTLs were shared across brain regions, whereas others displayed regional specificity. These "regionally ubiquitous" and "regionally specific" sQTLs showed distinct positional distributions of single-nucleotide polymorphisms (SNPs) within and outside essential splice sites, respectively, suggesting their regulation by distinct molecular mechanisms. Integrating the binding motifs and expression patterns of RNA binding proteins with exon splicing profiles, we uncovered likely causal variants underlying brain region-specific sQTLs. Notably, SNP rs17651213 created a putative binding site for the splicing factor RBFOX2 and was associated with increased splicing of MAPT exon 3 in cerebellar tissues, where RBFOX2 was highly expressed. Overall, our study reveals a more comprehensive spectrum and regional variation of sQTLs in human brain and demonstrates that such regional variation can be used to fine map potential causal variants of sQTLs and their associated neurological diseases.

Deep-learning augmented RNA-seq analysis of transcript splicing.

A major limitation of RNA sequencing (RNA-seq) analysis of alternative splicing is its reliance on high sequencing coverage. We report DARTS (https://github.com/Xinglab/DARTS), a computational framework that integrates deep-learning-based predictions with empirical RNA-seq evidence to infer differential alternative splicing between biological samples. DARTS leverages public RNA-seq big data to provide a knowledge base of splicing regulation via deep learning, thereby helping researchers better characterize alternative splicing using RNA-seq datasets even with modest coverage.

Model construction and application for effluent prediction in wastewater treatment plant: Data processing method optimization and process parameters integration.

Wastewater treatment based on the activated sludge process is complex process, which is easily affected by influent quality, aeration time and other factors, leading to unstable effluent. Facing increasingly stringent sewage discharge standards in China, it is necessary to build a prediction model for early warning of effluent quality. In this study, nine machine learning algorithms were adopted to construct models for the prediction of effluent Chemical Oxygen Demand (COD). In order to improve the prediction accuracy of the models, model optimization was conducted by introducing the hysteresis condition [Hydraulic Retention Time (HRT) of 18 h], data processing method (K-FOLD) and process parameters [dissolved oxygen (DO), sludge return ratio (SRR) and mixed liquid suspended solids (MLSS)]. Results showed that both K-Nearest Neighbour (KNN) and Gradient Boosting Decision Tree (GBDT) displayed excellent prediction effects, the best results of MAPE, RMSE and R2 were 7.34%/1.29/0.92(COD, KNN). The optimized models were further applied to the prediction of effluent total phosphorus (TP), total nitrogen (TN) and pH. The MAPE/RMSE/R2 were 7.43%/0.92/0.93(TN, GBDT), 17.81%/0.19/0.99(TP, KNN), 0.53%/0.16/0.99 (pH, KNN) respectively, indicating high prediction accuracy. The change and comparison of modeling conditions provide a new insight to wastewater prediction models. In addition, this study is close to the actual application scenario of WWTPs operation and management, also laying a foundation for the reverse regulation of energy saving and consumption reduction of wastewater treatment plants (WWTPs).

The Expanding Landscape of Alternative Splicing Variation in Human Populations.

Alternative splicing is a tightly regulated biological process by which the number of gene products for any given gene can be greatly expanded. Genomic variants in splicing regulatory sequences can disrupt splicing and cause disease. Recent developments in sequencing technologies and computational biology have allowed researchers to investigate alternative splicing at an unprecedented scale and resolution. Population-scale transcriptome studies have revealed many naturally occurring genetic variants that modulate alternative splicing and consequently influence phenotypic variability and disease susceptibility in human populations. Innovations in experimental and computational tools such as massively parallel reporter assays and deep learning have enabled the rapid screening of genomic variants for their causal impacts on splicing. In this review, we describe technological advances that have greatly increased the speed and scale at which discoveries are made about the genetic variation of alternative splicing. We summarize major findings from population transcriptomic studies of alternative splicing and discuss the implications of these findings for human genetics and medicine.

