An automated workflow for multi-omics screening of microbial model organisms

Donati, Stefano; Mattanovich, Matthias; Hjort, Pernille; Jacobsen, Simo Abdessamad Baallal; Blomquist, Sarah Dina; Mangaard, Drude; Gurdo, Nicolás; Pastor, Felix Pacheco; Maury, Jérôme; Hanke, René; Wulff, Tune; Jakočiūnas, Tadas; Nielsen, Lars K.; McCloskey, Douglas

doi:10.1101/2022.07.18.500181

Cited by 3 publications

(2 citation statements)

References 46 publications

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“…In recent years, data-driven modeling approaches have gained popularity in life science research, enabled by the advances in sequencing technologies and (ultra)high-throughput phenotypic assays (Payne et al 2020 , Zeng et al 2020 ), including multiomics assays (Donati et al 2022 ). Data-driven approaches can use large datasets of phenotypes and genotypes to train machine learning (ML) models.…”

Section: Introductionmentioning

confidence: 99%

From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry

Karlsen

Rau

Sánchez

et al. 2023

FEMS Microbiology Reviews

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When selecting microbial strains for the production of fermented foods, various microbial phenotypes need to be taken into account to achieve target product characteristics, such as biosafety, flavor, texture, and health-promoting effects. Through continuous advances in sequencing technologies, microbial whole-genome sequences of increasing quality can now be obtained both cheaper and faster, which increases the relevance of genome-based characterization of microbial phenotypes. Prediction of microbial phenotypes from genome sequences makes it possible to quickly screen large strain collections in silico to identify candidates with desirable traits. Several microbial phenotypes relevant to the production of fermented foods can be predicted using knowledge-based approaches, leveraging our existing understanding of the genetic and molecular mechanisms underlying those phenotypes. In the absence of this knowledge, data-driven approaches can be applied to estimate genotype-phenotype relationships based on large experimental datasets. Here, we review computational methods that implement knowledge- and data-driven approaches for phenotype prediction, as well as methods that combine elements from both approaches. Furthermore, we provide examples of how these methods have been applied in industrial biotechnology, with special focus on the fermented food industry.

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Section: Introductionmentioning

confidence: 99%

From genotype to phenotype: computational approaches for inferring microbial traits relevant to the food industry

Karlsen

Rau

Sánchez

et al. 2023

FEMS Microbiology Reviews

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“…However, deriving meaningful insights from such data analysis is challenging. Automating omics data analysis is essential for easier comprehension of biological processes and overcoming the computational resource limitations of managing and analysing omics data (14).…”

Section: Introductionmentioning

confidence: 99%

Analysis of Metabolomics and Transcriptomics Data to Assess Interactions in Microalgal Co-culture ofSkeletonema marinoiandPrymnesium parvum

Zulfiqar,

Abel,

Barth

et al. 2023

Preprint

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In marine ecosystems, microbial communities often interact using specialised metabolites, which play a central role in shaping the dynamics of the ecological networks and maintaining the balance of the ecosystem. With metabolomics and transcriptomics analyses, this study explores the interactions between two marine microalgae,Skeletonema marinoiandPrymnesium parvum, grown in mono-cultures and non-contact co-cultures. As a growth indicator, the photosynthetic potential, measured via fluorescence, suggested chemical interaction betweenS. marinoiandP. parvum. Using Liquid Chromatography-Mass Spectrometry (LC-MS) data, we identified 346 and 521 differentially produced features in the endo- and exometabolome ofS. marinoiandP. parvum, respectively. Despite limited tandem mass spectrometry data (MS2) for these features, we structurally annotated 14 compounds, most of which were previously under-studied specialised metabolites. Differential gene expression analysis was then performed on the transcriptomes of the microalgae, which uncovered differentially expressed genes involved in energy metabolism and cellular repair for both species. These metabolic and transcriptomics changes depict the adaptation of both species in the co-culture. However, further data acquisition and investigation will be necessary to confirm the type of interaction and the underlying mechanisms.ImportanceMarine microalgae have great ecological importance and biochemical potential. Among these microbes are the diatomSkeletonema marinoi, known for its marine biogeochemical cycling, and the haptophytePrymnesium parvum, which poses adverse environmental consequences. Given these opposing roles for the two cosmopolitan microalgae, we designed a study using untargeted metabolomics and transcriptomics to acquire a comprehensive snapshot of their interactions, grown as mono-cultures and co-cultures. The statistical analysis of the chlorophyllafluorescence levels, and the metabolomics and transcriptomics dataset revealed metabolic communication occurring among the two species via specialised metabolites and activated cellular repair mechanisms. These findings reveal the complexity of the interactions within marine microbial ecosystems, offering a foundation for future research to understand and harness marine ecological systems.

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