MetalPredator: a web server to predict iron-sulfur cluster binding proteomes

Proteomic strategies to interrogate the Fe-S proteome

Iron‑sulfur (Fe-S) clusters, inorganic cofactors composed of iron and sulfide, participate in numerous essential redox, non-redox, structural, and regulatory biological processes within the cell. Though structurally and functionally diverse, the list of all proteins in an organism capable of binding one or more Fe-S clusters is referred to as its Fe-S proteome. Importantly, the Fe-S proteome is highly dynamic, with continuous cluster synthesis and delivery by complex Fe-S cluster biogenesis pathways. This cluster delivery is balanced out by processes that can result in loss of Fe-S cluster binding, such as redox state changes, iron availability, and oxygen sensitivity. Despite continued expansion of the Fe-S protein catalogue, it remains a challenge to reliably identify novel Fe-S proteins. As such, high-throughput techniques that can report on native Fe-S cluster binding are required to both identify new Fe-S proteins, as well as characterize the in vivo dynamics of Fe-S cluster binding. Due to the recent rapid growth in mass spectrometry, proteomics, and chemical biology, there has been a host of techniques developed that are applicable to the study of native Fe-S proteins. This review will detail both the current understanding of the Fe-S proteome and Fe-S cluster biology as well as describing state-of-the-art proteomic strategies for the study of Fe-S clusters within the context of a native proteome.

The Toxoplasma gondii mitochondrial transporter ABCB7L is essential for the biogenesis of cytosolic and nuclear iron-sulfur cluster proteins and cytosolic translation

Iron-sulfur (Fe-S) clusters are ubiquitous inorganic cofactors required for numerous essential cellular pathways. Since they cannot be scavenged from the environment, Fe-S clusters are synthesized de novo in cellular compartments such as the apicoplast, mitochondrion, and cytosol. The cytosolic Fe-S cluster biosynthesis pathway relies on the transport of an intermediate from the mitochondrial pathway. An ATP-binding cassette (ABC) transporter called ABCB7 is responsible for this role in numerous commonly studied organisms, but its role in the medically important apicomplexan parasites has not yet been studied. Here we identify and characterize a Toxoplasma gondii ABCB7 homolog, which we name ABCB7-like (ABCB7L). Genetic depletion shows that it is essential for parasite growth and that its disruption triggers partial stage conversion. Characterization of the knock-down line highlights a defect in the biogenesis of cytosolic and nuclear Fe-S proteins leading to defects in protein translation and other pathways including DNA and RNA replication and metabolism. Our work provides support for a broad conservation of the connection between mitochondrial and cytosolic pathways in Fe-S cluster biosynthesis and reveals its importance for parasite survival.

IMPORTANCE

Iron-sulfur (Fe-S) clusters are inorganic cofactors of proteins that play key roles in numerous essential biological processes, for example, respiration and DNA replication. Cells possess dedicated biosynthetic pathways to assemble Fe-S clusters, including a pathway in the mitochondrion and cytosol. A single transporter, called ABCB7, connects these two pathways, allowing an essential intermediate generated by the mitochondrial pathway to be used in the cytosolic pathway. Cytosolic and nuclear Fe-S proteins are dependent on the mitochondrial pathway, mediated by ABCB7, in numerous organisms studied to date. Here, we study the role of a homolog of ABCB7, which we name ABCB7-like (ABCB7L), in the ubiquitous unicellular apicomplexan parasite Toxoplasma gondii. We generated a depletion mutant of Toxoplasma ABCB7L and showed its importance for parasite fitness. Using comparative quantitative proteomic analysis and experimental validation of the mutants, we show that ABCB7L is required for cytosolic and nuclear, but not mitochondrial, Fe-S protein biogenesis. Our study supports the conservation of a protein homologous to ABCB7 and which has a similar function in apicomplexan parasites and provides insight into an understudied aspect of parasite metabolism.