Downregulation of Cypher induces apoptosis in cardiomyocytes via Akt/p38 MAPK signaling pathway.

Background: Dilated cardiomyopathy (DCM) is considered as the most common form of non-ischemic cardiomyopathy with a high mortality worldwide. Cytoskeleton protein Cypher plays an important role in maintaining cardiac function. Genetic studies in human and animal models revealed that Cypher is involved in the development of DCM. However, the underlying molecular mechanism is not fully understood. Accumulating evidences suggest that apoptosis in myocytes may contribute to DCM. Thus, the purpose of this study is to define whether lack of Cypher in cardiomyocytes can elevate apoptosis signaling and lead to DCM eventually. Methods and Results: Cypher-siRNA sufficiently inhibited Cypher expression in cardiomyocytes. TUNEL-positive cardiomyocytes were increased in both Cypher knockdown neonatal rat cardiomyocytes and Cypher knockout mice hearts, which were rare in the control group. Flow cytometry further confirmed that downregulation of Cypher significantly increased myocytes apoptosis in vitro. Cell counting kit-8 assay revealed that Cypher knockdown in H9c2 cells significantly reduced cell viability. Cypher knockdown was found to increase cleaved caspase-3 expression and suppress p21, ratio of bcl-2 to Bax. Cypher-deficiency induced apoptosis was linked to downregulation of Akt activation and elevated p-p38 MAPK accumulation. Pharmacological activation of Akt with SC79 attenuated apoptosis with enhanced phosphorylation of Akt and reduced p-p38 MAPK and Bax expression. Conclusions: Downregulation of Cypher participates in the promotion of cardiomyocytes apoptosis through inhibiting Akt dependent pathway and enhancing p38 MAPK phosphorylation. These findings may provide a new potential therapeutic strategy for the treatment of DCM.

[Research advance of ANRIL on atherosclerosis by regulating cell proliferation and apoptosis].

Atherosclerosis is an important pathological basis for coronary artery disease. ANRIL is an antisense non-coding RNA located in Chr9p21 locus, which was identified as the most significant risk locus associated with atherosclerosis. ANRIL can produce multiple transcripts including linear and circular transcripts after various transcript splicing. It has been illustrated that ANRIL plays important roles in the pathology of atherosclerosis by regulating the proliferation and apoptosis of vascular cells. Linear ANRIL can regulate the proliferation of vascular smooth muscle cells (VSMCs) in plaques by chromatin modification, as well as influence the proliferation and the apoptosis of macrophages in post transcription; circular ANRIL can affect the proliferation and apoptosis of VSMCs by chromatin modification as well as interfering with rRNA maturation. In this review, we describe the ANRIL evolution, different transcripts characteristics, and their roles in the proliferation and apoptosis of vascular cells to participate in the process of atherosclerosis, for further understanding the pathogenesis of atherosclerosis and finding potential targets for diagnosis and treatment of atherosclerosis.

Novel polymorphisms in PDLIM3 and PDLIM5 gene encoding Z-line proteins increase risk of idiopathic dilated cardiomyopathy.

Idiopathic dilated cardiomyopathy (IDCM), characterized by ventricular dilation and impaired systolic function, is a primary cardiomyopathy resulting in heart failure. During heart contraction, the Z-line is responsible for transmitting force between sarcomeres and is also a hot spot for muscle cell signalling. Mutations in Z-line proteins have been linked to cardiomyopathies in both humans and mice. Actinin-associated LIM protein (ALP) and enigma homolog protein (ENH), encoded by PDLIM3 and PDLIM5, are components of the muscle cytoskeleton and localize to the Z-line. A PDLIM3 or PDLIM5 deficiency in mice leads to dilated cardiomyopathy. Since PDLIM3 and PDLIM5 are candidate IDCM susceptibility genes, the current study aims to investigate whether polymorphisms within PDLIM3 and PDLIM5 could be correlated with IDCM. We designed a case-control study, and exons of the PDLIM3 and PDLIM5 were amplified by polymerase chain reactions in 111 IDCM patients and 137 healthy controls. We found that five synonymous polymorphisms had statistical distribution differences between IDCM patients and controls, including rs4861669, rs4862543, c.731 + 131 T > G, c.1789-3 C > T and rs7690296, according to genotype and allele distribution. Haplotype G-C-C-C and A-T-C-T (rs2306705, rs10866276, rs12644280 and rs4635850 synthesized) were regarded as risk factors for IDCM patients when compared with carriers of other haplotypes (all P < .05). Furthermore, IDCM patients with two novel polymorphisms (c.731 + 131 T > G and c.1789-3 C > T) had lower systolic blood pressure. In conclusion, these five synonymous polymorphisms might constitute a genetic background that increases the risk of the development of IDCM in the Chinese Han population.

