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Fungi are diverse organisms with various characteristics and functions. Some play a role in recycling essential elements, such as nitrogen and carbon, while others are utilized in the food and drink production industry. Some others are known to cause diseases in various organisms, including humans. Fungal pathogens cause superficial, subcutaneous, and systemic infections. Consequently, many scientists have focused on studying the factors contributing to the development of human diseases. Therefore, multiple approaches have been assessed to examine the biology of these intriguing organisms. The genome-scale metabolic models (GEMs) have demonstrated many advantages to microbial metabolism studies and the ability to propose novel therapeutic alternatives. Despite significant advancements, much remains to be elucidated regarding the use of this tool for investigating fungal metabolism. This review aims to compile the data provided by the published GEMs of human fungal pathogens. It gives specific examples of the most significant contributions made by these models, examines the advantages and difficulties associated with using such models, and explores the novel approaches suggested to enhance and refine their development.

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IMPORTANCE: Flavonoids are a group of compounds generally produced by plants with proven biological activity, which have recently beeen recommended for the treatment and prevention of diseases and ailments with diverse causes. In this study, naringenin was produced in adequate amounts in yeast after in silico design. The four genes of the involved enzymes from several organisms (bacteria and plants) were multi-expressed in two vectors carrying each two genes linked by a short viral peptide sequence. The batch kinetic behavior of the product, substrate, and biomass was described at lab scale. The engineered strain might be used in a more affordable and viable bioprocess for industrial naringenin procurement.

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Wound healing is a complex process involving blood cells, extracellular matrix, and parenchymal cells. Research on biomimetics in amphibian skin has identified the CW49 peptide from Odorrana grahami, which has been demonstrated to promote wound regeneration. Additionally, lavender essential oil exhibits anti-inflammatory and antibacterial activities. Given these considerations, we propose an innovative emulsion that combines the CW49 peptide with lavender oil. This novel formulation could serve as a potent topical treatment, potentially fostering the regeneration of damaged tissues and providing robust antibacterial protection for skin wounds. This study investigates the physicochemical properties, biocompatibility, and in vitro regenerative capacity of the active components and the emulsion. The results show that the emulsion possesses appropriate rheological characteristics for topical application. Both the CW49 peptide and lavender oil exhibit high viability in human keratinocytes, indicating their biocompatibility. The emulsion induces hemolysis and platelet aggregation, an expected behavior for such topical treatments. Furthermore, the lavender-oil emulsion demonstrates antibacterial activity against both Gram-positive and Gram-negative bacterial strains. Finally, the regenerative potential of the emulsion and its active components is confirmed in a 2D wound model using human keratinocytes. In conclusion, the formulated emulsion, which combines the CW49 peptide and lavender oil, shows great promise as a topical treatment for wound healing. Further research is needed to validate these findings in more advanced in vitro models and in vivo settings, potentially leading to improved wound-care management and novel therapeutic options for patients with skin injuries.

