Integrating multi-omics data of childhood asthma using a deep association model

Childhood asthma is one of the most common respiratory diseases with rising mortality and morbidity.The multi-omics data is providing a new chance to explore collaborative biomarkers and corresponding diagnostic models of childhood asthma.To capture the nonlinear association of multi-omics data and improve interpretability of diagnostic model,we proposed a novel deep association model(DAM)and corresponding efficient analysis framework.First,the Deep Subspace Reconstruction was used to fuse the omics data and diagnostic information,thereby correcting the distribution of the original omics data and reducing the influence of unnecessary data noises.Second,the Joint Deep Semi-Negative Matrix Factorization was applied to identify different latent sample patterns and extract biomarkers from different omics data levels.Third,our newly proposed Deep Orthogonal Canonical Correlation Analysis can rank features in the collaborative module,which are able to construct the diagnostic model considering nonlinear correlation between different omics data levels.Using DAM,we deeply analyzed the transcriptome and methylation data of childhood asthma.The effectiveness of DAM is verified from the perspectives of algorithm performance and biological significance on the independent test dataset,by ablation experiment and comparison with many baseline methods from clinical and biological studies.The DAM-induced diagnostic model can achieve a prediction AUC of o.912,which is higher than that of many other alternative methods.Meanwhile,relevant pathways and biomarkers of childhood asthma are also recognized to be collectively altered on the gene expression and methylation levels.As an interpretable machine learning approach,DAM simultaneously considers the non-linear associations among samples and those among biological features,which should help explore interpretative biomarker candidates and efficient diagnostic models from multi-omics data analysis for human complexdiseases.

肝胆肿瘤中肿瘤特异性CircRNA衍生抗原肽的鉴定

基于肿瘤抗原的免疫治疗的应用受到验证免疫原性肽稀缺性的阻碍。本研究旨在研究环状RNA(circRNA)在肝胆肿瘤类器官中作为肿瘤抗原肽新来源的潜力。使用RNA测序(RNA-seq)和基于算法的评分工具,预测3950个翻译的肿瘤特异性环状RNA在27个类器官中产生18971个抗原肽。从抗原格局来看,11个氨基酸长度(mer)肽和人白细胞抗原(HLA)-A结合肽具有最高的免疫原性相关评分。在分析的3/5类器官中,有13个预测抗原肽通过质谱(MS)免疫肽组学被直接确认为HLA-A、HLA-B和HLA-C(HLA-ABC)结合肽。在流式细胞术和酶联免疫吸附试验(ELISA)中,由HLA-ABC分子呈递的circRNA衍生的肿瘤特异性肽刺激CD8(CD8)T细胞,显示CD107a干扰素γ(IFNγ)共表达和IFNγ分泌增加。免疫原性环状RNA衍生肽诱导的靶向类器官的细胞毒性T细胞活性在杀伤实验中得到验证。值得注意的是,来自circTBC1D15的抗原肽YGFNEILKK不仅被认为是类器官的HLA-ABC呈递肽,而且还显著降低了肿瘤类器官的存活率。本研究的发现强调了产生肿瘤抗原的一个关键亚群,这对靶向肿瘤特异性circRNA具有重要意义。

Ginsenoside compound-K attenuates OVX-induced osteoporosis via the suppression of RANKL-induced osteoclastogenesis and oxidative stress

Osteoporosis(OP),a systemic and chronic bone disease,is distinguished by low bone mass and destruction of bone microarchitecture.Ginsenoside Compound-K(CK),one of the metabolites of ginsenoside Rb1,has anti-aging,anti-inflammatory,anti-cancer,and hypolipidemic activities.We have demonstrated CK could promote osteogenesis and fracture healing in our previous study.However,the contribution of CK to osteoporosis has not been examined.In the present study,we investigated the effect of CK on osteoclastogenesis and ovariectomy(OVX)-induced osteoporosis.The results showed that CK inhibited receptor activator for nuclear factor-κB ligand(RANKL)-mediated osteoclast differentiation and reactive oxygen species(ROS)activity by inhibiting the phosphorylation of NF-κB p65 and oxidative stress in RAW264.7 cells.In addition,we also demonstrated that CK could inhibit bone resorption using bone marrow-derived macrophages.Furthermore,we demonstrated that CK attenuated bone loss by suppressing the activity of osteoclast and alleviating oxidative stress in vivo.Taken together,these results showed CK could inhibit osteoclastogenesis and prevent OVX-induced bone loss by inhibiting NF-κB signaling pathway.

