SOIL AND MICRO-ORGANISM ANALYSIS

The growing interest in soil microbiology is due to the contribution of microorganisms to agricultural productivity and sustainability. The awareness of researchers, rural producers and biological input companies about the importance of biological activity for agriculture has influenced the creation and adaptation of techniques and tools that allow for the expansion of knowledge.
on micro-organisms.

STAGE 1. DNA extraction and library preparation.

1. DNA extraction is performed with the DNeasy PowerLyzer PowerSoil DNeasy kit from Qiagen (Hilden, Germany).
2. BeCrop uses custom primers for PCR amplification specifically targeting the 16S rRNA V4 region and the ITS1 region.
3. The amplicons are purified using the KAPA Pure Beads kit (Roche, Basel, Switzerland), while the correct amplification of 16S and ITS is assessed using agarose gel.
4. The purified PCR products are then subjected to library preparation, following a two-step Illumina PCR protocol.
5. DNA is then quantified using a Qubit fluorometer with the Qubit HS 500 assay kit (Thermo Fisher Scientific, Waltham, MA, USA).
6. Finally, libraries are sequenced on an Illumina MiSeq instrument (Illumina, San Diego, CA, USA) using 2 × 251 paired-end reads.

STAGE 2. Bioinformatics Processing

STAGE 3. Calculation of microbiome indices and network properties.

Local network properties are determined following the procedure described by Ortiz-Alvarez et al:

1. Microbial community networks for 16S and ITS samples are constructed independently following the methodology reported in a previous publication.
2. The presence-absence meta-network with all samples is constructed using rarefied counts and ASV pairs, which occur with a significantly higher or lower frequency than expected, are preserved and the co-occurrence or co-exclusion network is determined, respectively.
3. Local network properties for both markers are calculated for both co-occurrence and co-exclusion: modularity, transitivity and average path length.

• The modularity describes the separation between groups of micro-organisms (modules) that tend to co-exist or often exclude each other in specific ecological niches.

• The transitivity (clustering coefficient) measures the tendency of connected nodes to form closed triangles.

• The average road length quantifies the degree of connectivity to get from one side of the network to the other.

The BeCrop indexes are indicators used to assess the state of health of soils based on metagenomic data as described by Acedo et al. These indicators assess relevant traits related to soil health ranging from metabolic potential to biocontrol and hormone estimates.

The underlying databases infer the adaptation to stress based on several mechanisms: abscisic acid (ABA), 1-aminocyclopropane-1-carboxylate (ACC) deaminase, exopolysaccharide (EPS) production, heavy metal solubilisation, salicylic acid, salt tolerance and siderophore production.

In addition, they offer a potential hormone production based on the production of cytokinins, gibberellin and IAA. All potential mechanism abundances are based on the combination of relevant prokaryotic and fungal abundances and are scaled to an index from 1 to 6, where 1 indicates low abundance and 6 indicates high abundance in the respective soil sample.

Do not hesitate to contact Tiloom Technological Solutions to carry out one of these tests and give a definitive boost to your product.