Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm.

Tumor heterogeneity presents a challenge for inferring clonal evolution and driver gene identification. Here, we describe a method for analyzing the cancer genome at a single-cell nucleotide level. To perform our analyses, we first devised and validated a high-throughput whole-genome single-cell sequencing method using two lymphoblastoid cell line single cells. We then carried out whole-exome single-cell sequencing of 90 cells from a JAK2-negative myeloproliferative neoplasm patient. The sequencing data from 58 cells passed our quality control criteria, and these data indicated that this neoplasm represented a monoclonal evolution. We further identified essential thrombocythemia (ET)-related candidate mutations such as SESN2 and NTRK1, which may be involved in neoplasm progression. This pilot study allowed the initial characterization of the disease-related genetic architecture at the single-cell nucleotide level. Further, we established a single-cell sequencing method that opens the way for detailed analyses of a variety of tumor types, including those with high genetic complex between patients.

Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor.

Clear cell renal cell carcinoma (ccRCC) is the most common kidney cancer and has very few mutations that are shared between different patients. To better understand the intratumoral genetics underlying mutations of ccRCC, we carried out single-cell exome sequencing on a ccRCC tumor and its adjacent kidney tissue. Our data indicate that this tumor was unlikely to have resulted from mutations in VHL and PBRM1. Quantitative population genetic analysis indicates that the tumor did not contain any significant clonal subpopulations and also showed that mutations that had different allele frequencies within the population also had different mutation spectrums. Analyses of these data allowed us to delineate a detailed intratumoral genetic landscape at a single-cell level. Our pilot study demonstrates that ccRCC may be more genetically complex than previously thought and provides information that can lead to new ways to investigate individual tumors, with the aim of developing more effective cellular targeted therapies.

Exploration of whole genome amplification generated chimeric sequences in long-read sequencing data.

MOTIVATION: Multiple displacement amplification (MDA) has become the most commonly used method of whole genome amplification, generating a vast amount of DNA with higher molecular weight and greater genome coverage. Coupling with long-read sequencing, it is possible to sequence the amplicons of over 20 kb in length. However, the formation of chimeric sequences (chimeras, expressed as structural errors in sequencing data) in MDA seriously interferes with the bioinformatics analysis but its influence on long-read sequencing data is unknown. RESULTS: We sequenced the phi29 DNA polymerase-mediated MDA amplicons on the PacBio platform and analyzed chimeras within the generated data. The 3rd-ChimeraMiner has been constructed as a pipeline for recognizing and restoring chimeras into the original structures in long-read sequencing data, improving the efficiency of using TGS data. Five long-read datasets and one high-fidelity long-read dataset with various amplification folds were analyzed. The result reveals that the mis-priming events in amplification are more frequently occurring than widely perceived, and the propor tion gradually accumulates from 42% to over 78% as the amplification continues. In total, 99.92% of recognized chimeric sequences were demonstrated to be artifacts, whose structures were wrongly formed in MDA instead of existing in original genomes. By restoring chimeras to their original structures, the vast majority of supplementary alignments that introduce false-positive structural variants are recycled, removing 97% of inversions on average and contributing to the analysis of structural variation in MDA-amplified samples. The impact of chimeras in long-read sequencing data analysis should be emphasized, and the 3rd-ChimeraMiner can help to quantify and reduce the influence of chimeras. AVAILABILITY AND IMPLEMENTATION: The 3rd-ChimeraMiner is available on GitHub, https://github.com/dulunar/3rdChimeraMiner.

SNP-based and haplotype-based genome-wide association on drug dependence in Han Chinese.

BACKGROUND: Drug addiction is a serious problem worldwide and is influenced by genetic factors. The present study aimed to investigate the association between genetics and drug addiction among Han Chinese. METHODS: A total of 1000 Chinese users of illicit drugs and 9693 healthy controls were enrolled and underwent single nucleotide polymorphism (SNP)-based and haplotype-based association analyses via whole-genome genotyping. RESULTS: Both single-SNP and haplotype tests revealed associations between illicit drug use and several immune-related genes in the major histocompatibility complex (MHC) region (SNP association: log10BF = 15.135, p = 1.054e-18; haplotype association: log10BF = 20.925, p = 2.065e-24). These genes may affect the risk of drug addiction via modulation of the neuroimmune system. The single-SNP test exclusively reported genome-wide significant associations between rs3782886 (SNP association: log10BF = 8.726, p = 4.842e-11) in BRAP and rs671 (SNP association: log10BF = 7.406, p = 9.333e-10) in ALDH2 and drug addiction. The haplotype test exclusively reported a genome-wide significant association (haplotype association: log10BF = 7.607, p = 3.342e-11) between a region with allelic heterogeneity on chromosome 22 and drug addiction, which may be involved in the pathway of vitamin B12 transport and metabolism, indicating a causal link between lower vitamin B12 levels and methamphetamine addiction. CONCLUSIONS: These findings provide new insights into risk-modeling and the prevention and treatment of methamphetamine and heroin dependence, which may further contribute to potential novel therapeutic approaches.

