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Long-Read Single-Cell Sequencing Using scCOLOR-seq.
Single-cell sequencing allows for the measurement of sequence information from individual cells with next-generation sequencing (NGS). However, its application to third-generation sequencing platforms such as Oxford Nanopore has been challenging because of its lower basecalling accuracy. Here we describe the method to perform highly accurate single-cell COrrected Long-Read sequencing (scCOLOR-seq) by droplet-based encapsulation of cells and sequencing using the Oxford Nanopore Sequencing system.
Complex Age- and Cancer-Related Changes in Human Blood Transcriptome-Implications for Pan-Cancer Diagnostics.
Early cancer detection is the key to a positive clinical outcome. While a number of early diagnostics methods exist in clinics today, they tend to be invasive and limited to a few cancer types. Thus, a clear need exists for non-invasive diagnostics methods that can be used to detect the presence of cancer of any type. Liquid biopsy based on analysis of molecular components of peripheral blood has shown significant promise in such pan-cancer diagnostics; however, existing methods based on this approach require improvements, especially in sensitivity of early-stage cancer detection. The improvement would likely require diagnostics assays based on multiple different types of biomarkers and, thus, calls for identification of novel types of cancer-related biomarkers that can be used in liquid biopsy. Whole-blood transcriptome, especially its non-coding component, represents an obvious yet under-explored biomarker for pan-cancer detection. In this study, we show that whole transcriptome analysis using RNA-seq could indeed serve as a viable biomarker for pan-cancer detection. Furthermore, a class of long non-coding (lnc) RNAs, very long intergenic non-coding (vlinc) RNAs, demonstrated superior performance compared with protein-coding mRNAs. Finally, we show that age and presence of non-blood cancers change transcriptome in similar, yet not identical, directions and explore implications of this observation for pan-cancer diagnostics.
Oral nanotherapeutics based on Antheraea pernyi silk fibroin for synergistic treatment of ulcerative colitis.
Ulcerative colitis (UC) with its rapidly increasing incidence has become an emerging challenge for public health. Therapeutic agents are required to be specifically delivered to colon epithelial cells and macrophages with controlled release in the cytoplasm for wound healing, inflammation alleviation, and microbiota rebalance. As a promising biomaterial for accomplishing this, Antheraea pernyi silk fibroin (ApSF) was selected and engineered to form nanoparticles (NPs) loaded with the anti-inflammatory drug, resveratrol (Res). The intrinsic features of these fabricated Res-ApNPs included targeting of colonic epithelial cells and macrophages, lysosomal escape capacity, and responsiveness to pH, reactive oxygen species (ROS), and glutathione, which were pertinent to their functional units such as arginine-glycine-aspartate tripeptides, α-helixes, β-sheets, and disulfide bonds, enabling on-demand release of Res molecules in the cytoplasm of target cells. The Res-ApNP treatment restored damaged colonic epithelial barriers, polarized macrophages to type M2, alleviated inflammatory reactions, and reduced the level of intracellular ROS. Oral treatment with chitosan-alginate hydrogels embedded with Res-ApNPs substantially relieved UC symptoms, as evidenced by decreased colonic inflammation, increased synthesis of tight-junction proteins, and rebalanced intestinal microbiota. Our findings suggest that these high-performance ApSF-based NPs can be developed as effective drug carriers for oral UC treatment.
PSRR: A Web Server for Predicting the Regulation of miRNAs Expression by Small Molecules.