Robust and rapid algorithms facilitate large-scale whole genome sequencing downstream analysis in an integrative framework.

Whole genome sequencing (WGS) is a promising strategy to unravel variants or genes responsible for human diseases and traits. However, there is a lack of robust platforms for a comprehensive downstream analysis. In the present study, we first proposed three novel algorithms, sequence gap-filled gene feature annotation, bit-block encoded genotypes and sectional fast access to text lines to address three fundamental problems. The three algorithms then formed the infrastructure of a robust parallel computing framework, KGGSeq, for integrating downstream analysis functions for whole genome sequencing data. KGGSeq has been equipped with a comprehensive set of analysis functions for quality control, filtration, annotation, pathogenic prediction and statistical tests. In the tests with whole genome sequencing data from 1000 Genomes Project, KGGSeq annotated several thousand more reliable non-synonymous variants than other widely used tools (e.g. ANNOVAR and SNPEff). It took only around half an hour on a small server with 10 CPUs to access genotypes of ∼60 million variants of 2504 subjects, while a popular alternative tool required around one day. KGGSeq's bit-block genotype format used 1.5% or less space to flexibly represent phased or unphased genotypes with multiple alleles and achieved a speed of over 1000 times faster to calculate genotypic correlation.

MiR-27b Promotes Muscle Development by Inhibiting MDFI Expression.

BACKGROUND/AIMS: Skeletal muscle plays an essential role in the body movement. However, injuries to the skeletal muscle are common. Lifelong maintenance of skeletal muscle function largely depends on preserving the regenerative capacity of muscle. Muscle satellite cells proliferation, differentiation, and myoblast fusion play an important role in muscle regeneration after injury. Therefore, understanding of the mechanisms associated with muscle development during muscle regeneration is essential for devising the alternative treatments for muscle injury in the future. METHODS: Edu staining, qRT-PCR and western blot were used to evaluate the miR-27b effects on pig muscle satellite cells (PSCs) proliferation and differentiation in vitro. Then, we used bioinformatics analysis and dual-luciferase reporter assay to predict and confirm the miR-27b target gene. Finally, we elucidate the target gene function on muscle development in vitro and in vivo through Edu staining, qRT-PCR, western blot, H&E staining and morphological observation. RESULT: miR-27b inhibits PSCs proliferation and promotes PSCs differentiation. And the miR-27b target gene, MDFI, promotes PSCs proliferation and inhibits PSCs differentiation in vitro. Furthermore, interfering MDFI expression promotes mice muscle regeneration after injury. CONCLUSION: our results conclude that miR-27b promotes PSCs myogenesis by targeting MDFI. These results expand our understanding of muscle development mechanism in which miRNAs and genes work collaboratively in regulating skeletal muscle development. Furthermore, this finding has implications for obtaining the alternative treatments for patients with the muscle injury.

AMPK/NF-κB signaling pathway regulated by ghrelin participates in the regulation of HUVEC and THP1 Inflammation.