Deep-Cloud: A Deep Neural Network-Based Approach for Analyzing Differentially Expressed Genes of RNA-seq Data.

Presently, the field of analyzing differentially expressed genes (DEGs) of RNA-seq data is still in its infancy, with new approaches constantly being proposed. Taking advantage of deep neural networks to explore gene expression information on RNA-seq data can provide a novel possibility in the biomedical field. In this study, a novel approach based on a deep learning algorithm and cloud model was developed, named Deep-Cloud. Its main advantage is not only using a convolutional neural network and long short-term memory to extract original data features and estimate gene expression of RNA-seq data but also combining the statistical method of the cloud model to quantify the uncertainty and carry out in-depth analysis of the DEGs between the disease groups and the control groups. Compared with traditional analysis software of DEGs, the Deep-cloud model further improves the sensitivity and accuracy of obtaining DEGs from RNA-seq data. Overall, the proposed new approach Deep-cloud paves a new pathway for mining RNA-seq data in the biomedical field.

Analysis of mutational genotyping using correctable decoding sequencing with superior specificity.

The ability to accurately identify SNPs or low-abundance mutations is important for early clinical diagnosis of diseases, but the existing high-throughput sequencing platforms are limited in terms of their accuracy. Here, we propose a correctable decoding sequencing strategy that may be used for high-throughput sequencing platforms. This strategy is based on adding a mixture of two types of mononucleotides, natural nucleotide and cyclic reversible termination (CRT), for cyclic sequencing. Using the synthetic characteristic of CRTs, about 75% of the calls are unambiguous for a single sequencing run, and the remaining ambiguous sequence can be accurately deduced by two parallel sequencing runs. We demonstrate the feasibility of this strategy, and its cycle efficiency can reach approximately 99.3%. This strategy is proved to be effective for correcting errors and identifying whether the sequencing information is correct or not. And its conservative theoretical error rate was determined to be 0.0009%, which is lower than that of Sanger sequencing. In addition, we establish that the information of only a single sequencing run can be used to detect samples with known mutation sites. We apply this strategy to accurately identify a mutation site in mitochondrial DNA from human cells.

Inferring single cell expression profiles from overlapped pooling sequencing data with compressed sensing strategy.

Though single cell RNA sequencing (scRNA-seq) technologies have been well developed, the acquisition of large-scale single cell expression data may still lead to high costs. Single cell expression profile has its inherent sparse properties, which makes it compressible, thus providing opportunities for solutions. Here, by computational simulation as well as experiment of 54 single cells, we propose that expression profiles can be compressed from the dimension of samples by overlapped assigning each cell into plenty of pools. And we prove that expression profiles can be inferred from these pool expression data with overlapped pooling design and compressed sensing strategy. We also show that by combining this approach with plate-based scRNA-seq measurement, it can maintain its superiorities in gene detection sensitivity and individual identity and recover the expression profile with high precision, while saving about half of the library cost. This method can inspire novel conceptions on the measurement, storage or computation improvements for other compressible signals in many biological areas.

Symbiodiniaceae microRNAs and their targeting sites in coral holobionts: A transcriptomics-based exploration.

Corals should make excellent models for cross-kingdom research because of their natural animal-photobiont holobiont composition, yet a lack of studies and experimental data restricts their use. Here we integrate new full-length transcriptomes and small RNAs of four common reef-building corals with the published Cladocopium genomes to gain deeper insight into gene regulation in coral-Symbiodiniaceae holobionts. Eleven novel Symbiodiniaceae miRNAs get identified, and enrichment results of their target genes show that they might play a role in downregulating rejection from host coral cells, protecting symbiont from autophagy and apoptosis in parallel. This work provides evidence for the early origin of cross-kingdom regulation as a mechanism of self-defense autotrophs can use against heterotrophs, sheds more light on coral-Symbiodiniaceae holobionts, and contributes valuable data for further coral research.

