Biology subjects

Ajaz, S.

Publications and source records attributed to Ajaz, S..

2 recordsLinked to original sources

bioRxiv2026

Unveiling Hidden Endophytes by Optimising Identification of Endophytic Bacterial Communities from Wild Grassland Plant Roots

Endophytic bacteria are increasingly recognised for their roles in plant health through symbiosis. However, methodological challenges, such as inconsistent root sterilisation, inefficient microbial DNA extraction, and co-amplification of plant organellar DNA, limit accurate characterisation of these communities, especially in wild grassland plants and non model plant in general. To address this, we developed and tested a streamlined protocol for bacterial endophyte detection from wild grassland plant roots, encompassing surface sterilisation of roots, DNA extraction, clamping of plant internal mitochondrial and chloroplast DNA, and 16S rRNA amplicon sequencing. Our approach minimises plant DNA contamination and yields high-quality microbial profiles. The protocol is adaptable and specific to grassland plant species, offering a standardised foundation for endophyte studies in wild and non-model plants. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=141 HEIGHT=200 SRC="FIGDIR/small/706108v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@157df36org.highwire.dtl.DTLVardef@1ff645aorg.highwire.dtl.DTLVardef@1580ecorg.highwire.dtl.DTLVardef@1c31b89_HPS_FORMAT_FIGEXP M_FIG C_FIG (Haskins and Ajaz, 2026) https://BioRender.com/47gd2xr

plant biology↗

bioRxiv2025

Maximum entropy networks show that plant-arbuscular mycorrhizal fungal associations are anti-nested and modular

Many applications of network theory to plant-mycorrhizal associations have used a bipartite description, in which one set of nodes is the plants, and the other set is the fungi. Most applications have relied on null models from algorithms that randomly rewire the observed connections to test for non-random patterns in the network. We used existing plant-arbuscular mycorrhizal (AM) fungal datasets to apply a new, well validated generation of network models relaxing the very limiting assumptions of traditional null models. We focused on nestedness and modularity, which have been related to the functioning and stability of communities. Given the existent literature, we expected nestedness and modularity to be prevalent. We modelled plant-AM fungal associations using maximum entropy networks with a degree sequence, soft constraint to generate null distributions for nestedness and modularity. Most plant-AM fungal associations were anti-nested and modular. This pattern was consistent across habitat types and multiple spatial scales. Anti-nestedness can easily emerge from modularity when network patterns are determined by the identity of the plant and AM fungal nodes. Future studies will have to test how the observed patterns determine the ability of the associations to adapt to environmental changes.

ecology↗