Background: MicroRNAs (miRNAs) play key roles in a variety of pathological processes by interacting with their specific target mRNAs for translation repression and may function as oncogenes (oncomiRs) or tumor suppressors (TSmiRs). Therefore, a web server that could predict the regulation relations between miRNAs and small molecules is expected to achieve implications for identifying potential therapeutic targets for anti-tumor drug development. Methods: Upon obtaining positive/known small molecule-miRNA regulation pairs from SM2miR, we generated a multitude of high-quality negative/unknown pairs by leveraging similarities between the small molecule structures. Using the pool of the positive and negative pairs, we created the Dataset1 and Dataset2 datasets specific to up-regulation and down-regulation pairs, respectively. Manifold machine learning algorithms were then employed to construct models of predicting up-regulation and down-regulation pairs on the training portion of pairs in Dataset1 and Dataset2, respectively. Prediction abilities of the resulting models were further examined by discovering potential small molecules to regulate oncogenic miRNAs identified from miRNA sequencing data of endometrial carcinoma samples. Results: The random forest algorithm outperformed four machine-learning algorithms by achieving the highest AUC values of 0.911 for the up-regulation model and 0.896 for the down-regulation model on the testing datasets. Moreover, the down-regulation and up-regulation models yielded the accuracy values of 0.91 and 0.90 on independent validation pairs, respectively. In a case study, our model showed highly-reliable results by confirming all top 10 predicted regulation pairs as experimentally validated pairs. Finally, our predicted binding affinities of oncogenic miRNAs and small molecules bore a close resemblance to the lowest binding energy profiles using molecular docking. Predictions of the final model are freely accessible through the PSRR web server at https://rnadrug.shinyapps.io/PSRR/. Conclusion: Our study provides a novel web server that could effectively predict the regulation of miRNAs expression by small molecules.
Near-Infrared-Enpowered Nanomotor-Mediated Targeted Chemotherapy and Mitochondrial Phototherapy to Boost Systematic Antitumor Immunity.
Phototherapy is an important strategy to inhibit tumor growth and activate antitumor immunity. However, the effect of photothermal/photodynamic therapy (PTT/PDT) is restricted by limited tumor penetration depth and unsatisfactory potentiation of antitumor immunity. Here, a near-infrared (NIR)-driven nanomotor is constructed with a mesoporous silicon nanoparticle (MSN) as the core, end-capped with Antheraea pernyi silk fibroin (ApSF) comprising arginine-glycine-aspartate (RGD) tripeptides. Upon NIR irradiation, the resulting ApSF-coated MSNs (DIMs) loading with photosensitizers (ICG derivatives, IDs) and chemotherapeutic drugs (doxorubicin, Dox) can efficiently penetrate into the internal tumor tissues and achieve effective phototherapy. Combined with chemotherapy, a triple-modal treatment (PTT, PDT, and chemotherapy) approach is developed to induce the immunogenic cell death of tumor cells and to accelerate the release of damage-associated molecular patterns. In vivo results suggest that DIMs can promote the maturation of dendritic cells and surge the number of infiltrated immune cells. Meanwhile, DIMs can polarize macrophages from M2 to M1 phenotypes and reduce the percentages of immunosuppressive Tregs, which reverse the immunosuppressive tumor microenvironment and activate systemic antitumor immunity. By achieving synergistic effects on the tumor inhibition and the antitumor immunity activation, DIMs show great promise as new nanoplatforms to treat metastatic breast cancer.
DeepdlncUD: Predicting regulation types of small molecule inhibitors on modulating lncRNA expression by deep learning.
Targeting lncRNAs by small molecules (SM-lncR) to alter their expression levels has emerged as an important therapeutic modality for disease treatment. To date, no computational tools have been dedicated to predicting small molecule-mediated upregulation or downregulation of lncRNA expression. Here, we introduce DeepdlncUD, which integrates predictions of nine deep learning algorithms together, to infer the regulation types of small molecules on modulating lncRNA expression. Through systematic optimization on a training set of 771 upregulation and 739 downregulation SM-lncR pairs, each encoding 1369 sequence, representational, and physiochemical features, this method outperforms a recently released program, DeepsmirUD, by achieving 0.674 in AUC (area under the receiver operating characteristic curve), 0.722 in AUCPR (area under the precision-recall curve), 0.681 in F1-score, and 0.516 in Jaccard Index on a test set of 222 SM-lncR pairs. By extracting 125 upregulation and 46 downregulation SM-lncR pairs that involve disease-associated lncRNAs, DeepdlncUD is shown to gain an accuracy of 0.700 in the pathological context. Using connectivity scores, around half of the small molecules are correctly estimated as drugs to treat lncRNA-regulated diseases. This tool can be run at a fast speed to assist the discovery of potential small molecule drugs of lncRNA targets on a large scale. DeepdlncUD is publicly available at https://github.com/2003100127/deepdlncud.
