里氏木霉及其纤维素酶高产菌株的研究进展

随着纤维素在能源、材料及化工等领域的广泛开发和应用,里氏木霉作为一种重要的产纤维素酶工业用菌种,越来越受到人们的广泛关注。为了提高其酶活,人们做了大量的工作,获得了一些相当好的突变株。对里氏木霉及其突变株的基因组进行研究,有助于人们理解其高效产酶的机制,同时也有利于构建其基因工程菌。介绍里氏木霉Trichoderma reesei的背景及其部分高产纤维素酶突变株,并阐述近些年来对其突变株的基因组的研究进展。

DNA Base Editing Induces Substantial Off-target RNA Mutations and Their Elimination by Mutagenesis

In a study published in Nature on June 10, researchers from Dr. YANG Hui’s Lab at the CAS Institute of Neuroscience (ION), and collaborators from the CAS-MPG Partner Institute for Computational Biology (PICB) and Sichuan University demonstrated that DNA base editors generated tens of thousands of off-target RNA single nucleotide variants (SNVs) and these off-target SNVs could be eliminated by introducing point mutations to the deaminases, built-in enzymes that act to rewrite the DNA bases.

烟用接装纸的FT-NIRs模式识别及内在质量稳定性评价

为了评判烟用接装纸内在质量稳定性,应用近红外光纤漫反射技术扫描烟用接装纸,对所得光谱进行标准正则变换(Standard Normal Variation,SNV)和一阶微分处理后,采用主成分分析(PrincipalComponent Analysis,PCA)法进行特征抽提,根据主成分空间下的马氏距离建立校正集,对不同厂家烟用接装纸进行模式识别,并建立评价模型对相同厂家接装纸内在质量稳定性进行评判。结果表明,建立的校正集模型可有效识别不同厂家的烟用接装纸,评价模型对相同厂家接装纸内在质量稳定性的判别完全准确。

生物组织拉曼光谱定量测量的方法研究

不同的生物样本和切片,在进行拉曼探测时,会因为聚焦位置的差异及系统自身的原因导致同一光谱多次检测得到的信号强度不同,难以作定量分析。因此,需要找到一种可行性强的内标或外标的方法,从而有利于比较分析不同强度的拉曼带,实现定量检测。本文以犬类髋关节软骨切片为例,在偏振与非偏振情况下,研究标准正态变换(SNV)及多元散射校正(MSC)等常用方法对拉曼光谱的处理效果,并探究伊红染色剂(Eosin)作为一种新的内标的实际使用效果。结果发现:在非偏振条件下,MSC处理效果更理想;在偏振条件下,以伊红的特征峰1501cm^-1作为拉曼内标效果更理想。本研究证明了伊红的拉曼光谱不具有各向异性,有利于实现生物组织偏振拉曼光谱测量的归一化,且操作简单,结果更可靠。本文的结果为生物样本的拉曼光谱定量分析提供了一种可行的新方法。

S_NV反应的研究进展

SNV反应是双键碳原子上的亲核取代反应,利用该反应可以制备含有双键的多官能团化合物。对SNV反应进行了总结,描述了碳负离子中间体结构特征及异头效应的作用。展望了其在有机合成中的应用前景。

禽网状内皮组织增生症病毒SNV株人工感染诱导细胞异常增殖研究

试验旨在研究实验室条件下人工感染对禽网状内皮组织增生症病毒(REV)非缺陷型毒株SNV致瘤能力的影响。通过将SNV株人工接种于7日胚龄SPF鸡胚、1日龄SPF雏鸡以及鸡胚成纤维细胞(CEF),动态观察被感染鸡只内脏器官肿瘤发生的特点与规律,同时监测CEF的分裂增殖能力及生长状态。结果表明:鸡胚接种SNV株后肿瘤发生的时间提前,最早可在2周龄雏鸡的肝脏、肾脏等器官发现网状细胞瘤及其他细胞的异常增生灶;免疫组化检测结果进一步显示这些异常生长的细胞内增殖细胞核抗原(PCNA)表达量上升。CCK-8及P-半乳糖苷酶检测结果显示,感染SNV株的CEF增殖能力增强且衰老减缓;Western-blot结果进一步显示,感染组CEF中的PCNA表达量显著上升。由此可知,在实验室条件及人工接种等因素的影响下,原先具有慢性致瘤作用的SNV株致病能力已发生改变,表现为机体发生肿瘤的时间提前和体外培养的细胞异常增殖。

Selective Enrichment of Low-Abundance DNA Variants Based on Programmable Peptide Nucleic Acid Probes

