Leptospirillum ferriphilum plays a major role in acidic, metal-rich environments, where it represents one of the most prevalent iron oxidizers. These milieus include acid rock and mine drainage as well as biomining operations. Despite its perceived importance, no complete genome sequence of the type strain of this model species is available, limiting the possibilities to investigate the strategies and adaptations that Leptospirillum ferriphilum DSM 14647T (here referred to as Leptospirillum ferriphilumT) applies to survive and compete in its niche. This study presents a complete, circular genome of Leptospirillum ferriphilumT obtained by PacBio single-molecule real-time (SMRT) long-read sequencing for use as a high-quality reference. Analysis of the functionally annotated genome, mRNA transcripts, and protein concentrations revealed a previously undiscovered nitrogenase cluster for atmospheric nitrogen fixation and elucidated metabolic systems taking part in energy conservation, carbon fixation, pH homeostasis, heavy metal tolerance, the oxidative stress response, chemotaxis and motility, quorum sensing, and biofilm formation. Additionally, mRNA transcript counts and protein concentrations were compared between cells grown in continuous culture using ferrous iron as the substrate and those grown in bioleaching cultures containing chalcopyrite (CuFeS2). Adaptations of Leptospirillum ferriphilumT to growth on chalcopyrite included the possibly enhanced production of reducing power, reduced carbon dioxide fixation, as well as elevated levels of RNA transcripts and proteins involved in heavy metal resistance, with special emphasis on copper efflux systems. Finally, the expression and translation of genes responsible for chemotaxis and motility were enhanced.IMPORTANCE Leptospirillum ferriphilum is one of the most important iron oxidizers in the context of acidic and metal-rich environments during moderately thermophilic biomining. A high-quality circular genome of Leptospirillum ferriphilumT coupled with functional omics data provides new insights into its metabolic properties, such as the novel identification of genes for atmospheric nitrogen fixation, and represents an essential step for further accurate proteomic and transcriptomic investigation of this acidophile model species in the future. Additionally, light is shed on adaptation strategies of Leptospirillum ferriphilumT for growth on the copper mineral chalcopyrite. These data can be applied to deepen our understanding and optimization of bioleaching and biooxidation, techniques that present sustainable and environmentally friendly alternatives to many traditional methods for metal extraction.
HighlightsDeep learning has become widely used in different fields of computer science such as face recognition, but also in biology, for example to detect malignant skin cancers based on images.Deep learning applied to microscopy images of biofilm colonization patterns accurately characterized its bacterial composition.Deep learning applications only rarely outperform human experts in classification tasks. Here however, deep learning reached an accuracy of 90%, therefore clearly outperforming human experts (50% accurate).Our method provides an accurate alternative to standard, time-consuming biochemical methods, using visual information only.
Palm oil production in Indonesia increased constantly over the last decades, which led to massive deforestation, especially on Sumatra island. The ongoing conversion of rainforest to agricultural systems results in high biodiversity loss. Here, we present the first RNA-based study on the effects of rainforest transformation to rubber and oil palm plantations in Indonesia for the active soil bacterial communities. For this purpose, bacterial communities of three different converted systems (jungle rubber, rubber plantation, and oil palm plantation) were studied in two landscapes with rainforest as reference by RT-PCR amplicon-based analysis of 16S rRNA gene transcripts. Active soil bacterial communities were dominated by Frankiales (Actinobacteria), subgroup 2 of the Acidobacteria and Alphaproteobacteria (mainly Rhizobiales and Rhodospirillales). Community composition differed significantly between the converted land use systems and rainforest reference sites. Alphaproteobacteria decreased significantly in oil palm samples compared to rainforest samples. In contrast, relative abundances of taxa within the Acidobacteria increased. Most important abiotic drivers for shaping soil bacterial communities were pH, calcium concentration, base saturation and C:N ratio. Indicator species analysis showed distinct association patterns for the analyzed land use systems. Nitrogen-fixing taxa including members of Rhizobiales and Rhodospirillales were associated with rainforest soils while nitrifiers and heat-resistant taxa including members of Actinobacteria were associated with oil palm soils. Predicted metabolic profiles revealed that the relative abundances of genes associated with fixation of nitrogen significantly decreased in plantation soils. Furthermore, predicted gene abundances regarding motility, competition or gene transfer ability indicated rainforest conversion-induced changes as well.
Acidithiobacillus ferrivorans is an acidophile implicated in low-temperature biomining for the recovery of metals from sulfide minerals. Acidithiobacillus ferrivorans obtains its energy from the oxidation of inorganic sulfur compounds, and genes encoding several alternative pathways have been identified. Next-generation sequencing of At. ferrivorans RNA transcripts identified the genes coding for metabolic and electron transport proteins for energy conservation from tetrathionate as electron donor. RNA transcripts suggested that tetrathionate was hydrolyzed by the tetH1 gene product to form thiosulfate, elemental sulfur and sulfate. Despite two of the genes being truncated, RNA transcripts for the SoxXYZAB complex had higher levels than for thiosulfate quinone oxidoreductase (doxDAgenes). However, a lack of heme-binding sites in soxX suggested that DoxDA was responsible for thiosulfate metabolism. Higher RNA transcript counts also suggested that elemental sulfur was metabolized by heterodisulfide reductase (hdrgenes) rather than sulfur oxygenase reductase (sor). The sulfite produced as a product of heterodisulfide reductase was suggested to be oxidized by a pathway involving the sat gene product or abiotically react with elemental sulfur to form thiosulfate. Finally, several electron transport complexes were involved in energy conservation. This study has elucidated the previously unknown At. ferrivorans tetrathionate metabolic pathway that is important in biomining.
Sulfide mineral processing often produces large quantities of wastewaters containing acid-generating inorganic sulfur compounds. If released untreated, these wastewaters can cause catastrophic environmental damage. In this study, microbial fuel cells were inoculated with acidophilic microorganisms to investigate whether inorganic sulfur compound oxidation can generate an electrical current. Cyclic voltammetry suggested that acidophilic microorganisms mediated electron transfer to the anode, and that electricity generation was catalyzed by microorganisms. A cation exchange membrane microbial fuel cell, fed with artificial wastewater containing tetrathionate as electron donor, reached a maximum whole cell voltage of 72 ± 9 mV. Stepwise replacement of the artificial anolyte with real mining process wastewater had no adverse effect on bioelectrochemical performance and generated a maximum voltage of 105 ± 42 mV. 16S rRNA gene sequencing of the microbial consortia resulted in sequences that aligned within the genera Thermoplasma, Ferroplasma, Leptospirillum, Sulfobacillus and Acidithiobacillus. This study opens up possibilities to bioremediate mining wastewater using microbial fuel cell technology.
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