基于RNA-Seq技术对不同品种猪排卵前卵泡差异表达基因的筛选与注释

[目的]筛选淮猪和大约克猪排卵前卵泡组织差异表达基因。[方法]采集淮猪和大约克猪各3头的排卵前卵泡,提取RNA,个体独立建库。采用RNA测序技术以及GO和pathway方法对所获序列进行显著性富集分析。[结果]测序后,获得的淮猪clean reads数分别为85958912、91701082、84448828,其中有效Reads数分别达到98.39%、98.59%、96.04%;获得的大约克猪clean reads数分别为87914166、80480542、85994612,其中有效reads数分别达到98.61%、96.95%、98.80%,测序饱和度良好(证明测序数据真实可靠)。结果表明,筛选到上调基因67个,下调基因54个。GO和Pathway分析发现,差异表达基因富集在繁殖过程、生长过程和代谢过程及WNT信号通路、卵巢类固醇生成通路和类固醇生物合成通路等。[结论]获得了猪排卵前卵泡基因表达谱,并筛选到与繁殖性状相关的关键候选基因。

基于RNA-seq对复合益生菌作用于肉鸡回肠中新转录本预测及可变剪接、SNP分析

为分析复合益生菌饲喂肉鸡回肠转录组中的新转录本预测、可变剪接事件和SNP,选择200只1日龄黄麻肉鸡随机分为2组,每组5个重复,每个重复20只。对照组正常饮水,益生菌组饮用水中补充1%复合菌制剂,试验分为生长前期(1~21 d)、后期(22~42 d)两个阶段。42日龄时进行肉鸡屠宰试验,取对照组和益生菌组回肠各3个样本进行转录组测序。对测序数据进行分析,共发现1047个新基因,20176个新转录本,276个新基因在GO数据库中得到归类注释。对转录组数据进行可变剪接分析,外显子跳跃(SE)占可变剪接类型的比例最高,共筛选到1131个显著的差异剪接基因,在外显子跳跃(SE)、第一个外显子可变剪接(A5SS)、最后一个外显子可变剪接(A3SS)、外显子选择性跳跃(MXE)、内含子滞留(RI)事件中鉴定到的差异剪接基因数分别为588、139、176、136、92。对获得的差异剪接基因进行GO富集分析,主要富集在代谢和免疫GO term。KEGG富集到了胞吞作用、MAPK信号通路等。在各样品中大多数碱基取代均为转换大于颠换,其中转换单核苷酸多态性(SNP)所占百分比为73.53%~73.94%;颠换型SNP所占百分比为26.05%~26.47%。试验对对照组、益生菌组肉鸡的回肠进行了RNA-seq测序分析,为发掘复合益生菌作用于肉鸡回肠中的可变剪接事件提供基础,为进一步了解复合益生菌作用于肉鸡回肠中新转录本的发现和SNP位点提供了数据支撑。

RNA-sequencing expression profile and functional analysis of retinal pigment epithelium in atrophic age-related macular degeneration

The retinal pigment epithelium(RPE)is fundamental to sustaining retinal homeostasis.RPE abnormality leads to visual defects and blindness,including age-related macular degeneration(AMD).Although breakthroughs have been made in the treatment of neovascular AMD,effective intervention for atrophic AMD is largely absent.The adequate knowledge of RPE pathology is hindered by a lack of the patients'RPE datasets,especially at the single-cell resolution.In the current study,we delved into a large-scale single-cell resource of AMD donors,in which RPE cells were occupied in a substantial proportion.Bulk RNA-seq datasets of atrophic AMD were integrated to extract molecular characteristics of RPE in the pathogenesis of atrophic AMD.Both in vivo and in vitro models revealed that carboxypeptidase X,M14 family member 2(CPXM2),was specifically expressed in the RPE cells of atrophic AMD,which might be induced by oxidative stress and involved in the epithelial-mesenchymal transition of RPE cells.Additionally,silencing of CPXM2 inhibited the mesenchymal phenotype of RPE cells in an oxidative stress cell model.Thus,our results demonstrated that CPXM2 played a crucial role in regulating atrophic AMD and might serve as a potential therapeutic target for atrophic AMD.

