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[前沿资讯] 植物免疫机制研究取得进展 进入全文

中国科学院

植物与病原微生物长期协同进化过程中,形成了多层次的防御体系抑制病原的侵染。近日,生物互作卓越中心研究员周俭民团队在植物免疫机制研究中取得新进展。             次生代谢物在植物抵御病原侵染中发挥着重要的作用,目前发现的植物次生代谢物种类繁多、结构各异,但对其作用机制的认识匮乏。对植物抗菌代谢物活性的认知主要基于多数抗菌代谢物在体外具有杀菌或抑菌的活性,但不加选择地杀灭病原微生物和有益微生物显然不利于植物的正常生长。大多数革兰氏阴性病原细菌,比如丁香假单胞菌,利用其分泌系统向宿主细胞内分泌效应蛋白,干扰宿主的生命活动,导致病害。研究团队首次发现十字花科植物特有天然产物SFN能够特异共价修饰调控分泌系统表达的转录因子,从而抑制病原细菌的致病力、增强植物的抗病性。更为重要的是,SFN不影响有益微生物在植物上的定殖(Wang et al., 2020 Cell Host & Microbe)。            植物调动次生代谢物抑制病原细菌效应表达的同时,也利用其胞内免疫受体(主要是NLR蛋白)识别进入到其细胞内部的效应蛋白,激活免疫反应,保护自身免受伤害。前期,研究团队与其他研究团队合作,在体外组装了激活形式的ZAR1复合物(抗病小体),解析了第一个植物NLR抗病蛋白激活前后的结构。然而,在植物体内病原微生物效应蛋白是否能够诱导抗病蛋白复合物的形成还不清楚。抗病蛋白ZAR1能够与一类不具有酶活的蛋白激酶结合,识别多个病原微生物效应蛋白,如AvrAC与HopZ1a。团队首先发现了AvrAC能够在植物细胞内诱导ZAR1形成大约900 kDa分子量的复合物,这与之前报道的体外ZAR1抗病小体分子量相似。进一步研究发现,HopZ1a也同样能够诱导ZAR1在植物细胞内形成寡聚复合物。研究人员结合ZAR1抗病小体的晶体结构,对ZAR1及假激酶ZED1进行位点突变,发现体外组装抗病小体所需的结构位点对HopZ1a诱导ZAR1在植物细胞内寡聚和抗病性是不可缺少的(Hu et al., 2020 Mol Plant)。            该研究工作首次发现植物合成识别敌友的“机智”天然产物,加深了人们对植物抗菌代谢物的认知,同时发现效应蛋白在植物细胞内诱导抗病蛋白寡聚,并证实抗病小体参与不同效应蛋白诱导的植物抗病,对解析植物抗病蛋白激活的分子机制有重要意义。

