Transcriptomic response of Debaryomyces hansenii during mixed culture in a liquid model cheese medium with Yarrowia lipolytica

2 years ago
Malek, R. ; Bonnarme, P. ; Irlinger, F. ; Frey-Klett, P. ; Onesime, D. ; Aubert, J. ; Loux, V. ; Beckerich, J.-M.
International Journal of Food Microbiology, 2018, 264 : 53-62.
Article
Yeasts play a crucial role in cheese ripening. They contribute to the curd deacidification, the establishment of acid-sensitive bacterial communities, and flavour compounds production via proteolysis and catabolism of amino acids (AA). Negative yeast-yeast interaction was observed between the yeast Yarrowia lipolytica 1E07 (YL1E07) and the yeast Debaryomyces hansenii 1L25 (DH1L25) in a model cheese but need elucidation. YL1E07 and DH1L25 were cultivated in mono and co-cultures in a liquid synthetic medium (SM) mimicking the cheese environment and the growth inhibition of DH1L25 in the presence of YL1E07 was reproduced. We carried out microbiological, biochemical (lactose, lactate, AA consumption and ammonia production) and transcriptomic analyses by microarray technology to highlight the interaction mechanisms. We showed that the DH1L25 growth inhibition in the presence of YL1E07 was neither due to the ammonia production nor to the nutritional competition for the medium carbon sources between the two yeasts. The transcriptomic study was the key toward the comprehension of yeast-yeast interaction, and revealed that the inhibition of DH1L25 in co-culture is due to a decrease of the mitochondrial respiratory chain functioning.

Unraveling the evolution and coevolution of small regulatory RNAs and coding genes in Listeria

2 years 2 months ago
Cerutti, F. ; Mallet, L. ; Painset, A. ; Hoede, C. ; Moisan, A. ; Becavin, C. ; Duval, M. ; Dussurget, O. ; Cossart, P. ; Gaspin, C. ; Chiapello, H.
BMC Genomics, 2017, 18 (882)
Article
Pièces jointes : Unraveling the evolution and coevolution of small regulatory RNAs and coding genes in Listeria.pdf
Background: Small regulatory RNAs (sRNAs) are widely found in bacteria and play key roles in many important physiological and adaptation processes. Studying their evolution and screening for events of coevolution with other genomic features is a powerful way to better understand their origin and assess a common functional or adaptive relationship between them. However, evolution and coevolution of sRNAs with coding genes have been sparsely investigated in bacterial pathogens. Results: We designed a robust and generic phylogenomics approach that detects correlated evolution between sRNAs and protein-coding genes using their observed and inferred patterns of presence-absence in a set of annotated genomes. We applied this approach on 79 complete genomes of the Listeria genus and identified fifty-two accessory sRNAs, of which most were present in the Listeria common ancestor and lost during Listeria evolution. We detected significant coevolution between 23 sRNA and 52 coding genes and inferred the Listeria sRNA-coding genes coevolution network. We characterized a main hub of 12 sRNAs that coevolved with genes encoding cell wall proteins and virulence factors. Among them, an sRNA specific to L. monocytogenes species, rli133, coevolved with genes involved either in pathogenicity or in interaction with host cells, possibly acting as a direct negative post-transcriptional regulation. Conclusions: Our approach allowed the identification of candidate sRNAs potentially involved in pathogenicity and host interaction, consistent with recent findings on known pathogenicity actors. We highlight four sRNAs coevolving with seven internalin genes, some of which being important virulence factors in Listeria.

The bacterial interlocked process ONtology (BiPON): a systemic multi-scale unified representation of biological processes in prokaryotes

2 years 2 months ago
Henry, V. J. ; Goelzer, A. ; Ferré, A. ; Fischer, S. ; Dinh, M. ; Loux, V. ; Froidevaux ; Fromion, V.
Journal of Biomedical Semantics, 2017, 8 (53) : 16 pages.
Article
Pièces jointes : Henry_et_al-2017-Journal_of_Biomedical_Semantics.pdf
Background: High-throughput technologies produce huge amounts of heterogeneous biological data at all cellular levels. Structuring these data together with biological knowledge is a critical issue in biology and requires integrative tools and methods such as bio-ontologies to extract and share valuable information. In parallel, the development of recent whole-cell models using a systemic cell description opened alternatives for data integration. Integrating a systemic cell description within a bio-ontology would help to progress in whole-cell data integration and modeling synergistically. Results: We present BiPON, an ontology integrating a multi-scale systemic representation of bacterial cellular processes. BiPON consists in of two sub-ontologies, bioBiPON and modelBiPON. bioBiPON organizes the systemic description of biological information while modelBiPON describes the mathematical models (including parameters) associated with biological processes. bioBiPON and modelBiPON are related using bridge rules on classes during automatic reasoning. Biological processes are thus automatically related to mathematical models. 37% of BiPON classes stem from different well-established bio-ontologies, while the others have been manually defined and curated. Currently, BiPON integrates the main processes involved in bacterial gene expression processes. Conclusions: BiPON is a proof of concept of the way to combine formally systems biology and bio-ontology. The knowledge formalization is highly flexible and generic. Most of the known cellular processes, new participants or new mathematical models could be inserted in BiPON. Altogether, BiPON opens up promising perspectives for knowledge integration and sharing and can be used by biologists, systems and computational biologists, and the emerging community of whole-cell modeling.

PhylOligo: a package to identify contaminant or untargeted organism sequences in genome assemblies

2 years 3 months ago
Mallet, L. ; Bitard-Feildel, T. ; Cerutti, F. ; Chiapello, H.
Bioinformatics, 2017, 33 (20) : 3283 - 3285.
Article
Pièces jointes : PhylOligo a package to identify contaminant or untargetatd organism sequences in genome assemblies.pdf
Motivation: Genome sequencing projects sometimes uncover more organisms than expected, especially for complex and/or non-model organisms. It is therefore useful to develop software to identify mix of organisms from genome sequence assemblies. Results: Here we present PhylOligo, a new package including tools to explore, identify and extract organism-specific sequences in a genome assembly using the analysis of their DNA compositional characteristics.