Soda lakes are intriguing ecosystems harboring extremely productive microbial communities in spite of their extreme environmental conditions. This makes them valuable model systems for studying the connection between community structure and abiotic parameters such as pH and salinity. For the first time, we apply high-throughput sequencing to accurately estimate phylogenetic richness and composition in five soda lakes, located in the Ethiopian Rift Valley. The lakes were selected for their contrasting pH, salinities and stratification and several depths or spatial positions were covered in each lake. DNA was extracted and analyzed from all lakes at various depths and RNA extracted from two of the lakes, analyzed using both amplicon- and shotgun sequencing. We reveal a surprisingly high biodiversity in all of the studied lakes, similar to that of freshwater lakes. Interestingly, diversity appeared uncorrelated or positively correlated to pH and salinity, with the most “extreme” lakes showing the highest richness. Together, pH, dissolved oxygen, sodium- and potassium concentration explained approximately 30% of the compositional variation between samples. A diversity of prokaryotic and eukaryotic taxa could be identified, including several putatively involved in carbon-, sulfur- or nitrogen cycling. Key processes like methane oxidation, ammonia oxidation and ‘nitrifier denitrification’ were also confirmed by mRNA transcript analyses.

The effect of salinity on prokaryotic community diversity in Abijata-Shalla Soda Ash Concentration Pond system was investigated by using high-throughput 16S rRNA gene 454 pyrosequencing. Surface water and brine samples from five sites spanning a salinity range of 3.4 % (Lake Abijata) to 32 % (SP230F, crystallizer pond) were analyzed. Overall, 33 prokaryotic phyla were detected, and the dominant prokaryotic phyla accounted for more than 95 % of the reads consisting of Planctomycetes, Bacteroidetes, candidate division TM7, Deinococcus-Thermus, Firmicutes, Actinobacteria, Proteobacteria, and Euryarchaeota. Diversity indices indicated that operational taxonomic unit (OTU) richness decreases drastically with increasing salinity in the pond system. A total of 471 OTUs were found at 3.4 % salinity whereas 49 OTUs were detected in pond SP211 (25 % salinity), and only 19 OTUs in the crystallization pond at 32 % salinity (SP230F). Along the salinity gradient, archaeal community gradually replaced bacterial community. Thus, archaeal community accounted for 0.4 % in Lake Abijata while 99.0 % in pond SP230F. This study demonstrates that salinity appears to be the key environmental parameter in structuring the prokaryotic communities of haloalkaline environments. Further, it confirmed that the prokaryotic diversity in Lake Abijata is high and it harbors taxa with low or no phylogenetic similarities to existing prokaryotic taxa and thus represents novel microorganisms.

This review aims to elucidate the contemporary methods of measuring and estimating methane (CH4) emissions from ruminants. Six categories of methods for measuring and estimating CH4 emissions from ruminants are discussed. The widely used methods in most CH4 abatement experiments comprise the gold standard respiration chamber, in vitro incubation, and the sulfur hexafluoride (SF6) techniques. In the spot sampling methods, the paper discusses the sniffer method, the GreenFeed system, the face mask method, and the portable accumulation chamber. The spot sampling relies on the measurement of short-term breath data adequately on spot. The mathematical modeling methods focus on predicting CH4 emissions from ruminants without undertaking extensive and costly experiments. For instance, the Intergovernmental Panel on Climate Change (IPCC) provides default values for regional emission factors and other parameters using three levels of estimation (Tier 1, 2 and 3 levels), with Tier 1 and Tier 3 being the simplest and most complex methods, respectively. The laser technologies include the open-path laser technique and the laser CH4 detector. They use the laser CH4 detector and wireless sensor networks to measure CH4 flux. The micrometeorological methods rely on measurements of meteorological data in line with CH4 concentration. The last category of methods for measuring and estimating CH4 emissions in this paper is the emerging technologies. They include the blood CH4 concentration tracer, infrared thermography, intraruminal telemetry, the eddy covariance (EC) technique, carbon dioxide as a tracer gas, and polytunnel. The emerging technologies are essential for the future development of effective quantification of CH4 emissions from ruminants. In general, adequate knowledge of CH4 emission measurement methods is important for planning, implementing, interpreting, and comparing experimental results.

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Extremophiles provide a one-of-a-kind source of enzymes with properties that allow them to endure the rigorous industrial conversion of lignocellulose biomass into fermentable sugars. However, the fact that most of these organisms fail to grow under typical culture conditions limits the accessibility to these enzymes. In this study, we employed a functional metagenomics approach to identify carbohydrate-degrading enzymes from Ethiopian soda lakes, which are extreme environments harboring a high microbial diversity. Out of 21,000 clones screened for the five carbohydrate hydrolyzing enzymes, 408 clones were found positive. Cellulase and amylase, gave high hit ratio of 1:75 and 1:280, respectively. A total of 378 genes involved in the degradation of complex carbohydrates were identified by combining high-throughput sequencing of 22 selected clones and bioinformatics analysis using a customized workflow. Around 41% of the annotated genes belonged to the Glycoside Hydrolases (GH). Multiple GHs were identified, indicating the potential to discover novel CAZymes useful for the enzymatic degradation of lignocellulose biomass from the Ethiopian soda Lakes. More than 73% of the annotated GH genes were linked to bacterial origins, with Halomonas as the most likely source. Biochemical characterization of the three enzymes from the selected clones (amylase, cellulase, and pectinase) showed that they are active in elevated temperatures, high pH, and high salt concentrations. These properties strongly indicate that the evaluated enzymes have the potential to be used for applications in various industrial processes, particularly in biorefinery for lignocellulose biomass conversion.

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Soda lakes are unique poly-extreme environments with high alkalinity and salinity that support diverse microbial communities despite their extreme nature. In this study, prokaryotic and eukaryotic microbial diversity in samples of the three soda lakes, Lake Abijata, Lake Chitu and Lake Shala in the East African Rift Valley, were determined using amplicon sequencing. Culture-independent analysis showed higher diversity of prokaryotic and eukaryotic microbial communities in all three soda lakes than previously reported. A total of 3,603 prokaryotic and 898 eukaryotic operational taxonomic units (OTUs) were found through culture-independent amplicon sequencing, whereas only 134 bacterial OTUs, which correspond to 3%, were obtained by enrichment cultures. This shows that only a fraction of the microorganisms from these habitats can be cultured under laboratory conditions. Of the three soda lakes, samples from Lake Chitu showed the highest prokaryotic diversity, while samples from Lake Shala showed the lowest diversity. Pseudomonadota (Halomonas), Bacillota (Bacillus, Clostridia), Bacteroidota (Bacteroides), Euryarchaeota (Thermoplasmata, Thermococci, Methanomicrobia, Halobacter), and Nanoarchaeota (Woesearchaeia) were the most common prokaryotic microbes in the three soda lakes. A high diversity of eukaryotic organisms were identified, primarily represented by Ascomycota and Basidiomycota. Compared to the other two lakes, a higher number of eukaryotic OTUs were found in Lake Abijata. The present study showed that these unique habitats harbour diverse microbial genetic resources with possible use in biotechnological applications, which should be further investigated by functional metagenomics.

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