Single-nucleotide variants(SNVs)are crucial in disease development,but their accurate detection is challenging due to their low abundance and interference from wild-type targets.Although nucleic acid analogs like peptide nucleic acids(PNAs)have been used for SNV detection,they often lack programmable sensitivity and specificity due to poorly calculated thermodynamics and kinetics.Here,we present a computational method for calculating the stacking energy of PNA and DNA hybrids,leveraging nearest neighbor parameters.Validation against experimental data from 16 sequences under varied hybridization conditions yielded good agreement using Bland-Altman analysis,with all data points falling within the confidence interval.Our findings indicate that PNA-DNA hybridization is thermodynamically more stable and exhibits kinetics 140-fold faster than DNA-DNA hybridization for identical sequences.Utilizing this computational framework,we designed PNA toehold probes,which were screened via simulations and experiments.This combined approach facilitated the identification of highly sensitive and specific PNA toehold probes for single point mutation detection via strand displacement reaction.Our results demonstrate the successful application of PNA toehold probes for detecting point mutations with high sensitivity and specificity,achieving a selective amplification of approximately 200-fold for variants with a variant allele frequency(VAF)of 0.5%using quantitative polymerase chain reaction.

Whole-genome sequencing identifies novel genes for autism in Chinese trios

Autism spectrum disorder(ASD)is a neurodevelopmental disorder with high genetic heritability but heterogeneity.Fully understanding its genetics requires whole-genome sequencing(WGS),but the ASD studies utilizing WGS data in Chinese population are limited.In this study,we present a WGS study for 334 individuals,including 112 ASD patients and their non-ASD parents.We identified 146 de novo variants in coding regions in 85 cases and 60 inherited variants in coding regions.By integrating these variants with an association model,we identified 33 potential risk genes(P<0.001)enriched in neuron and regulation related biological process.Besides the well-known ASD genes(SCN2A,NF1,SHANK3,CHD8 etc.),several high confidence genes were highlighted by a series of functional analyses,including CTNND1,DGKZ,LRP1,DDN,ZNF483,NR4A2,SMAD6,INTS1,and MRPL12,with more supported evidence from GO enrichment,expression and network analysis.We also integrated RNA-seq data to analyze the effect of the variants on the gene expression and found 12 genes in the individuals with the related variants had relatively biased expression.We further presented the clinical phenotypes of the proband carrying the risk genes in both our samples and Caucasian samples to show the effect of the risk genes on phenotype.Regarding variants in noncoding regions,a total of 74 de novo variants and 30 inherited variants were predicted as pathogenic with high confidence,which were mapped to specific genes or regulatory features.The number of de novo variants found in patient was significantly associated with the parents’ages at the birth of the child,and gender with trend.We also identified small de novo structural variants in ASD trios.The results in this study provided important evidence for understanding the genetic mechanism of ASD.

近红外光谱技术对圆枣产地的判别分析

采用近红外高光谱成像系统对3类不同产地的圆枣进行判别分析,快速鉴别圆枣产地。应用近红外高光谱获取3类圆枣样本的光谱数据,对光谱采用标准正则变换(SNV)方法预处理,从原始光谱数据中提取特征波长,并建立全波段与特征波段下的线性判别模型来判别3类圆枣的产地。结果表明,近红外光谱结合线性判别法对圆枣产地鉴别的特征波段模型可有效替代全波段模型,模型准确率均大于99%,为实现农产品产地鉴别和自动分类提供理论依据。

Characterization and validation of somatic mutation spectrum to reveal heterogeneity in gastric cancer by single cell sequencing

Gastric cancer(GC) is a highly heterogeneous disease with multiple cellular types and poor prognosis.However, the cellular evolution and molecular basis of GC at the individual intra-tumor level has not been well demonstrated. We performed single-cell whole exome sequencing to detect somatic singlenucleotide variants(SNVs) and significantly mutated genes(SMGs) among 34 tumor cells and 9 normal cells from a patient with GC. The Complete Prediction for Protein Conformation(CPPC) approach directly predicting the folding conformation of the protein 3D structure with Protein Folding Shape Code, combined with functional experiments were used to confirm the characterization of mutated SMGs in GC cells. We identified 201 somatic SNVs, including 117 non-synonymous mutations in GC cells. Further analysis identified 24 significant mutated genes(SMGs) in single cells, for which a single amino acid change might affect protein conformation. Among them, two genes(CDC27 and FLG) that were mutated only in single cells but not in the corresponding tumor tissue, were recurrently present in another GC tissue cohort, and may play a potential role to promote carcinogenesis, as confirmed by functional characterization. Our findings showed a mutational landscape of GC at intra-tumor level for the first time and provided opportunities for understanding the heterogeneity and individualized target therapy for this disease.

Gut microbiome evolution impacts the clinical outcomes of diseases

The human digestive tract is home to trillions of microbes,and owing to the advent of next-generation sequencing at the turn of the century and the development of bioinformatics technology,researchers have been able to unravel the picture of the gut microbiome(1).

