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  • 1.
    Bellenberg, Sören
    et al.
    Univ Duisburg Essen, Germany.
    Buetti-Dinh, Antoine
    Univ Svizzera Italiana, Switzerland;Swiss Inst Bioinformat, Switzerland.
    Galli, Vanni
    Univ Appl Sci Southern Switzerland, Switzerland.
    Ilie, Olga
    Univ Svizzera Italiana, Switzerland;Swiss Inst Bioinformat, Switzerland.
    Herold, Malte
    Univ Luxembourg, Luxembourg.
    Christel, Stephan
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.
    Boretska, Mariia
    Univ Duisburg Essen, Germany.
    Pivkin, Igor V.
    Univ Svizzera Italiana, Switzerland;Swiss Inst Bioinformat, Switzerland.
    Wilmes, Paul
    Univ Luxembourg, Luxembourg.
    Sand, Wolfgang
    Univ Duisburg Essen, Germany;Donghua Univ, Peoples Republic of China;Tech Univ Bergakad Freiberg, Germany.
    Vera, Mario
    Pontificia Univ Catolica Chile, Chile.
    Dopson, Mark
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.
    Automated Microscopic Analysis of Metal Sulfide Colonization by Acidophilic Microorganisms2018In: Applied and Environmental Microbiology, ISSN 0099-2240, E-ISSN 1098-5336, Vol. 84, no 20, article id UNSP e01835-18Article in journal (Refereed)
    Abstract [en]

    Industrial biomining processes are currently focused on metal sulfides and their dissolution, which is catalyzed by acidophilic iron(II)- and/or sulfur-oxidizing microorganisms. Cell attachment on metal sulfides is important for this process. Biofilm formation is necessary for seeding and persistence of the active microbial community in industrial biomining heaps and tank reactors, and it enhances metal release. In this study, we used a method for direct quantification of the mineral-attached cell population on pyrite or chalcopyrite particles in bioleaching experiments by coupling high-throughput, automated epifluorescence microscopy imaging of mineral particles with algorithms for image analysis and cell quantification, thus avoiding human bias in cell counting. The method was validated by quantifying cell attachment on pyrite and chalcopyrite surfaces with axenic cultures of Acidithiobacillus caldus, Leptospirillum ferriphilum, and Sulfobacillus thermosulfidooxidans. The method confirmed the high affinity of L. ferriphilum cells to colonize pyrite and chalcopyrite surfaces and indicated that biofilm dispersal occurs in mature pyrite batch cultures of this species. Deep neural networks were also applied to analyze biofilms of different microbial consortia. Recent analysis of the L. ferriphilum genome revealed the presence of a diffusible soluble factor (DSF) family quorum sensing system. The respective signal compounds are known as biofilm dispersal agents. Biofilm dispersal was confirmed to occur in batch cultures of L. ferriphilum and S. thermosulfidooxidans upon the addition of DSF family signal compounds. IMPORTANCE The presented method for the assessment of mineral colonization allows accurate relative comparisons of the microbial colonization of metal sulfide concentrate particles in a time-resolved manner. Quantitative assessment of the mineral colonization development is important for the compilation of improved mathematical models for metal sulfide dissolution. In addition, deep-learning algorithms proved that axenic or mixed cultures of the three species exhibited characteristic biofilm patterns and predicted the biofilm species composition. The method may be extended to the assessment of microbial colonization on other solid particles and may serve in the optimization of bioleaching processes in laboratory scale experiments with industrially relevant metal sulfide concentrates. Furthermore, the method was used to demonstrate that DSF quorum sensing signals directly influence colonization and dissolution of metal sulfides by mineral-oxidizing bacteria, such as L. ferriphilum and S. thermosulfidooxidans.

