Exploring the dynamics of bacterial community composition in soil: the pan-bacteriome approach

Bacci, Giovanni; Ceccherini, Maria Teresa; Bani, Aïda; Bazzicalupo, Marco; Castaldini, M.; Giovannetti, Luciana; Mocali, Stefano; Pastorelli, Roberta; Pantani, Ottorino‐Luca; Arfaioli, Paola; Pietramellara, Giacomo; Viti, Carlo; Nannipieri, Paolo; Mengoni, Alessio

doi:10.1007/s10482-014-0372-4

Cited by 8 publications

(2 citation statements)

References 54 publications

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“…To study the composition of bacterial communities in the rhizosphere samples, we used 16S rRNA amplicon sequencing. For a bacterial community, the number of specimens in which its members are detected (persistence) should be correlated to the abundance of those members, which is usually expressed as the normalized number of reads assigned to a given ASV (Shade and Handelsman, 2012;Bacci et al, 2015Bacci et al, , 2018. In agreement with this definition, the abundance of all ASVs in different sampling sites was significantly correlated with the number of sites inhabited by those ASVs (linear regression on log-transformed abundance; R 2 = 0.66, F(1, 6126) = 11,947.91, p < 0.001) ( Supplementary Table S5 in Supplementary Material S9 and Figure 5A).…”

Section: Effect Of Biochar Soil Amendment On Rhizosphere Bacterial Comentioning

confidence: 99%

The Impact of Soil-Applied Biochars From Different Vegetal Feedstocks on Durum Wheat Plant Performance and Rhizospheric Bacterial Microbiota in Low Metal-Contaminated Soil

et al. 2019

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Section: Effect Of Biochar Soil Amendment On Rhizosphere Bacterial Comentioning

confidence: 99%

The Impact of Soil-Applied Biochars From Different Vegetal Feedstocks on Durum Wheat Plant Performance and Rhizospheric Bacterial Microbiota in Low Metal-Contaminated Soil

et al. 2019

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“…Consequently, obtaining an accurate dynamic model of a community would require longitudinal metagenomics or, more in general, methods to infer growth dynamics of single bacterial species from metagenomics (Korem et al, 2015). However, this might not be straight-forward, since time-resolved metagenomics shows that some species can grow faster than others increasing their abundance quickly enough to significantly change the whole community structure (Bacci et al, 2015). Therefore, given the paradigm of CB metabolic modeling, this could effectively represent a problem when trying to infer the metabolic phenotype of a microbial community.…”

Section: Strengths and Weaknesses Of Community Modelsmentioning

confidence: 99%

Perspectives and Challenges in Microbial Communities Metabolic Modeling

et al. 2017

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Bacteria have evolved to efficiently interact each other, forming complex entities known as microbial communities. These “super-organisms” play a central role in maintaining the health of their eukaryotic hosts and in the cycling of elements like carbon and nitrogen. However, despite their crucial importance, the mechanisms that influence the functioning of microbial communities and their relationship with environmental perturbations are obscure. The study of microbial communities was boosted by tremendous advances in sequencing technologies, and in particular by the possibility to determine genomic sequences of bacteria directly from environmental samples. Indeed, with the advent of metagenomics, it has become possible to investigate, on a previously unparalleled scale, the taxonomical composition and the functional genetic elements present in a specific community. Notwithstanding, the metagenomic approach per se suffers some limitations, among which the impossibility of modeling molecular-level (e.g., metabolic) interactions occurring between community members, as well as their effects on the overall stability of the entire system. The family of constraint-based methods, such as flux balance analysis, has been fruitfully used to translate genome sequences in predictive, genome-scale modeling platforms. Although these techniques have been initially developed for analyzing single, well-known model organisms, their recent improvements allowed engaging in multi-organism in silico analyses characterized by a considerable predictive capability. In the face of these advances, here we focus on providing an overview of the possibilities and challenges related to the modeling of metabolic interactions within a bacterial community, discussing the feasibility and the perspectives of this kind of analysis in the (near) future.

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