BSA-Seq结合RNA-Seq技术挖掘大豆叶片提前黄化衰老基因

大豆产量与其生殖生长的持续时间呈正相关,延缓其开花后叶片的衰老,提高其生理功能,有利于增加植物的粒重。叶片黄化是植物衰老的显著特征之一。关于在大豆鼓粒后期叶片黄化的研究鲜见报道。本研究对鼓粒后期叶片提前黄化突变体ly和野生型ofc杂交组合进行遗传分析,结果表明,大豆鼓粒后期叶片提前黄化性状受单隐性核基因控制。通过BSA-Seq在19号染色体得到一个2.23Mb的初定位区间,经开发标记图位克隆后将区间缩短至1.75 Mb,区间内有219个基因,再结合RNA-Seq分析,得到了区间内12个候选基因,其中有4个SNP变异基因和8个差异表达基因。本研究结果为大豆鼓粒后期叶片黄化衰老基因的克隆奠定了基础。

基于RNA-Seq分析大白猪和巴马香猪背最长肌组织基因表达差异

以180日龄的大白猪(n=6)与巴马香猪(n=6)为研究对象,屠宰后测定肌内脂肪、肉色、嫩度、pH、滴水损失等肉质性状,采集背最长肌组织提取总RNA,利用RNA-Seq技术鉴定大白猪和巴马香猪背最长肌组织中的表达差异基因及其功能富集通路。结果发现,以大白猪为对照组,在巴马香猪和大白猪背最长肌组织中共鉴定到5 037个差异表达基因,其中有3 326个上调表达基因,1 711个下调表达基因。经过KEGG富集通路分析,发现巴马香猪和大白猪一共有168条显著富集通路,其中上调DEGs有92条富集路径,下调DEGs有76条富集路径;GO功能分析中上调表达基因富集到BP有3 058条、CC有413条、MF有559条,下调表达基因富集到BP有3 050条、CC有415条、MF有557条。大白猪与巴马香猪肉色的亮度L在45min时呈极显著差异;大白猪与巴马香猪之间的肌内脂肪、水分均呈现显著差异。本文对大白猪与巴马香猪肌内脂肪含量进行转录组测序分析,初步认为CAPN3、MTM1、TMOD4等基因在巴马香猪肉质中发挥重要作用。

High-throughput calculations combining machine learning to investigate the corrosion properties of binary Mg alloys

Magnesium(Mg)alloys have shown great prospects as both structural and biomedical materials,while poor corrosion resistance limits their further application.In this work,to avoid the time-consuming and laborious experiment trial,a high-throughput computational strategy based on first-principles calculations is designed for screening corrosion-resistant binary Mg alloy with intermetallics,from both the thermodynamic and kinetic perspectives.The stable binary Mg intermetallics with low equilibrium potential difference with respect to the Mg matrix are firstly identified.Then,the hydrogen adsorption energies on the surfaces of these Mg intermetallics are calculated,and the corrosion exchange current density is further calculated by a hydrogen evolution reaction(HER)kinetic model.Several intermetallics,e.g.Y_(3)Mg,Y_(2)Mg and La_(5)Mg,are identified to be promising intermetallics which might effectively hinder the cathodic HER.Furthermore,machine learning(ML)models are developed to predict Mg intermetallics with proper hydrogen adsorption energy employing work function(W_(f))and weighted first ionization energy(WFIE).The generalization of the ML models is tested on five new binary Mg intermetallics with the average root mean square error(RMSE)of 0.11 eV.This study not only predicts some promising binary Mg intermetallics which may suppress the galvanic corrosion,but also provides a high-throughput screening strategy and ML models for the design of corrosion-resistant alloy,which can be extended to ternary Mg alloys or other alloy systems.