  • 2.
    Bellenberg, Sören
    et al.
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science. Univ Duisburg Essen, Germany.
    Huynh, Dieu
    TU Bergakademie Freiberg, Germany.
    Poetsch, Ansgar
    Ruhr Univ Bochum, Germany;Univ Plymouth, UK.
    Sand, Wolfgang
    Univ Duisburg Essen, Germany;TU Bergakad Freiberg, Germany;Donghua Univ, Peoples Republic of China.
    Vera, Mario
    Pontificia Univ Catolica Chile, Chile.
    Proteomics Reveal Enhanced Oxidative Stress Responses and Metabolic Adaptation in Acidithiobacillus ferrooxidans Biofilm Cells on Pyrite2019In: Frontiers in Microbiology, ISSN 1664-302X, E-ISSN 1664-302X, Vol. 10, p. 1-14, article id 592Article in journal (Refereed)
    Abstract [en]

    Reactive oxygen species (ROS) cause oxidative stress and growth inhibition by inactivation of essential enzymes, DNA and lipid damage in microbial cells. Acid mine drainage (AMD) ecosystems are characterized by low pH values, enhanced levels of metal ions and low species abundance. Furthermore, metal sulfides, such as pyrite and chalcopyrite, generate extracellular ROS upon exposure to acidic water. Consequently, oxidative stress management is especially important in acidophilic leaching microorganisms present in industrial biomining operations, especially when forming biofilms on metal sulfides. Several adaptive mechanisms have been described, but the molecular repertoire of responses upon exposure to pyrite and the presence of ROS are not thoroughly understood in acidophiles. In this study the impact of the addition of H2O2 on iron oxidation activity in Acidithiobacillus ferrooxidans DSM 14882(T) was investigated. Iron(II)- or sulfur-grown cells showed a higher sensitivity toward H2O2 than pyrite-grown ones. In order to elucidate which molecular responses may be involved, we used shot-gun proteomics and compared proteomes of cells grown with iron(II)-ions against biofilm cells, grown for 5 days in presence of pyrite as sole energy source. In total 1157 proteins were identified. 213 and 207 ones were found to have increased levels in iron(II) ion-grown or pyrite-biofilm cells, respectively. Proteins associated with inorganic sulfur compound (ISC) oxidation were among the latter. In total, 80 proteins involved in ROS degradation, thiol redox regulation, macromolecule repair mechanisms, biosynthesis of antioxidants, as well as metal and oxygen homeostasis were found. 42 of these proteins had no significant changes in abundance, while 30 proteins had increased levels in pyrite-biofilm cells. New insights in ROS mitigation strategies, such as importance of globins for oxygen homeostasis and prevention of unspecific reactions of free oxygen that generate ROS are presented for A. ferrooxidans biofilm cells. Furthermore, proteomic analyses provide insights in adaptations of carbon fixation and oxidative phosphorylation pathways under these two growth conditions.

  • 3.
    Buetti-Dinh, Antoine
    et al.
    Università della Svizzera italiana, Switzerland;Swiss Institute of Bioinformatics, Switzerland.
    Galli, Vanni
    University of Applied Sciences of Southern Switzerland, Switzerland.
    Bellenberg, Sören
    Universität Duisburg-Essen, Germany.
    Ilie, Olga
    Università della Svizzera italiana, Switzerland;Swiss Institute of Bioinformatics, Switzerland.
    Herold, Malte
    University of Luxembourg, Luxembourg.
    Christel, Stephan
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.
    Boretska, Mariia
    Universität Duisburg-Essen, Germany.
    Pivkin, Igor V.
    Università della Svizzera italiana, Switzerland;Swiss Institute of Bioinformatics, Switzerland.
    Wilmes, Paul
    University of Luxembourg, Luxembourg.
    Sand, Wolfgang
    Universität Duisburg-Essen, Germany;Donghua University, People's Republic of China;Mining Academy and Technical University Freiberg, Germany.
    Vera, Mario
    Pontificia Universidad Católica de Chile, Chile.
    Dopson, Mark
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.
    Deep neural networks outperform human expert's capacity in characterizing bioleaching bacterial biofilm composition2019In: Biotechnology Reports, ISSN 0156-1383, E-ISSN 2215-017X, Vol. 22, p. 1-5, article id e00321Article in journal (Refereed)
    Abstract [en]

    Background: Deep neural networks have been successfully applied to diverse fields of computer vision. However, they only outperform human capacities in a few cases. Methods: The ability of deep neural networks versus human experts to classify microscopy images was tested on biofilm colonization patterns formed on sulfide minerals composed of up to three different bioleaching bacterial species attached to chalcopyrite sample particles. Results: A low number of microscopy images per category (<600) was sufficient for highly efficient computational analysis of the biofilm's bacterial composition. The use of deep neural networks reached an accuracy of classification of ∼90% compared to ∼50% for human experts. Conclusions: Deep neural networks outperform human experts’ capacity in characterizing bacterial biofilm composition involved in the degradation of chalcopyrite. This approach provides an alternative to standard, time-consuming biochemical methods. © 2019 The Author

