The soil microbiomes of forest ecosystems in Kenya: their diversity and environmental drivers.

Soil microbiomes in forest ecosystems act as both nutrient sources and sinks through a range of processes including organic matter decomposition, nutrient cycling, and humic compound incorporation into the soil. Most forest soil microbial diversity studies have been performed in the northern hemisphere, and very little has been done in forests within African continent. This study examined the composition, diversity and distribution of prokaryotes in Kenyan forests top soils using amplicon sequencing of V4-V5 hypervariable region of the 16S rRNA gene. Additionally, soil physicochemical characteristics were measured to identify abiotic drivers of prokaryotic distribution. Different forest soils were found to have statistically distinct microbiome compositions, with Proteobacteria and Crenarchaeota taxa being the most differentially abundant across regions within bacterial and archaeal phyla, respectively. Key bacterial community drivers included pH, Ca, K, Fe, and total N while archaeal diversity was shaped by Na, pH, Ca, total P and total N. To contextualize the prokaryote diversity of Kenyan forest soils on a global scale, the sample set was compared to amplicon data obtained from forest biomes across the globe; displaying them to harbor distinct microbiomes with an over-representation of uncultured taxa such as TK-10 and Ellin6067 genera.

Evolution of fungal and non-fungal eukaryotic communities in response to thermophilic co-composting of various nitrogen-rich green feedstocks.

Thermophilic composting is a promising soil and waste management approach involving diverse micro and macro-organisms, including eukaryotes. Due to sub-optimal amounts of nutrients in manure, supplemental feedstock materials such as Lantana camara, and Tithonia diversifolia twigs are used in composting. These materials have, however, been reported to have antimicrobial activity in in-vitro experiments. Furthermore, the phytochemical analysis has shown differences in their complexities, thus possibly requiring various periods to break down. Therefore, it is necessary to understand these materials' influence on the biological and physical-chemical stability of compost. Most compost microbiome studies have been bacterial-centric, leaving out eukaryotes despite their critical role in the environment. Here, the influence of different green feedstock on the fungal and non-fungal eukaryotic community structure in a thermophilic compost environment was examined. Total community fungal and non-fungal eukaryotic DNA was recovered from triplicate compost samples of four experimental regimes. Sequencing for fungal ITS and non-fungal eukaryotes; 18S rDNA was done under the Illumina Miseq platform, and bioinformatics analysis was done using Divisive Amplicon Denoising Algorithm version 2 workflow in R version 4.1. Samples of mixed compost and composting day 84 recorded significantly (P<0.05) higher overall fungal populations, while Lantana-based compost and composting day 84 revealed the highest fungal community diversity. Non-fungal eukaryotic richness was significantly (P< 0.05) more abundant in Tithonia-based compost and composting day 21. The most diverse non-fungal eukaryotic biome was in the Tithonia-based compost and composting day 84. Sordariomycetes and Holozoa were the most contributors to the fungal and non-fungal community interactions in the compost environment, respectively. The findings of this study unravel the inherent influence of diverse composting materials and days on the eukaryotic community structure and compost's biological and chemical stability.

Composition, structure, and functional shifts of prokaryotic communities in response to co-composting of various nitrogenous green feedstocks.

BACKGROUND: Thermophilic composting is a promising method of sanitizing pathogens in manure and a source of agriculturally important thermostable enzymes and microorganisms from organic wastes. Despite the extensive studies on compost prokaryotes, shifts in microbial profiles under the influence of various green materials and composting days are still not well understood, considering the complexity of the green material sources. Here, the effect of regimens of green composting material on the diversity, abundance, and metabolic capacity of prokaryotic communities in a thermophilic compost environment was examined. METHODS: Total community 16S rRNA was recovered from triplicate compost samples of Lantana-based, Tithonia-based, Grass-based, and mixed (Lantana + Tithonia + Grass)- based at 21, 42, 63, and 84 days of composting. The 16S rRNA was sequenced using the Illumina Miseq platform. Bioinformatics analysis was done using Divisive Amplicon Denoising Algorithm version 2 (DADA2) R version 4.1 and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States version 2 (PICRUSt2) pipelines for community structure and metabolic profiles, respectively. In DADA2, prokaryotic classification was done using the Refseq-ribosomal database project (RDP) and SILVA version 138 databases. RESULTS: Our results showed apparent differences in prokaryotic community structure for total diversity and abundance within the four compost regimens and composting days. The study showed that the most prevalent phyla during composting included Acidobacteriota, Actinobacteriota, Bacteroidota, Chloroflexi, and Proteobacteria. Additionally, there were differences in the overall diversity of metabolic pathways but no significant differences among the various compost treatments on major metabolic pathways like carbohydrate biosynthesis, carbohydrate degradation, and nitrogen biosynthesis. CONCLUSION: Various sources of green material affect the succession of compost nutrients and prokaryotic communities. The similarity of amounts of nutrients, such as total Nitrogen, at the end of the composting process, despite differences in feedstock material, indicates a significant influence of composting days on the stability of nutrients during composting.

Prokaryotic diversity and composition within equatorial lakes Olbolosat and Oloiden in Kenya (Africa).

