Exploration of Bioinformatics on Microbial Fuel Cell Technology: Trends, Challenges, and Future Prospects

Nachammai, K. T.; Ramachandran, Srinithi; Nagarajan, Chitra; Kulanthaivel, Langeswaran; Subbaraj, Gowtham Kumar; Chandrasekaran, Kirubhanand; Paramasivan, Vijayalakshmi; Subramanian, Senthilkumar

doi:https://doi.org/10.1155/2023/6902054

Journal of Chemistry

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Abstract Introduction Conclusion Abbreviations Data Availability Ethical Approval Consent Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

Review Article | Open Access

Volume 2023 | Article ID 6902054 | https://doi.org/10.1155/2023/6902054

Exploration of Bioinformatics on Microbial Fuel Cell Technology: Trends, Challenges, and Future Prospects

K. T. Nachammai,¹Srinithi Ramachandran,¹Chitra Nagarajan,²Langeswaran Kulanthaivel,¹Gowtham Kumar Subbaraj,³Kirubhanand Chandrasekaran,⁴Vijayalakshmi Paramasivan,⁵and Senthilkumar Subramanian⁶

Academic Editor: Suresh Kannan Balasingam

Received06 Oct 2022

Revised16 Dec 2022

Accepted07 Jan 2023

Published27 Jan 2023

Abstract

Microbial fuel cells (MFCs) are a cost-effective and environmentally friendly alternative energy method. MFC technology has gained much interest in recent decades owing to its effectiveness in remediating wastewater and generating bioelectricity. The microbial fuel cell generates energy mainlybecause of oxidation-reduction reactions. In this reaction, electrons were transferred between two reactants. Bioinformatics is expanding across a wide range of microbial fuel cell technology. Electroactive species in the microbial community were evaluated using bioinformatics methodologies in whole genome sequencing, RNA sequencing, transcriptomics, metagenomics, and phylogenetics. Technology advancements in microbial fuel cells primarily produce power from organic and inorganic waste from various sources. Reduced chemical oxygen demand and waste degradation are two added advantages for microbial fuel cells. From plants, bacteria, and algae, microbial fuel cells were developed. Due to the rapid advancement of sequencing techniques, bioinformatics approaches are currently widely used in the technology of microbial fuel cells. In addition, they play an important role in determining the composition of electroactive species in microorganisms. The metabolic pathway is also possible to determine with bioinformatics resources. A computational technique that reveals the nature of the mediators and the substrate was also used to predict the electrochemical properties. Computational strategies were used to tackle significant challenges in experimental procedures, such as optimization and understanding microbiological systems. The main focus of this review is on utilizing bioinformatics techniques to improve microbial fuel cell technology.

1. Introduction

Modern scientific fields are continuously targeting energy generation, storage, and consumption. Recent studies have shown positive results in various sectors, including energy generation from biomass and the sun, wind, tidal, and other sources. The study of alternate renewable energy sources is expanding dramatically. Integrating renewable energy sources, such as solar-hydrogen fuel cells and solar-wind hybrids, maybe a different course that should be investigated. No renewable energy source can strive with or completely replace the conventional fossil fuel-basedenergy-producing technique. Microbial fuel cells are one likely source of alternate energy. Despite significant limitations, MFCs offer a more environmentally friendly solution for producing bioelectricity and handling sewage. Therefore, more research is required to develop cost-effective, sustainable integrated solutions based on MFC [1]. The microbial fuel cell is generated using the organic waste from landfills, domestic wastewater, industrial waste, agro-based, brewery, and textile industries. Further, MFC has a potential role in generating electricity, reducing chemical oxygen demand, and helping degrade various wastages. Schematic representation shows the MFC technology utilizing organic waste that leads to generating electricity, as depicted in Figure 1.