  • 4.
    Buetti-Dinh, Antoine
    et al.
    Linnaeus University, Faculty of Health and Life Sciences, Department of Chemistry and Biomedical Sciences. Università della Svizzera italiana, Switzerland;Swiss Institute of Bioinformatics, Switzerland.
    Herold, Malte
    University of Luxembourg, Luxembourg.
    Christel, Stephan
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.
    El Hajjami, Mohamed
    QNLM, China.
    Delogu, Francesco
    Norwegian University of Life Sciences, Norway.
    Ilie, Olga
    Università della Svizzera italiana, Switzerland;Swiss Institute of Bioinformatics, Switzerland.
    Bellenberg, Sören
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.
    Wilmes, Paul
    University of Luxembourg, Luxembourg.
    Poetsch, Ansgar
    Ruhr University Bochum, Germany;QNLM, China;Ocean University of China, China.
    Sand, Wolfgang
    University Duisburg-Essen, Germany;Donghua University, China;3Mining Academy and Technical University Freiberg, Germany.
    Vera, Mario
    Pontificia Universidad Católica de Chile, Chile.
    Pivkin, Igor V.
    Università della Svizzera italiana, Switzerland;Swiss Institute of Bioinformatics, Switzerland.
    Friedman, Ran
    Linnaeus University, Faculty of Health and Life Sciences, Department of Chemistry and Biomedical Sciences.
    Dopson, Mark
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.
    Reverse engineering directed gene regulatory networks from transcriptomics and proteomics data of biomining bacterial communities with approximate Bayesian computation and steady-state signalling simulations2020In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 21, no 1, p. 1-15, article id 23Article in journal (Refereed)
    Abstract [en]

    Background: Network inference is an important aim of systems biology. It enables the transformation of OMICs datasets into biological knowledge. It consists of reverse engineering gene regulatory networks from OMICs data, such as RNAseq or mass spectrometry-based proteomics data, through computational methods. This approach allows to identify signalling pathways involved in specific biological functions. The ability to infer causality in gene regulatory networks, in addition to correlation, is crucial for several modelling approaches and allows targeted control in biotechnology applications. Methods: We performed simulations according to the approximate Bayesian computation method, where the core model consisted of a steady-state simulation algorithm used to study gene regulatory networks in systems for which a limited level of details is available. The simulations outcome was compared to experimentally measured transcriptomics and proteomics data through approximate Bayesian computation. Results: The structure of small gene regulatory networks responsible for the regulation of biological functions involved in biomining were inferred from multi OMICs data of mixed bacterial cultures. Several causal inter- and intraspecies interactions were inferred between genes coding for proteins involved in the biomining process, such as heavy metal transport, DNA damage, replication and repair, and membrane biogenesis. The method also provided indications for the role of several uncharacterized proteins by the inferred connection in their network context. Conclusions: The combination of fast algorithms with high-performance computing allowed the simulation of a multitude of gene regulatory networks and their comparison to experimentally measured OMICs data through approximate Bayesian computation, enabling the probabilistic inference of causality in gene regulatory networks of a multispecies bacterial system involved in biomining without need of single-cell or multiple perturbation experiments. This information can be used to influence biological functions and control specific processes in biotechnology applications.

  • 5.
    Christel, Stephan
    et al.
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.
    Herold, Malte
    University of Luxembourg, Luxembourg.
    Bellenberg, Sören
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science. Universität Duisburg-Essen, Germany.
    Buetti-Dinh, Antoine
    Università della Svizzera italiana, Switzerland;Swiss Institute of Bioinformatics (SIB), Switzerland.
    El Hajjami, Mohamed
    Ruhr-Universität Bochum, Germany.
    Pivkin, Igor
    Università della Svizzera italiana, Switzerland;Swiss Institute of Bioinformatics (SIB), Switzerland.
    Sand, Wolfgang
    Universität Duisburg-Essen, Germany;Donghua University, Peoples Republic of China;Mining Academy, Germany;Technical University Freiberg, Germany.
    Wilmes, Paul
    University of Luxembourg, Luxembourg.
    Poetsch, Ansgar
    Ruhr-Universität Bochum, Germany;Plymouth University, United Kingdom.
    Vera, Mario
    Pontificia Universidad Católica de Chile, Chile.
    Dopson, Mark
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.
    Weak Iron Oxidation by Sulfobacillus thermosulfidooxidans Maintains a Favorable Redox Potential for Chalcopyrite Bioleaching2018In: Frontiers in Microbiology, ISSN 1664-302X, E-ISSN 1664-302X, Vol. 9, article id 3059Article in journal (Refereed)
    Abstract [en]