生物审编组织管理模式及证据源质量控制和评价

阐述国内外生物审编工作开展现状,从审编组织管理模式、证据源质量控制和评价两方面分析生物审编工作当前面临的主要挑战,提出我国发展生物审编相关建议,推进生物审编工作科学开展。

宏基因组大数据分析的质量控制流程规范

宏基因组数据具有数据量大、复杂度高的特点,从数据类型来看,其涵盖了元数据和测序数据。为了保证宏基因组数据后续功能分析的有效性和正确性,需要对这些元数据和测序数据进行严格的质量控制检测。详细描述了宏基因组数据的质量控制流程,包括元数据和测序数据的信息检查、低质量片段的过滤等过程,从而为宏基因组数据分析提供了预处理的规范,这将为微生物组大数据分析提供坚实的基础。

前沿信息技术在生物医学大数据中的应用及展望

近年来,随着以高通量组学检测技术为代表的生物技术(biological technology,BT)的发展,生物医学研究领域开始进入大数据时代。面对高维度、跨层次、多模态生物医学大数据,科学研究需要数据密集型科研新范式。云计算、区块链、人工智能等前沿信息技术(information technology,IT)的蓬勃发展为这种新型研究范式的实践提供了技术手段。本文对云计算、区块链、人工智能等前沿信息技术在生物医学大数据中的应用进行了描述,并对数据密集型科研新范式支撑环境的构建提出了前瞻展望,以期建立融合BT&IT技术的新型研究方案和科研新范式,最终推动生物医学研究跨越式发展。

A cell marker-based clustering strategy(cmCluster)for precise cell type identification of scRNA-seq data

Background:The precise and efficient analysis of single-cell transcriptome data provides powerful support for studying the diversity of cell functions at the single-cell level.The most important and challenging steps are cell clustering and recognition of cell populations.While the precision of clustering and annotation are considered separately in most current studies,it is worth attempting to develop an extensive and flexible strategy to balance clustering accuracy and biological explanation comprehensively.Methods:The cell marker-based clustering strategy(cmCluster),which is a modified Louvain clustering method,aims to search the optimal clusters through genetic algorithm(GA)and grid search based on the cell type annotation results.Results:By applying cmCluster on a set of single-cell transcriptome data,the results showed that it was beneficial for the recognition of cell populations and explanation of biological function even on the occasion of incomplete cell type information or multiple data resources.In addition,cmCluster also produced clear boundaries and appropriate subtypes with potential marker genes.The relevant code is available in GitHub website(huangyuwei301/cmCluster).Conclusions:We speculate that cmCluster provides researchers effective screening strategies to improve the accuracy of subsequent biological analysis,reduce artificial bias,and facilitate the comparison and analysis of multiple studies.

Early warning of emerging infectious diseases based on multimodal data

The coronavirus disease 2019 (COVID-19) pandemic has dramatically increased the awareness of emerging infectious diseases. The advancement of multiomics analysis technology has resulted in the development of several databases containing virus information. Several scientists have integrated existing data on viruses to construct phylogenetic trees and predict virus mutation and transmission in different ways, providing prospective technical support for epidemic prevention and control. This review summarized the databases of known emerging infectious viruses and techniques focusing on virus variant forecasting and early warning. It focuses on the multi-dimensional information integration and database construction of emerging infectious viruses, virus mutation spectrum construction and variant forecast model, analysis of the affinity between mutation antigen and the receptor, propagation model of virus dynamic evolution, and monitoring and early warning for variants. As people have suffered from COVID-19 and repeated flu outbreaks, we focused on the research results of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses. This review comprehensively viewed the latest virus research and provided a reference for future virus prevention and control research.