Most of the MFC is derived from bacteria, plants, and algae. Algae can be grown on undesirable wasteland and use a wide range of sources, such as saline and wastewater, which minimize the rivalry with farming properties. Algae proficiently reprocess carbon-rich biogas, absorbing more than 40% of the atmospheric carbon dioxide emissions. According to other potential biofuel crops, including oil palm soybean, jatropha, sunflower, and soybean, algae strains are better able to collect significant amounts of neutral lipids, especially triacylglycerol (TAG). In addition, algal strains can be more easily engineered for the effective synthesis of biofuel precursors and other valuable bioactive co-products due to the reduced complexity of the cellular structure [2]. Genomic studies of DNA, mRNA, proteins, and metabolites are referred to as “omics” in biological sciences, broadly including transcriptomics, proteomics, and metabolomics. Since “omics” is currently required to discover novel biomarkers for medication and vaccine development [3]. One of the potential solutions is improving algae’s capability to generate lipids by establishing nutritionally inadequate conditions to direct metabolic fluxes towards lipid biosynthesis. MFC increased the rate of total organic carbon (TOC) removal and the rate of chemical oxygen demand (COD) reduction in wastewater. Microbial fuel cells (MFCs) have recently gained prominence due to their great capability for long-term effluent management and pollutant elimination [4]. Overall, pollutants can be eliminated either as electron acceptors by reduction at the cathode or as electron donors through microbial catalyzed oxidation at the anode. Some pollutants act as electron mediators at the cathode or anode [5].

1.1. Components of Microbial Fuel Cells (MFCs)

In a microbial-mediated electrochemical system, electrodes and microorganisms operate as the two main structural components of MFCs. A proton exchange membrane (PEM), which serves as a membrane separator in the standard MFC, divides the two chambers. The MFC is split into two different anodic and cathodic regions by this membrane separator. Microorganisms in the anodic compartment are known to transmit electrons to an anode, which is then used to oxidize organic waste to produce energy. Biodegradable waste sources, such sewage wastewater and brewery wastewater, which are naturally abundant in organic compounds such as glucose, sucrose, acetate, lignocellulose, and biomass materials, can be utilized in MFC for the generation of bioelectricity. The MFC anode should possess characteristics that lead to high electrical and chemical stability, biocompatibility, and high surface area. Carbon paper, carbon cloth, carbon felt, graphene, and carbon nanotubes have lately been employed to improve the anode surface. Due to their low cost and lack of corrosion, carbon-based materials are frequently utilized to modify electrodes. Using titanium, gold, and stainless steel as additional materials enhance the surface properties of anodic materials and give bacteria a suitable surface on which to produce biofilm. One of the crucial interactions in biological respiration and energy conversion systems like MFCs, the oxygen reduction reaction (ORR), occurs in the cathode compartment. Protons transferred via PEM and electrons received by the cathode compartment via the external circuit both play significant roles in a reduction reaction. Protons and electrons react during the reduction reaction, which produces water molecules (H₂O). The cathode has a significant impact on the entire cell voltage output and should have a high redox potential. Cathode modifications using carbon materials such as carbon paper and carbon cloth modified with an active catalyst such as platinum (Pt) have been recommended to enhance the reduction rate. A membrane that allows charge to be passed between the electrodes is placed between the cathode chamber, where the electrons react with the oxygen, and the anode chamber, where the bacteria grow, allowing the biocatalyst to be separated from oxygen [6].

1.2. Types of Microbial Fuel Cells (MFCs)

Different types of MFC are double-chamber MFC, single-chamber MFC, upflow MFC, and stacked MFC. A double-chamber model is the simplest MFC. Typically, PEM is used to separate two bottles, one of which is employed as the cathode and the other as the anode in a design. Energy is typically generated in the two-chamber MFC using a defined catholyte solution and a specific medium (or substrate) in the anode. A single-chamber MFC consists of one chamber that contains both the anode and the cathode. The single-chamber MFCs often have an anodic chamber and do not require a cathodic chamber to contain air. The upflow MFC is cylinder-shaped. The cathode chamber is located at the top of the MFC, while the anode is located at the bottom. Glass wool and glass bead layers divide both compartments. The substrate is supplied from the anode’s bottom up to the cathode and finally to the top. The formation of a gradient between the electrodes contributes to the favourable action of the fuel cell. There are no separate anolyte and catholyte in the upflow MFC design. To increase power production, the stacked MFCs typically consist of several MFCs that are connected either in series or parallel [7].