    Bioleaching is an emerging technology, describing the microbially assisted dissolution of sulfidicores that provides a more environmentally friendly alternative to many traditional metal extractionmethods, such as roasting or smelting. Industrial interest increases steadily and today, circa 15-20%of the world’s copper production can be traced back to this method. However, bioleaching of theworld’s most abundant copper mineral chalcopyrite suffers from low dissolution rates, oftenattributed to passivating layers, which need to be overcome to use this technology to its full potential.To prevent these passivating layers from forming, leaching needs to occur at a lowoxidation/reduction potential (ORP), but chemical redox control in bioleaching heaps is difficult andcostly. As an alternative, selected weak iron-oxidizers could be employed that are incapable ofscavenging exceedingly low concentrations of iron and therefore, raise the ORP just above the onsetof bioleaching, but not high enough to allow for the occurrence of passivation. In this study, wereport that microbial iron oxidation by Sulfobacillus thermosulfidooxidans meets these specifications.Chalcopyrite concentrate bioleaching experiments with S. thermosulfidooxidans as the sole ironoxidizer exhibited significantly lower redox potentials and higher release of copper compared tocommunities containing the strong iron oxidizer Leptospirillum ferriphilum. Transcriptomic responseto single and co-culture of these two iron oxidizers was studied and revealed a greatly decreasednumber of mRNA transcripts ascribed to iron oxidation in S. thermosulfidooxidans when cultured inthe presence of L. ferriphilum. This allowed for the identification of genes potentially responsible forS. thermosulfidooxidans’ weaker iron oxidation to be studied in the future, as well as underlined theneed for mechanisms to control the microbial population in bioleaching heaps

  • 6.
    Christel, Stephan
    et al.
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.
    Herold, Malte
    University of Luxembourg, Luxembourg.
    Bellenberg, Sören
    Universität Duisburg-Essen, Germany.
    El Hajjami, Mohamed
    Ruhr Universität Bochum, Germany.
    Buetti-Dinh, Antoine
    Università della Svizzera Italiana, Switzerland;Swiss Institute of Bioinformatics, Switzerland.
    Pivkine, Igor V.
    Università della Svizzera Italiana, Switzerland;Swiss Institute of Bioinformatics, Switzerland.
    Sand, Wolfgang
    Universität Duisburg-Essen, Germany;Donghua UniversityMining Academy and Technical University Freiberg, Germany, PR China;.
    Wilmes, Paul
    University of Luxembourg, Luxembourg.
    Poetsch, Ansgar
    Ruhr Universität Bochum, Germany;Plymouth University, UK.
    Dopson, Mark
    Linnaeus University, Faculty of Health and Life Sciences, Department of Biology and Environmental Science.
    Multi-omics reveal the lifestyle of the acidophilic, mineral-oxidizing model species Leptospirillum ferriphilumT2018In: Applied and Environmental Microbiology, ISSN 0099-2240, E-ISSN 1098-5336, Vol. 4, no 3, article id UNSP e02091-17Article in journal (Refereed)
    Abstract [en]

    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 this model species' type strain is available, limiting the possibilities to investigate the strategies and adaptations Leptospirillum ferriphilumT applies to survive and compete in its niche. This study presents a complete, circular genome of Leptospirillum ferriphilumT DSM 14647 obtained by PacBio 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, 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 substrate and bioleaching cultures containing chalcopyrite (CuFeS2). Leptospirillum ferriphilumT adaptations to growth on chalcopyrite included a possibly enhanced production of reducing power, reduced carbon dioxide fixation, as well as elevated RNA transcripts and proteins involved in heavy metal resistance, with special emphasis on copper efflux systems. Finally, expression and translation of genes responsible for chemotaxis and motility were enhanced.

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