日本生態学会　関東地区会

公開オンラインシンポジウム
　 謎解き！? 複雑な微生物の相互作用：機能の理解と群集動態の予測に向けて
Unraveling the complexity of microbial interactions: Toward predictive understanding of community dynamics and functions

概要

■日時　 2021年7月21日（水）　13：30～19：30
第1部　日本語　13:30-15:00
第2部　英語　　15:00-19:30

■開催形式　 オンラインのみ（Zoom）

■企画者　 鏡味麻衣子（横浜国立大学大学院環境情報研究院），三木健（龍谷大学先端理工学部）

微生物は生態系において物質循環を駆動し、生物多様性の創出・維持において重要な役割を担っている。微生物は相互に競争・捕食・寄生・共生など様々な形で作用し合うだけでなく、環境や宿主との相互作用を通じ、複雑で多様な群集を形成する。このように複雑な微生物群集の相互作用は未だ「謎」が多い。さらに各種撹乱に対する微生物群集の応答についての知見は乏しく、環境変動に対する微生物群集および生態系の応答を予測することは困難である。 超並列シーケンサーを用いた網羅的微生物群集解析やメタゲノムなど各種オミックス解析により、野外における微生物群集の組成や機能を詳細に把握できるようになった。ただし、これらのデータはある時点ある群集における構成種や活性のスナップショットである。これらの知見から動態や機能を理解し予測につなげるためには、モデルや実験を活用することが有効である。
本シンポジウムでは、メタゲノム、長期変動解析(Empirical Dynamic Modeling)、大規模・網羅的実験などを組み合わせ微生物群集の動態や機能について最新の研究結果を得ている研究者を招聘し、成果を発表していただく。第一部の日本語セッションでは、第二部の英語講演の内容が分かりやすくなるような解説をおこなう。ライトニングトーク として話題提供は日本語及び英語セッションそれぞれで募り、様々な研究事例をふまえて、言語の壁が低い形で今後の発展性について議論する。

Microbial communities play essential roles in biogeochemical cycles and maintaining biodiversity. The dynamics of microbial community are driven by a multitude of interactions between microbial populations (competition, predation, parasitism, symbiosis etc.), as well as by environmental and host organisms. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. High-throughput sequencing and multi-omic approaches, such as metagenomics, now allow us to catalogue the diversity of microbial communities and functions in situ. These data represent a compositional snapshot of the species and genes that are present in a given microbial community. The key challenge now is to convert this empirical knowledge into fundamental insights and testable predictions, by integrating mathematical models, integrative data analysis methods and experiments.
In this symposium, we invite 4 researchers who successfully integrate between field observation, theory, data analysis methods and experiments. We divide 2 parts; 1st part in Japanese and 2nd part in English. In each session, we will invite speakers for lightning talks, who challenge to unravel the complexity of microbial interactions.

■Ecological Research誌特集号への論文投稿のお誘い
本シンポジウムに関連して，Ecological Research誌で特集” Unraveling the complexity of microbial interactions to understand community dynamics and function”を企画しました（詳細はこちらのファイル [PDF:278KB]をご覧ください)。 興味のある方はシンポジウム参加フォームもしくはメールで鏡味までご連絡ください（kagami-maiko-bd(at)ynu.ac.jp）。投稿締め切りは2022年3月です。

■参加申し込み
事前に以下のサイトよりお申し込みください（締め切り7月10日(土）23:59）。受付後，Zoomのミーティング情報をお知らせします。
Please register in advance at the following website (deadline: Saturday, July 10, 23:59). After the deadline, we will inform you of the Zoom meeting information.
https://forms.gle/Njp1FChhRgYsExUm8

プログラム(第一部）

プログラム(第二部）

ライトニングトーク

本シンポジウムのホームページ

要旨（abstract）

三木健「大規模相互作用網の再構成に関するノンパラメトリック手法の日本語解説」

生態学にとどまらず、「力学方程式」・「数理モデル」を用いた理論によって発展してきた研究分野においては、なかなか「モデル数式」(Theoretical Dynamic Modeling: TDM)から離れて事象を理解しその動態を定量化しようという機運がなかなか高まらない。21世紀に入って、Cross-Convergence Mapping (CCM)やS-mapと言った具体的なモデル数式に依存しない「ノンパラメトリック」な手法で相互作用する要素間の因果関係を明らかにするEmpirical Dynamic Modeling (EDM)が勃興した現在においても、TDMの呪縛から逃れられない人は多い。斯く言う私もEDMに初めて出会った（というか謝志豪氏に出会った）2005年以来、TDMの支配から逃れるのに10数年以上もかかってしまったし、その心変わりの原因を言語化するのも難しい。本講演では、参加者の心の呪縛を少しでも解けるよう、EDMの基盤的コンセプトの解説から始めた上で、英語セッションのChang博士の講演についての平易な紹介を行いたい。現状のEDMの手法では「次元の呪い」のせいで100ノードを超えるような大規模ネットワーク（たとえば100種が相互作用する生物群集）に適用する際の正確さについての性能評価が行われておらず、実データへの適用の有効性に疑問が残る（最適埋め込み次元がノード数よりもだいぶ小さいからである）。この問題を解決するために講演者も参加してChang博士が主導した研究によって開発された、“multiview distance regularized S-map”について、みなさんに紹介する予定である。