[前沿资讯] 番茄中的细胞外蛋白水解级联激活免疫蛋白酶Rcr3在植物免疫应答中的作用机制 进入全文

植物生物技术Pbj

木瓜蛋白酶样半胱氨酸蛋白酶(PLCPs)的分泌是整个植物界免疫应答的重要组成部分。PLCPs在免疫中的相关性在植物物种中很明显。例如,柑橘的细菌黄龙病病原体分泌效应子SDE1抑制柑橘类PLCPs,而玉米黑穗病菌可能分泌效应子Pit2抑制玉米PLCPs。因此,大多数质外体植物病原体产生抑制剂来抑制宿主植物分泌的与防御相关的PLCP。Rcr3是番茄的一种分泌的木瓜蛋白酶样半胱氨酸蛋白酶(PLCP),可作为Cf-2抗性蛋白的共受体来检测黄萎病菌分泌的效应子Avr2。Cf-2对Avr2的识别导致局部程序性细胞死亡。Cf-2编码具有细胞外亮氨酸重复序列的受体样蛋白,而Avr2是一种小的分泌的富含半胱氨酸的蛋白。Avr2结合并抑制Rcr3,这种Avr2-Rcr3复合物被Cf-2识别。因此RCR3抵抗病原菌的能力依靠Cf-2,当cf-2突变之后,Rcr3并不能对番茄叶霉病产生抗性。所以Rcr3对番茄抵御病原菌有着至关重要的作用。而且蛋白水解级联调节动物的免疫力和发育,但是尚未报道植物中的这些级联。             近日英国牛津大学在PNAS上发表了题为“Extracellular proteolytic cascade in tomato activates immune protease Rcr3”的论文。该文章报道了番茄的细胞外免疫蛋白酶Rcr3被P69B和其他枯草蛋白酶(SBTs)激活,揭示了调节茄科植物细胞外免疫的蛋白水解级联反应。                    该项研究中作者检测了Rcr3在Avr2感知中的作用是否需要催化活性。基于所描述的PLCPs的pH依赖性自激活机制,假设缺少催化半胱氨酸的Rcr3突变体将无法激活自身。当递送到酸性环境中时,PLCPs的前结构域会展开,并且蛋白酶会通过在前结构域和蛋白酶结构域之间裂解来激活自身。由于认为Avr2通过与底物结合槽相互作用来抑制Rcr3,因此Rcr3前结构域将禁止Avr2与proRcr3结合。因此,催化失活的proRcr3应该不能去除其前结构域,所以并不能与Avr2相互作用以触发HR。但是,作者发现催化失活的Rcr3仍在加工中,能够结合Avr2并触发HR。研究表明,proRcr3由一类被称为枯草杆菌蛋白酶(SBT)的质外生丝氨酸蛋白酶加工而成。此类包括P69B,也称为致病相关7(PR7),一种在番茄的质外体中丰富的免疫相关SBT。有趣的是,P69B和其他SBT受到E.pf的抑制,EPI1是由致病疫霉产生的一种类似SBT抑制剂效应物,表明该病原体可以通过抑制上游蛋白酶来阻止诱导的免疫PLCPs的激活。研究表明茄科植物中多余的蛋白水解级联会激活免疫蛋白酶以提供强大的质外性免疫。             综上:木瓜蛋白酶样半胱氨酸蛋白酶(PLCPs)的分泌是整个植物界免疫应答的重要组成部分。文章中显示免疫蛋白酶Rcr3被分泌的枯草杆菌蛋白酶激活,枯草杆菌蛋白酶是植物中常见的丝氨酸蛋白酶。枯草蛋白酶P69B通过在天冬氨酸在Rcr3前体的自抑制原结构域和蛋白酶结构域之间的连接处裂解后激活proRcr3,从而激活Rcr3。不同亚科的枯草蛋白酶促进了烟草亲缘种中proRcr3的加工,表明这种蛋白水解级联可能在植物中很常见。因此,分泌枯草杆菌蛋白酶抑制剂的病原体可能会间接阻止免疫蛋白酶的激活。