合成生物学赋能:从学科发展到产业转化

合成生物学通过引入工程科学“自下而上”认识生命体系的理念,在生命科学研究中采用“设计—构建—测试—学习”迭代的研究范式,并在基因组和系统生物学的基础上,构建工程化的新生命体系,为生命科学提供了“从创造到理解”的新途径,不仅颠覆了从整体到局部的“格物致知—还原论”的传统研究策略,还开启了理解生命本质的“建物致知”新文化。同时,合成生物学将生物技术由“模拟自然过程”和“遗传工程改造”上升至“定量理性设计”和“标准化构建测试”的高度,推动生物工程和代谢工程朝着对标生命过程的高效率、普适性的工程化“重编程”,甚至是“从头构建”的新高度,以实现“建物致用”的目标。文章梳理了合成生物学近年来在生命科学基础研究中“建物致知”方面的重要进展,列举了其在化工、医药、食品、环境等应用领域中“建物致用”的具体案例,展现了合成生物学对人类社会全面发展正在发挥的重大影响,强调了为确保合成生物学的健康、快速发展,构建会聚生态系统与治理体系的重要性。

Comparative analysis of NovaSeq 6000 and MGISEQ 2000 single-cell RNA sequencing data

Background:Single-cell RNA sequencing(scRNA-seq)technology is now becoming a widely applied method of transcriptome exploration that helps to reveal cell-type composition as well as cell-state heterogeneity for specific biological processes.Distinct sequencing platforms and processing pipelines may contribute to various results even for the same sequencing samples.Therefore,benchmarking sequencing platforms and processing pipelines was considered as a necessary step to interpret scRNA-seq data.However,recent comparing efforts were constrained in sequencing platforms or analyzing pipelines.There is still a lack of knowledge of analyzing pipelines matched with specific sequencing platforms in aspects of sensitivity,precision,and so on.Methods:We downloaded public scRNA-seq data that was generated by two distinct sequencers,NovaSeq 6000 and MGISEQ 2000.Then data was processed through the Drop-seq-tools,UMI-tools and Cell Ranger pipeline respectively.We calculated multiple measurements based on the expression profiles of the six platform-pipeline combinations.Results:We found that all three pipelines had comparable performance,the Cell Ranger pipeline achieved the best performance in precision while UMI-tools prevailed in terms of sensitivity and marker calling.Conclusions:Our work provided an insight into the selection of scRNA-seq data processing tools for two sequencing platforms as well as a framework to evaluate platform-pipeline combinations.

生物合成科技与应用的监管

以生物体系为工具合成物质的生物合成技术,既是生物技术的重要组成,经历了从传统向现代的跨越;也是生物技术的重要出口,在食品、医药、化工等产业领域发挥着重要作用.合成生物学的兴起促进了生物合成技术及相关产业的变革.与基因工程技术相比,合成生物学技术会聚程度更高、赋能能力更强、产品类型更多,给现有监管体系带来的挑战也更大.如何科学有效地监管生物合成技术的研发与应用,使之在保障人民健康、保护环境、维护社会稳定的前提下,加速赋能技术研发和产业转化,已成为不可回避的紧迫问题.本文回顾了传统生物技术到近代生物技术,再到现代生物技术的三个阶段中生物合成监管的发展历史,系统梳理了生物合成监管的内涵;选取医药、传染病防控、食品和环境四个典型应用场景中的案例,就新技术、新产品带来的监管挑战进行了分析.最后,提出了明确监管政策衔接、调整、突破或创新的重点,开发科学评估的工具、标准和方法,建立综合治理与科学传播体系等针对性建议.

Characterization of heavy-chain antibody gene repertoires in Bactrian camels

Camelids are the only mammals that can produce functional heavy-chain antibodies(HCAbs).Although HCAbs were discovered over 30 years ago,the antibody gene repertoire of Bactrian camels remains largely underexplored.To characterize the diversity of variable genes of HCAbs(VHHs),germline and rearranged VHH repertoires are constructed.Phylogenetics analysis shows that all camelid VHH genes are derived from a common ancestor and the nucleotide diversity of VHHs is similar across all camelid species.While species-specific hallmark sites are identified,the non-canonical cysteines specific to VHHs are distinct in Bactrian camels and dromedaries compared with alpacas.Though low divergence at the germline repertoire between wild and domestic Bactrian camels,higher expression of VHHs is observed in some wild Bactrian camels than that of domestic ones.This study not only adds our understanding of VHH repertoire diversity across camelids,but also provides useful resources for HCAb engineering.

