Mesoscale DNA feature in antibody-coding sequence facilitates somatic hypermutation.

Somatic hypermutation (SHM), initiated by activation-induced cytidine deaminase (AID), generates mutations in the antibody-coding sequence to allow affinity maturation. Why these mutations intrinsically focus on the three nonconsecutive complementarity-determining regions (CDRs) remains enigmatic. Here, we found that predisposition mutagenesis depends on the single-strand (ss) DNA substrate flexibility determined by the mesoscale sequence surrounding AID deaminase motifs. Mesoscale DNA sequences containing flexible pyrimidine-pyrimidine bases bind effectively to the positively charged surface patches of AID, resulting in preferential deamination activities. The CDR hypermutability is mimicable in in vitro deaminase assays and is evolutionarily conserved among species using SHM as a major diversification strategy. We demonstrated that mesoscale sequence alterations tune the in vivo mutability and promote mutations in an otherwise cold region in mice. Our results show a non-coding role of antibody-coding sequence in directing hypermutation, paving the way for the synthetic design of humanized animal models for optimal antibody discovery and explaining the AID mutagenesis pattern in lymphoma.

ExosomePurity: tumour purity deconvolution in serum exosomes based on miRNA signatures.

Exosomes cargo tumour-characterized biomolecules secreted from cancer cells and play a pivotal role in tumorigenesis and cancer progression, thus providing their potential for non-invasive cancer monitoring. Since cancer cell-derived exosomes are often mixed with those from healthy cells in liquid biopsy of tumour patients, accurately measuring the purity of tumour cell-derived exosomes is not only critical for the early detection but also essential for unbiased identification of diagnosis biomarkers. Here, we propose 'ExosomePurity', a tumour purity deconvolution model to estimate tumour purity in serum exosomes of cancer patients based on microribonucleic acid (miRNA)-Seq data. We first identify the differently expressed miRNAs as signature to distinguish cancer cell- from healthy cell-derived exosomes. Then, the deconvolution model was developed to estimate the proportions of cancer exosomes and normal exosomes in serum. The purity predicted by the model shows high correlation with actual purity in simulated data and actual data. Moreover, the model is robust under the different levels of noise background. The tumour purity was also used to correct differential expressed gene analysis. ExosomePurity empowers the research community to study non-invasive early diagnosis and to track cancer progression in cancers more efficiently. It is implemented in R and is freely available from GitHub (https://github.com/WangHYLab/ExosomePurity).

TRAmHap: accurate prediction of transcriptional activity from DNA methylation haplotypes in bisulfite-sequencing data.

Deoxyribonucleic acid (DNA) methylation (DNAm) is an important epigenetic mechanism that plays a role in chromatin structure and transcriptional regulation. Elucidating the relationship between DNAm and gene expression is of great importance for understanding its role in transcriptional regulation. The conventional approach is to construct machine-learning-based methods to predict gene expression based on mean methylation signals in promoter regions. However, this type of strategy only explains about 25% of gene expression variation, and hence is inadequate in elucidating the relationship between DNAm and transcriptional activity. In addition, using mean methylation as input features neglects the heterogeneity of cell populations that can be reflected by DNAm haplotypes. We here developed TRAmaHap, a novel deep-learning framework that predicts gene expression by utilizing the characteristics of DNAm haplotypes in proximal promoters and distal enhancers. Using benchmark data of human and mouse normal tissues, TRAmHap shows much higher accuracy than existing machine-learning based methods, by explaining 60~80% of gene expression variation across tissue types and disease conditions. Our model demonstrated that gene expression can be accurately predicted by DNAm patterns in promoters and long-range enhancers as far as 25 kb away from transcription start site, especially in the presence of intra-gene chromatin interactions.

E-value: a superior alternative to P-value and its adjustments in DNA methylation studies.