基于RNA-Seq探究蔗糖影响黑果枸杞枝刺发生的机理

【目的】黑果枸杞Lycium ruthenicum是集生态、经济和药用价值等为一体的灌木。然而,其密集的枝刺严重阻碍其作为经济林推广,本研究旨在探究蔗糖(Suc)促进黑果枸杞(黑杞)枝刺的发生机理,为培育其无刺优良品种提供依据。【方法】在证明Suc通过能量和信号路径促进黑杞枝刺发生的基础上,利用RNA-Seq对无刺茎顶芽(TleCK)、有刺茎顶芽(ThoCK)、黑暗处理的有刺茎顶芽(ThoDCK)、减少内源Suc处理后有刺变无刺茎顶芽(TleDPL)和喷施外源Suc后无刺变有刺茎顶芽(ThoSuc)5种样品的差异表达基因(DEG)和叶绿体差异基因(cpDEG)进行筛选和分析,并采用RT-qPCR验证了9个DEG的表达情况。【结果】5组有刺和无刺材料顶芽之间(ThoCK vs.TleCK、ThoCK vs.TleDPL、ThoDCK vs.TleDPL、TleCK vs.ThoSuc和TleDPL vs.ThoSuc)的DEG分别为5281、4363、3125、5226和3278个,这5组DEG共同显著富集在9个GO条目和4个KEGG通路(“类黄酮生物合成”“二萜生物合成”“苯丙烷生物合成”、“芪类、二芳基庚烷和姜辣素生物合成”)。韦恩图显示5组比较组共有的DEG是196个,其中33个被预测为转录因子基因,较多转录因子基因被预测为NAC和WRKY家族。5组比较组有3个共有DEG注释在植物激素信号转导KEGG通路,分别参与生长素、茉莉酸和水杨酸信号传导。3个关键有刺和无刺比较组(ThoCK vs.TleCK、ThoCK vs.TleDPL和TleCK vs.ThoSuc)中共有2个DEG被预测为TCP转录因子基因,这3个比较组均有cpDEG被注释到光合作用KEGG通路。9个DEG的RT-qPCR验证了RNA-Seq结果的可靠性。【结论】Suc可能通过影响生长素、茉莉酸和水杨酸信号传导以及赤霉素合成基因表达来促进黑杞枝刺发生,枝刺发生也可能与类黄酮积累和木质素减少有关,而且增施外源和减少内源Suc并非通过简单地逆转cpDEG的表达来影响枝刺发生。注释到WRKY、TCP和NAC转录因子家族的共有DEG可能是调控枝刺发生的关键基因。本研究建立了Suc影响黑杞枝刺发生的机理模型图。

Integrating artificial intelligence and high-throughput phenotyping for crop improvement

Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.

A review on advances of high-throughput experimental technology for titanium alloys

Ti alloys,as leading lightweight and high-strength metallic materials,exhibit significant application potential in aerospace,marine engineering,biomedical,and other industries.However,the lack of fundamental understanding of the microstructure−property relationship results in prolonged research and development(R&D)cycles,hindering the optimization of the performance of Ti alloys.Recently,the advent of high-throughput experimental(HTE)technology has shown promise in facilitating the efficient and demand-driven development of next-generation Ti alloys.This work reviews the latest advancements in HTE technology for Ti alloys.The high-throughput preparation(HTP)techniques commonly used in the fabrication of Ti alloys are addressed,including diffusion multiple,additive manufacturing(AM),vapor deposition and others.The current applications of high-throughput characterization(HTC)techniques in Ti alloys are shown.Finally,the research achievements in HTE technology for Ti alloys are summarized and the challenges faced in their industrial application are discussed.

From the perspective of experimental practice: High-throughput computational screening in photocatalysis

Photocatalysis,a critical strategy for harvesting sunlight to address energy demand and environmental concerns,is underpinned by the discovery of high-performance photocatalysts,thereby how to design photocatalysts is now generating widespread interest in boosting the conversion effi-ciency of solar energy.In the past decade,computational technologies and theoretical simulations have led to a major leap in the development of high-throughput computational screening strategies for novel high-efficiency photocatalysts.In this viewpoint,we started with introducing the challenges of photocatalysis from the view of experimental practice,especially the inefficiency of the traditional“trial and error”method.Sub-sequently,a cross-sectional comparison between experimental and high-throughput computational screening for photocatalysis is presented and discussed in detail.On the basis of the current experimental progress in photocatalysis,we also exemplified the various challenges associated with high-throughput computational screening strategies.Finally,we offered a preferred high-throughput computational screening procedure for pho-tocatalysts from an experimental practice perspective(model construction and screening,standardized experiments,assessment and revision),with the aim of a better correlation of high-throughput simulations and experimental practices,motivating to search for better descriptors.