Spatial Transcriptomic Analysis Reveals Regional Transcript Changes in Early and Late Stages of rd1 Model Mice with Retinitis Pigmentosa.

Retinitis pigmentosa (RP) is the leading cause of inherited blindness with a genetically heterogeneous disorder. Currently, there is no effective treatment that can protect vision for those with RP. In recent decades, the rd1 mouse has been used to study the pathological mechanisms of RP. Molecular biological studies using rd1 mice have clarified the mechanism of the apoptosis of photoreceptor cells in the early stage of RP. However, the pathological changes in RP over time remain unclear. The unknown pathology mechanism of RP over time and the difficulty of clinical treatment make it urgent to perform more refined and spatially informed molecular biology studies of RP. In this study, spatial transcriptomic analysis is used to study the changes in different retinal layers of rd1 mice at different ages. The results demonstrate the pattern of photoreceptor apoptosis between rd1 mice and the control group. Not only was oxidative stress enhanced in the late stage of RP, but it was accompanied by an up-regulation of the VEGF pathway. Analysis of temporal kinetic trends has further identified patterns of changes in the key pathways of the early and late stages, to help understand the important pathogenesis of RP. Overall, the application of spatial transcriptomics to rd1 mice can help to elucidate the important pathogenesis of RP involving photoreceptor apoptosis and retinal remodeling.

Constructing the boundary between potent and ineffective siRNAs by MG-algorithm with C-features.

BACKGROUND: In siRNA based antiviral therapeutics, selection of potent siRNAs is an indispensable step, but these commonly used features are unable to construct the boundary between potent and ineffective siRNAs. RESULTS: Here, we select potent siRNAs by removing ineffective ones, where these conditions for removals are constructed by C-features of siRNAs, C-features are generated by MG-algorithm, Icc-cluster and the different combinations of some commonly used features, MG-algorithm and Icc-cluster are two different algorithms to search the nearest siRNA neighbors. For the ineffective siRNAs in test data, they are removed from test data by I-iteration, where I-iteration continually updates training data by adding these successively removed siRNAs. Furthermore, the efficacy of siRNAs of test data is predicted by their nearest neighbors of training data. CONCLUSIONS: By siRNAs of Hencken dataset, results show that our algorithm removes almost ineffective siRNAs from test data, gives the clear boundary between potent and ineffective siRNAs, and accurately predicts the efficacy of siRNAs also. We suggest that our algorithm can provide new insights for selecting the potent siRNAs.

Soil causes gut microbiota to flourish and total serum IgE levels to decrease in mice.

Traditional farm environments induce protection from allergic diseases. In this study, farm environmental factors were classified into three categories, environmental microbes, soil, and organic matter. To explore the impact of soil and environmental microorganisms on gut microbiota and immune function, mice were fed sterilized soil and inhaling microbes, soil microbes, or non-sterilized soil. Metagenomic sequencing results showed the intake of sterile soil, that is, inhaling a small amount of soil microbes in the air increased gut microbial diversity and the abundance of type III secretion system (T3SS) genes, and decreased serum immune IgE levels induced by 2-4-dinitrofluorobenzene (DNFB). The intake of soil microbes increased the abundance of genes involved in the metabolism of short-chain fatty acids and amino acid biosynthesis. Meanwhile, the intake of soil increased gut microbial diversity, the abundance of T3SS genes and related infectious elements, and genes associated with the metabolism of short-chain fatty acids and amino acid biosynthesis, and decreased serum IgE levels. Therefore, soil may be useful as a potential 'prebiotic' promoting the reproduction and growth of some intestinal microorganisms that harbour bacterial secretion system genes, especially those of T3SS, whose abundance was positively and significantly correlated with innate immune function of mice.

Transmembrane transport process and endoplasmic reticulum function facilitate the role of gene cel1b in cellulase production of Trichoderma reesei.