mRNA therapeutics:New vaccination and beyond

The idea of mRNA therapy had been conceived for decades before it came into reality during the Covid-19 pandemic.The mRNA vaccine emerges as a powerful and general tool against new viral infections,largely due to its versatility and rapid development.In addition to prophylactic vaccines,mRNA technology also offers great promise for new applications as a versatile drug modality.However,realizing the conceptual potential faces considerable challenges,such as minimal immune stimulation,high and long-term expression,and efficient delivery to target cells and tissues.Here we review the applications of mRNA-based therapeutics,with emphasis on the innovative design and future challenges/solutions.In addition,we also discuss the next generation of mRNA therapy,including circular mRNA and self-amplifying RNAs.We aim to provide a conceptual overview and outlook on mRNA therapeutics beyond prophylactic vaccines.

生物医学大数据生产要素价值的实现:从数据元素起步

基因组学/系统生物医学、转化医学、精准医学时代以来形成的生物医学大数据不仅是生物医学领域开展数据密集型研究的基石,成为与人口健康、社会发展和国家安全相关的战略资源,而且还是利用人工智能赋能“大健康”产业发展的核心生产要素(常简称为“数据要素”)。生物医学数据元素具有与生物和医学相关的“跨尺度、多源性、高维度、细粒度”等异质性复杂体系特征,因此,具有4V特征(Volume、Velocity、Variety、Veracity)的海量生物医学数据的数据元素必须经标准化规范整合并供共享分析,才能将海量生物医学数据质变转化为生物医学大数据,发挥生产要素的功能,实现生产要素的价值。这个价值释放的“要素化”过程,面临着特有的机遇与挑战,特别是已经成为生物学与健康医疗大数据最核心的基础的多组学及多模态数据,与欧美相比,我国数据“多而不强”,由于开放共享程度低、集中程度不高,难以评估数据质量。数据库是生物医学数据共享的主要载体,其数据来源和共享模式直接影响数据要素的价值释放过程。数据中心是数据库的建设及运维主体,也是各类数据元素转换为适用各类应用场景的数据要素的重要参与者和推动者,处于数据要素化不可或缺的核心环节。在从数据元素转换到数据要素的过程中,我们面临着存量数据规模与数据规范化集成的治理能力不匹配、已开放的数据规模与数据分析挖掘的治理能力不匹配的挑战,需要在数据、数据库、数据中心三个层面上加强数据治理和数据共享等基础性工作。我们建设了1(套整合交互共享导向的数据资源服务体系)-2(个标准化数据分析平台)-3(种科学/技术问题驱动的健康医学数据治理平台)-X(类面向应用场景的智能分析服务体系)的生物医学大数据技术体系,秉承“安全管理、信息共享、标准增值、技术创新、尊重产权、高效利用”理念,努力将数据中心从成本中心转换为价值中心,可为生物医学大数据“要素化”提供借鉴。

多组学大数据共享平台研究进展

高通量检测技术的快速发展催生了海量的多组学数据,数据驱动型研究规模正逐步超越传统假设型研究。不同层次组学数据的组合,通过对系统生物学和疾病发展更深入和全面的解读,持续改变生物医学研究方式。同时,多组学数据庞大的数据规模、异质的数据特性,以及强烈的数据共享内源性需求,都推动组学数据向规模化、平台化、标准化共享的方向发展。该文首先介绍了代表性的多组学平台和各组学数据的特点,接着以多维组学数据百科全书NODE为例,从多组学数据融合和多组学数据安全共享两方面对相应的方法和技术进行了细致的阐述,并展望了多组学数据平台未来的发展方向。

Current progress,challenges,and future perspectives of language models for protein representation and protein design

The sequence-structure-function paradigm of protein is the basis of molecular biology.What is the underlying mechanism of such sequence and structure/function corresponding relationship?We reviewed the methods for protein representation and protein design.With these protein representation models,we can accurately predict many properties of proteins,such as stability and binding affinity.