Endothelial inflammation and monocyte plays an essential role in the initiation and progression of atherosclerosis. Ghrelin is beneficial for atherosclerosis progression. However, the detailed and precise molecular mechanisms of how ghrelin regulates endothelial inflammation are not clear. In this study, we investigated the regulation mechanism of ghrelin on TNF-α-activated endothelial inflammation and monocyte adhesion. It was found that TNF-α-induced monocyte adhesion on HUVEC was significantly attenuated by ghrelin. Furthermore, we found that ghrelin effectively suppressed TNF-α-induced inflammatory factors' (including ICAM-1, VCAM-1, MCP-1, and IL-1ß) expression through inhibiting AMPK phosphorylation and p65 expression both in HUVEC and THP-1. This phenomenon was further demonstrated by using AMPK agonist AICAR and inhibitor compound C, respectively. Our findings suggest that ghrelin may mediate TNF-α-induced endothelial inflammation and monocyte adhesion, in part via AMPK/NF-κB signaling pathway. These novel anti-inflammatory and immunoregulatory actions of ghrelin may play a certain role in understanding the formation and development of atherosclerosis.

Inheritance-mode specific pathogenicity prioritization (ISPP) for human protein coding genes.

MOTIVATION: Exome sequencing studies have facilitated the detection of causal genetic variants in yet-unsolved Mendelian diseases. However, the identification of disease causal genes among a list of candidates in an exome sequencing study is still not fully settled, and it is often difficult to prioritize candidate genes for follow-up studies. The inheritance mode provides crucial information for understanding Mendelian diseases, but none of the existing gene prioritization tools fully utilize this information. RESULTS: We examined the characteristics of Mendelian disease genes under different inheritance modes. The results suggest that Mendelian disease genes with autosomal dominant (AD) inheritance mode are more haploinsufficiency and de novo mutation sensitive, whereas those autosomal recessive (AR) genes have significantly more non-synonymous variants and regulatory transcript isoforms. In addition, the X-linked (XL) Mendelian disease genes have fewer non-synonymous and synonymous variants. As a result, we derived a new scoring system for prioritizing candidate genes for Mendelian diseases according to the inheritance mode. Our scoring system assigned to each annotated protein-coding gene (N = 18 859) three pathogenic scores according to the inheritance mode (AD, AR and XL). This inheritance mode-specific framework achieved higher accuracy (area under curve = 0.84) in XL mode. CONCLUSION: The inheritance-mode specific pathogenicity prioritization (ISPP) outperformed other well-known methods including Haploinsufficiency, Recessive, Network centrality, Genic Intolerance, Gene Damage Index and Gene Constraint scores. This systematic study suggests that genes manifesting disease inheritance modes tend to have unique characteristics. AVAILABILITY AND IMPLEMENTATION: ISPP is included in KGGSeq v1.0 (http://grass.cgs.hku.hk/limx/kggseq/), and source code is available from (https://github.com/jacobhsu35/ISPP.git). CONTACT: mxli@hku.hkSupplementary information: Supplementary data are available at Bioinformatics online.

Predicting regulatory variants with composite statistic.

MOTIVATION: Prediction and prioritization of human non-coding regulatory variants is critical for understanding the regulatory mechanisms of disease pathogenesis and promoting personalized medicine. Existing tools utilize functional genomics data and evolutionary information to evaluate the pathogenicity or regulatory functions of non-coding variants. However, different algorithms lead to inconsistent and even conflicting predictions. Combining multiple methods may increase accuracy in regulatory variant prediction. RESULTS: Here, we compiled an integrative resource for predictions from eight different tools on functional annotation of non-coding variants. We further developed a composite strategy to integrate multiple predictions and computed the composite likelihood of a given variant being regulatory variant. Benchmarked by multiple independent causal variants datasets, we demonstrated that our composite model significantly improves the prediction performance. AVAILABILITY AND IMPLEMENTATION: We implemented our model and scoring procedure as a tool, named PRVCS, which is freely available to academic and non-profit usage at http://jjwanglab.org/PRVCS CONTACT: wang.junwen@mayo.edu, jliu@stat.harvard.edu, or limx54@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MiCroKiTS 4.0: a database of midbody, centrosome, kinetochore, telomere and spindle.