BACKGROUND: A total of 11 ß-glucosidases are predicted in the genome of Trichoderma reesei, which are of great importance for regulating cellulase biosynthesis. Nevertheless, the relevant function and regulation mechanism of each ß-glucosidase remained unknown. RESULTS: We evidenced that overexpression of cel1b dramatically decreased cellulase synthesis in T. reesei RUT-C30 both at the protein level and the mRNA level. In contrast, the deletion of cel1b did not noticeably affect cellulase production. Protein CEL1B was identified to be intracellular, being located in vacuole and cell membrane. The overexpression of cel1b reduced the intracellular pNPGase activity and intracellular/extracellular glucose concentration without inducing carbon catabolite repression. On the other hand, RNA-sequencing analysis showed the transmembrane transport process and endoplasmic reticulum function were affected noticeably by overexpressing cel1b. In particular, some important sugar transporters were notably downregulated, leading to a compromised cellular uptake of sugars including glucose and cellobiose. CONCLUSIONS: Our data suggests that the cellulase inhibition by cel1b overexpression was not due to the ß-glucosidase activity, but probably the dysfunction of the cellular transport process (particularly sugar transport) and endoplasmic reticulum (ER). These findings advance the knowledge of regulation mechanism of cellulase synthesis in filamentous fungi, which is the basis for rationally engineering T. reesei strains to improve cellulase production in industry.

Solid-state nanopore analysis on the conformation change of DNA polymerase I induced by a DNA substrate.

Proteins with a changeable conformation, such as polymerases, play a very important role in various life activities. Their conformational changes can be reflected in their structural size and flexibility, which may influence their transport kinetics. Recently, solid-state nanopore sensors have been widely applied to characterize the conformation of proteins and other complex structures as sensitive and high throughput single-molecule detectors. In this work, we used a SiN nanopore sensor to study the conformational changes between the Klenow fragment (KF) and its monomer complex with a DNA substrate (KF-DNA). By calculating their hydrodynamic radii, pore volume, the duration of translocation events, drift velocity, and molecular dynamics simulations, we found that the KF-DNA monomer complex has a tighter structure and transports slower. The study performed here can be potentially used to identify single polymerases in real time and may ultimately reveal conformation changes and the interaction between polymerases and their substrates.

dCITI-Seq: droplet combinational indexed transposon insertion sequencing.

The rapid development of high-throughput parallel sequencing poses new challenges for large-scale barcoding and sequencing library construction. Here, we present droplet combinational indexed transposon insertion sequencing (dCITI-Seq), in which samples are indexed by the direct insertion of index-containing adaptors through transposition. The random combination of two sets of adaptors with known barcodes and massively parallel transposition was realized via a robust droplet pairing and merging platform. This strategy potentially enlarges the indexing capacity and decreases index crosstalk. Also, dCITI-Seq exhibited a lower GC base preference than conventional in-tube transposition library preparation. With a custom bioinformatic processing, it could be further applied to large-scale single-cell sequencing.

A molecular dynamics investigation of Taq DNA polymerase and its complex with a DNA substrate using a solid-state nanopore biosensor.

Proteins have a small volume difference by the diversity of amino acids, which make protein detection and identification a great challenge. Solid-state nanopore as label-free biosensors has attracted attention with high sensitivity. In this work, we investigated the Taq DNA polymerase before and after combining it with a DNA substrate on a solid-state nanopore through molecular dynamics. In simulation, we analyzed the contribution source of nanopore current blockage. In addition to considering the traditional physical exclusion volume model, the non-covalent interaction between the protein molecules and the pore wall also showed to affect the current blockage in the nanopore. When choosing pores of comparable size to protein molecules, the two states of Taq DNA polymerase produce differentiated non-covalent interactions with the pore wall, which enhanced the amplitude difference in current blockage. As a result, the two DNA polymerases can be distinguished through the distinct current blockage. However, when applying additional pulling force or increasing the pore size of the nanopore, the differences between the current blockages are not significant enough to distinguish. The introduction of the non-covalent interaction makes it clear to understand the current blockage differences, which guide the mechanism between molecules with similar structures or volumes.

DNA nanotechnology assisted nanopore-based analysis.

Nanopore technology is a promising label-free detection method. However, challenges exist for its further application in sequencing, clinical diagnostics and ultra-sensitive single molecule detection. The development of DNA nanotechnology nonetheless provides possible solutions to current obstacles hindering nanopore sensing technologies. In this review, we summarize recent relevant research contributing to efforts for developing nanopore methods associated with DNA nanotechnology. For example, DNA carriers can capture specific targets at pre-designed sites and escort them from nanopores at suitable speeds, thereby greatly enhancing capability and resolution for the detection of specific target molecules. In addition, DNA origami structures can be constructed to fulfill various design specifications and one-pot assembly reactions, thus serving as functional nanopores. Moreover, based on DNA strand displacement, nanopores can also be utilized to characterize the outputs of DNA computing and to develop programmable smart diagnostic nanodevices. In summary, DNA assembly-based nanopore research can pave the way for the realization of impactful biological detection and diagnostic platforms via single-biomolecule analysis.