DNA methylation plays a crucial role in transcriptional regulation. Reduced representation bisulfite sequencing (RRBS) is a technique of increasing use for analyzing genome-wide methylation profiles. Many computational tools such as Metilene, MethylKit, BiSeq and DMRfinder have been developed to use RRBS data for the detection of the differentially methylated regions (DMRs) potentially involved in epigenetic regulations of gene expression. For DMR detection tools, as for countless other medical applications, P-values and their adjustments are among the most standard reporting statistics used to assess the statistical significance of biological findings. However, P-values are coming under increasing criticism relating to their questionable accuracy and relatively high levels of false positive or negative indications. Here, we propose a method to calculate E-values, as likelihood ratios falling into the null hypothesis over the entire parameter space, for DMR detection in RRBS data. We also provide the R package 'metevalue' as a user-friendly interface to implement E-value calculations into various DMR detection tools. To evaluate the performance of E-values, we generated various RRBS benchmarking datasets using our simulator 'RRBSsim' with eight samples in each experimental group. Our comprehensive benchmarking analyses showed that using E-values not only significantly improved accuracy, area under ROC curve and power, over that of P-values or adjusted P-values, but also reduced false discovery rates and type I errors. In applications using real RRBS data of CRL rats and a clinical trial on low-salt diet, the use of E-values detected biologically more relevant DMRs and also improved the negative association between DNA methylation and gene expression.

A novel hypergraph model for identifying and prioritizing personalized drivers in cancer.

Cancer development is driven by an accumulation of a small number of driver genetic mutations that confer the selective growth advantage to the cell, while most passenger mutations do not contribute to tumor progression. The identification of these driver genes responsible for tumorigenesis is a crucial step in designing effective cancer treatments. Although many computational methods have been developed with this purpose, the majority of existing methods solely provided a single driver gene list for the entire cohort of patients, ignoring the high heterogeneity of driver events across patients. It remains challenging to identify the personalized driver genes. Here, we propose a novel method (PDRWH), which aims to prioritize the mutated genes of a single patient based on their impact on the abnormal expression of downstream genes across a group of patients who share the co-mutation genes and similar gene expression profiles. The wide experimental results on 16 cancer datasets from TCGA showed that PDRWH excels in identifying known general driver genes and tumor-specific drivers. In the comparative testing across five cancer types, PDRWH outperformed existing individual-level methods as well as cohort-level methods. Our results also demonstrated that PDRWH could identify both common and rare drivers. The personalized driver profiles could improve tumor stratification, providing new insights into understanding tumor heterogeneity and taking a further step toward personalized treatment. We also validated one of our predicted novel personalized driver genes on tumor cell proliferation by vitro cell-based assays, the promoting effect of the high expression of Low-density lipoprotein receptor-related protein 1 (LRP1) on tumor cell proliferation.

Assessing base-resolution DNA mechanics on the genome scale.

Intrinsic DNA properties including bending play a crucial role in diverse biological systems. A recent advance in a high-throughput technology called loop-seq makes it possible to determine the bendability of hundred thousand 50-bp DNA duplexes in one experiment. However, it's still challenging to assess base-resolution sequence bendability in large genomes such as human, which requires thousands of such experiments. Here, we introduce 'BendNet'-a deep neural network to predict the intrinsic DNA bending at base-resolution by using loop-seq results in yeast as training data. BendNet can predict the DNA bendability of any given sequence from different species with high accuracy. To explore the utility of BendNet, we applied it to the human genome and observed DNA bendability is associated with chromatin features and disease risk regions involving transcription/enhancer regulation, DNA replication, transcription factor binding and extrachromosomal circular DNA generation. These findings expand our understanding on DNA mechanics and its association with transcription regulation in mammals. Lastly, we built a comprehensive resource of genomic DNA bendability profiles for 307 species by applying BendNet, and provided an online tool to assess the bendability of user-specified DNA sequences (http://www.dnabendnet.com/).

Neuromelanin-sensitive magnetic resonance imaging: Possibilities and promises as an imaging biomarker for Parkinson's disease.