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Computational modeling and simulation of biological systems have become valuable tools for understanding and predicting cellular performance and phenotype generation. This work aimed to construct, model, and dynamically simulate the virulence factor pyoverdine (PVD) biosynthesis in Pseudomonas aeruginosa through a systemic approach, considering that the metabolic pathway of PVD synthesis is regulated by the quorum-sensing (QS) phenomenon. The methodology comprised three main stages: (i) Construction, modeling, and validation of the QS gene regulatory network that controls PVD synthesis in P. aeruginosa strain PAO1; (ii) construction, curating, and modeling of the metabolic network of P. aeruginosa using the flux balance analysis (FBA) approach; (iii) integration and modeling of these two networks into an integrative model using the dynamic flux balance analysis (DFBA) approximation, followed, finally, by an in vitro validation of the integrated model for PVD synthesis in P. aeruginosa as a function of QS signaling. The QS gene network, constructed using the standard System Biology Markup Language, comprised 114 chemical species and 103 reactions and was modeled as a deterministic system following the kinetic based on mass action law. This model showed that the higher the bacterial growth, the higher the extracellular concentration of QS signal molecules, thus emulating the natural behavior of P. aeruginosa PAO1. The P. aeruginosa metabolic network model was constructed based on the iMO1056 model, the P. aeruginosa PAO1 strain genomic annotation, and the metabolic pathway of PVD synthesis. The metabolic network model included the PVD synthesis, transport, exchange reactions, and the QS signal molecules. This metabolic network model was curated and then modeled under the FBA approximation, using biomass maximization as the objective function (optimization problem, a term borrowed from the engineering field). Next, chemical reactions shared by both network models were chosen to combine them into an integrative model. To this end, the fluxes of these reactions, obtained from the QS network model, were fixed in the metabolic network model as constraints of the optimization problem using the DFBA approximation. Finally, simulations of the integrative model (CCBM1146, comprising 1123 reactions and 880 metabolites) were run using the DFBA approximation to get (i) the flux profile for each reaction, (ii) the bacterial growth profile, (iii) the biomass profile, and (iv) the concentration profiles of metabolites of interest such as glucose, PVD, and QS signal molecules. The CCBM1146 model showed that the QS phenomenon directly influences the P. aeruginosa metabolism to PVD biosynthesis as a function of the change in QS signal intensity. The CCBM1146 model made it possible to characterize and explain the complex and emergent behavior generated by the interactions between the two networks, which would have been impossible to do by studying each system's individual components or scales separately. This work is the first in silico report of an integrative model comprising the QS gene regulatory network and the metabolic network of P. aeruginosa.

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Parkinson's disease (PD) is the second most common neurodegenerative disorder after Alzheimer's disease. Therefore, development of novel technologies and strategies to treat PD is a global health priority. Current treatments include administration of Levodopa, monoamine oxidase inhibitors, catechol-O-methyltransferase inhibitors, and anticholinergic drugs. However, the effective release of these molecules, due to the limited bioavailability, is a major challenge for the treatment of PD. As a strategy to solve this challenge, in this study we developed a novel multifunctional magnetic and redox-stimuli responsive drug delivery system, based on the magnetite nanoparticles functionalized with the high-performance translocating protein OmpA and encapsulated into soy lecithin liposomes. The obtained multifunctional magnetoliposomes (MLPs) were tested in neuroblastoma, glioblastoma, primary human and rat astrocytes, blood brain barrier rat endothelial cells, primary mouse microvascular endothelial cells, and in a PD-induced cellular model. MLPs demonstrated excellent performance in biocompatibility assays, including hemocompatibility (hemolysis percentages below 1%), platelet aggregation, cytocompatibility (cell viability above 80% in all tested cell lines), mitochondrial membrane potential (non-observed alterations) and intracellular ROS production (negligible impact compared to controls). Additionally, the nanovehicles showed acceptable cell internalization (covered area close to 100% at 30 min and 4 h) and endosomal escape abilities (significant decrease in lysosomal colocalization after 4 h of exposure). Moreover, molecular dynamics simulations were employed to better understand the underlying translocating mechanism of the OmpA protein, showing key findings regarding specific interactions with phospholipids. Overall, the versatility and the notable in vitro performance of this novel nanovehicle make it a suitable and promising drug delivery technology for the potential treatment of PD.

