References of "Martinez Arbas, Susana 50009487"
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See detailRoles of bacteriophages, plasmids and CRISPR immunity in microbial community dynamics revealed using time-series integrated meta-omics
Martinez Arbas, Susana UL; Narayanasamy, Shaman; Herold, Malte et al

in Nature Microbiology (2020)

Viruses and plasmids (invasive mobile genetic elements (iMGEs)) have important roles in shaping microbial communities, but their dynamic interactions with CRISPR-based immunity remain unresolved. We ... [more ▼]

Viruses and plasmids (invasive mobile genetic elements (iMGEs)) have important roles in shaping microbial communities, but their dynamic interactions with CRISPR-based immunity remain unresolved. We analysed generation-resolved iMGE–host dynamics spanning one and a half years in a microbial consortium from a biological wastewater treatment plant using integrated meta-omics. We identified 31 bacterial metagenome-assembled genomes encoding complete CRISPR–Cas systems and their corresponding iMGEs. CRISPR-targeted plasmids outnumbered their bacteriophage counterparts by at least fivefold, highlighting the importance of CRISPR-mediated defence against plasmids. Linear modelling of our time-series data revealed that the variation in plasmid abundance over time explained more of the observed community dynamics than phages. Community-scale CRISPR-based plasmid–host and phage–host interaction networks revealed an increase in CRISPR-mediated interactions coinciding with a decrease in the dominant ‘Candidatus Microthrix parvicella’ population. Protospacers were enriched in sequences targeting genes involved in the transmission of iMGEs. Understanding the factors shaping the fitness of specific populations is necessary to devise control strategies for undesirable species and to predict or explain community-wide phenotypes. [less ▲]

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See detailIntegration of time-series meta-omics data reveals how microbial ecosystems respond to disturbance
Herold, Malte; Martinez Arbas, Susana UL; Narayanasamy, Shaman et al

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 ... [more ▼]

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. [less ▲]

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See detailA multi-omic view of invasive genetic elements and their linked prokaryotic population dynamics within a mixed microbial community
Martinez Arbas, Susana UL; Narayanasamy, Shaman; Herold, Malte et al

Poster (2018, September 11)

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See detailIntegrated time-resolved multi-omics for understanding microbial niche ecology
Herold, Malte UL; Narayanasamy, Shaman UL; Martinez Arbas, Susana UL et al

Poster (2018, August)

Microbial communities are strongly shaped by the niche breadths of their constituent populations. However, a detailed understanding of microbial niche ecology is typically lacking. Integrated multi-omic ... [more ▼]

Microbial communities are strongly shaped by the niche breadths of their constituent populations. However, a detailed understanding of microbial niche ecology is typically lacking. Integrated multi-omic analyses of host- or environment-derived samples offer the prospect of resolving fundamental and realised niches in situ. In turn, this is considered a prerequisite for niche engineering in order to drive an individual population or a community towards a specific phenotype, e.g., improvement of a biotechnological process. Here, we sampled floating islets on the surface of an activated sludge tank in a time-series spanning 51 time-points over 14 months. Multi-omics datasets (metagenomics, metatranscriptomics, metaproteomics, and (meta-)metabolomics) were generated for all time-points. Leveraging nucleotide sequencing data, we analyzed the community structure and reconstructed genomes for specific populations of interest. Moreover, based on their metabolic potential, three major groups emerged, serving as proxies for their respective fundamental niches . Time-resolved linkage of the proteomic and transcriptomic data to the reconstructed genomes revealed a fine-grained picture of niche realization. In particular, environmental factors (temperature, metabolites, oxygen) were significantly associated with gene expression of individual populations. Furthermore, we subjected the community to controlled oxygen conditions (stable or dynamic) in a bioreactor experiment and measured the transcriptomic response. Our results suggest short-term adaptations of populations of interest with respect to lipid metabolism, among other pathways. In conclusion, our work demonstrates how longitudinal multi-omic datasets can be integrated in order to further our understanding of microbial niche ecology within a biotechnological process with potential applications beyond waste water treatment. [less ▲]

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See detailUsing metabolic networks to resolve ecological properties of microbiomes
Muller, Emilie UL; Faust, Karoline; Widder, Stefanie et al

in Current Opinion in Systems Biology (2018)

The systematic collection, integration and modelling of high-throughput molecular data (multi-omics) allows the detailed characterisation of microbiomes in situ. Through metabolic trait inference ... [more ▼]

The systematic collection, integration and modelling of high-throughput molecular data (multi-omics) allows the detailed characterisation of microbiomes in situ. Through metabolic trait inference, metabolic network reconstruction and modelling, we are now able to define ecological interactions based on metabolic exchanges, identify keystone genes, functions and species, and resolve ecological niches of constituent microbial populations. The resulting knowledge provides detailed information on ecosystem functioning. However, as microbial communities are dynamic in nature the field needs to move towards the integration of time- and space-resolved multi-omic data along with detailed environmental information to fully harness the power of community- and population-level metabolic network modelling. Such approaches will be fundamental for future targeted management strategies with wide-ranging applications in biotechnology and biomedicine. [less ▲]

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See detailA multi-omic view of phage-host dynamics within a mixed microbial community
Martinez Arbas, Susana UL

Presentation (2017, December 07)

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See detailA systems biology approach to identify niche determinants of cellular phenotypes
Ravichandran, Srikanth UL; Okawa, Satoshi UL; Martinez Arbas, Susana UL et al

in Stem Cell Research (2016)

Recent reports indicate a dominant role for cellular microenvironment or niche for stably maintaining cellular phenotypic states. Identification of key niche mediated signaling that maintains stem cells ... [more ▼]

Recent reports indicate a dominant role for cellular microenvironment or niche for stably maintaining cellular phenotypic states. Identification of key niche mediated signaling that maintains stem cells in specific phenotypic states remains a challenge, mainly due to the complex and dynamic nature of stem cell-niche interactions. In order to overcome this, we consider that stem cells maintain their phenotypic state by experiencing a constant effect created by the niche by integrating its signals via signaling pathways. Such a constant niche effect should induce sustained activation/inhibition of specific stem cell signaling pathways that controls the gene regulatory program defining the cellular phenotypic state. Based on this view, we propose a computational approach to identify the most likely receptor mediated signaling responsible for transmitting niche signals to the transcriptional regulatory network that maintain cell-specific gene expression patterns, termed as niche determinants. We demonstrate the utility of our method in different stem cell systems by identifying several known and novel niche determinants. Given the key role of niche in several degenerative diseases, identification of niche determinants can aid in developing strategies for potential applications in regenerative medicine. [less ▲]

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