[前沿资讯] AI to help create a smarter post-COVID-19 agriculture 进入全文

Future Farming

AI is well on its way to helping create a post-COVID-19 agriculture world that’s more efficient, less wasteful, and truly smarter than the one before. While the world slowly begins to reopen after its initial COVID-19 lockdown, we’re still wrapping our heads around its potential long-lasting effects. One thing that has likely changed forever: how companies work – especially with technology. And the agriculture industry has put itself in a unique position to revolutionise its operations with these new and existing technologies. Adoption of AI-driven technology quickened by COVID-19 Before the pandemic, the agriculture sector’s adoption of AI-driven technology was already on the rise, and COVID-19 has only quickened its growth. Here at DroneDeploy, we saw a 32% increase in drone flights across the agriculture vertical from 2018-2019. And while the beginning of 2020 was understandably volatile, we actually saw a 33% increase in takeoffs across U.S. agriculture users from mid-March to mid-April – the heart of COVID-19 lockdown. Agriculture professionals have been quick to realise that investing in drone data solutions still allows for valuable work like field surveying and seeding to occur remotely, all while keeping workers safely off the job site. This rise in agricultural automation will continue to drive industry innovation post-pandemic and possibly revolutionise their processes for the better. Smart Planting One of the first activities that could evolve is the planting process. Currently, drone software can automatically count plants shortly after they emerge from the ground to gauge if areas need to be re-planted (e.g., DroneDeploy’s Count AI tool can automatically calculate trees). It can also help understand which variety of seed performs the best in different types of soil, locations, climates, etc. Drone software integrated into equipment management Drone software is increasingly being integrated into equipment management tools, enabling not just detecting low crop population areas, but also providing the data as input to the planter to re-plant only particular sections. This automation could also potentially make a recommendation about which variety of seed/crop to plant. Based on the previous 10-20 years of data, agronomists can determine which varieties performed best under predicted climate conditions. FBN currently offers a similar service through crowd-sourced data, but AI has the power to analyse, predict, and provide such recommendations more smartly and accurately. Growing re-imagined Second, the growing season will become more efficient and sustainable. Currently, AI tools can detect nitrogen deficiencies, water issues (e.g., ponding or lack of water), weeds, and specific diseases or pests in surveyed land plots. Blue River Technology uses AI and a camera on a sprayer to detect and spot-spray weeds. Precise soil moisture sensors and field weather stations can also indicate whether the field moisture level is adequate. Drone data to help detect field issues Drone data can help detect these field issues then automatically activate a solution. For example, a drone map could detect a nitrogen deficiency, then inform a sprayer to spray only deficient areas. In the same vein, a drone solution could spot a lack of water or weed growth and feed the map to an AI, so it only irrigates specific field positions or directs herbicide only on weeds. Overhauling the harvest Finally, harvesting practices have the potential to change for the better with the help of AI. The order in which you harvest your fields depends on which ones have “dried down” first. For example, typically corn needs to be harvested with a moisture level of 24-33% (and a maximum moisture level of 40%). Cornfields that have not yet turned yellow or brown will have to be mechanically dried post-harvest. Drone imagery can help growers gauge which fields have dried down to the optimum levels and can determine which ones to harvest first. Additionally, AI in combination with variables, modeling, and seed genetics can also predict which fields planted from existing seed varieties will be ready to harvest first. This level of data removes the guesswork from growing and enables growers to harvest more efficiently. Farming’s future As automation in agriculture continues to develop, it’s exciting to see what innovation will come out of this season. COVID-19 has undoubtedly presented the agriculture sector with several challenges, but it has also enabled many opportunities. Bill Gates once said, “We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten.” While the changes we’ve predicted may not happen right away, there are vast possibilities over the next several decades. We’ll see drones and AI utilised in ways that we could never have imagined. Post-COVID-19 agriculture For now, though, in 2020, change is already afoot. AI is well on its way to helping create a post-COVID-19 agriculture world that’s more efficient, less wasteful, and truly smarter than the one before.

[前沿资讯] Kverneland refines variable rate control for spreaders 进入全文

Future Farming

A more sophisticated Variable Rate Control option for Kverneland Group’s IsoMatch Geocontrol fertiliser spreading system enables material to be applied at more than one rate within the overall spread pattern. The aim is to achieve better rate matching where different rate zones in a variable rate prescription map meet. The new Multirate facility was made available first on the range-topping Kverneland Exacta TLX and Vicon RotaFlow RO-XXL twin disc tractor-mounted spreaders but it is also available for all Geospread-capable models as a software installation. Wider working widths The TLX and XXL machines are capable of wider working widths – 12m to 54m versus up to 28m – and faster working speeds than other Exacta and RotaFlow spreaders, resulting in increased daily outputs. They have a different version of the dosing system that distinguishes these machines from other twin disc spreaders – a rotating bowl in the base of the hopper that accelerates the fertiliser granules or prills before feeding them on to each disc. This aims to minimise impact damage as the fast-rotating disc vanes engage the fertiliser; there are eight vanes on each disc – six long, two short. Variable rate application For variable rate application, all Geospread-capable machines are equipped with load cells and a ‘correction’ reference sensor to compensate when working up and down or across sloping ground. Together with a satellite location connection and Geospread software, they can automatically follow a prescription map to vary the rate of fertiliser applied in different zones across a field. With Multirate, the control system is further refined to make it possible to apply different rates within the overall spreading width of the machines by altering the distribution pattern. This enables the system to more effectively respond by getting as close as possible to the required rates in parts of the field where different rate zones meet in the prescription map. Up to eight different rates The system can cope with up to eight different rates (two, four, six or eight) provided by a prescription map, which must be converted to an ISO-XML file. Growers using Kverneland’s IsoMatch FarmCentre cloud-based telematics solution can prepare the prescription file offline and upload it wirelessly ready for the operator to commence spreading. Similarly, data recording the completed job can be transmitted back to FarmCentre for enterprise and assurance records.