Total community 16S rDNA was used to determine the diversity and composition of bacteria and archaea within lakes Olbolosat and Oloiden in Kenya. The V3-V4 hypervariable region of the 16S rRNA gene was targeted since it's highly conserved and has a higher resolution for lower rank taxa. High throughput sequencing was performed on 15 samples obtained from the two lakes using the Illumina Miseq platform. Lakes Olbolosat and Oloiden shared 280 of 10,523 Amplicon Sequence Variants (ASVs) recovered while the four sample types (water, microbial mats, dry and wet sediments) shared 4 ASVs. The composition of ASVs in lake Olbolosat was highly dependent on Cu+, Fe2+, NH4 +, and Mn2+, while L. Oloiden was dependent on Mg2+, Na+, Ca2+, and K+. All the alpha diversity indices except Simpson were highest in the dry sediment sample (EC1 and 2) both from lake Oloiden. The abundant phyla included Proteobacteria (33.8%), Firmicutes (27.3%), Actinobacteriota (21.2%), Chloroflexi (6.8%), Cyanobacteria (3.8%), Acidobacteriota (2.8%), Planctomycetota (1.9%) and Bacteroidota (1.1%). Analysis of similarity (ANOSIM) revealed a significant difference in ASV composition between the two lakes (r = 0.191, p = 0.048), and between the sample types (r = 0.6667, p = 0.001). The interaction network for prokaryotic communities within the two lakes displayed Proteobacteria to be highly positively connected with other microbes. PERMANOVA results suggest that temperature controls the functioning of the two ecosystems.

Diversity and structure of prokaryotic communities within organic and conventional farming systems in central highlands of Kenya.

Management practices such as tillage, crop rotation, irrigation, organic and inorganic inputs application are known to influence diversity and function of soil microbial populations. In this study, we investigated the effect of conventional versus organic farming systems at low and high input levels on structure and diversity of prokaryotic microbial communities. Soil samples were collected from the ongoing long-term farming system comparison trials established in 2007 at Chuka and Thika in Kenya. Physicochemical parameters for each sample were analyzed. Total DNA and RNA amplicons of variable region (V4-V7) of the 16S rRNA gene were generated on an Illumina platform using the manufacturer's instructions. Diversity indices and statistical analysis were done using QIIME2 and R packages, respectively. A total of 29,778,886 high quality reads were obtained and assigned to 16,176 OTUs at 97% genetic distance across both 16S rDNA and 16S rRNA cDNA datasets. The results pointed out a histrionic difference in OTUs based on 16S rDNA and 16S rRNA cDNA. Precisely, while 16S rDNA clustered by site, 16S rRNA cDNA clustered by farming systems. In both sites and systems, dominant phylotypes were affiliated to phylum Actinobacteria, Proteobacteria and Acidobacteria. Conventional farming systems showed a higher species richness and diversity compared to organic farming systems, whilst 16S rRNA cDNA datasets were similar. Physiochemical factors were associated differently depending on rRNA and rDNA. Soil pH, electrical conductivity, organic carbon, nitrogen, potassium, aluminium, zinc, iron, boron and micro-aggregates showed a significant influence on the observed microbial diversity. The observed higher species diversity in the conventional farming systems can be attributed to the integration of synthetic and organic agricultural inputs. These results show that the type of inputs used in a farming system not only affect the soil chemistry but also the microbial population dynamics and eventually the functional roles of these microbes.

Bacteria and Archaea diversity within the hot springs of Lake Magadi and Little Magadi in Kenya.

BACKGROUND: Lake Magadi and little Magadi are hypersaline, alkaline lakes situated in the southern part of Kenyan Rift Valley. Solutes are supplied mainly by a series of alkaline hot springs with temperatures as high as 86 °C. Previous culture-dependent and culture-independent studies have revealed diverse groups of microorganisms thriving under these conditions. Previous culture independent studies were based on the analysis of 16S rDNA but were done on less saline lakes. For the first time, this study combined illumina sequencing and analysis of amplicons of both total community rDNA and 16S rRNA cDNA to determine the diversity and community structure of bacteria and archaea within 3 hot springs of L. Magadi and little Magadi. METHODS: Water, wet sediments and microbial mats were collected from springs in the main lake at a temperature of 45.1 °C and from Little Magadi "Nasikie eng'ida" (temperature of 81 °C and 83.6 °C). Total community DNA and RNA were extracted from samples using phenol-chloroform and Trizol RNA extraction protocols respectively. The 16S rRNA gene variable region (V4 - V7) of the extracted DNA and RNA were amplified and library construction performed following Illumina sequencing protocol. Sequences were analyzed done using QIIME while calculation of Bray-Curtis dissimilarities between datasets, hierarchical clustering, Non Metric Dimensional Scaling (NMDS) redundancy analysis (RDA) and diversity indices were carried out using the R programming language and the Vegan package. RESULTS: Three thousand four hundred twenty-six and one thousand nine hundred thirteen OTUs were recovered from 16S rDNA and 16S rRNA cDNA respectively. Uncultured diversity accounted for 89.35 % 16S rDNA and 87.61 % 16S rRNA cDNA reads. The most abundant phyla in both the 16S rDNA and 16S rRNA cDNA datasets included: Proteobacteria (8.33-50 %), Firmicutes 3.52-28.92 %, Bacteroidetes (3.45-26.44 %), Actinobacteria (0.98-28.57 %) and Euryarchaeota (3.55-34.48 %) in all samples. NMDS analyses of taxonomic composition clustered the taxa into three groups according to sample types (i.e. wet sediments, mats and water samples) with evident overlap of clusters between wet sediments and microbial mats from the three sample types in both DNA and cDNA datasets. The hot spring (45.1 °C) contained less diverse populations compared to those in Little Magadi (81-83 °C). CONCLUSION: There were significant differences in microbial community structure at 95 % level of confidence for both total diversity (P value, 0.009) based on 16S rDNA analysis and active microbial diversity (P value, 0.01) based on 16S rRNA cDNA analysis, within the three hot springs. Differences in microbial composition and structure were observed as a function of sample type and temperature, with wet sediments harboring the highest diversity.