Development of a High-throughput Sequencing Platform for Detection of Viral Encephalitis Pathogens Based on Amplicon Sequencing

Objective Viral encephalitis is an infectious disease severely affecting human health.It is caused by a wide variety of viral pathogens,including herpes viruses,flaviviruses,enteroviruses,and other viruses.The laboratory diagnosis of viral encephalitis is a worldwide challenge.Recently,high-throughput sequencing technology has provided new tools for diagnosing central nervous system infections.Thus,In this study,we established a multipathogen detection platform for viral encephalitis based on amplicon sequencing.Methods We designed nine pairs of specific polymerase chain reaction(PCR)primers for the 12 viruses by reviewing the relevant literature.The detection ability of the primers was verified by software simulation and the detection of known positive samples.Amplicon sequencing was used to validate the samples,and consistency was compared with Sanger sequencing.Results The results showed that the target sequences of various pathogens were obtained at a coverage depth level greater than 20×,and the sequence lengths were consistent with the sizes of the predicted amplicons.The sequences were verified using the National Center for Biotechnology Information BLAST,and all results were consistent with the results of Sanger sequencing.Conclusion Amplicon-based high-throughput sequencing technology is feasible as a supplementary method for the pathogenic detection of viral encephalitis.It is also a useful tool for the high-volume screening of clinical samples.

High-throughput microfluidic production of carbon capture microcapsules:fundamentals,applications,and perspectives

In the last three decades,carbon dioxide(CO_(2)) emissions have shown a significant increase from various sources.To address this pressing issue,the importance of reducing CO_(2) emissions has grown,leading to increased attention toward carbon capture,utilization,and storage strategies.Among these strategies,monodisperse microcapsules,produced by using droplet microfluidics,have emerged as promising tools for carbon capture,offering a potential solution to mitigate CO_(2) emissions.However,the limited yield of microcapsules due to the inherent low flow rate in droplet microfluidics remains a challenge.In this comprehensive review,the high-throughput production of carbon capture microcapsules using droplet microfluidics is focused on.Specifically,the detailed insights into microfluidic chip fabrication technologies,the microfluidic generation of emulsion droplets,along with the associated hydrodynamic considerations,and the generation of carbon capture microcapsules through droplet microfluidics are provided.This review highlights the substantial potential of droplet microfluidics as a promising technique for large-scale carbon capture microcapsule production,which could play a significant role in achieving carbon neutralization and emission reduction goals.

High-throughput studies and machine learning for design of β titanium alloys with optimum properties

Based on experimental data,machine learning(ML) models for Young's modulus,hardness,and hot-working ability of Ti-based alloys were constructed.In the models,the interdiffusion and mechanical property data were high-throughput re-evaluated from composition variations and nanoindentation data of diffusion couples.Then,the Ti-(22±0.5)at.%Nb-(30±0.5)at.%Zr-(4±0.5)at.%Cr(TNZC) alloy with a single body-centered cubic(BCC) phase was screened in an interactive loop.The experimental results exhibited a relatively low Young's modulus of(58±4) GPa,high nanohardness of(3.4±0.2) GPa,high microhardness of HV(520±5),high compressive yield strength of(1220±18) MPa,large plastic strain greater than 30%,and superior dry-and wet-wear resistance.This work demonstrates that ML combined with high-throughput analytic approaches can offer a powerful tool to accelerate the design of multicomponent Ti alloys with desired properties.Moreover,it is indicated that TNZC alloy is an attractive candidate for biomedical applications.

Revealing the role of honokiol in human glioma cells by RNA-seq analysis

Background:Glioma is a kind of tumor that easily deteriorates and originates from glial cells in nerve tissue.Honokiol is a bisphenol compound that is an essential monomeric compound extracted from the roots and bark of Magnoliaceae plants.It also has anti-infection,antitumor,and immunomodulatory effects.In this study,we found that honokiol induces cell apoptosis in the human glioma cell lines U87-MG and U251-MG.However,the mechanism through which honokiol regulates glioma cell apoptosis is still unknown.Methods:We performed RNA-seq analysis of U251-MG cells treated with honokiol and control cells.Protein-protein interaction(PPI)network analysis was performed,and the 10 top hub unigenes were examined via real-time quantitative PCR.Furthermore,MAPK signaling and ferroptosis were detected via western blotting.Results:332 differentially expressed genes(DEGs)were found,comprising 163 increased and 169 decreased genes.Analysis of the DEGs revealed that various biological processes were enriched,including‘response to hypoxia’,‘cerebellum development cellular response to hypoxia,’‘iron ion binding,’‘oxygen transporter activity,’‘oxygen binding,’‘ferric iron binding,’and‘structural constituent of cytoskeleton.’Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analysis revealed that the DEGs were enriched in the following pathways:‘mitogen-activated protein kinases(MAPK)’,‘Hypoxia-inducible factor 1(HIF-1)’,‘ferroptosis,’‘Peroxisome proliferator-activated receptor(PPAR),’‘Phosphatidylinositol-4,5-bisphosphate 3-kinase(PI3K)-protein kinase B(Akt),’and‘phagosome.’Among these pathways,the MAPK signaling pathway and ferroptosis were verified.Conclusion:This study revealed the potential mechanism by which honokiol induces apoptosis and provided a comprehensive analysis of DEGs in honokiol-treated U251-MG cells and the associated signaling pathways.These data could lead to new ideas for future research and therapy for patients with glioma.