[前沿资讯] CNH: Connectivity for all Brazilian farmers next step 进入全文

Future Farming

CNH工业公司的目标是到2021年将巴西的互联互通扩大到1300万公顷的面积上。CNH工业公司是ConectarAGRO的创始人和领导者之一。ConectarAGRO是一个非营利性协会,旨在将互联网连接到巴西的所有农业和偏远地区。 互联互通面积扩大到510多万公顷 2019年,ConectarAGRO通过4G LTE 700mhz宽带将巴西农村地区的连接面积扩大到510多万公顷,约占该国谷物和甘蔗种植面积的8%,比比利时、荷兰和瑞士的总和还要大。根据CNH的数据,这项服务惠及了超过57.5万人、218个城镇和8个州,以及超过2.4万公里的公路受益。 最近CNH任命了公司南美数字技术总监Gregory Riordan为新成立的ConnectarAGRO协会主席。“到2021年,我们希望拥有更多的合作伙伴,将巴西的连通性扩大到1300万公顷,并开发一些项目,旨在提高在新的数字现实中生活和工作在农村的人们的技能,”Riordan先生说。 Riordan同时表示,将对ConectarAGRO的国际扩张进行分析,因为南美其他国家/地区的机构已经与该公司进行了接触。 提高效率 CNH表示,联网设备能够显著提高生产力和效率:使用联网技术时,Case IH和New Holland Agriculture种植者可以按照计划路径并使用实时监控,将效率提高多达5%,以最大程度地减少停机时间。 CNH说,收割物流的规划可以使机器利用率提高20%,从而避免计划外的停工,并促进进一步的作物处理物流。 减少15%的投入使用量 该公司进一步声称,Case IH和New Holland Agriculture喷雾机可以遵循预先准备的处方率图,并使用实时卫星或无人机图像,从而使投入使用量减少了约15%,降低了运营成本并提高了整体效率。 “因此,实现自动化和数字化有助于更好地管理机队、农业资产和生产计划,从而能够做出准确、基于事实的决策。这种部署将使整个农村社区及其工作方式受益,”CNH工业公司说。

[前沿资讯] The contribution of photosynthesis traits and plant height components to plant height in wheat at the individual quantitative trait locus level 进入全文

Nature

株高是小麦形态发生和产量形成的重要农艺性状。本研究以穗长、节间数、第一节间至第六节间长度为指标,对收获期株高进行正态和多变量条件数量性状位点(QTL)分析,以重组自交系群体为基础,研究小麦苗期和抽穗期的光合特性。本研究共检测到49个正常QTL和312个条件QTL。2D染色体上的Xbcd1970-Xbcd262基因区的qtl最多,有6个正常QTL和39个条件性QTL。常规QTL定位与条件QTL定位的比较表明,第三节间、第四节间和第五节间的长度与株高的遗传相关性很高,而所有光合性状的相关性较弱。这种比较分析可以作为一个平台,在利用QTL聚类进行基因配置之前,分析客观性状与其他表型性状之间的遗传关系。

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