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The molecule (2S)-naringenin is a scaffold molecule with several nutraceutical properties. Currently, (2S)-naringenin is obtained through chemical synthesis and plant isolation. However, these methods have several drawbacks. Thus, heterologous biosynthesis has emerged as a viable alternative to its production. Recently, (2S)-naringenin production studies in Escherichia coli have used different tools to increase its yield up to 588 mg/L. In this study, we designed and assembled a bio-factory for (2S)-naringenin production. Firstly, we used several parametrized algorithms to identify the shortest pathway for producing (2S)-naringenin in E. coli, selecting the genes phenylalanine ammonia lipase (pal), 4-coumarate: CoA ligase (4cl), chalcone synthase (chs), and chalcone isomerase (chi) for the biosynthetic pathway. Then, we evaluated the effect of oxygen transfer on the production of (2S)-naringenin at flask (50 mL) and bench (4 L culture) scales. At the flask scale, the agitation rate varied between 50 rpm and 250 rpm. At the bench scale, the dissolved oxygen was kept constant at 5% DO (dissolved oxygen) and 40% DO, obtaining the highest (2S)-naringenin titer (3.11 ± 0.14 g/L). Using genome-scale modeling, gene expression analysis (RT-qPCR) of oxygen-sensitive genes was obtained.

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Coffee is one of the most consumed products worldwide. Among the varieties of this product, specialty coffee is a type of coffee that has been growing in the world market. This paper aims to assess the effects that the conditions derived from coffee roasting at different altitude levels have on the quality of the product. It was discovered that processing coffee at a higher altitude level yielded a smaller increase in bitterness. This led to a better Specialty Coffee Association (SCA) score in cupping and, consequently, to better preservation of the coffee quality. The storage time affected the aroma by associating roaster aromas with older coffees. Although the assessed origins had the same NIR spectra, differences in peak intensity lead to variations in the flavor and aroma of the coffee. Furthermore, although green beans prolong quality allowing a SCA score of 84.73 ± 2.81 after 4 months of storage, roasted coffee at higher altitudes could also maintain the quality between production and consumption (SCA score of 80.22 ± 0.91 after 2 months). Finally, this research found that the instrumental equipment helped to find minor changes in the sensorial profile, and with these changes correlated with the sensorial panel, the best conditions to preserve coffee quality were found.

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Community acquired infections caused by Meticillin-resistant Staphylococcus aureus (MRSA) have become a growing concern due to its impact on the world public health. This microorganism is a commonly spreading pathogen associated predominantly with skin infections and connected to other more severe conditions (septic shock, and generalized infection). The lack of highly effective antibiotics and treatments to control skin infections with S. aureus has led to the search of novel therapies using alternative agents such as antimicrobial peptides (AMPs). In order to obtain a viable administration route to counteract superficial skin infections (impetigo, abscesses, furuncles, and cellulitis), a topical formulation based on Magnetite-Buforin-II-silver nanobioconjugates as active antibacterial agents was designed by their dispersion in O/W concentrated emulsions. The prepared topical characterization indicated that O/W emulsions were stable in time, the droplets size remained within the appropriate values (∼1 µm) and their rheological properties, such as pseudoplastic and shear-thinning behavior, remained unchanged for up to 3 months. Additionally, hemolysis and platelet aggregation tests were acceptable (i.e., 14.72 ± 2.62% and 8.06 ± 2.90%, respectively) in compliance with the ISO-10993 standard. Furthermore, the treatment reduced significantly (p < 0.0001) the growth of both clinical isolated MRSA and wild Type S. aureus strains as evidenced by the contact diffusion method. These results are important in the context of proposing new alternatives that allow manage effectively the threat posed by the antibiotic resistant bacterial strains, which jeopardize the lives of thousands of people every year.

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As the world enters its second year of the pandemic caused by SARS-CoV-2, intense efforts have been directed to develop an effective diagnosis, prevention, and treatment strategies. One promising drug target to design COVID-19 treatments is the SARS-CoV-2 Mpro. To date, a comparative understanding of Mpro dynamic stereoelectronic interactions with either covalent or non-covalent inhibitors (depending on their interaction with a pocket called S1' or oxyanion hole) has not been still achieved. In this study, we seek to fill this knowledge gap using a cascade in silico protocol of docking, molecular dynamics simulations, and MM/PBSA in order to elucidate pharmacophore models for both types of inhibitors. After docking and MD analysis, a set of complex-based pharmacophore models was elucidated for covalent and non-covalent categories making use of the residue bonding point feature. The highest ranked models exhibited ROC-AUC values of 0.93 and 0.73, respectively for each category. Interestingly, we observed that the active site region of Mpro protein-ligand complex undergoes large conformational changes, especially within the S2 and S4 subsites. The results reported in this article may be helpful in virtual screening (VS) campaigns to guide the design and discovery of novel small-molecule therapeutic agents against SARS-CoV-2 Mpro protein.