We reported an updated database of MiCroKiTS 4.0 (http://microkit.biocuckoo.org) for proteins temporally and spatially localized in distinct subcellular positions including midbody, centrosome, kinetochore, telomere and mitotic spindle during cell division/mitosis. The database was updated from our previously developed database of MiCroKit 3.0, which contained 1489 proteins mostly forming super-complexes at midbody, centrosome and kinetochore from seven eukaryotes. Since the telomere and spindle apparatus are critical for cell division, the proteins localized at the two positions were also integrated. From the scientific literature, we curated 1872 experimentally identified proteins which at least locate in one of the five positions from eight species. Then the ortholog detection was performed to identify potential MiCroKiTS proteins from 144 eukaryotic organisms, which contains 66, 45 and 33 species of animals, fungi and plants, respectively. In total, 87,983 unique proteins with corresponding localization information were integrated into the database. The primary references of experimentally identified localizations were provided and the fluorescence microscope figures for the localizations of human proteins were shown. The orthologous relations between predicted and experimental localizations were also present. Taken together, we anticipate the database can serve as a useful resource for further analyzing the molecular mechanisms during cell division.

Multifunctional Mixed Micelles Cross-Assembled from Various Polyurethanes for Tumor Therapy.

A challenge in the development of multifunctional drug delivery systems is to establish a reasonable and effective synthetic route for multifunctional polymer preparation. Herein, we propose a unique protocol to prepare multifunctional micelles by a cross-assembly process using three different functional polyurethanes incorporating acidic sensitive hydrazone, folic acid for active targeting, and gemini quaternary ammonium (GQA) as efficient cell uptake ligands, respectively. These multifunctional mixed micelles (GFHPMs) have been endowed tunable particle sizes and zeta potential and a unique three-order-layer cross-assemble structure. Their drug-loading contents have been significantly improved, and drug release profiles displayed controlled release of their payloads under acid condition. The folate and GQA ligands showed a synergistic effect to enhance the cell uptake. Biodistribution and antitumor effect of these micelles were systematically investigated in vivo, the mixed micelles could penetrate into the depths of tumors, and drug concentrations in tumors reached the maximum of 6.5% ID/g at 24 h, resulting in an excellent therapeutic effect that the volumes of tumors treated with GFHPM are five times smaller than those treated with blank micelles. Our present work provides an effective approach to the design of multifunctional nanocarriers for tumor-targeted and programmed intracellular drug delivery.

A new method for classifying different phenotypes of kidney transplantation.

For end-stage renal diseases, kidney transplantation is the most efficient treatment. However, the unexpected rejection caused by inflammation usually leads to allograft failure. Thus, a systems-level characterization of inflammation factors can provide potentially diagnostic biomarkers for predicting renal allograft rejection. Serum of kidney transplant patients with different immune status were collected and classified as transplant patients with stable renal function (ST), impaired renal function with negative biopsy pathology (UNST), acute rejection (AR), and chronic rejection (CR). The expression profiles of 40 inflammatory proteins were measured by quantitative protein microarrays and reduced to a lower dimensional space by the partial least squares (PLS) model. The determined principal components (PCs) were then trained by the support vector machines (SVMs) algorithm for classifying different phenotypes of kidney transplantation. There were 30, 16, and 13 inflammation proteins that showed statistically significant differences between CR and ST, CR and AR, and CR and UNST patients. Further analysis revealed a protein-protein interaction (PPI) network among 33 inflammatory proteins and proposed a potential role of intracellular adhesion molecule-1 (ICAM-1) in CR. Based on the network analysis and protein expression information, two PCs were determined as the major contributors and trained by the PLS-SVMs method, with a promising accuracy of 77.5 % for classification of chronic rejection after kidney transplantation. For convenience, we also developed software packages of GPS-CKT (Classification phenotype of Kidney Transplantation Predictor) for classifying phenotypes. By confirming a strong correlation between inflammation and kidney transplantation, our results suggested that the network biomarker but not single factors can potentially classify different phenotypes in kidney transplantation.