Parkinson's disease (PD) is an age-related progressive neurodegenerative disorder characterized by both motor and non-motor symptoms resulting from the death of dopaminergic neurons in the substantia nigra pars compacta (SNpc) and noradrenergic neurons in the locus coeruleus (LC). The current diagnosis of PD primarily relies on motor symptoms, often leading to diagnoses in advanced stages, where a significant portion of SNpc dopamine neurons has already succumbed. Therefore, the identification of imaging biomarkers for early-stage PD diagnosis and disease progression monitoring is imperative. Recent studies propose that neuromelanin-sensitive magnetic resonance imaging (NM-MRI) holds promise as an imaging biomarker. In this review, we summarize the latest findings concerning NM-MRI characteristics at various stages in patients with PD and those with atypical parkinsonism. In conclusion, alterations in neuromelanin within the LC are associated with non-motor symptoms and prove to be a reliable imaging biomarker in the prodromal phase of PD. Furthermore, NM-MRI demonstrates efficacy in differentiating progressive supranuclear palsy (PSP) from PD and multiple system atrophy with predominant parkinsonism. The spatial patterns of changes in the SNpc can be indicative of PD progression and aid in distinguishing between PSP and synucleinopathies. We recommend that patients with PD and individuals at risk for PD undergo regular NM-MRI examinations. This technology holds the potential for widespread use in PD diagnosis.

THBS1 promotes angiogenesis and accelerates ESCC malignant progression by the HIF-1/VEGF signaling pathway.

Previously, we demonstrated that the expression of THBS1 is increased in esophageal squamous cell carcinoma (ESCC) tissues and is correlated with lymph node metastasis and poor prognosis, indicating that THBS1 might be a candidate oncogene in ESCC. In this study, we future studied the specific role of THBS1 in ESCC and its molecular mechanism. Silencing THBS1 expression resulted in inhibition of cell migration and cell invasion of ESCC cells, the decrease of colony formation and proliferation. Tube formation of human umbilical vein endothelial cells (HUVECs) in vitro was decreased when cultured with conditioned medium from THBS1-silenced cells. The expression of CD31, a marker for blood vessel endothelial cells, was decreased in tumor tissues derived from THBS1-silenced tumors in vivo. Silencing THBS1 leaded the decreased of hypoxia-inducible factor-1α (HIF-1α), HIF-1ß, and VEGFA protein. The expression of p-ERK and p-AKT were declined in HUVECs following incubation with conditioned medium from THBS1-silenced ESCC cells compared conditioned medium from control cells. Furthermore, the treatment with bevacizumab boosted the decrease of the p-ERK and p-AKT levels in HUVECs incubated with the conditioned medium from THBS1-silenced ESCC cells. THBS1 silencing combined with bevacizumab blocked VEGF, inhibited to the tube formation, colony formation and migration of HUVECs, which were superior to that of bevacizumab alone. We presumed that THBS1 can enhance HIF-1/VEGF signaling and subsequently induce angiogenesis by activating the AKT and ERK pathways in HUVECs, resulting in bevacizumab resistance. THBS1 would be a potential target in tumor antiangiogenesis therapies.

Syringe-assisted test-aspiration with mechanical aspiration thrombectomy results in good safety and short-term outcomes in the treatment of patients with deep venous thrombosis.

OBJECTIVE: To evaluate the short-term outcomes and safety of syringe-assisted test-aspiration with mechanical aspiration thrombectomy in the treatment of deep venous thrombosis. METHODS: This was a single-center, retrospective study of hospitalized patients with iliofemoral and/or inferior vena caval deep venous thrombosis, excluding those with pulmonary embolism. We collected the following patient data from the electronic medical records: age, sex, provoked/unprovoked deep venous thrombosis, symptom duration, thrombosed segments, and the presence of a tumor, thrombophilia, diabetes, and/or iliac vein compression syndrome. Venography and computed tomographic venography were performed in all patients before the procedure. All patients underwent syringe-assisted test-aspiration with mechanical aspiration thrombectomy under local anesthesia and sedation, and all received low-molecular-weight heparin peri-operatively. All patients underwent implantation of an inferior vena caval filter. Rivaroxaban was administered post-procedure, instead of heparin, for 3-6 months, with lower extremity compression. RESULTS: Overall, 29 patients with deep venous thrombosis underwent syringe-assisted test-aspiration with mechanical aspiration thrombectomy from January 2022 to October 2022 in our institution. Technical success (>70% thrombus resolution) was achieved in all patients, and using a single procedure in 25/29 patients (86%). Concomitant stenting was performed in 18/29 (62%) of the patients, and 21/29 (69%) underwent angioplasty. The median (interquartile range) procedure time was 110 min (100-122), the median intra-operative bleeding volume was 150 mL (120-180), and the median decrease in the hemoglobin concentration from pre- to post-operative was 7 g/L (4-14). The median follow-up duration was 7 months (5-9). All patients obtained symptomatic relief, and 27/29 achieved near-remission or full remission (combined total). No patients experienced peri-operative bleeding complications, or symptom recurrence or post-thrombectomy syndrome during follow-up. CONCLUSION: The short-term outcomes following syringe-assisted test-aspiration with mechanical aspiration thrombectomy in the treatment of deep venous thrombosis were excellent, and the procedure was safe.

