Isolation and characterization of bacterial isolates from Lake Magadi

Microorganisms from soda lakes have attracted attention as a possible source of novel enzymes and metabolites for use in industrial applications. Isolation and characterization of bacteria from Kenyan soda lakes has been done mainly in Lakes Elmenteita, Bogoria and Nakuru. Only a few studies have been documented on Lake Magadi, a hyper saline lake with up to 30% salinity levels. This study sought to isolate alkaliphilic bacteria from Lake Magadi that could produce novel enzymes and antimicrobial compounds, and document for further exploitation. Nearly 55 isolates were obtained using different media prepared with filter-sterilised water from the lake, which were characterized and screened for production of extracellular enzymes and/or antimicrobial compounds. Bacteria retrieved grew well at pH ranging from 5 – 10, temperature range of 25 – 50 oC and sodium chloride range of 0- 30 %. The isolates produced amylases, lipases, proteases and esterases and exhibited a range of inhibitory effects on various test organisms. Analysis of partial sequences of 16S rRNA genes using Blast showed that 80 % of the isolates were affiliated to the genus Bacillus, while 20% were affiliated to members of Gammaproteobacteria. Five (5) isolates showed identity of 95 - 97 % similarity with the previously known sequences and could represent novel bacterial species, while 4 isolates had a sequence identity of 80 - 93% similarity to known organisms, and could represent novel genera. This study demonstrated that the extreme environment of Lake Magadi harbors novel alkaliphilic bacteria with potential for production of enzymes and antimicrobial compounds.Keywords: Soda lakes, Alkaliphiles, extreme environment, enzymes and antimicrobial compoundsJ. Trop. Microbiol. Biotechnol. 8:17-25 1

Additional file 8 of Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes

Additional file 8. Figure S8-A. Predicted prokaryotic Shannon biodiversity index values (expressed as natural log scale) in soils of the 9 sub-Saharan Africa countries used in this study, for 2040-2060 and 2080-2100 under two distinct GH emission scenarios (SSP126 and SSP585), and comparison with current predicted Shannon biodiversity as estimated by SEM. Pairwise significance values of differences in biodiversity means between the different years and scenarios are represented by the brackets with the following nomenclature: * - p-value \u3c 0.05; ** - p-value \u3c 0.01; *** - p-value \u3c 0.001

Additional file 11 of Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes

Additional file 11. Figure S8-D. Predicted abundance values of PGPF (expressed as natural log scale) in soils of the 9 sub-Saharan Africa countries used in this study, for 2040-2060 and 2080-2100 under two distinct GH emission scenarios (SSP126 and SSP585), and comparison with current predicted Shannon biodiversity as estimated by SEM. Pairwise significance values of differences in biodiversity means between the different years and scenarios are represented by the brackets with the following nomenclature: * - p-value \u3c 0.05; ** - p-value \u3c 0.01; *** - p-value \u3c 0.001

Additional file 18 of Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes

Additional file 18. Table S7. Number of samples allocated for each country, and number of samples collected

Additional file 1 of Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes

Additional file 1. Figure S1. Distribution of samples across the 9 African countries according to their land cover (LC) classification. Land cover codes used were the following: LC_1 - Rainfed croplands; LC_2 - Mosaic Cropland (50-70%) / Vegetation (grassland, shrubland, forest) (20-50%); LC_3 - Mosaic Vegetation (grassland, shrubland, forest) (50-70%) / Cropland (20-50%); LC_4 - Closed to open (\u3e15%) broadleaved evergreen and/or semi-deciduous forest (\u3e5m); LC_5 - Closed (\u3e40%) broadleaved deciduous forest (\u3e5m); LC_6 - Open (15-40%) broadleaved deciduous forest (\u3e5m); LC_10 - Mosaic Forest/Shrubland (50-70%) / Grassland (20-50%); LC_11 - Mosaic Grassland (50-70%) / Forest/Shrubland (20-50%); LC_12 - Closed to open (\u3e15%) shrubland (\u3c5m); LC_13 - Closed to open (\u3e15%) grassland; LC_14 - Sparse (\u3e15%) vegetation (woody vegetation, shrubs, grassland); LC_17 - Closed to open (\u3e15%) vegetation (grassland, shrubland, woody vegetation) on regularly flooded or waterlogged soil; LC_18 - Artificial surfaces and associated areas (urban areas \u3e50%); LC_19 - Bare areas

Additional file 10 of Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes

Additional file 10. Figure S8-C. Predicted fungal Shannon biodiversity values (expressed as natural log scale) in soils of the 9 sub-Saharan Africa countries used in this study, for 2040-2060 and 2080-2100 under two distinct GH emission scenarios (SSP126 and SSP585), and comparison with current predicted Shannon biodiversity as estimated by SEM. Pairwise significance values of differences in biodiversity means between the different years and scenarios are represented by the brackets with the following nomenclature: * - p-value \u3c 0.05; ** - p-value \u3c 0.01; *** - p-value \u3c 0.001

Additional file 7 of Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes

Additional file 7. Figure S7. MIROC6 model predictions for mean annual temperature (oC) (A) and mean annual precipitation (mm) (B) under too different GH emission scenarios (SSP126 and SSP585), predicted for 2040-2060 and 2080-2100 temporal windows. The predicted datasets are grouped according to country, as indicated by the vertical dashed lines

Additional file 14 of Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes

Additional file 14. Table S3. Metadata of the dominant phylotypes, including taxonomy, functional predictions (based on FAPROTAX and manual curation), and ecological groups based on the main environmental predictor

Additional file 5 of Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes

Additional file 5. Figure S5. Relationship between the relative abundance of dominant phylotypes across soil samples and their main environmental predictors, as determined by semipartial correlation analysis. Phylotypes were grouped into environmental categories based on the correlation between phylotype and its major environmental predictor: positive correlation with pH – high pH; negative correlation with pH – low pH; positive correlation with phosphate – high Phosphate; negative correlation with phosphate – low Phosphate; negative correlation with Sodium – low Sodium

Additional file 19 of Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes

Additional file 19. Table S8. Variable codes, meaning and units for the environmental variables used in this study