Conditional essentiality of the 11-subunit complex I-like enzyme in strict anaerobes: the case of Desulfitobacterium hafniense strain DCB-2

In oxidative phosphorylation, respiratory complex I serves as an entry point in the electron transport chain for electrons generated in catabolic processes in the form of NADH. An ancestral version of the complex, lacking the NADH-oxidising module, is encoded in a significant number of bacterial genomes. Amongst them is Desulfitobacterium hafniense, a strict anaerobe capable of conserving energy via organohalide respiration. This study investigates the role of the complex I-like enzyme in D. hafniense energy metabolism using rotenone as a specific complex I inhibitor under different growth conditions. The investigation revealed that the complex I-like enzyme was essential for growth with lactate and pyruvate but not in conditions involving H2 as an electron donor. In addition, a previously published proteomic dataset of strain DCB-2 was analysed to reveal the predominance of the complex under different growth conditions and to identify potential redox partners. This approach revealed seven candidates with expression patterns similar to Nuo homologues, suggesting the use of diverse electron sources. Based on these results, we propose a model where the complex I-like enzyme serves as an electron entry point into the respiratory chain for substrates delivering electrons within the cytoplasm, such as lactate or pyruvate, with ferredoxins shuttling electrons to the complex.

Omics technology draws a comprehensive heavy metal resistance strategy in bacteria

The rapid industrial revolution significantly increased heavy metal pollution, becoming a major global environmental concern. This pollution is considered as one of the most harmful and toxic threats to all environmental components (air, soil, water, animals, and plants until reaching to human). Therefore, scientists try to find a promising and eco-friendly technique to solve this problem i.e., bacterial bioremediation. Various heavy metal resistance mechanisms were reported. Omics technologies can significantly improve our understanding of heavy metal resistant bacteria and their communities. They are a potent tool for investigating the adaptation processes of microbes in severe conditions. These omics methods provide unique benefits for investigating metabolic alterations, microbial diversity, and mechanisms of resistance of individual strains or communities to harsh conditions. Starting with genome sequencing which provides us with complete and comprehensive insight into the resistance mechanism of heavy metal resistant bacteria. Moreover, genome sequencing facilitates the opportunities to identify specific metal resistance genes, operons, and regulatory elements in the genomes of individual bacteria, understand the genetic mechanisms and variations responsible for heavy metal resistance within and between bacterial species in addition to the transcriptome, proteome that obtain the real expressed genes. Moreover, at the community level, metagenome, meta transcriptome and meta proteome participate in understanding the microbial interactive network potentially novel metabolic pathways, enzymes and gene species can all be found using these methods. This review presents the state of the art and anticipated developments in the use of omics technologies in the investigation of microbes used for heavy metal bioremediation.

Iron-sulfur protein odyssey: exploring their cluster functional versatility and challenging identification

Iron-sulfur (Fe-S) clusters are an essential and ubiquitous class of protein-bound prosthetic centers that are involved in a broad range of biological processes (e.g. respiration, photosynthesis, DNA replication and repair and gene regulation) performing a wide range of functions including electron transfer, enzyme catalysis, and sensing. In a general manner, Fe-S clusters can gain or lose electrons through redox reactions, and are highly sensitive to oxidation, notably by small molecules such as oxygen and nitric oxide. The [2Fe-2S] and [4Fe-4S] clusters, the most common Fe-S cofactors, are typically coordinated by four amino acid side chains from the protein, usually cysteine thiolates, but other residues (e.g. histidine, aspartic acid) can also be found. While diversity in cluster coordination ensures the functional variety of the Fe-S clusters, the lack of conserved motifs makes new Fe-S protein identification challenging especially when the Fe-S cluster is also shared between two proteins as observed in several dimeric transcriptional regulators and in the mitoribosome. Thanks to the recent development of in cellulo, in vitro, and in silico approaches, new Fe-S proteins are still regularly identified, highlighting the functional diversity of this class of proteins. In this review, we will present three main functions of the Fe-S clusters and explain the difficulties encountered to identify Fe-S proteins and methods that have been employed to overcome these issues.

