Soil microbial functional gene dataset associated with Agathis australis
Description
Kauri (Agathis australis) is a significant and iconic native tree of Aotearoa, New Zealand. A Phytophthora infection known as the kauri-dieback disease is currently destroying kauri trees. Studies have revealed that soil microorganisms can positively impact the plant immune system. These microorganisms compete with pathogens for nutrients, thereby hindering pathogen colonization. Moreover, the root microbiome is a complex system of diverse microbes with their own genetic elements and interactions that can have an impact on plant health. Investigating and evaluating the genetic aspects of microorganisms that impact the diversity of microbial populations is a pivotal area of emphasis in ecological research. The GeoChip 5.0M (Glomics Inc., USA) is a microarray-based metagenomic tool that specifically characterizes the functional gene profile of soil microbial diversity. It includes 1,447 gene families from a range of microorganisms, including archaea, bacteria, fungi, protists, algae, and viruses. The Auckland Botanical Gardens in Aotearoa, New Zealand is maintained by the local council, and it features kauri trees in three distinct garden areas: native forest, kauri grove, and rose garden. The native forest is designed to simulate a natural forest setting with indigenous trees and New Zealand native plants. Kauri grove has around 100 kauri trees that are approximately 20 years old. Soil samples were collected from four cardinal points of each stand at 10cm depth. The CTAB hot phenol-chloroform DNA extraction method was used to recover DNA from the soil. The pooled environmental DNA (eDNA) recovered from all four cardinal points were pooled and sent to Glomics Inc. USA for the microarray analysis. There were 946 genes and 4342 taxa identified from the pooled eDNA. This data offers valuable insight into the functional genes linked to kauri soil, serving as a useful foundation for future microbiome analyses of kauri soil in forest-scale studies. Data table header descriptions Unique ID - Probe ID ProtienGI - GI number of protein sequence accessible through the NCBI search AccessionNo - NCBI accession number for the sequence Gene - The name of the gene detected Species - The organism the gene derives from Lineage - The phylogenetic information of the organism Gene category - The categories according to gene functions Subcategory1 - Coarse division of gene categories Subcategory2 - Fine division of gene categories Samples - NF1 (Native Forest tree 1), NF2 (Native Forest tree 2), KG3 (Kauri grove tree 3), KG5 (Kauri grove tree 5). Values - Normalized signal intensity
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Steps to reproduce
Two trees were chosen from the native forest location and another two from the kauri grove site. The eDNA was extracted from 0.5g of soil taken from each cardinal point using the CTAB hot phenol-chloroform DNA extraction method. The samples were incubated in phosphate buffer, SDS, CTAB, lysozyme, and proteinaseK at 60oC for one hour. Recovered eDNA was quantified using a Quant-iT dsDNA Assay kit (Invitrogen, California USA) on a Qubit 2 Fluorometer (Invitrogen, California USA) following the manufacturer’s instructions. The equimolar eDNA from each cardinal point was pooled and sent to Glomics for GeoChip 5.0M analysis. The DNA was labelled with Cy3 random priming using the Klenow fragment and purified using a QIAquick purification kit (Qiagen, CA, USA) following the manufacturer's instructions. The purified labelled DNA was placed in 10% formamide hybridization solution and loaded onto Agilent slide covered by array slide and sealed with SureHyb chamber. The setup was then put in the Agilent G2545A Hybridization Oven at 67°C for 24 hours. The slides were rinsed and analysed using NimbleGen microarray scanner MS200 (Roche NimbleGen, Madison, WI, USA). The Agilent Feature Extraction program v11.5, was used to generate the microarray data which was then loaded onto the GeoChip data analysis pipeline (http://www.ou.edu/ieg/tools/data-analysispipeline.html). In accordance with Shi et al. 2019, a two-step process was utilized to normalize and filter data in all arrays involved in the experiment. Shi, Z., Yin, H., Van Nostrand, J. D., Voordeckers, J. W., Tu, Q., Deng, Y., Yuan, M., Zhou, A., Zhang, P., Xiao, N., Ning, D., He, Z., Wu, L., & Zhou, J. (2019). Functional Gene Array-Based Ultrasensitive and Quantitative Detection of Microbial Populations in Complex Communities. MSystems. https://doi.org/10.1128/msystems.00296-19