生物谷報(bào)道：以往我們的常識(shí)告訴我們只有具備完整神經(jīng)系統(tǒng)的生物才可以主動(dòng)預(yù)知環(huán)境的改變而作好應(yīng)變準(zhǔn)備,。可是美國(guó)普林斯頓大學(xué)的科學(xué)家近日研究表明細(xì)菌也具有某種形式的學(xué)習(xí)記憶能力,，能夠預(yù)先判斷環(huán)境的變化而做出反應(yīng),。
研究人員利用計(jì)算機(jī)模型模擬了細(xì)菌的進(jìn)化，并在實(shí)驗(yàn)室中培育大腸桿菌：首先將溫度從25攝氏度提高到37攝氏度,，然后將氧含量從20%降低到零,。研究人員監(jiān)控了幾百代細(xì)菌的反應(yīng)，結(jié)果發(fā)現(xiàn),，幾周之后,，細(xì)菌已經(jīng)可以預(yù)料氧氣的下降，即溫度一改變它們就會(huì)調(diào)整自己的代謝,。這說(shuō)明,，大腸桿菌能夠發(fā)展出將高溫（如人類口腔內(nèi)）與缺氧（如人類腸道內(nèi)）建立聯(lián)系的能力,。
近百年來(lái)，關(guān)于細(xì)菌行為的流行觀點(diǎn)是動(dòng)態(tài)平衡（homeostasis）,，即認(rèn)為細(xì)菌對(duì)環(huán)境刺激的反應(yīng)是以一種作用-反作用的方式進(jìn)行的,。這一研究結(jié)果則顛覆了以往的認(rèn)知。這是細(xì)菌具有聯(lián)想學(xué)習(xí)的首個(gè)證據(jù),，這種反應(yīng)與巴甫洛夫條件反射作用（條件反射訓(xùn)練后,，狗聞鈴聲便分泌過(guò)量唾液）相似。當(dāng)然,，這并不是說(shuō)單細(xì)胞的細(xì)菌與狗或人具有相同的學(xué)習(xí)方式,。研究醫(yī)院表示，人或狗的聯(lián)想學(xué)習(xí)跨越整個(gè)生命周期,，并且包括對(duì)腦部神經(jīng)元之間連接強(qiáng)度的修正；而細(xì)菌的學(xué)習(xí)要經(jīng)過(guò)很長(zhǎng)時(shí)間的進(jìn)化過(guò)程,，包括基因網(wǎng)絡(luò)之間連接的改變,。
此次研究對(duì)于處理細(xì)菌感染和抗藥性具有潛在的意義。另外,，預(yù)見(jiàn)細(xì)菌的行為可能還有助于指導(dǎo)釀造等依賴它們的工業(yè)過(guò)程,。這項(xiàng)研究的報(bào)告5月8日在線發(fā)表于Science雜志。（生物谷www.bioon.com）
生物谷推薦原始出處：
Science,，DOI: 10.1126/science.1154456,，Ilias Tagkopoulos，Saeed Tavazoie
Anticipatory Behavior Within Microbial Genetic Networks
Ilias Tagkopoulos 1, Yir-Chung Liu 2, Saeed Tavazoie 2*
1 Department of Electrical Engineering, Princeton University, Princeton, NJ 08544, USA.; Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.
2 Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.; Department of Molecular Biology, Princeton University, Princeton, NJ 08544, USA.
* To whom correspondence should be addressed.
Saeed Tavazoie , E-mail: [email protected]
These authors contributed equally to this work.
We question whether homeostasis alone adequately explains microbial responses to environmental stimuli, and explore the capacity of intra-cellular networks for predictive behavior in a fashion similar to metazoan nervous systems. We show that in silico biochemical networks, evolving randomly under precisely defined complex habitats, capture the dynamical, multi-dimensional structure of diverse environments by forming internal models that allow prediction of environmental change. We provide evidence for such anticipatory behavior by revealing striking correlations of Escherichia coli transcriptional responses to temperature and oxygen perturbations—precisely mirroring the co-variation of these parameters upon transitions between the outside world and the mammalian gastrointestinal-tract. We further show that these internal correlations reflect a true associative learning paradigm, since they show rapid de-coupling upon exposure to novel environments.