Comprehensive analysis of cuproptosis genes and cuproptosis-related genes as prognosis factors in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a common invasive and pernicious cancer with a low five-year survival rate. To identify potential therapeutic targets, we first investigated the characteristics of cuproptosis genes (CUGs) in ESCC. The expression patterns of 10 CUGs (FDX1, LIPT1, LIAS, DLAT, DLD, PDHA1, PDHB, GLS, MTF1, and CDKN2A) were analyzed to identify ESCC-relevant targets. Weighted correlation network analysis (WGCNA) was performed to obtain CUG-related genes (CRGs). A total of seven differentially expressed genes were identified (FDX1, DLAT, LIAS, PDHB, MTF1, GLS, and CDKN2A). DLAT was upregulated in stage III, and LIPT1 was upregulated in N0 + N1 cancers. The high expression of CDKN2A, and PDHA1, was related to better overall survival, whereas the low expression of LIAS was related to better clinical outcomes. WGCNA was performed to get CUG-related genes (CRGs) and showed three key modules that related to FDX1, DLAT, and LIPT1. Moreover, CRGs (BTLA, CT47A1, and PRRX1) were selected to construct a risk score model in order to predict the survival and prognosis of patients with ESCC. Additionally, the cuproptosis score based on CUGs and a nomogram constructed based on it helped accurately predict the prognosis of patients with ESCC; thus, maybe it can be used for the clinical diagnosis of ESCC. The results also showed that milciclib might inhibit the proliferation and migration of KYSE150 and KYSE510 cells by targeting CDKN2A. In conclusion, the abovementioned CUGs and CRGs play a crucial role in tumorigenesis and cancer progression in ESCC, indicating their potential as therapeutic targets.

mHapTk: a comprehensive toolkit for the analysis of DNA methylation haplotypes.

SUMMARY: Bisulfite sequencing remains the gold standard technique to detect DNA methylation profiles at single-nucleotide resolution. The DNA methylation status of CpG sites on the same fragment represents a discrete methylation haplotype (mHap). The mHap-level metrics were demonstrated to be promising cancer biomarkers and explain more gene expression variation than average methylation. However, most existing tools focus on average methylation and neglect mHap patterns. Here, we present mHapTk, a comprehensive python toolkit for the analysis of DNA mHap. It calculates eight mHap-level summary statistics in predefined regions or across individual CpG in a genome-wide manner. It identifies methylation haplotype blocks, in which methylations of pairwise CpGs are tightly correlated. Furthermore, mHap patterns can be visualized with the built-in functions in mHapTk or external tools such as IGV and deepTools. AVAILABILITY AND IMPLEMENTATION: https://jiantaoshi.github.io/mhaptk/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Secuer: Ultrafast, scalable and accurate clustering of single-cell RNA-seq data.

Identifying cell clusters is a critical step for single-cell transcriptomics study. Despite the numerous clustering tools developed recently, the rapid growth of scRNA-seq volumes prompts for a more (computationally) efficient clustering method. Here, we introduce Secuer, a Scalable and Efficient speCtral clUstERing algorithm for scRNA-seq data. By employing an anchor-based bipartite graph representation algorithm, Secuer enjoys reduced runtime and memory usage over one order of magnitude for datasets with more than 1 million cells. Meanwhile, Secuer also achieves better or comparable accuracy than competing methods in small and moderate benchmark datasets. Furthermore, we showcase that Secuer can also serve as a building block for a new consensus clustering method, Secuer-consensus, which again improves the runtime and scalability of state-of-the-art consensus clustering methods while also maintaining the accuracy. Overall, Secuer is a versatile, accurate, and scalable clustering framework suitable for small to ultra-large single-cell clustering tasks.