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The impact of cocoa lipid content on chocolate quality has been extensively described. Nevertheless, few studies have elucidated the cocoa lipid composition and their bioactive properties, focusing only on specific lipids. In the present study the lipidome of fine-flavor cocoa fermentation was analyzed using LC-MS-QTOF and a Machine Learning model to assess potential bioactivity was developed. Our results revealed that the cocoa lipidome, comprised mainly of fatty acyls and glycerophospholipids, remains stable during fine-flavor cocoa fermentations. Also, several Machine Learning algorithms were trained to explore potential biological activity among the identified lipids. We found that K-Nearest Neighbors had the best performance. This model was used to classify the identified lipids as bioactive or non-bioactive, nominating 28 molecules as potential bioactive lipids. None of these compounds have been previously reported as bioactive. Our work is the first untargeted lipidomic study and systematic effort to investigate potential bioactivity in fine-flavor cocoa lipids.

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For many years, Colombia was one of the countries with the largest illegal cultivation of cannabis around the world. Currently, it is going through a period of transition with a new government law that recently allows the cultivation, transformation, and commercialization of such plant species. In this sense, the identification of strategies for the valorization of products or by-products from Cannabis sativa represent a great opportunity to improve the value chain of this crop. One of these products is hemp seeds, which are exceptionally nutritious and rich in healthy lipids (with high content of three polyunsaturated fatty acids: linoleic acid, alpha-linolenic acid, and gamma-linolenic acid), good quality protein, and several minerals. In addition, hemp seeds contain THC (tetrahydrocannabinol) or CBD (cannabidiol) in traces, molecules that are responsible for the psychoactive and therapeutic properties of cannabis. These low terpenophenolic contents make it more attractive for food applications. This fact, together with the constant search for proteins of vegetable origin and natural food ingredients, have aroused an important interest in the study of this biomass. Some bioactivities of phytochemical compounds (polyphenols and terpenoids, mainly) present in hemp seeds have provided antioxidant, antimicrobial, and anti-inflammatory properties. This review summarizes and discusses the context of hemp use in Latin-American and the new opportunities for hemp seeds culture in Colombia considering the valuable nutritional value, main functional bioactivities, and recent advances in food market applications of hemp seeds.

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Cyclin-Dependent Kinase 2 (CDK2) and Vascular-Endothelial Growth Factor Receptor 2 (VEGFR2) are promising targets for the design of novel inhibitors in anticancer therapeutics. In a recent work, our group designed a set of potential dual inhibitors predicted to occupy an allosteric back pocket near the active site of both enzymes, but their dynamic and unbinding behavior was unclear. Here, we used molecular dynamics (MD) and metadynamics (meta-D) simulations to study two of these virtual candidates (herein called IQ2 and IQ3). Their binding mode was predicted to be similar to that observed in LQ5 and BAX, well-known back-pocket binders of CDK2 and VEGFR2, respectively, including H-bonding with critical residues such as Leu83/Cys113 and Asp145/Asp190 (but excepting H-bonding with Glu51/Glu111) in CDK2/VEGFR2, correspondingly. Likewise, while LQ5 and BAX unbound through the allosteric channel as expected for type-IIA inhibitors, IQ2 and IQ3 unbound via the ATP channel (except for CDK2-IQ2) as expected for type-I½A inhibitors. Interestingly, a C-C single/double bond difference between IQ2/IQ3, respectively, resulted associated with differences in the AS/T loop flexibility observed for CDK2. These insights will help developing scaffold modifications during an optimization stage, serving as a starting point to develop dual kinase inhibitors in challenging biological targets with a promising anticancer potential.Communicated by Ramaswamy H. Sarma.