Back to Science in Searching for SARS-CoV-2 Origins

In recent decades,emerging and re-emerging human-infecting pathogens have been represented as huge threats to public health and have become a global concern(1).After outbreaks of two coronaviruses(CoVs),severe acute respiratory syndrome coronavirus(SARS-CoV)and Middle East respiratory syndrome coronavirus(MERS-CoV),severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)became the first-known pandemic hastening CoV with tremendous wrecking to the world(2).The origin tracing of these emerging pathogens is of great significance in infectious disease prevention and control(3–4).The origin of SARS-CoV-2 remains elusive after the more than 3-year pandemic,though scientists around the world are making great efforts.From the experience of studying many other infectious pathogens,origin tracing is systematic and time-consuming work.The supposed origins of many infectious pathogens are still in debate,including SARS-CoV and human immunodeficiency virus,etc(5).

生物医学大数据发展的新挑战与趋势

生物医学数据从PB量级的组学时代进入到EB量级的多维度大数据时代,引发了生物医学研究向数据密集型的第四科学范式的深刻变革。如何将临床数据与研究数据进行高维度多层次的汇交共享,实现从"组学"到临床与健康人群数据的生物医学大数据的综合管理利用,从而使大数据迅速转化为新知识,成为生物医学大数据所面临的挑战。发展以递交为基础、以整合为导向的数据存储技术,以主题为基础、以交互为导向的数据共享技术,以及以传统信息技术为基础、以前沿信息技术为导向的数据分析挖掘技术,并同时开展标准质控相关研究,是生物医学大数据存储、共享和转化的新思路,也是构建新一代生物医学大数据研究中心的技术关键和未来趋势。

A systematic survey of PRMT interactomes reveals the key roles of arginine methylation in the global control of RNA splicing and translation

Thousands of proteins undergo arginine methylation,a widespread post-translational modification catalyzed by several protein arginine methyltransferases(PRMTs).However,global understanding of their biological functions is limited due to the lack of a complete picture of the catalytic network for each PRMT.Here,we systematically identified interacting proteins for all human PRMTs and demonstrated their functional importance in mRNA splicing and translation.We demonstrated significant overlapping of interactomes of human PRMTs with the known methylarginine-containing proteins.Different PRMTs are functionally redundant with a high degree of overlap in their substrates and high similarities between their putative methylation motifs.Importantly,RNA-binding proteins involved in regulating RNA splicing and translation contain highly enriched arginine methylation regions.Moreover,inhibition of PRMTs globally alternates alternative splicing(AS)and suppresses translation.In particular,ribosomal proteins are extensively modified with methylarginine,and mutations in their methylation sites suppress ribosome assembly,translation,and eventually cell growth.Collectively,our study provides a global view of different PRMT networks and uncovers critical functions of arginine methylation in regulating mRNA splicing and translation.

Peripheral CD4^(+) T cell signatures in predicting the responses to anti-PD-1/PD-L1 monotherapy for Chinese advanced non-small cell lung cancer

Limited benefit population of immune checkpoint inhibitors makes it urgent to screen predictive biomarkers for stratifying the patients.Herein,we have investigated peripheral CD4^(+) T cell signatures in advanced non-small cell lung cancer(NSCLC)patients receiving anti-PD-1/PD-L1 treatments.It was found that the percentages of IFN-γand IL-17A secreting naïve CD4^(+) T cells(Tn),and memory CD4^(+) T cells(Tm)expressing PD-1,PD-L1 and CTLA-4 were significantly higher in responder(R)than non-responder(NonR)NSCLC patients associated with a longer progression free survival(PFS).Logistic regression analysis revealed that the baseline IFN-γ-producing CD4^(+) Tn cells and PD-1^(+)CD4^(+) Tm cells were the most significant signatures with the area under curve(AUC)value reaching 0.849.This was further validated in another anti-PD-1 monotherapy cohort.Conversely,high percentage of CTLA-4^(+)CD4^(+) Tm cells was associated with a shorter PFS in patients receiving anti-PD-L1 monotherapy.Our study therefore elucidates the significance of functional CD4^(+) Tn and Tm subpopulations before the treatment in predicting the responses to anti-PD-1 treatment in Chinese NSCLC patients.The fact that there display distinct CD4^(+) T cell signatures in the prediction to anti-PD-1 and anti-PD-L1 monotherapy from our study provides preliminary evidence on the feasibility of anti-PD-1 and anti-PD-L1 combination therapy for advanced NSCLC patients.