Transcription-wide impact by RESCUE-induced off-target single-nucleotide variants in mammalian cells

RNA base editing is a promising tool in precise molecular therapy. Currently, there are two widely used RNA base editors, REPAIRand RESCUE. REPAIR only facilitates A-to-I conversions, while RESCUE performs both A-to-I and C-to-U conversions. Thus, RESCUEcan generate twice the number of mutations compared to REPAIR. However, transcription-wide impact due to RESCUE-induced offtarget single-nucleotide variants (SNVs) is not fully appreciated. Therefore, to determine the off-target effects of RESCUE-mediatedediting, we employed transcription-wide sequencing on cells edited by RESCUE. The SNVs showed different off-target effects onmRNA, circRNA, lncRNA, and miRNA expression patterns and their interacting networks. Our results illustrate the transcriptionwide impact of RESCUE-induced off-target SNVs and highlight the need for careful characterization of the off-target impact by thisediting platform.

Genomic and functional evaluation of TNFSF14 in multiple sclerosis susceptibility

Among multiple sclerosis(MS)susceptibility genes,the strongest non-human leukocyte antigen(HLA)signal in the Italian population maps to the TNFSF14 gene encoding LIGHT,a glycoprotein involved in dendritic cell(DC)maturation.Through fine-mapping in a large Italian dataset(4,198 patients with MS and3,903 controls),we show that the TNFSF14 intronic SNP rs1077667 is the primarily MS-associated variant in the region.Expression quantitative trait locus(e QTL)analysis indicates that the MS risk allele is significantly associated with reduced TNFSF14 messenger RNA levels in blood cells,which is consistent with the allelic imbalance in RNA-Seq reads(P<0.0001).The MS risk allele is associated with reduced levels of TNFSF14 gene expression(P<0.01)in blood cells from 84 Italian patients with MS and 80 healthy controls(HCs).Interestingly,patients with MS are lower expressors of TNFSF14 compared to HC(P<0.007).Individuals homozygous for the MS risk allele display an increased percentage of LIGHT-positive peripheral blood myeloid DCs(CD11 c+,P=0.035)in 37 HCs,as well as in in vitro monocyte-derived DCs from 22 HCs(P=0.04).Our findings suggest that the intronic variant rs1077667 alters the expression of TNFSF14 in immune cells,which may play a role in MS pathogenesis.

Finding neoepitopes in mouse models of personalized cancer immunotherapy

BACKGROUND： Cancer immunotherapy uses one＇s own immune system to fight cancerous cells. As immune system is hard- wired to distinguish self and non-self, cancer immunotherapy is predicted to target cancerous cells specifically, therefore is less toxic than chemotherapy and radiation therapy, two major treatments for cancer. Cancer immunologists have spent decades to search for the specific targets in cancerous cells. METHODS： Due to the recent advances in high throughput sequencing and bioinformatics, evidence has merged that the neoantigens in cancerous cells are probably the cancer-specific targets that lead to the destruction of cancer. We will review the transplantable murine tumor models for cancer immunotherapy and the bioinformatics tools used to navigate mouse genome to identify tumor-rejecting neoantigens. RESULTS： Several groups have independently identified point mutations that can be recognized by T cells of host immune system. It is consistent with the note that the formation ofpeptide-MHC I-TCR complex is critical to activate T cells. Both anchor residue and TCR-facing residue mutations have been reported. While TCR-facing residue mutations may directly activate specific T cells, anchor residue mutations improve the binding of peptides to MHC I molecules, which increases the presentation of peptides and the T cell activation indirectly. CONCLUSIONS： Our work indicates that the affinity of neoepitopes for MHC I is not a predictor for anti-tumor immune responses in mice. Instead differential agretopic index （DAI）, the numerical difference of epitope-MHC I affinities between the mutated and un-mutated sequences is a significant predictor. A similar bioinformatics pipeline has been developed to generate personalized vaccines to treat human ovarian cancer in a Phase I clinical trial.

Analysis of Allele Specific Expression——A Survey

Allele specific expression is essential for cellular programming and development and the diversity of cellular phenotypes. Traditional analysis methods utilize RNA and depend on single nucleotide polymorphisms,thus to suffer from limited amount of materials for analysis. The rapid development of next-generation sequencing technologies provides more comprehensive and powerful approaches to analyze the genomic, epigenetic, and transcriptomic data, and further to detect and measure allele specific expressions. It will potentially enhance the understanding of the allele specific expressions, their complexities, and the effect on biological processes. In this paper, we extensively review the state-of-art enabling technologies and tools to analyze, detect, and measure allele specific expressions, compare their features, and point out the future trend of the methods.