1.3. Factors Affecting Microbial Fuel Cells (MFCs)

Within microbial electrochemical systems, a single cell’s electrochemical behaviour can vary greatly depending on a number of variables, including the microorganism’s proximity to the electrode (electron transfer method), ambient conditions (such as temperature, salinity, and pH), biofilm formation, and so forth, as well as specific system characteristics such as materials, architecture, and configuration. Especially when additional parameters such as system performance and intrinsic microbial metabolism are considered, this might cause one to ignore important EET processes such as multilayer cell aggregation or early substrate depletion [8].

2. Bioinformatics for Microbial Fuel Cells (MFCs) Technology

Microbial fuel cells are bioelectrical devices that use microbes’ built-in metabolism to generate electricity. Treatment of waste, sensor development, and renewable energy generation can be achieved through a promising technology MFC [9, 10]. Based on the MFC concept, it is clear that the microbes can transfer electrons formed by the metabolic oxidation of organic substrates to insoluble and extracellular electron-accepting compounds. Electrons generated in bacterial cell membranes by cytochromes, pili, microbial nanowires, and protein complexes can be transferred directly to electrodes. Alternately, some microbes indirectly use extracellular electron mediators, and they produce or can obtain from the environment to transfer electrons [11, 12]. Through bioinformatics techniques, it is possible to analyze the genomic characteristics of deoxyribose nucleic acid (DNA) and ribose nucleic acid (RNA) transcription rates in microbial electrochemical systems. This information may be used to understand important facts about bacteria. Genetic identification (identification of species), projected gene coding sequences, and assessment of gene and transcript expression are revealed by genomic and transcriptomic research, which increases our understanding of the electron transfer machinery in bacterial colonies [13]. An electroactive biofilm microorganism composition may be revealed by DNA analysis, and RNA analysis reveals the discrepancy between gene and transcript expression under various circumstances [14]. Microbes were used to enhance the microbial fuel cell technology, and its database is tabulated in Table 1.

3. rRNA Gene Sequencing Technology

3.1. 16S rRNA Gene Sequencing Technology

Using 16S ribosomal RNA (rRNA) gene sequencing to identify species in electroactive microbial systems demonstrated the importance of bioinformatics analysis to the microbial electrochemical community [15]. The highly conserved introns of the 16s rRNA gene allow for the global primer design and amplification of bacterial species’ whole gene region. In the genome, intron sequences impact 16S rRNA gene primers. The 16S rRNA gene comprises mutable exon sections that can be employed in species differentiation and variable exon regions that differ between species. Typically, 16S rRNA gene analysis is inadequate. The region does not encode virulence factors due to the lack of variation [16, 17]. The sequencing of 16S rRNA gene helpful in recognizing novel electroactive species followed by characterization. bacteria, fungi, and Archaea have been discovered to contain electroactive species, resulting in a diverse range of species classification. Depending on the species, microorganisms-electrogenes may use a wide variety of substrates. As a result, the MFC bacteria Thermincola ferriacetica, Escherichia coli, Desulfuromonas acetoxidans, Geobacter sulfurreducens, and Bacillus subtilis produced an electric current [18]. Candida melibiosica, Pichia anomala, P. polymorpha, Saccharomyces cerevisiae, and Blastobotrys adeninivorans are being recognized as potential catalysts in the MFC [19]. Under fuel cell operation, the microbial composition dependence in a wastewater-fed-batch MFC system was determined using the 16S rRNA gene sequencing. The diverse microbial systems’ proficiency in fabricating EET and communal environmental factors triggered fluctuations in microbial community composition can be studied with the help of sequencing the 16S rRNA gene. Researchers reported that salt, potential, and inoculum-dependent microbial communities would modify the 16S rRNA gene [20, 21]. The initial discovery of electroactive species can be achieved using 16S rRNA gene sequencing, which must be supplemented by other genomic strategies [22].