Improved sequence-based prediction of interaction sites in α-helical transmembrane proteins by deep learning.
Interactions between transmembrane (TM) proteins are fundamental for a wide spectrum of cellular functions, but precise molecular details of these interactions remain largely unknown due to the scarcity of experimentally determined three-dimensional complex structures. Computational techniques are therefore required for a large-scale annotation of interaction sites in TM proteins. Here, we present a novel deep-learning approach, DeepTMInter, for sequence-based prediction of interaction sites in α-helical TM proteins based on their topological, physiochemical, and evolutionary properties. Using a combination of ultra-deep residual neural networks with a stacked generalization ensemble technique DeepTMInter significantly outperforms existing methods, achieving the AUC/AUCPR values of 0.689/0.598. Across the main functional families of human transmembrane proteins, the percentage of amino acid sites predicted to be involved in interactions typically ranges between 10% and 25%, and up to 30% in ion channels. DeepTMInter is available as a standalone package at https://github.com/2003100127/deeptminter. The training and benchmarking datasets are available at https://data.mendeley.com/datasets/2t8kgwzp35.
E-Index for Differentiating Complex Dynamic Traits.
While it is a daunting challenge in current biology to understand how the underlying network of genes regulates complex dynamic traits, functional mapping, a tool for mapping quantitative trait loci (QTLs) and single nucleotide polymorphisms (SNPs), has been applied in a variety of cases to tackle this challenge. Though useful and powerful, functional mapping performs well only when one or more model parameters are clearly responsible for the developmental trajectory, typically being a logistic curve. Moreover, it does not work when the curves are more complex than that, especially when they are not monotonic. To overcome this inadaptability, we therefore propose a mathematical-biological concept and measurement, E-index (earliness-index), which cumulatively measures the earliness degree to which a variable (or a dynamic trait) increases or decreases its value. Theoretical proofs and simulation studies show that E-index is more general than functional mapping and can be applied to any complex dynamic traits, including those with logistic curves and those with nonmonotonic curves. Meanwhile, E-index vector is proposed as well to capture more subtle differences of developmental patterns.
DeepHelicon: Accurate prediction of inter-helical residue contacts in transmembrane proteins by residual neural networks.
Accurate prediction of amino acid residue contacts is an important prerequisite for generating high-quality 3D models of transmembrane (TM) proteins. While a large number of compositional, evolutionary, and structural properties of proteins can be used to train contact prediction methods, recent research suggests that coevolution between residues provides the strongest indication of their spatial proximity. We have developed a deep learning approach, DeepHelicon, to predict inter-helical residue contacts in TM proteins by considering only coevolutionary features. DeepHelicon comprises a two-stage supervised learning process by residual neural networks for a gradual refinement of contact maps, followed by variance reduction by an ensemble of models. We present a benchmark study of 12 contact predictors and conclude that DeepHelicon together with the two other state-of-the-art methods DeepMetaPSICOV and Membrain2 outperforms the 10 remaining algorithms on all datasets and at all settings. On a set of 44 TM proteins with an average length of 388 residues DeepHelicon achieves the best performance among all benchmarked methods in predicting the top L/5 and L/2 inter-helical contacts, with the mean precision of 87.42% and 77.84%, respectively. On a set of 57 relatively small TM proteins with an average length of 298 residues DeepHelicon ranks second best after DeepMetaPSICOV. DeepHelicon produces the most accurate predictions for large proteins with more than 10 transmembrane helices. Coevolutionary features alone allow to predict inter-helical residue contacts with an accuracy sufficient for generating acceptable 3D models for up to 30% of proteins using a fully automated modeling method such as CONFOLD2.
Missense Variant of Endoplasmic Reticulum Region of WFS1 Gene Causes Autosomal Dominant Hearing Loss without Syndromic Phenotype.