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Cocoa fermentation plays a crucial role in producing flavor and bioactive compounds of high demand for food and nutraceutical industries. Such fermentations are frequently described as a succession of three main groups of microorganisms (i.e., yeast, lactic acid, and acetic acid bacteria), each producing a relevant metabolite (i.e., ethanol, lactic acid, and acetic acid). Nevertheless, this view of fermentation overlooks two critical observations: the role of minor groups of microorganisms to produce valuable compounds and the influence of environmental factors (other than oxygen availability) on their biosynthesis. Dissecting the metabolome during spontaneous cocoa fermentation is a current challenge for the rational design of controlled fermentations. This study evaluates variations in the metabolic fingerprint during spontaneous fermentation of fine flavor cocoa through a multiplatform metabolomics approach. Our data suggested the presence of two phases of differential metabolic activity that correlate with the observed variations on temperature over fermentations: an exothermic and an isothermic phase. We observed a continuous increase in temperature from day 0 to day 4 of fermentation and a significant variation in flavonoids and peptides between phases. While the second phase, from day four on, was characterized for lower metabolic activity, concomitant with small upward and downward fluctuations in temperature. Our work is the first to reveal two phases of metabolic activity concomitant with two temperature phases during spontaneous cocoa fermentation. Here, we proposed a new paradigm of cocoa fermentation that considers the changes in the global metabolic activity over fermentation, thus changing the current paradigm based only on three main groups of microorganism and their primary metabolic products.

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Recent developments in High Throughput Sequencing (HTS) technologies and bioinformatics, including improved read lengths and genome assemblers allow the reconstruction of complex genomes with unprecedented quality and contiguity. Sugarcane has one of the most complicated genomes among grassess with a haploid length of 1Gbp and a ploidies between 8 and 12. In this work, we present a genome assembly of the Colombian sugarcane hybrid CC 01-1940. Three types of sequencing technologies were combined for this assembly: PacBio long reads, Illumina paired short reads, and Hi-C reads. We achieved a median contig length of 34.94 Mbp and a total genome assembly of 903.2 Mbp. We annotated a total of 63,724 protein coding genes and performed a reconstruction and comparative analysis of the sucrose metabolism pathway. Nucleotide evolution measurements between orthologs with close species suggest that divergence between Saccharum officinarum and Saccharum spontaneum occurred <2 million years ago. Synteny analysis between CC 01-1940 and the S. spontaneum genome confirms the presence of translocation events between the species and a random contribution throughout the entire genome in current sugarcane hybrids. Analysis of RNA-Seq data from leaf and root tissue of contrasting sugarcane genotypes subjected to water stress treatments revealed 17,490 differentially expressed genes, from which 3,633 correspond to genes expressed exclusively in tolerant genotypes. We expect the resources presented here to serve as a source of information to improve the selection processes of new varieties of the breeding programs of sugarcane.