Proteome profiling of Paenibacillus sonchi genomovar Riograndensis SBR5T under conventional and alternative nitrogen fixation

Paenibacillus sonchi SBR5T is a Gram-positive, endospore-forming facultative aerobic diazotrophic bacterium that can fix nitrogen via an alternative Fe-only nitrogenase (AnfHDGK). In several bacteria, this alternative system is expressed under molybdenum (Mo)-limiting conditions when the conventional Mo-dependent nitrogenase (NifHDK) production is impaired. The regulatory mechanisms, metabolic processes, and cellular functions of N2 fixation by alternative and/or conventional systems are poorly understood in the Paenibacillus genus. We conducted a comparative proteomic profiling study of P. sonchi SBR5T grown under N2-fixing conditions with and without Mo supply through an LC-MS/MS and label-free quantification analysis to address this gap. Protein abundances revealed overrepresented processes related to anaerobiosis growth adaption, Fe-S cluster biosynthesis, ammonia assimilation, electron transfer, and sporulation under N2-fixing conditions compared to non-fixing control. Under Mo limitation, the Fe-only nitrogenase components were overrepresented together with the Mo-transporter system, while the dinitrogenase component (NifDK) of Mo‑nitrogenase was underrepresented. The dinitrogenase reductase component (NifH) and accessory proteins encoded by the nif operon had no significant differential expression, suggesting post-transcriptional regulation of nif gene products in this strain. Overall, this was the first comprehensive proteomic analysis of a diazotrophic strain from the Paenibacillaceae family, and it provided insights related to alternative N2-fixation by Fe-only nitrogenase. SIGNIFICANCE: In this work, we try to understand how the alternative nitrogen fixation system, presented by some diazotrophic bacteria, works. For this, we used the SBR5 lineage of P. sonchi, which presents the alternative system in which the nitrogenase cofactor is composed only of iron. In addition, we tried to unravel the proteome of this strain in different situations of nitrogen fixation, since, for Gram-positive bacteria, these systems are little known. The results achieved, through LC-MS/MS and label-free quantitative analysis, showed an overrepresentation of proteins related to different processes involved with growth under stressful conditions in situations of nitrogen deficiency, in addition to suggesting that some encoded proteins by the nif operon may be regulated at post-transcriptional levels. Our findings represent important steps toward the elucidation of nitrogen fixation systems in Gram-positive diazotrophic bacteria.

Biochemical and cellular characterization of the CISD3 protein: Molecular bases of cluster release and destabilizing effects of nitric oxide

The NEET proteins, an important family of iron-sulfur (Fe-S) proteins, have generated a strong interest due to their involvement in diverse diseases such as cancer, diabetes, and neurodegenerative disorders. Among the human NEET proteins, CISD3 has been the least studied, and its functional role is still largely unknown. We have investigated the biochemical features of CISD3 at the atomic and in cellulo levels upon challenge with different stress conditions i.e., iron deficiency, exposure to hydrogen peroxide, and nitric oxide. The redox and cellular stability properties of the protein agree on a predominance of reduced form of CISD3 in the cells. Upon the addition of iron chelators, CISD3 loses its Fe-S clusters and becomes unstructured, and its cellular level drastically decreases. Chemical shift perturbation measurements suggest that, upon cluster oxidation, the protein undergoes a conformational change at the C-terminal CDGSH domain, which determines the instability of the oxidized state. This redox-associated conformational change may be the source of cooperative electron transfer via the two [Fe2S2] clusters in CISD3, which displays a single sharp voltammetric signal at -31 mV versus SHE. Oxidized CISD3 is particularly sensitive to the presence of hydrogen peroxide in vitro, whereas only the reduced form is able to bind nitric oxide. Paramagnetic NMR provides clear evidence that, upon NO binding, the cluster is disassembled but iron ions are still bound to the protein. Accordingly, in cellulo CISD3 is unaffected by oxidative stress induced by hydrogen peroxide but it becomes highly unstable in response to nitric oxide treatment.

MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier

In biological organisms, metal ion-binding proteins participate in numerous metabolic activities and are closely associated with various diseases. To accurately predict whether a protein binds to metal ions and the type of metal ion-binding protein, this study proposed a classifier named MIBPred. The classifier incorporated advanced Word2Vec technology from the field of natural language processing to extract semantic features of the protein sequence language and combined them with position-specific score matrix (PSSM) features. Furthermore, an ensemble learning model was employed for the metal ion-binding protein classification task. In the model, we independently trained XGBoost, LightGBM, and CatBoost algorithms and integrated the output results through an SVM voting mechanism. This innovative combination has led to a significant breakthrough in the predictive performance of our model. As a result, we achieved accuracies of 95.13% and 85.19%, respectively, in predicting metal ion-binding proteins and their types. Our research not only confirms the effectiveness of Word2Vec technology in extracting semantic information from protein sequences but also highlights the outstanding performance of the MIBPred classifier in the problem of metal ion-binding protein types. This study provides a reliable tool and method for the in-depth exploration of the structure and function of metal ion-binding proteins.

Genomics of Preaxostyla Flagellates Illuminates the Path Towards the Loss of Mitochondria

The notion that mitochondria cannot be lost was shattered with the report of an oxymonad Monocercomonoides exilis, the first eukaryote arguably without any mitochondrion. Yet, questions remain about whether this extends beyond the single species and how this transition took place. The Oxymonadida is a group of gut endobionts taxonomically housed in the Preaxostyla which also contains free-living flagellates of the genera Trimastix and Paratrimastix. The latter two taxa harbour conspicuous mitochondrion-related organelles (MROs). Here we report high-quality genome and transcriptome assemblies of two Preaxostyla representatives, the free-living Paratrimastix pyriformis and the oxymonad Blattamonas nauphoetae. We performed thorough comparisons among all available genomic and transcriptomic data of Preaxostyla to further decipher the evolutionary changes towards amitochondriality, endobiosis, and unstacked Golgi. Our results provide insights into the metabolic and endomembrane evolution, but most strikingly the data confirm the complete loss of mitochondria for all three oxymonad species investigated (M. exilis, B. nauphoetae, and Streblomastix strix), suggesting the amitochondriate status is common to a large part if not the whole group of Oxymonadida. This observation moves this unique loss to 100 MYA when oxymonad lineage diversified.

Respiratory protein interactions in Dehalobacter sp. strain 8M revealed through genomic and native proteomic analyses

Dehalobacter (Firmicutes) encompass obligate organohalide-respiring bacteria used for bioremediation of groundwater contaminated with halogenated organics. Various aspects of their biochemistry remain unknown, including the identities and interactions of respiratory proteins. Here, we sequenced the genome of Dehalobacter sp. strain 8M and analysed its protein expression. Strain 8M encodes 22 reductive dehalogenase homologous (RdhA) proteins. RdhA D8M_v2_40029 (TmrA) was among the two most abundant proteins during growth with trichloromethane and 1,1,2-trichloroethane. To examine interactions of respiratory proteins, we used blue native gel electrophoresis together with dehalogenation activity tests and mass spectrometry. The highest activities were found in gel slices with the highest abundance of TmrA. Protein distributions across gel lanes provided biochemical evidence that the large and small subunits of the membrane-bound [NiFe] uptake hydrogenase (HupL and HupS) interacted strongly and that HupL/S interacted weakly with RdhA. Moreover, the interaction of RdhB and membrane-bound b-type cytochrome HupC was detected. RdhC proteins, often encoded in rdh operons but without described function, migrated in a protein complex not associated with HupL/S or RdhA. This study provides the first biochemical evidence of respiratory protein interactions in Dehalobacter, discusses implications for the respiratory architecture and advances the molecular comprehension of this unique respiratory chain.

Adaptation of the late ISC pathway in the anaerobic mitochondrial organelles of Giardia intestinalis

