Back

From acidic extreme environments to agriculture: a multi-stress-tolerant Talaromyces pseudofuniculosus enhances soybean growth under abiotic stress

Staiano, I.; Castaldi, s.; Masi, M.; Salvatore, M. M.; de Tommasis, G.; Gentile, M. C.; Andolfi, A.; Cimmino, A.; Isticato, R.

2026-10-06 microbiology
10.64898/2026.10.06.756979 bioRxiv
Show abstract

Extremophilic fungi are a relatively unexplored source of stress-resilient microbes for sustainable agriculture. Here, we describe Talaromyces pseudofuniculosus FAR, isolated from the highly acidic site of Piccolo Inferno in southern Italy and identified by ITS and {beta}-tubulin (BenA) sequencing. Despite its origin from an extreme environment, T. pseudofuniculosus FRA remained fully functional under non-stress conditions and displayed tolerance to salinity and heavy metals while maintaining key plant growth-promoting traits, including phosphate solubilization, auxin-related compound production and hydrolytic activities. In soybean (Glycine max L.), the live fungus promoted plant growth under both non-stress conditions and agriculturally relevant salinity and copper stress, and successfully colonized the roots. A cell-free extracellular extract (exCFS) promoted early seedling development in vitro, but its effects were not sustained in pot experiments. GC-MS analysis provided a qualitative characterization of the extracellular metabolite profile and revealed condition-dependent differences between control, NaCl and CuSO4; growth conditions, indicating environment-dependent modulation of fungal metabolism. Overall, these findings highlight the physiological and metabolic plasticity of T. pseudofuniculosus FRA and support its potential as a fungal biostimulant for saline- and metal-impacted agricultural soils.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.