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Selecting appropriate metabolic engineering targets to build efficient cell factories maximizing the bioconversion of industrial by-products to valuable compounds taking into account time restrictions is a significant challenge in industrial biotechnology. Microbial metabolism engineering following a rational design has been widely studied. However, it is a cost-, time-, and laborious-intensive process because of the cell network complexity; thus, it is important to use tools that allow predicting gene deletions. An in silico experiment was performed to model and understand the metabolic engineering effects on the cell factory considering a second complexity level by transcriptomics data integration. In this study, a systems-based metabolic engineering target prediction was used to increase glycerol bioconversion to succinic acid based on Escherichia coli. Transcriptomics analysis suggests insights on how to increase cell glycerol utilization to further design efficient cell factories. Three E. coli models were used: a core model, a second model based on the integration of transcriptomics data obtained from growth in an optimized culture media, and a third one obtained after integration of transcriptomics data from adaptive laboratory evolution (ALE) experiments. A total of 2,402 strains were obtained with fumarase and pyruvate dehydrogenase being frequently predicted for all the models, suggesting these reactions as essential to increase succinic acid production. Finally, based on using flux balance analysis (FBA) results for all the mutants predicted, a machine learning method was developed to predict new mutants as well as to propose optimal metabolic engineering targets and mutants based on the measurement of the importance of each knockout's (feature's) contribution. Glycerol has become an interesting carbon source for industrial processes due to biodiesel business growth since it has shown promising results in terms of biomass/substrate yields. The combination of transcriptome, systems metabolic modeling, and machine learning analyses revealed the versatility of computational models to predict key metabolic engineering targets in a less cost-, time-, and laborious-intensive process. These data provide a platform to improve the prediction of metabolic engineering targets to design efficient cell factories. Our results may also work as a guide and platform for the selection/engineering of microorganisms for the production of interesting chemical compounds.

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The global demand for fine-flavour cocoa has increased worldwide during the last years. Fine-flavour cocoa offers exceptional quality and unique fruity and floral flavour attributes of high demand by the world's elite chocolatiers. Several studies have highlighted the relevance of cocoa fermentation to produce such attributes. Nevertheless, little is known regarding the microbial interactions and biochemistry that lead to the production of these attributes on farms of industrial relevance, where traditional fermentation methods have been pre-standardized and scaled up. In this study, we have used metagenomic approaches to dissect on-farm industrial fermentations of fine-flavour cocoa. Our results revealed the presence of a shared core of nine dominant microorganisms (i.e. Limosilactobacillus fermentum, Saccharomyces cerevisiae, Pestalotiopsis rhododendri, Acetobacter aceti group, Bacillus subtilis group, Weissella ghanensis group, Lactobacillus_uc, Malassezia restricta and Malassezia globosa) between two farms located at completely different agro-ecological zones. Moreover, a community metabolic model was reconstructed and proposed as a tool to further elucidate the interactions among microorganisms and flavour biochemistry. Our work is the first to reveal a core of microorganisms shared among industrial farms, which is an essential step to process engineering aimed to design starter cultures, reducing fermentation times, and controlling the expression of undesirable phenotypes.

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The oil and gas industry generates large amounts of oil-derived effluents such as Heavy Crude Oil (HCO) in water (W) emulsions, which pose a significant remediation and recovery challenge due to their high stability and the presence of environmentally concerning compounds. Nanomaterials emerge as a suitable alternative for the recovery of such effluents, as they can separate them under mild conditions. Additionally, different biomolecules with bioremediation and interfacial capabilities have been explored to functionalize such nanomaterials to improve their performance even further. Here, we put forward the notion of combining these technologies for the simultaneous separation and treatment of O/W effluent emulsions by a novel co-immobilization approach where both OmpA (a biosurfactant) and Laccase (a remediation enzyme) were effectively immobilized on polyether amine (PEA)-modified magnetite nanoparticles (MNPs). The obtained bionanocompounds (i.e., MNP-PEA-OmpA, MNP-PEA-Laccase, and MNP-PEA-OmpA-Laccase) were successfully characterized via DLS, XRD, TEM, TGA, and FTIR. The demulsification of O/W emulsions was achieved by MNP-PEA-OmpA and MNP-PEA-OmpA-Laccase at 5000 ppm. This effect was further improved by applying an external magnetic field to approach HCO removal efficiencies of 81% and 88%, respectively. The degradation efficiencies with these two bionanocompounds reached levels of between 5% and 50% for the present compounds. Taken together, our results indicate that the developed nanoplatform holds significant promise for the efficient treatment of emulsified effluents from the oil and gas industry.