Mitochondrial metabolism is entirely dependent on the biosynthesis of the [4Fe-4S] clusters, which are part of the subunits of the respiratory chain. The mitochondrial late ISC pathway mediates the formation of these clusters from simpler [2Fe-2S] molecules and transfers them to client proteins. Here, we characterized the late ISC pathway in one of the simplest mitochondria, mitosomes, of the anaerobic protist Giardia intestinalis that lost the respiratory chain and other hallmarks of mitochondria. In addition to IscA2, Nfu1 and Grx5 we identified a novel BolA1 homologue in G. intestinalis mitosomes. It specifically interacts with Grx5 and according to the high-affinity pulldown also with other core mitosomal components. Using CRISPR/Cas9 we were able to establish full bolA1 knock out, the first cell line lacking a mitosomal protein. Despite the ISC pathway being the only metabolic role of the mitosome no significant changes in the mitosome biology could be observed as neither the number of the mitosomes or their capability to form [2Fe-2S] clusters in vitro was affected. We failed to identify natural client proteins that would require the [2Fe-2S] or [4Fe-4S] cluster within the mitosomes, with the exception of [2Fe-2S] ferredoxin, which is itself part of the ISC pathway. The overall uptake of iron into the cellular proteins remained unchanged as also observed for the grx5 knock out cell line. The pull-downs of all late ISC components were used to build the interactome of the pathway showing specific position of IscA2 due to its interaction with the outer mitosomal membrane proteins. Finally, the comparative analysis across Metamonada species suggested that the adaptation of the late ISC pathway identified in G. intestinalis occurred early in the evolution of this supergroup of eukaryotes.

Integrating reduced amino acid composition into PSSM for improving copper ion-binding protein prediction

Copper ion-binding proteins play an essential role in metabolic processes and are critical factors in many diseases, such as breast cancer, lung cancer, and Menkes disease. Many algorithms have been developed for predicting metal ion classification and binding sites, but none have been applied to copper ion-binding proteins. In this study, we developed a copper ion-bound protein classifier, RPCIBP, which integrating the reduced amino acid composition into position-specific score matrix (PSSM). The reduced amino acid composition filters out a large number of useless evolutionary features, improving the operational efficiency and predictive ability of the model (feature dimension from 2900 to 200, ACC from 83 % to 85.1 %). Compared with the basic model using only three sequence feature extraction methods (ACC in training set between 73.8 %-86.2 %, ACC in test set between 69.3 %-87.5 %), the model integrating the evolutionary features of the reduced amino acid composition showed higher accuracy and robustness (ACC in training set between 83.1 %-90.8 %, ACC in test set between 79.1 %-91.9 %). Best copper ion-binding protein classifiers filtered by feature selection progress were deployed in a user-friendly web server (http://bioinfor.imu.edu.cn/RPCIBP). RPCIBP can accurately predict copper ion-binding proteins, which is convenient for further structural and functional studies, and conducive to mechanism exploration and target drug development.

Gut microbiota interspecies interactions shape the response of Clostridioides difficile to clinically relevant antibiotics

In the human gut, the growth of the pathogen Clostridioides difficile is impacted by a complex web of interspecies interactions with members of human gut microbiota. We investigate the contribution of interspecies interactions on the antibiotic response of C. difficile to clinically relevant antibiotics using bottom-up assembly of human gut communities. We identify 2 classes of microbial interactions that alter C. difficile's antibiotic susceptibility: interactions resulting in increased ability of C. difficile to grow at high antibiotic concentrations (rare) and interactions resulting in C. difficile growth enhancement at low antibiotic concentrations (common). Based on genome-wide transcriptional profiling data, we demonstrate that metal sequestration due to hydrogen sulfide production by the prevalent gut species Desulfovibrio piger increases the minimum inhibitory concentration (MIC) of metronidazole for C. difficile. Competition with species that display higher sensitivity to the antibiotic than C. difficile leads to enhanced growth of C. difficile at low antibiotic concentrations due to competitive release. A dynamic computational model identifies the ecological principles driving this effect. Our results provide a deeper understanding of ecological and molecular principles shaping C. difficile's response to antibiotics, which could inform therapeutic interventions.