Meiotic transcriptional reprogramming mediated by cell-cell communications in humans and mice revealed by scATACseq and scRNA-seq

Meiosis is a highly complex process significantly influenced by transcriptional regulation.However,studies on the mechanisms that govern transcriptomic changes during meiosis,especially in prophase I,are limited.Here,we performed single-cell ATAC-seq of human testis tissues and observed reprogramming during the transition from zygotene to pachytene spermatocytes.This event,conserved in mice,involved the deactivation of genes associated with meiosis after reprogramming and the activation of those related to spermatogenesis before their functional onset.Furthermore,we identified 282 transcriptional regulators(TRs)that underwent activation or deactivation subsequent to this process.Evidence suggested that physical contact signals from Sertoli cells may regulate these TRs in spermatocytes,while secreted ENHO signals may alter metabolic patterns in these cells.Our results further indicated that defective transcriptional reprogramming may be associated with non-obstructive azoospermia(NOA).This study revealed the importance of both physical contact and secreted signals between Sertoli cells and germ cells in meiotic progression.

Interpretable Machine Learning-Assisted High-Throughput Screening for Understanding NRR Electrocatalyst Performance Modulation between Active Center and C-N Coordination

Understanding the correlation between the fundamental descriptors and catalytic performance is meaningful to guide the design of high-performance electrochemical catalysts.However,exploring key factors that affect catalytic performance in the vast catalyst space remains challenging for people.Herein,to accurately identify the factors that affect the performance of N2 reduction,we apply interpretable machine learning(ML)to analyze high-throughput screening results,which is also suited to other surface reactions in catalysis.To expound on the paradigm,33 promising catalysts are screened from 168 carbon-supported candidates,specifically single-atom catalysts(SACs)supported by a BC_(3)monolayer(TM@V_(B/C)-N_(n)=_(0-3)-BC_(3))via high-throughput screening.Subsequently,the hybrid sampling method and XGBoost model are selected to classify eligible and non-eligible catalysts.Through feature interpretation using Shapley Additive Explanations(SHAP)analysis,two crucial features,that is,the number of valence electrons(N_(v))and nitrogen substitution(N_(n)),are screened out.Combining SHAP analysis and electronic structure calculations,the synergistic effect between an active center with low valence electron numbers and reasonable C-N coordination(a medium fraction of nitrogen substitution)can exhibit high catalytic performance.Finally,six superior catalysts with a limiting potential lower than-0.4 V are predicted.Our workflow offers a rational approach to obtaining key information on catalytic performance from high-throughput screening results to design efficient catalysts that can be applied to other materials and reactions.

High-throughput calculation-based rational design of Fe-doped MoS_(2) nanosheets for electrocatalytic p H-universal overall water splitting

Electrocatalytic water splitting is crucial for H2generation via hydrogen evolution reaction(HER)but subject to the sluggish dynamics of oxygen evolution reaction(OER).In this work,single Fe atomdoped MoS_(2)nanosheets(SFe-DMNs)were prepared based on the high-throughput density functional theory(DFT)calculation screening.Due to the synergistic effect between Fe atom and MoS_(2)and optimized intermediate binding energy,the SFe-DMNs could deliver outstanding activity for both HER and OER.When assembled into a two-electrode electrolytic cell,the SFe-DMNs could achieve the current density of 50 mA cm^(-2)at a low cell voltage of 1.55 V under neutral condition.These results not only confirmed the effectiveness of high-throughput screening,but also revealed the excellent activity and thus the potential applications in fuel cells of SFe-DMNs.