3.2. 18S rRNA Gene Sequencing Technology

The sequencing of 18S rRNA genes may be astonishing by sequences from food or the mammalian host, which are sequences that are maybe more prevalent. An 18S rRNA gene amplicon was created to avoid human and plant sequences. The 18S, 5.8S, and 28S ribosomal subunits in eukaryotes are encoded in a single locus separated by the first and second internal transcribed spacers (ITSs). The ITS RNAs are less conserved than the 18S and 28S RNAs because they are destroyed soon after transcription and do not become integrated into the ribosome [23]. From the 18S rRNA gene sequencing technology results in diversity analysis and evolutionary analysis using the MEGA software [24].

4. Whole Genome Sequencing (WGS) Technology

A microorganism’s entire genome is analyzed using the whole genome analysis, which goes beyond taxonomic identification based on the 16S rRNA gene. Because of its advances in the next-generation sequencing [25], genome sequencing has recently been widely accessible, making it possible for the area of microbial electrochemistry to quickly, cheaply, and effectively sequence bacterial genomes [26]. The genome sequences can be loaded into bioinformatics systems and genetic data to annotate the genome. Transcriptional start sites and regions of the genome with unknown activities, RNA-coding sequences, and protein-coding sequences are all given functions via functional genome annotation. The possible function can then be assigned by comparing the sequence of amino acid residues to databases of existing protein sequences. With the help of this resource, it is possible to determine from the genome sequence whether a microbe may indicate electroactivity due to specific genes, such as outer membrane cytochromes [27]. Metagenomics, which allows genome sequencing of multiple strains at once, is commonly used in gene sequencing studies for electroactive microbial communities [28]. Pure strain genomics for model electroactive species, in addition to that various pure culture studies of electroactive microorganisms, have been conducted [29]. The transcriptomic analysis is an effective alternative to genomic study because expressed messenger RNA (mRNA) transcripts validate that the genes are not only present but also actively used and expressed by microorganisms.

5. Analysis Using Transcriptomics

Gene expression analysis can reveal the appropriate mRNA transcripts for EET or the genes impacted by the EET stimuli by applying methods for measuring mRNA transcripts. RNA is obtained from the microbial culture for transcriptional analysis. The sequence of the target marker gene is required for experimental planning and expression analysis using polymerase chain reaction and microarray techniques. With the development of next-generation sequencing technology, RNA sequencing has recently become more prevalent in this sector because it is inexpensive, yields a plethora of information, and does not require prior knowledge of interesting gene sequences. RNA sequencing facilitates the identification of new RNAs and their expression levels that may not have been discovered in earlier research since it thoroughly studies all the RNA in a system [30, 31]. Transcriptomic data can determine whether the amounts of essential genes associated with electron transport pathways are correlated with bioelectrochemical performance, and bioinformatics tools can reveal genetic markers suggesting the presence of electron transfer processes. The complexity of complete systems, however, prevents any stated strategy from being applied alone to provide an answer for a system attribute, which supports the combination of various computational approaches to provide an answer for a microbiological system’s qualities. RNA sequencing has recently been utilized to clarify differences in an electrocatalytic activity between bacterial cells, in addition to helping to identify transcriptomic alterations brought on by an applied potential in microorganisms [32].

6. Metatranscriptomics

The first investigation of extracellular electron transport with metatranscriptomics followed differentially expressed profiles across a potential gradient. In the first step, every genome in an electroactive community is compiled and organized using metagenomics. Then, metatranscriptomics is used to align the RNA readings to the generated metagenomics for the system. It is useful because it increases the chance of finding new organisms in electroactive communities and aligns the RNA readings to the precise metagenomics in the system rather than aiming for the expected species [33]. Metatranscriptomics, such as metagenomics, can examine the gene or transcript expression from a sample of a multispecies microbial culture to identify differential expression in different circumstances. Metatranscriptomics’ accessibility makes it an ideal tool for researching any known gene. Using this approach, it is possible to explore the electroactive microbial community in marine sediment and discover new types of sulfate-reducing bacteria that responded differently to the EET stimuli by expressing genes that were similar to the c-type cytochrome genes omcX and omcS, which are known to be associated to EET in Geobacter [34]. Metatranscriptomics analysis can be used to understand interspecies electrochemical communication and to distinguish between the interspecies transfer of electrons and hydrogen interspecies transfer, where hydrogen acts as an electron donor [35].