Classifying metal-binding sites with neural networks

To advance our ability to predict impacts of the protein scaffold on catalysis, robust classification schemes to define features of proteins that will influence reactivity are needed. One of these features is a protein's metal-binding ability, as metals are critical to catalytic conversion by metalloenzymes. As a step toward realizing this goal, we used convolutional neural networks (CNNs) to enable the classification of a metal cofactor binding pocket within a protein scaffold. CNNs enable images to be classified based on multiple levels of detail in the image, from edges and corners to entire objects, and can provide rapid classification. First, six CNN models were fine-tuned to classify the 20 standard amino acids to choose a performant model for amino acid classification. This model was then trained in two parallel efforts: to classify a 2D image of the environment within a given radius of the central metal binding site, either an Fe ion or a [2Fe-2S] cofactor, with the metal visible (effort 1) or the metal hidden (effort 2). We further used two sub-classifications of the [2Fe-2S] cofactor: (1) a standard [2Fe-2S] cofactor and (2) a Rieske [2Fe-2S] cofactor. The accuracy for the model correctly identifying all three defined features was >95%, despite our perception of the increased challenge of the metalloenzyme identification. This demonstrates that machine learning methodology to classify and distinguish similar metal-binding sites, even in the absence of a visible cofactor, is indeed possible and offers an additional tool for metal-binding site identification in proteins.

Metalloproteome of human-infective RNA viruses: a study towards understanding the role of metal ions in virology

Metalloproteins and metal-based inhibitors have been shown to effectively combat infectious diseases, particularly those caused by RNA viruses. In this study, a diverse set of bioinformatics methods was employed to identify metal-binding proteins of human RNA viruses. Seventy-three viral proteins with a high probability of being metal-binding proteins were identified. These proteins included 40 zinc-, 47 magnesium- and 14 manganese-binding proteins belonging to 29 viral species and eight significant viral families, including Coronaviridae, Flaviviridae and Retroviridae. Further functional characterization has revealed that these proteins play a critical role in several viral processes, including viral replication, fusion and host viral entry. They fall under the essential categories of viral proteins, including polymerase and protease enzymes. Magnesium ion is abundantly predicted to interact with these viral enzymes, followed by zinc. In addition, this study also examined the evolutionary aspects of predicted viral metalloproteins, offering essential insights into the metal utilization patterns among different viral species. The analysis indicates that the metal utilization patterns are conserved within the functional classes of the proteins. In conclusion, the findings of this study provide significant knowledge on viral metalloproteins that can serve as a valuable foundation for future research in this area.

Metal trafficking in the cell: Combining atomic resolution with cellular dimension

Metals are widely present in biological systems as simple ions or complex cofactors, and are involved in a variety of processes essential for life. Their transport inside cells and insertion into the binding sites of the proteins that need metals to function occur through complex and selective pathways involving dedicated multiprotein machineries specifically and transiently interacting with each other, often sharing the coordination of metal ions and/or cofactors. The understanding of these machineries requires integrated approaches, ranging from bioinformatics to experimental investigations, possibly in the cellular context. In this review, we report two case studies where the use of integrated in vitro and in cellulo approaches is necessary to clarify at atomic resolution essential aspects of metal trafficking in cells.

The zinc proteome of SARS-CoV-2

Zinc is an essential element for human health. Among its many functions, zinc(II) modulates the immune response to infections and, at high concentrations or in the presence of ionophores, inhibits the replication of various RNA viruses. Structural biology studies on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) revealed that zinc(II) is the most common metal ion that binds to viral proteins. However, the number of zinc(II)-binding sites identified by experimental methods is far from exhaustive, as metal ions may be lost during protein purification protocols. To better define the zinc(II)-binding proteome of coronavirus, we leveraged the wealth of deposited structural data and state-of-the-art bioinformatics methods. Through this in silico approach, 15 experimental zinc(II) sites were identified and a further 22 were predicted in Spike, open reading frame (ORF)3a/d, ORF8, and several nonstructural proteins, highlighting an essential role of zinc(II) in viral replication. Furthermore, the structural relationships between viral and eukaryotic sites (typically zinc fingers) indicate that SARS-CoV-2 can compete with human proteins for zinc(II) binding. Given the double-edged effect of zinc(II) ions, both essential and toxic to coronavirus, only the complete elucidation of the structural and regulatory zinc(II)-binding sites can guide selective antiviral strategies based on zinc supplementation.

