Biology subjects

Gumiere, T.

Publications and source records attributed to Gumiere, T..

3 recordsLinked to original sources

bioRxiv2024

Tomato cystatin SlCYS8 as a trigger of drought tolerance and tuber yield in potato

Current climate change scenarios predict an increased incidence of drought episodes likely to affect potato crops worldwide. Potato exhibits a low-density, shallow root system that makes it particularly vulnerable to water shortage and any successful attempt to implement drought tolerance in cultivated potato varieties is potentially relevant from an agronomic standpoint. In this study, we assessed the potential of tomato cystatin SlCYS8 to promote drought tolerance in SlCYS8-expressing potato lines by induction of stress-related pleiotropy. Up to now, protease inhibitors of the cystatin protein superfamily have been mostly considered as biotechnological tools to engineer pest or pathogen resistance in crops, but several recent studies have also revealed a possible link between abiotic stress tolerance and these regulatory proteins in leaf tissue. SlCYS8-expressing plantlets grown on culture medium containing the drought mimic polyethylene glycol (PEG) exhibited an elevated root-to-shoot ratio, an indicator of drought tolerance in potato. A similar conclusion could be drawn with greenhouse-grown acclimated plants, confirming a relative root growth-promoting effect for the recombinant inhibitor upon water deficit. SlCYS8-potato lines also showed a high tuber yield compared to the control line under both limiting and non-limiting water regimes, suggesting an improved efficiency of the primary metabolism and the avoidance of a growth- stress response tradeoff in the modified lines. Accordingly, SlCYS8 expression was associated with a stress response-oriented proteome in leaves likely explained by pleiotropic effects of the recombinant cystatin driving the constitutive expression of stress-related proteins and the upregulation of primary metabolism-associated proteins. Overall, these data suggest the potential of cystatins as molecular triggers of tuber biomass production and drought resilience in potato. Complementary studies will be warranted to assess tuber yield of the SlCYS8-lines under different water regimes in field conditions.

plant biology↗

bioRxiv2021

Fungal succession on the decomposition of three plant species from a Brazilian mangrove

Leaf decomposition is the primary process in release nutrients in the dynamic mangrove habitat, supporting the ecosystem food webs. On most environments, fungi are an essential part of this process. However, due to the peculiarities of mangrove forests, this group is currently neglected. Thus, this study tests the hypothesis that fungal community display an specific succession pattern in different mangrove species. A molecular approach was employed to investigate the dynamics of the fungal community during the decomposition of three common plant species (Rhizophora mangle, Laguncularia racemosa, and Avicennia schaueriana) from the mangrove habitat located at the southeast of Brazil. Plant material was the primary driver of fungi communities but time also was marginally significant for the process, and evident changes in the fungal community during the decomposition process were observed. The five most abundant classes common to all the three plant species were Saccharomycetes, Sordariomycetes, Tremellomycetes, Eurotiomycetes, and Dothideomycetes, all belonging to the Phylum Ascomycota. Microbotryomycetes class were shared only by A. schaueriana and L. racemosa, while Agaricomycetes class were shared by L. racemosa and R. mangle. The class Glomeromycetes were shared by A. schaueriana and R. mangle. The analysis of the core microbiome showed that Saccharomycetes was the most abundant class. In the variable community, Sordariomycetes was the most abundant one, mainly in the Laguncularia racemosa plant. The results presented in this work shows a specialization of the fungal community regarding plant material during mangrove litter decomposition.

microbiology↗

bioRxiv2018

A probabilistic model to identify the core microbial community

The core microbial community has been hypothesized to have essential functions ranging from maintaining health in animals to protection against plant disease. However, the identification of the core microbial community is frequently based on arbitrary thresholds, selecting only the most abundant microorganisms. Here, we developed and tested an approach to identify the core community based on a probabilistic model. The Poisson distribution was used to identify OTUs with a probable occurrence in every sample of a given dataset. We identified the core communities of four extensive microbial datasets, and compared the results with conventional, but arbitrary, methods. The datasets were composed of the microbiomes of humans (tongue, gut, and skin), mice (gut), plant (grapevine) tissue, and the maize rhizosphere. Our proposed method revealed core microbial communities with higher richness and diversity than those previously described. This method also includes a greater number of rare taxa in the core, which are often neglected by arbitrary threshold methods. We demonstrated that our proposed method revels a probable core microbial community for each different habitat, which extend our knowledge about shared microbial communities. Our proposed method may help the next steps proving the essential functions of core microbial communities.\n\nOriginality-Significance StatementMore rigorous and less arbitrary statistical methods could increase knowledge regarding the role of microorganisms and their interactions. Here, we suggest a probabilistic method to identify the microbial core community across systems. Our method identifies a large proportion of the rare community that likely belongs to the microbial core community, which was not identified by conventional methods. Our probabilistic model is a non-arbitrary approach to defining the microbial core community, which may help in the next step of the microbial core community studies.

microbiology↗