Prof. Hans-Peter Grossart「The multitude of microbial interactions: the need for new molecular tools and theoretical concepts」

Microbes are basically everywhere on earth and greatly interact with their abiotic and biotic environment. In particular, interactions with organisms - including us humans - are of great interest since they can shape many important physiological functions and behavior of organisms and thus can to a large extent shape food web structure and functioning. These interactions often include the intensive exchange of signaling molecules, hormone like substance, vitamins, trace metals and metabolic products such as sugars and proteins. As such microbes can greatly interlink organisms and create trophic links which would otherwise not exist. To disentangle the multitude of possible interactions, new molecular tools are of great value. On the one hand, genomic features can be used to discover interaction traits via bioinformatics, which then can be specifically searched for by using various OMICS tools. On the other hand, we are lacking profound theoretical understanding on how these interactions shape food web structure and biogeochemical cycles. New concepts and models are needed to account for the central role microbes play in many environments. Lately, cross feedings has been accepted as an important ecological concept which can be well implemented into theoretical food web models. Also, it has been shown that microbes being parasites or saprophytes are crucial for community assembly and hence ecosystem functions. In times of rapid global, such knowledge is urgently needed to mitigate arising negative consequences, e.g. due to increasing temperatures and anthropogenic interferences.

Dr. Thijs Frenken「The effect of global change on phytoplankton disease, and consequences for the rest of the aquatic food web」

Aquatic systems are generally warming and receive an increased nutrient supply. During these conditions harmful phytoplankton can proliferate and form dense surface blooms that often consist of large-sized phytoplankton taxa, such as diatoms or filamentous cyanobacteria. In many cases these are inedible to zooplankton and thus form trophic bottlenecks, preventing efficient transfer of energy and elements to higher trophic levels. Just as any other organism cyanobacteria and diatoms can also get infected by pathogens, including fungal parasites and viruses. However, it remains unclear how warming and changes in nutrient supply affect these parasites. Here, we show how changes in temperature and stoichiometry may affect epidemic development in phytoplankton. Using large 1000-L mesocosms with a natural community we show that warming advances timing of aquatic viruses, and accelerates termination of a phytoplankton bloom by fungal parasites. Also, temperature led to changes in the stoichiometry of primary producers, which may have consequences for reproduction and stoichiometry of parasites. Indeed, by synthesizing literature, and with laboratory experiments, we confirm that changes in nutrient supply may create or alleviate stoichiometric mismatches between primary producers and their pathogens. This may have consequences for herbivores that rely on phytoplankton and parasites as food. Fungal parasites may not only provide zooplankton with a complementary food source in the form of fungal zoospores, they also fragmentate cyanobacterial filaments making them more edible. This work highlights the need to incorporate the complex dynamics between infections, stoichiometry and grazing to better understand how future aquatic food webs and their phytoplankton communities will respond to climate change.

Dr. Chun-Wei Chang「Reconstructing large networks with time-varying interactions」

Reconstructing interactions from observational data is a critical need for investigating natural biological networks, wherein network dimensionality (ie number of interacting components) is usually high and interactions are time-varying. These pose a challenge to existing methods that can quantify only small interaction networks or assume static interactions under steady state. Here, we proposed a novel approach to reconstruct high-dimensional, time-varying interaction networks using empirical time series. This method, named" multiview distance regularized S-map", generalized the state space reconstruction to accommodate high dimensionality and overcome difficulties in quantifying massive interactions with limited data. When we evaluated this method using the time series generated from a large theoretical model involving hundreds of interacting species, estimated interaction strengths were in good agreement with theoretical expectations. As a result, reconstructed networks preserved important topological properties, such as centrality, strength distribution and derived stability measures. Moreover, our method effectively forecasted the dynamic behavior of network nodes. Applying this method to a natural bacterial community helped identify keystone species from the interaction network and revealed the mechanisms governing the dynamical stability of bacterial community. Our method overcame the challenge of high dimensionality and disentangled complex time-varying interactions in large natural dynamical systems.

Dr. Kazufumi Hosoda「A high-throughput synthetic microbial ecosystem: stochastic community dynamics, inter-ecosystem interactions, predictions and controls」

Experiments on ecosystems have intrinsic difficulties in handling, reproducibility, and experimental commonality compared with individual organisms, where large-scale experiments using some “model organisms” have been commonly conducted. Synthetic assemblage of microorganisms that is axenically-culturable and cryopreservable is a type of ideal model ecosystems. However, most of previously-reported this type of synthetic ecosystems with trophic relationships had little diversity (typically each single species of producer, decomposer, and predator). Here we developed a synthetic ecosystem of 12 cryopreservable microbial species with diverse interactions as an experimental “model ecosystem.” We created a machine learning model that noninvasively distinguished the 12 species on micrographs. Our developments enable high-throughput experiments, for example, one researcher can simultaneously run 10,000 synthetic ecosystems in an ordinary experimental laboratory. This synthetic ecosystem was simple, but stochastic phenomena due to keystone species were observed, suggesting that it had some complexity as an ecosystem. In my talk, I will show some results about stochastic community dynamics, inter-ecosystem interactions, and predictions and controls of ecosystems.