The Fe-S proteome of Escherichia coli: prediction, function and fate

Iron-sulfur (Fe-S) clusters are inorganic ubiquitous and ancient cofactors. Fe-S bound proteins contribute to most cellular processes, including DNA replication and integrity, genetic expression and regulation, metabolism, biosynthesis and most bioenergetics systems. Also, Fe-S proteins hold a great biotechnological potential in metabolite and chemical production, including antibiotics. From classic biophysics and spectroscopy methodologies to recent development in bioinformatics, including structural modeling and chemoproteomics, our capacity to predict and identify Fe-S proteins has spectacularly increased over the recent years. Here, these developments are presented and collectively used to update the composition of Escherichia coli Fe-S proteome, for which we predict 181 occurrences, i.e. 40 more candidates than in our last catalog (Py and Barras, 2010), and equivalent to 4% of its total proteome. Besides, Fe-S clusters can be targeted by redox active compounds or reactive oxygen and nitrosative species, and even be destabilized by contaminant metals. Accordingly, we discuss how cells handle damaged Fe-S proteins, i.e. degradation, recycling or repair.

Arabidopsis Iron Superoxide Dismutase FSD1 Protects Against Methyl Viologen-Induced Oxidative Stress in a Copper-Dependent Manner

Iron superoxide dismutase 1 (FSD1) was recently characterized as a plastidial, cytoplasmic, and nuclear enzyme with osmoprotective and antioxidant functions. However, the current knowledge on its role in oxidative stress tolerance is ambiguous. Here, we characterized the role of FSD1 in response to methyl viologen (MV)-induced oxidative stress in Arabidopsis thaliana. In accordance with the known regulation of FSD1 expression, abundance, and activity, the findings demonstrated that the antioxidant function of FSD1 depends on the availability of Cu²⁺ in growth media. Arabidopsis fsd1 mutants showed lower capacity to decompose superoxide at low Cu²⁺ concentrations in the medium. Prolonged exposure to MV led to reduced ascorbate levels and higher protein carbonylation in fsd1 mutants and transgenic plants lacking a plastid FSD1 pool as compared to the wild type. MV induced a rapid increase in FSD1 activity, followed by a decrease after 4 h long exposure. Genetic disruption of FSD1 negatively affected the hydrogen peroxide-decomposing ascorbate peroxidase in fsd1 mutants. Chloroplastic localization of FSD1 is crucial to maintain redox homeostasis. Proteomic analysis showed that the sensitivity of fsd1 mutants to MV coincided with decreased abundances of ferredoxin and photosystem II light-harvesting complex proteins. These mutants have higher levels of chloroplastic proteases indicating an altered protein turnover in chloroplasts. Moreover, FSD1 disruption affects the abundance of proteins involved in the defense response. Collectively, the study provides evidence for the conditional antioxidative function of FSD1 and its possible role in signaling.

Identification of Iron-Sulfur (Fe-S) Cluster and Zinc (Zn) Binding Sites Within Proteomes Predicted by DeepMind's AlphaFold2 Program Dramatically Expands the Metalloproteome

DeepMind's AlphaFold2 software has ushered in a revolution in high quality, 3D protein structure prediction. In very recent work by the DeepMind team, structure predictions have been made for entire proteomes of twenty-one organisms, with >360,000 structures made available for download. Here we show that thousands of novel binding sites for iron-sulfur (Fe-S) clusters and zinc (Zn) ions can be identified within these predicted structures by exhaustive enumeration of all potential ligand-binding orientations. We demonstrate that AlphaFold2 routinely makes highly specific predictions of ligand binding sites: for example, binding sites that are comprised exclusively of four cysteine sidechains fall into three clusters, representing binding sites for 4Fe-4S clusters, 2Fe-2S clusters, or individual Zn ions. We show further: (a) that the majority of known Fe-S cluster and Zn binding sites documented in UniProt are recovered by the AlphaFold2 structures, (b) that there are occasional disputes between AlphaFold2 and UniProt with AlphaFold2 predicting highly plausible alternative binding sites, (c) that the Fe-S cluster binding sites that we identify in E. coli agree well with previous bioinformatics predictions, (d) that cysteines predicted here to be part of ligand binding sites show little overlap with those shown via chemoproteomics techniques to be highly reactive, and (e) that AlphaFold2 occasionally appears to build erroneous disulfide bonds between cysteines that should instead coordinate a ligand. These results suggest that AlphaFold2 could be an important tool for the functional annotation of proteomes, and the methodology presented here is likely to be useful for predicting other ligand-binding sites.