Bulked Segregant RNA-Seq Analysis of Pollinated Pistils Reveals Genes Influencing Spikelet Fertility in Rice

Prezygotic isolation is important for successful fertilization in rice, significantly affecting yield. This study focused on F_(5:6) generation plants derived from inter-subspecific crosses(Nipponbare × KDML105) with low(LS) and high seed-setting rates(HS), in which normal pollen fertility was observed. However, LS plants showed a reduced number of pollen grains adhering to the stigma and fewer pollen tubes reaching the ovules at 4-5 h post-pollination, compared with HS plants. Bulked segregant RNA-Seq analysis of pollinated pistils from the HS and LS groups revealed 249 and 473 differentially expressed genes(DEGs), respectively. Kyoto Encyclopedia of Genes and Genomes analysis of the HS and LS-specific DEGs indicated enrichment in metabolic pathways, pentose and glucuronate interconversions, and flavonoid biosynthesis. Several of these DEGs exhibited co-expression with pollen development genes and formed extensive clusters of co-expression networks. Compared with LS pistils, enzyme genes controlling pectin degradation, such as OsPME35 and OsPLL9, showed similar expression patterns, with higher levels in HS pistils pre-pollination. Os02g0467600, similar to cinnamate 4-hydroxylase gene(CYP73), involved in flavonoid biosynthesis, displayed higher expression in HS pistils post-pollination. Our findings suggest that OsPME35, OsPLL9, and Os02g0467600 contribute to prezygotic isolation by potentially modifying the stigma cell wall(OsPME35 and OsPLL9) and controlling later processes such as pollen-stigma adhesion(Os02g0467600) genes. Furthermore, several DEGs specific to HS and LS were co-localized with QTLs and functional genes associated with spikelet fertility. These findings provide valuable insights for further research on rice spikelet fertility, ultimately contributing to the development of high-yielding rice varieties.

High-throughput computational screening and in vitro evaluation identifies 5-(4-oxo-4H-3,1-benzoxazin-2-yl)-2-[3-(4-oxo-4H-3,1-benzoxazin-2-yl)phenyl]-1H-isoindole-1,3(2H)-dione(C3),as a novel EGFR—HER2 dual inhibitor in gastric tumors

Gastric cancers are caused primarily due to the activation and amplification of the EGFR or HER2 kinases resulting in cell proliferation,adhesion,angiogenesis,and metastasis.Conventional therapies are ineffective due to the intra-tumoral heterogeneity and concomitant genetic mutations.Hence,dual inhibition strategies are recommended to increase potency and reduce cytotoxicity.In this study,we have conducted computational high-throughput screening of the ChemBridge library followed by in vitro assays and identified novel selective inhibitors that have a dual impediment of EGFR/HER2 kinase activities.Diversity-based High-throughput Virtual Screening(D-HTVS)was used to screen the whole ChemBridge small molecular library against EGFR and HER2.The atomistic molecular dynamic simulation was conducted to understand the dynamics and stability of the protein-ligand complexes.EGFR/HER2 kinase enzymes,KATOIII,and Snu-5 cells were used for in vitro validations.The atomistic Molecular Dynamics simulations followed by solvent-based Gibbs binding free energy calculation of top molecules,identified compound C3(5-(4-oxo-4H-3,1-benzoxazin-2-yl)-2-[3-(4-oxo-4H-3,1-benzoxazin-2-yl)phenyl]-1H-isoindole-1,3(2H)-dione)to have a good affinity for both EGFR and HER2.The predicted compound,C3,was promising with better binding energy,good binding pose,and optimum interactions with the EGFR and HER2 residues.C3 inhibited EGFR and HER2 kinases with IC50 values of 37.24 and 45.83 nM,respectively.The GI50 values of C3 to inhibit KATOIII and Snu-5 cells were 84.76 and 48.26 nM,respectively.Based on these findings,we conclude that the identified compound C3 showed a conceivable dual inhibitory activity on EGFR/HER2 kinase,and therefore can be considered as a plausible lead-like molecule for treating gastric cancers with minimal side effects,though testing in higher models with pharmacokinetic approach is required.

RNA-seq技术在虾蟹研究中的应用

基于高通量测序技术的RNA-seq技术已被广泛应用于较高经济价值的虾蟹类动物研究中,从分子水平剖析虾蟹类生命周期中相关基因的调控功能和遗传信息。本文综述了对虾蟹类动物转录组研究现状,包括虾蟹类的生长发育、生殖繁育、环境胁迫、免疫应答、营养代谢和分子标记开发等,分析了RNA-seq技术在虾蟹类研究中面临的局限与挑战,以期为更深入开展虾蟹类分子生物学研究提供参考。