Drivers of change in China's energy-related CO2 emissions.

CO2 emissions are of global concern because of climate change. China has become the largest CO2 emitter in the world and presently accounts for 30% of global emissions. Here, we analyze the major drivers of energy-related CO2 emissions in China from 1978 when the reform and opening-up policy was launched. We find that 1) there has been a 6-fold increase in energy-related CO2 emissions, which was driven primarily (176%) by economic growth followed by population growth (16%), while the effects of energy intensity (-79%) and carbon intensity (-13%) slowed the growth of carbon emissions over most of this period; 2) energy-related CO2 emissions are positively related to per capita gross domestic product (GDP), population growth rate, carbon intensity, and energy intensity; and 3) a portfolio of command-and-control policies affecting the drivers has altered the total emission trend. However, given the major role of China in global climate change mitigation, significant future reductions in China's CO2 emissions will require transformation toward low-carbon energy systems.

Controlling the Balance between Remote, Pinhole, and van der Waals Epitaxy of Heusler Films on Graphene/Sapphire.

Remote epitaxy is promising for the synthesis of lattice-mismatched materials, exfoliation of membranes, and reuse of expensive substrates. However, clear experimental evidence of a remote mechanism remains elusive. Alternative mechanisms such as pinhole-seeded epitaxy or van der Waals epitaxy can often explain the resulting films. Here, we show that growth of the Heusler compound GdPtSb on clean graphene/sapphire produces a 30° rotated (R30) superstructure that cannot be explained by pinhole epitaxy. With decreasing temperature, the fraction of this R30 domain increases, compared to the direct epitaxial R0 domain, which can be explained by a competition between remote versus pinhole epitaxy. Careful graphene/substrate annealing and consideration of the relative lattice mismatches are required to obtain epitaxy to the underlying substrate across a series of other Heusler films, including LaPtSb and GdAuGe. The R30 superstructure provides a possible experimental fingerprint of remote epitaxy, since it is inconsistent with the leading alternative mechanisms.

Topsoil selenium (Se) under Se-rich farming in China: Current status, cropping impacts and ecological risk assessment.

Selenium (Se), as an essential microelement, can be supplied through Se-biofortified food from Se-rich soils and associated farming practices for human health, while it can also cause eco-risks if overapplied. In this study, a multi-scale spatiotemporal meta-analysis was conducted to guide sustainable Se-rich farming in China by combining a long-term survey with a reviewed database. The weighted mean concentration, spatial distribution of soil Se, nationwide topsoil Se variation from cropping impacts and its bioavailability-based ecological risks were assessed and quantified. The results showed that the weighted mean content (0.3 mg kg-1) of China was slightly higher than that of previous nationwide topsoil Se surveys, as more Se-rich areas were found in recent high-density sampling surveys. Cropping has overall reduced Se content by 9.5% from farmland across China and deprived more with the increase in farming rotation driven by geo-climatic conditions. Long-term cropping removed Se from Se-rich areas but accumulated it in Se-deficient areas. Additionally, the bioavailable Se content of topsoil in China ranged from 0 to 332 µg kg-1, and the bioavailability-based eco-risks indicated that high eco-risks only existed in overfertilized and extremely high-Se soils, such as in Enshi, Ziyang and some coalfield areas. This work provides evidence for the development of sustainable Se-rich farming with proper utilization of soil Se resources, simultaneously protecting the soil eco-environment.

Greenhouse gas emissions from extractive industries in a globalized era.

The extractive industry consumes vast amounts of energy and is a major contributor to greenhouse gas (GHG) emissions. However, its climatic impacts have not yet been fully accounted for. In this study, we estimated the GHG emissions from extractive activities globally with a focus on China, and assessed the main emission drivers. In addition, we predicted the Chinese extractive industry emissions in the context of global mineral demand and cycling. As of 2020, GHG emissions from the global extractive industry had reached 7.7 billion tons of CO2 equivalents (CO2e), accounting for approximately 15.0% of the global anthropogenic GHG emissions (excluding GHG emissions from land use, land-use change, and forestry activities (LULUCF), with China being the largest emitter, accounting for 3.5% of global emissions. Extractive industry GHG emissions are projected to peak by 2030 or even earlier to achieve low-carbon peak targets. The most critical pathway for reducing GHG emissions in the extractive industry is to control emissions from coal mining. Therefore, reducing methane emissions from mining and washing coal (MWC) should be prioritized.