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Clostridium (Ruminiclostridium) thermocellum is recognized for its ability to ferment cellulosic biomass directly, but it cannot naturally grow on xylose. Recently, C. thermocellum (KJC335) was engineered to utilize xylose through expressing a heterologous xylose catabolizing pathway. Here, we compared KJC335's transcriptomic responses to xylose versus cellobiose as the primary carbon source and assessed how the bacteria adapted to utilize xylose. Our analyses revealed 417 differentially expressed genes (DEGs) with log2 fold change (FC) >|1| and 106 highly DEGs (log2 FC >|2|). Among the DEGs, two putative sugar transporters, cbpC and cbpD, were up-regulated, suggesting their contribution to xylose transport and assimilation. Moreover, the up-regulation of specific transketolase genes (tktAB) suggests the importance of this enzyme for xylose metabolism. Results also showed remarkable up-regulation of chemotaxis and motility associated genes responding to xylose feeding, as well as widely varying gene expression in those encoding cellulosomal enzymes. For the down-regulated genes, several were categorized in gene ontology terms oxidation-reduction processes, ATP binding and ATPase activity, and integral components of the membrane. This study informs potentially critical, enabling mechanisms to realize the conceptually attractive Next-Generation Consolidated BioProcessing approach where a single species is sufficient for the co-fermentation of cellulose and hemicellulose.

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The opportunistic pathogen Malassezia pachydermatis causes bloodstream infections in preterm infants or individuals with immunodeficiency disorders and has been associated with a broad spectrum of diseases in animals such as seborrheic dermatitis, external otitis and fungemia. The current approaches to treat these infections are failing as a consequence of their adverse effects, changes in susceptibility and antifungal resistance. Thus, the identification of novel therapeutic targets against M. pachydermatis infections are highly relevant. Here, Gene Essentiality Analysis and Flux Variability Analysis was applied to a previously reported M. pachydermatis metabolic network to identify enzymes that, when absent, negatively affect biomass production. Three novel therapeutic targets (i.e., homoserine dehydrogenase (MpHSD), homocitrate synthase (MpHCS) and saccharopine dehydrogenase (MpSDH)) were identified that are absent in humans. Notably, L-lysine was shown to be an inhibitor of the enzymatic activity of MpHCS and MpSDH at concentrations of 1 mM and 75 mM, respectively, while L-threonine (1 mM) inhibited MpHSD. Interestingly, L- lysine was also shown to inhibit M. pachydermatis growth during in vitro assays with reference strains and canine isolates, while it had a negligible cytotoxic activity on HEKa cells. Together, our findings form the bases for the development of novel treatments against M. pachydermatis infections.

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Dye-sensitized solar cells (DSSCs) have been highlighted as the promising alternative to generate clean energy based on low pay-back time materials. These devices have been designed to mimic solar energy conversion processes from photosynthetic organisms (the most efficient energy transduction phenomenon observed in nature) with the aid of low-cost materials. Recently, light-harvesting complexes (LHC) have been proposed as potential dyes in DSSCs based on their higher light-absorption efficiencies as compared to synthetic dyes. In this work, photo-electrochemical hybrid devices were rationally designed by adding for the first time Leu and Lys tags to heterologously expressed light-harvesting proteins from Chlamydomonas reinhardtii, thus allowing their proper orientation and immobilization on graphene electrodes. The light-harvesting complex 4 from C. reinhardtii (LHC4) was initially expressed in Escherichia coli, purified via affinity chromatography and subsequently immobilized on plasma-treated thin-film graphene electrodes. A photocurrent density of 40.30 ± 9.26 µA/cm2 was measured on devices using liquid electrolytes supplemented with a phosphonated viologen to facilitate charge transfer. Our results suggest that a new family of graphene-based thin-film photovoltaic devices can be manufactured from rationally tagged LHC proteins and opens the possibility to further explore fundamental processes of energy transfer for biological components interfaced with synthetic materials.

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