Characterization of Microbes Colonizing Yellowing Diseased Coconut Plants Growing at the Kenyan Coast

Abstract

Coconut is a highly versatile plant with multiple uses for its different parts. Despite its economic importance, there is inadequate scientific documentation of diseases affecting coconuts along the Kenyan coast. This cross-sectional study aimed at identifying microbes occurring in coconut plants with symptoms of yellowing diseases growing along the Kenyan coast, specifically in Kwale, Kilifi, and Lamu counties. A survey study was conducted and out of 1,080 coconut plants surveyed, 162 had symptoms of yellowing disease. This data was used to compute disease prevalence and severity scores. Fifty-four diseased samples were collected using the purposive sampling technique while nine samples were collected from health coconuts making a total of sixty-three samples collected for microbial isolation, morphological and biochemical assays as well as genetic profiling experiments. Total genomic DNA was extracted from leaf samples using CTAB and profiling of the genetic structure of microbial communities using amplicons of 16S rRNA sequences (V4 region) with Illumina MiSeq. The sequence data was analyzed using the QIIME 2 pipeline, and machine learning for prediction of the presence of differential OTU using Random Forest classifier. Nested-PCR using the P1/P7 primers for the first reaction and Phyto3F/R for the second reaction was used to confirm the absence of phytoplasma strains in the samples. From the surveyed areas, the highest disease prevalence and severity were recorded in Kilifi with 16.67% and 72.22%, while the lowest were recorded in Lamu with 13.61% and 59.26%, respectively. Heatmaps were generated utilizing nine morphological descriptors for bacteria and ten morphological descriptors for fungi to discern relationships among the isolates and compare them to presumptive positive controls. The samples yielded a total of 172 bacterial isolates, with the majority being translucent (63.74%), gram-positive (83.63%), and rod-shaped (79.53%), resembling the genera Erwinia and Pseudomonas. For fungi, 109 isolates were obtained, most of which were grey or black in colour (45.37%), while 76.85% were fluffy, most of the characteristics belonging to the Phylum Ascomycota. A total of 113,330 reads were obtained with sequence clustering, yielding 285 OTUs for bacteria. The most abundant phylum was Actinobacteria (84.87%), while Streptomyces was the most prevalent genus (61.24%). The most defining microbial taxon in Kilifi was Streptomyces, while in Kwale it was Agrobacterium and Actinocatenispora, with Lamu having no distinct microbial taxon. Fungi were clustered into 28 OTUs with 1,806 reads. Ascomycota was the dominant phylum (98.56%), while Cyphellophora and Devriesia were the most prevalent genera at 22.42% and 21.93% respectively. The findings indicate that microbial diversity was higher in Kilifi and Kwale counties as compared to Lamu, which registered the lowest diversity. For both bacteria and fungi, healthy control samples exhibited low diversity with minimal microbial concentration. The results provide a comprehensive understanding of the prevalence, severity, and microbial communities present in yellowing, diseased coconut plants. This will help with future elucidation of the agents exacerbating coconut disease symptoms as well as in the adoption of research findings by stakeholders like KCDA to tackle the challenges encountered by coconut plants, thereby promoting the improvement and conservation of coconut germplasm.