7. Density Functional Theory (DFT)

According to the Quantum mechanics principle, DFT is estimated by electronic absorption spectra. The electronic transition occurs from the highest occupied molecular orbital (HOMO) to the lowest unoccupied molecular orbital (LUMO) at the wavelength with the highest absorption. DFT determines energy differences between HOMO and LUMO. Quantum mechanics calculations can support and guide various modeling and experimental methods, aid in understanding microbial electrochemical systems, and more with proper applications. In heterogeneous catalysis, these mechanisms primarily depend on the energy and pathways of the elementary steps of the density functional theory [36]. The density functional theory (DFT) was beneficial in elucidating the structure of pores in granular activated carbon cathodes. This provided important insights that have been essential to exploring the properties of the oxygen reduction reaction for an air-breathing cathode in the wastewater treatment [37]. Later efforts to test the oxygen reduction reaction with alternative cathode materials, such as platinum and pyrolyzed carbon, employed similar approaches [38–40]. The DFT has been utilized to improve understanding of the electron transfer processes. DFT calculations and Raman spectrum shifts revealed that in their system, electrons were moved from a native extracellular redox mediator to an electrode-immobilized mediator via a conformational change in the attached mediator [41]. For optimization and understanding microbial systems, which are typically very challenging to understand through direct experimental observations, computations can produce significant results because the wavelet of minute interactions between small molecules can be challenging in such complex systems. The modeling of microbial systems may benefit from these discoveries [42].

7.1. Bioinformatics Resources

The establishment of bioinformatics resources is necessary due to the expanding quantity of genome sequence data for many organisms. These resources should serve as a knowledge base for different metabolic pathways and a tool for comparative genomic analysis, which can functionally annotate and interpret newly discovered genes at proteomic and metabolic levels. The sample collected from the microbial community and another genome, DNA, or RNA will be extracted. Sequence amplification compared with the reference available sequences. Bioinformatics tools and databases are used to identify the electroactive microbes from the microbial community, as represented in Figure 2.

7.2. Metabolic Pathway Databases

Kyoto Encyclopedia of Genes and Genomes (KEGG) is one of the furthermost well-known and inclusive databases of metabolic pathways for various organisms. The database also provides reference route architecture for constructing organism-independent biochemical pathways using the user’s input entire genome [43]. MetaCyc (http://metacyc.org) is a well-known database that offers details on empirically verified metabolic pathways, including different species such as bacteria, plants, and humans [44]. MetaCyc is also ideal as a reference source to enhance the functional characterization of recently sequenced algae putative genes intricate in lipid production. Pathway databases, including PlantGDB and MapMan, are also included in the online portal’s expected and known biochemical route collection. Additionally integrated inside the application to support phylogenomic research are the Inparanoid and OrthoMCL-DB databases. ChlamyCyc is the only algal-specific web database currently available and is designed for in-depth analyses of metabolic pathways and crucial cellular functions (http://chlamycyc.mpimp-golm.mpg.de). The ChlamyCyc website also provides a web-based version of the standard BLAST (software) to assist in the functional annotation of uncharacterized genes and gene products [45]. The BFGR (Biofuel Feedstock Genomic Resource) and pDAWG are two more databases designed to assist genome-wide analyses of plant biofuel feedstock species [46, 47]. The BFGR database (http://bfgr.plantbiology.msu.edu) is a uniform and integrated manner with excellent quality. Database pDAWG (http://csbl1.bmb.uga.edu/pDAWG/) provides comprehensive information on plant cell wall genes and proteins, including phylogenomics, sequence, and structure-function relationships. Enhancing the microbial fuel cell technology using bioinformatics resources is represented in Figure 3.