OBJECTIVE: Genetic variants in the WFS1 gene can cause Wolfram syndrome (WS) or autosomal dominant nonsyndromic low-frequency hearing loss (HL). This study is aimed at investigating the molecular basis of HL in an affected Chinese family and the genotype-phenotype correlation of WFS1 variants. METHODS: The clinical phenotype of the five-generation Chinese family was characterized using audiological examinations and pedigree analysis. Target exome sequencing of 129 known deafness genes and bioinformatics analysis were performed among six patients and four normal subjects to screen suspected pathogenic variants. We built a complete WFS1 protein model to assess the potential effects of the variant on protein structure. RESULTS: A novel heterozygous pathogenic variant NM_006005.3 c.2020G>T (p.Gly674Trp) was identified in the WFS1 gene, located in the C-terminal domain of the wolframin protein. We further showed that HL-related WFS1 missense variants were mainly concentrated in the endoplasmic reticulum (ER) domain. In contrast, WS-related missense variants are randomly distributed throughout the protein. CONCLUSIONS: In this family, we identified a novel variant p.Gly674Trp of WFS1 as the primary pathogenic variant causing the low-frequency sensorineural HL, enriching the mutational spectrum of the WFS1 gene.
Favipiravir for COVID-19 in adults in the community in PRINCIPLE, an open-label, randomised, controlled, adaptive platform trial of short- and longer-term outcomes.
BACKGROUND: Evidence for the effect of favipiravir treatment of acute COVID-19 on recovery, hospital admissions and longer-term outcomes in community settings is limited. METHODS: In this multicentre. open-label, multi-arm, adaptive platform randomised controlled trial participants aged ≥18 years in the community with a positive test for SARS-CoV-2 and symptoms lasting ≤14 days were randomised to: usual care; usual care plus favipiravir tablets (loading dose of 3600mg in divided doses on day one, then 800mg twice a day for four days); or, usual care plus other interventions. Co-primary endpoints were time to first self-reported recovery and hospitalisation/death related to COVID-19, within 28 days, analysed using Bayesian models. Recovery at six months was the primary longer-term outcome. TRIAL REGISTRATION: ISRCTN86534580. FINDINGS: The primary analysis model included 8811 SARS-CoV-2 positive mostly COVID vaccinated participants, randomised to favipiravir (n=1829), usual care (n=3256), and other treatments (n=3726). Time to self-reported recovery was shorter in the favipiravir group than usual care (estimated hazard ratio 1·23 [95% credible interval 1·14 to 1·33]), a reduction of 2·98 days [1·99 to 3·94] from 16 days in median time to self-reported recovery for favipiravir versus usual care alone. COVID-19 related hospitalisations/deaths were similar (estimated odds ratio 0·99 [0·61 to 1·61]; estimated difference 0% [-0·9% to 0·6%]). 14 serious adverse events occurred in the favipiravir group and 4 in usual care. By six months, the proportion feeling fully recovered was 74·9% for favipiravir versus 71·3% for usual care (RR = 1·05, [1·02 to 1·08]). INTERPRETATION: In this open-label trial in a largely vaccinated population with COVID-19 in the community, favipiravir did not reduce hospital admissions, but shortened time to recovery and had a marginal positive impact on long term outcomes.
Deep learning models to automate the scoring of hand radiographs for rheumatoid arthritis
The van der Heijde modification of the Sharp (SvdH) score is a widely used radiographic scoring method to quantify damage in Rheumatoid Arthritis (RA) in clinical trials. However, its complexity with a necessity to score each individual joint, and the expertise required limit its application in clinical practice, especially in disease progression measurement. In this work, we addressed this limitation by developing a bespoke, automated pipeline that is capable of predicting the SvdH score and RA severity from hand radiographs without the need to localise the joints first. Using hand radiographs from RA and suspected RA patients, we first investigated the performance of the state-of-the-art architectures in predicting the total SvdH score for hands and wrists and its corresponding severity class. Secondly, we leveraged publicly available data sets to perform transfer learning with different finetuning schemes and ensemble learning, which resulted in substantial improvement in model performance being on par with an experienced human reader. The best model for RA scoring achieved a Pearson’s correlation coefficient (PCC) of 0.925 and root mean squared error (RMSE) of 18.02, while the best model for RA severity classification achieved an accuracy of 0.358 and PCC of 0.859. Our score prediction model attained almost comparable accuracy with experienced radiologists (PCC = 0.97, RMSE = 18.75). Finally, using Grad-CAM, we showed that our models could focus on the anatomical structures in hands and wrists which clinicians deemed as relevant to RA progression in the majority of cases.