The Expression Pattern and Clinical Significance of Lysyl Oxidase Family in Gliomas.

LOX (Lysyl oxidase) family participates in the catalysis of collagen and elastin to maintain ECM homeostasis. Glioma is the most common primary brain tumor and LOX family has not been systemic studied in glioma. In this study, we found LOX family members are upregulated expressed in gliomas samples. A protein-protein interaction network (PPIN) was construct to visualize and understand the differential expression pattern, as well as functional annotation, for LOX family and their interacting proteins, which involved in collagen fibril organization and MAPK signaling pathway. Through subcellular localization distribution, the LOX family members distribute both intracellular and extracellular. All five LOX members are consistently significantly correlate with dendritic cell both in immune infiltrate of GBM and LGG. Survival analysis showed that high expression of LOX family is associated with a poor prognosis of gliomas patients. These analyses provide important clues to identify the potential biological roles for LOX family in gliomas, which might serve as diagnosis markers.

DriverRWH: discovering cancer driver genes by random walk on a gene mutation hypergraph.

BACKGROUND: Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data. A critical challenge in cancer genomics is identification of a few cancer driver genes whose mutations cause tumor growth. However, the majority of existing computational approaches underuse the co-occurrence mutation information of the individuals, which are deemed to be important in tumorigenesis and tumor progression, resulting in high rate of false positive. RESULTS: To make full use of co-mutation information, we present a random walk algorithm referred to as DriverRWH on a weighted gene mutation hypergraph model, using somatic mutation data and molecular interaction network data to prioritize candidate driver genes. Applied to tumor samples of different cancer types from The Cancer Genome Atlas, DriverRWH shows significantly better performance than state-of-art prioritization methods in terms of the area under the curve scores and the cumulative number of known driver genes recovered in top-ranked candidate genes. Besides, DriverRWH discovers several potential drivers, which are enriched in cancer-related pathways. DriverRWH recovers approximately 50% known driver genes in the top 30 ranked candidate genes for more than half of the cancer types. In addition, DriverRWH is also highly robust to perturbations in the mutation data and gene functional network data. CONCLUSION: DriverRWH is effective among various cancer types in prioritizes cancer driver genes and provides considerable improvement over other tools with a better balance of precision and sensitivity. It can be a useful tool for detecting potential driver genes and facilitate targeted cancer therapies.

A comprehensive review of computational prediction of genome-wide features.

There are significant correlations among different types of genetic, genomic and epigenomic features within the genome. These correlations make the in silico feature prediction possible through statistical or machine learning models. With the accumulation of a vast amount of high-throughput data, feature prediction has gained significant interest lately, and a plethora of papers have been published in the past few years. Here we provide a comprehensive review on these published works, categorized by the prediction targets, including protein binding site, enhancer, DNA methylation, chromatin structure and gene expression. We also provide discussions on some important points and possible future directions.

The DNA methylation haplotype (mHap) format and mHapTools.

SUMMARY: Bisulfite sequencing (BS-seq) is currently the gold standard for measuring genome-wide DNA methylation profiles at single-nucleotide resolution. Most analyses focus on mean CpG methylation and ignore methylation states on the same DNA fragments [DNA methylation haplotypes (mHaps)]. Here, we propose mHap, a simple DNA mHap format for storing DNA BS-seq data. This format reduces the size of a BAM file by 40- to 140-fold while retaining complete read-level CpG methylation information. It is also compatible with the Tabix tool for fast and random access. We implemented a command-line tool, mHapTools, for converting BAM/SAM files from existing platforms to mHap files as well as post-processing DNA methylation data in mHap format. With this tool, we processed all publicly available human reduced representation bisulfite sequencing data and provided these data as a comprehensive mHap database. AVAILABILITY AND IMPLEMENTATION: https://jiantaoshi.github.io/mHap/index.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.