8. Role of Genome Annotation in the MFC Technology

Genome annotation or DNA annotation is the process of locating genes and total genome coding areas and figuring out those genes’ functions is known as DNA annotation or genome annotation. A remarkable discovery of carbon dioxide fixing enzymes in several Geobacteraceae resulted from genome annotation [48]. A pair of genes in the G. metallireducens genome is anticipated to encode an ATP-dependent citrate lyase, enabling acetyl-CoA production by the reverse TCA cycle. The genome of G. metallireducens also contains predicted genes for all of the recognized enzymes involved in the dicarboxylate/4-hydroxybutyrate cycle of carbon dioxide fixing [49]. G. sulfurreducens may convert protons to hydrogen using electrons from an electrode, potentially offering a renewable catalyst significantly cheaper than the metal catalysts commonly used to produce hydrogen [50, 51].

9. Future Perspectives of Bioinformatics Applied to MFC

Bioinformatics can significantly enhance the efficiency of identifying genetic variation in many microorganisms. Bioinformatics technology can provide researchers with an accurate and rapid to characterize bacteria through information on protein sequence, DNA sequence, and protein structure. Many microbial genome-related industries utilize bioinformatics, including waste cleanup, climate change, nanotechnology and biotechnology, and alternative energy. Bioinformatics is more critical in analyzing and identifying biological data using computer-based strategies to aid in understanding biology at the system level. Many bioinformatics approaches are used to analyze biological data and improve gene sequences. Using bioinformatics techniques, electroactive microbes can easily be identified. Bioinformatics resources help to compare the existing one with the new one. In future, bioinformatics techniques will significantly improve the MFC technology [52, 53].

10. Conclusion

Microbial fuel cell technology is a promising one for bioremediation as well as the production of electricity from organic wastage. Bioelectricity is the production of electricity by organisms as a result of the production of electrons as a byproduct of their metabolic reactions. These generated electrons can be confined to sustain a constant or continuous energy output source. Species identification, gene coding, microbiological behaviour, and gene evaluation are all made possible by the whole genome sequencing approach. Genomics studies provide complete genome information. Furthermore, the the16rRNA sequence is used in the early measurement of electrogenic species. Metagenomics has a broad scope because of its multiple-strain sequencing strategies. At the same time, metatranscriptomics is used to find new species from the electroactive community. Density functional theory has been used in the electron transfer mechanism. We can easily optimize microbial systems using computational methods such as DFT, while in experimental observation, it seems complicated. Bioinformatics source helps to find the novel gene and annotation from database microbes’ metabolic pathways and their evolutionary relationship between related organisms. Recent bioinformatics studies utilize omics data to determine the efficient microbial fuel-producing organisms. Finally, bioinformatics techniques, such as identifying new electroactive species, analysis, and optimization, are crucial in the MFC technology.

Abbreviations

MFCs:	Microbial fuel cells
TAG:	Triacylglycerol
TOC:	Total organic carbon
COD:	Chemical oxygen demand
PEM:	Proton exchange membrane
DNA:	Deoxyribonucleic acid
RNA:	Ribonucleic acid
rRNA:	Ribosomal RNA
EET:	Extracellular electron transfer
WGS:	Whole genome sequencing
mRNA:	Messenger RNA
DFT:	Density functional theory
HOMO:	Highest occupied molecular orbital
LUMO:	Lowest unoccupied molecular orbital
QMM:	Quantum mechanics method
KEGG:	Kyoto Encyclopedia of Genes and Genomes
BFGR:	Biofuel feedstock genomic resource.

Data Availability

All data were available with the corresponding author on request.

Ethical Approval

Not applicable.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Authors’ Contributions

NKT and SR conceptualized the study. KL and GS wrote the original draft. KC, CR, and VP wrote, reviewed, and edited the manuscript. SSK supervised the study. All authors have read and agreed to the published version of the manuscript. Nachammai KT, Langeswaran Kulanthaivel, and Gowtham Kumar Subbaraj contributed equally as first authors to this work.

Acknowledgments

Dr. KL thankfully acknowledges MHRD-RUSA 2.0 F.24/51/2014-U, Policy (TN Multi-Gen), Department of Education, Government of India.

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Copyright © 2023 K. T. Nachammai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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