Prediction of Time-Series Transcriptomic Gene Expression Based on Long Short-Term Memory with Empirical Mode Decomposition.

RNA degradation can significantly affect the results of gene expression profiling, with subsequent analysis failing to faithfully represent the initial gene expression level. It is urgent to have an artificial intelligence approach to better utilize the limited data to obtain meaningful and reliable analysis results in the case of data with missing destination time. In this study, we propose a method based on the signal decomposition technique and deep learning, named Multi-LSTM. It is divided into two main modules: One decomposes the collected gene expression data by an empirical mode decomposition (EMD) algorithm to obtain a series of sub-modules with different frequencies to improve data stability and reduce modeling complexity. The other is based on long short-term memory (LSTM) as the core predictor, aiming to deeply explore the temporal nonlinear relationships embedded in the sub-modules. Finally, the prediction results of sub-modules are reconstructed to obtain the final prediction results of time-series transcriptomic gene expression. The results show that EMD can efficiently reduce the nonlinearity of the original data, which provides reliable theoretical support to reduce the complexity and improve the robustness of LSTM models. Overall, the decomposition-combination prediction framework can effectively predict gene expression levels at unknown time points.

Full-Length Transcriptome Maps of Reef-Building Coral Illuminate the Molecular Basis of Calcification, Symbiosis, and Circadian Genes.

Coral transcriptomic data largely rely on short-read sequencing, which severely limits the understanding of coral molecular mechanisms and leaves many important biological questions unresolved. Here, we sequence the full-length transcriptomes of four common and frequently dominant reef-building corals using the PacBio Sequel II platform. We obtain information on reported gene functions, structures, and expression profiles. Among them, a comparative analysis of biomineralization-related genes provides insights into the molecular basis of coral skeletal density. The gene expression profiles of the symbiont Symbiodiniaceae are also isolated and annotated from the holobiont sequence data. Finally, a phylogenetic analysis of key circadian clock genes among 40 evolutionarily representative species indicates that there are four key members in early metazoans, including cry genes; Clock or Npas2; cyc or Arntl; and tim, while per, as the fifth member, occurs in Bilateria. In summary, this work provides a foundation for further work on the manipulation of skeleton production or symbiosis to promote the survival of these important organisms.

EasyNanopore: A Ready-to-Use Processing Software for Translocation Events in Nanopore Translocation Experiments.

We developed EasyNanopore which is a ready-to-use software to select the events of a nanopore molecular translocation experiment. The software is released as an executable file with a graphical user interface and provides several versions suitable for different operating systems without installing any running environment to execute it. We use the adaptive threshold which adapts to the low-frequency variation of the baseline to detect events and uses a multiprocess method to accelerate the process of event detection. After the event is identified, its duration and amplitude information will be extracted and a resulting txt file will be generated for further analysis. Our software runs fast and can effectively extract the data from data of large-scale nanopore molecular translocation experiments.

Epidemic of HIV infection among persons who inject drugs in mainland China: a series, cross-sectional study.

BACKGROUND: Studies have suggested that the injection drug use (IDU) was no longer the main transmission route of HIV/AIDS in China. However, there has never been a study to assess the national HIV epidemic among persons who inject drugs (PWIDs) based on a nationwide database. METHODS: PWIDs among new entrants in detoxification centers with HIV test results were extracted from the 2008-2016 National Dynamic Management and Control Database for Persons Who Use Drugs (NDMCD). Logistic regressions were used to analyze factors associated with HIV infection, and joinpoint regression were used to examine trends in the HIV prevalence. RESULTS: A total of 103,619 PWIDs among new entrants tested for HIV in detoxification centers between 2008 and 2016 were included in the analysis. The HIV prevalence was 5.0% (n = 5167) among PWIDs. A U-shaped curve of the HIV prevalence decreased from 4.9% in 2008 to 3.3% in 2010 (Annual Percent Change [APC] - 20.6, 95% CI - 32.5 to - 6.7, p < 0.05) and subsequently increased from 3.3% in 2010 to 8.6% in 2016 (APC 17.9, 95% CI 14.5-21.4, p < 0.05) was observed. The HIV prevalence in west regions in China all presented decreased trends, while central and eastern regions presented increased trends. CONCLUSIONS: Although the HIV prevalence has been declining in general population, the HIV prevalence among PWIDs has shown an increasing trend since 2010. Current policies on HIV control in PWIDs should be reassessed.