Integration of time-series meta-omics data reveals how microbial ecosystems respond to disturbance.
Malte HeroldSusana Martínez ArbasShaman NarayanasamyAbdul R SheikLuise A K Kleine-BorgmannLaura A LebrunBenoit Josef KunathHugo RoumeIrina BessarabRohan B H WilliamsJohn D GilleceJames M SchuppPaul S KeimChristian JägerMichael R HoopmannRobert L MoritzYuzhen YeSujun LiHaixu TangAnna Heintz-BuschartPatrick MayEmilie E L MullerCédric Christian LacznyPaul WilmesPublished in: Nature communications (2020)
The development of reliable, mixed-culture biotechnological processes hinges on understanding how microbial ecosystems respond to disturbances. Here we reveal extensive phenotypic plasticity and niche complementarity in oleaginous microbial populations from a biological wastewater treatment plant. We perform meta-omics analyses (metagenomics, metatranscriptomics, metaproteomics and metabolomics) on in situ samples over 14 months at weekly intervals. Based on 1,364 de novo metagenome-assembled genomes, we uncover four distinct fundamental niche types. Throughout the time-series, we observe a major, transient shift in community structure, coinciding with substrate availability changes. Functional omics data reveals extensive variation in gene expression and substrate usage amongst community members. Ex situ bioreactor experiments confirm that responses occur within five hours of a pulse disturbance, demonstrating rapid adaptation by specific populations. Our results show that community resistance and resilience are a function of phenotypic plasticity and niche complementarity, and set the foundation for future ecological engineering efforts.
Keyphrases
- wastewater treatment
- climate change
- single cell
- microbial community
- gene expression
- antibiotic resistance genes
- electronic health record
- healthcare
- mental health
- big data
- dna methylation
- blood pressure
- genome wide
- human health
- quality improvement
- data analysis
- social support
- genetic diversity
- current status
- depressive symptoms
- machine learning
- artificial intelligence
- cell wall
- subarachnoid hemorrhage