Table 1.
Brown algae collected from the Kenyan coast and taxonomically identified at KMFRI.
Table 2.
List of brown algae collected and their associated epiphytic fungal isolates.
Fig 1.
Agar plug-based screening of antibacterial activity in epiphytic fungi from brown algae.
Table 3.
Summary of the cultural and morphological characteristics of fungal epiphytes from brown seaweeds grown on PDA medium after 7-10 days of growth at 28 ± 2 °C.
Fig 2.
Macroscopic and Microscopic features of the epiphytic fungi grown on PDA medium, incubated at 28 ± 2°C for 7-10 days.
Front view (F), Reverse view (R), Microscopic view (M). Magnification of each slide was 40x by observing shape of conidia, and hyphae. The identification process was guided by the taxonomic keys and descriptions provided by Dugan (2008) [28].
Fig 3.
Phylogenetic tree of nine epiphytic fungal isolates showing the genetic relationships among fungal isolates based on ITS sequence data.
Table 4.
BLAST matches for fungal epiphytes isolated from the brown algae of Kenyan coast.
Table 5.
Pairwise genetic distances estimated from sequence data of fungal epiphytes from brown algae of the Kenyan coast.
Fig 4.
Antibacterial evaluation of Ethyl acetate and Methanol extracts of fungal epiphytes against drug resistant E. cloacae by the disc diffusion method.
The values are the mean diameter (mm) of triplicate readings (mean ± SD; n = 3). Bars with different letters are significantly different according to Tukey’s post hoc test at p = 0.05. Therefore, any observed difference between them is considered to be statistically significant.
Fig 5.
Antibacterial evaluation of Ethyl acetate and Methanol extracts of fungal epiphytes against drug resistant A. baumannii by the disc diffusion method.
The values are the mean diameter (mm) of triplicate readings (mean ± SD; n = 3). Bars with different letters are significantly different according to Tukey’s post hoc test at p = 0.05. Therefore, any observed difference between them is considered to be statistically significant.
Fig 6.
Antibacterial evaluation of Ethyl acetate and Methanol extracts of fungal epiphytes against drug resistant E. faecium by the disc diffusion method.
The values are the mean diameter (mm) of triplicate readings (mean ± SD; n = 3). Bars with different letters are significantly different according to Tukey’s post hoc test at p = 0.05. Therefore, any observed difference between them is considered to be statistically significant.
Fig 7.
Antibacterial evaluation of Ethyl acetate and Methanol extracts of fungal epiphytes against drug resistant S. aureus by the disc diffusion method.
The values are the mean diameter (mm) of triplicate readings (mean ± SD; n = 3). Bars with different letters are significantly different according to Tukey’s post hoc test at p = 0.05. Therefore, any observed difference between them is considered to be statistically significant.
Fig 8.
Antibacterial evaluation of Ethyl acetate and Methanol extracts of fungal epiphytes against drug resistant K. pneumoniae by the disc diffusion method.
The values are the mean diameter (mm) of triplicate readings (mean ± SD; n = 3). Bars with different letters are significantly different according to Tukey’s post hoc test at p = 0.05. Therefore, any observed difference between them is considered to be statistically significant.
Table 6.
Determination of MIC and MBC (mg/mL) of the ethyl acetate extracts of fungal epiphytes against the tested bacteria.
Table 7.
Determination of MIC and MBC (mg/mL) of the methanolic extracts of fungal epiphytes against the tested bacteria.
Table 8.
Determination of MIC and MBC (mg/mL) of the combined extracts of fungal epiphytes against the tested bacteria.
Table 9.
Summarizes the mode of action of ethyl acetate, methanolic, and combined fungal extracts against ESKAPE bacteria.
Table 10.
GC-MS-based secondary metabolite profiling of the Dib-3 ethyl acetate extract.
Table 15.
GC-MS-based secondary metabolite profiling of the Sac-12 methanolic extract.
Fig 9.
Antibacterial evaluation of Ethyl acetate and Methanol extracts of fungal epiphytes against drug resistant P. aeruginosa by the disc diffusion method.
The values are the mean diameter (mm) of triplicate readings (mean ± SD; n = 3). Bars with different letters are significantly different according to Tukey’s post hoc test at p = 0.05. Therefore, any observed difference between them is considered to be statistically significant.
Fig 10.
SEM micrographs showing the effects of Dib-3 extract (combined) at the concentration of 2x MIC mg/mL on S. aureus and E. cloacae cells.
(a) and (c) are 1x PBS-treated cells (controls). (b) and (d) are extract-treated cells of the test bacteria.
Fig 11.
SEM micrographs showing the effects of Dib-4 extract (combined) at the concentration of 2x MIC mg/mL on S. aureus and E. cloacae cells.
(a) and (c) are 1x PBS-treated cells (controls). (b) and (d) are extract-treated cells of the test bacteria.
Fig 12.
SEM micrographs showing the effects of Sac-12 extract (combined) at the concentration of 2x MIC mg/mL on S. aureus and E. cloacae cells.
(a) and (c) are 1x PBS-treated cells (controls). (b) and (d) are extract-treated cells of the test bacteria.
Fig 13.
GC-MS chromatogram illustration of detected compounds in ethyl acetate extract of Dib-3.
Numbers above the Peaks represent the individual chemical components.
Fig 14.
GC-MS chromatogram illustration of detected compounds in methanolic of Dib-3.
Numbers above the Peaks represent the individual chemical components.
Fig 15.
GC-MS chromatogram illustration of detected compounds in ethyl acetate extract of Dib-4.
Numbers above the Peaks represent the individual chemical components.
Fig 16.
GC-MS chromatogram illustration of detected compounds in methanolic extract of Dib-4.
Numbers above the Peaks represent the individual chemical components.
Fig 17.
GC-MS chromatogram illustration of detected compounds in ethyl acetate extract of Sac-12.
Numbers above the Peaks represent the individual chemical components.
Fig 18.
GC-MS chromatogram illustration of detected compounds in methanolic extract of Sac-12.
Numbers above the Peaks represent the individual chemical components.
Table 11.
GC-MS-based secondary metabolite profiling of the Dib-3 methanolic extract.
Table 12.
GC-MS-based secondary metabolite profiling of the Dib-4 ethyl acetate extract.
Table 13.
GC-MS-based secondary metabolite profiling of the Dib-4 methanolic extract.
Table 14.
GC-MS-based secondary metabolite profiling of the Sac-12 ethyl acetate extract.