Prediction of the Iron–Sulfur Binding Sites in Proteins Using the Highly Accurate Three-Dimensional Models Calculated by AlphaFold and RoseTTAFold

AlphaFold and RoseTTAFold are deep learning-based approaches that predict the structure of proteins from their amino acid sequences. Remarkable success has recently been achieved in the prediction accuracy of not only the fold of the target protein but also the position of its amino acid side chains. In this article, I question the accuracy of these methods to predict iron–sulfur binding sites. I analyze three-dimensional models calculated by AlphaFold and RoseTTAFold of Fe–S–dependent enzymes, for which no structure of a homologous protein has been solved experimentally. In all cases, the amino acids that presumably coordinate the cluster were gathered together and facing each other, which led to a quite accurate model of the Fe–S cluster binding site. Yet, cysteine candidates were often involved in intramolecular disulfide bonds, and the number and identity of the protein amino acids that should ligate the cluster were not always clear. The experimental structure determination of the protein with its Fe–S cluster and in complex with substrate/inhibitor/product is still needed to unambiguously visualize the coordination state of the cluster and understand the conformational changes occurring during catalysis.

The Mechanism of Metal Homeostasis in Plants: A New View on the Synergistic Regulation Pathway of Membrane Proteins, Lipids and Metal Ions

Heavy metal stress (HMS) is one of the most destructive abiotic stresses which seriously affects the growth and development of plants. Recent studies have shown significant progress in understanding the molecular mechanisms underlying plant tolerance to HMS. In general, three core signals are involved in plants' responses to HMS; these are mitogen-activated protein kinase (MAPK), calcium, and hormonal (abscisic acid) signals. In addition to these signal components, other regulatory factors, such as microRNAs and membrane proteins, also play an important role in regulating HMS responses in plants. Membrane proteins interact with the highly complex and heterogeneous lipids in the plant cell environment. The function of membrane proteins is affected by the interactions between lipids and lipid-membrane proteins. Our review findings also indicate the possibility of membrane protein-lipid-metal ion interactions in regulating metal homeostasis in plant cells. In this review, we investigated the role of membrane proteins with specific substrate recognition in regulating cell metal homeostasis. The understanding of the possible interaction networks and upstream and downstream pathways is developed. In addition, possible interactions between membrane proteins, metal ions, and lipids are discussed to provide new ideas for studying metal homeostasis in plant cells.

Differential contribution of two organelles of endosymbiotic origin to iron-sulfur cluster synthesis and overall fitness in Toxoplasma

Iron-sulfur (Fe-S) clusters are one of the most ancient and ubiquitous prosthetic groups, and they are required by a variety of proteins involved in important metabolic processes. Apicomplexan parasites have inherited different plastidic and mitochondrial Fe-S clusters biosynthesis pathways through endosymbiosis. We have investigated the relative contributions of these pathways to the fitness of Toxoplasma gondii, an apicomplexan parasite causing disease in humans, by generating specific mutants. Phenotypic analysis and quantitative proteomics allowed us to highlight notable differences in these mutants. Both Fe-S cluster synthesis pathways are necessary for optimal parasite growth in vitro, but their disruption leads to markedly different fates: impairment of the plastidic pathway leads to a loss of the organelle and to parasite death, while disruption of the mitochondrial pathway trigger differentiation into a stress resistance stage. This highlights that otherwise similar biochemical pathways hosted by different sub-cellular compartments can have very different contributions to the biology of the parasites, which is something to consider when exploring novel strategies for therapeutic intervention.

Pathways of Iron and Sulfur Acquisition, Cofactor Assembly, Destination, and Storage in Diverse Archaeal Methanogens and Alkanotrophs

Archaeal methanogens, methanotrophs, and alkanotrophs have a high demand for iron (Fe) and sulfur (S); however, little is known of how they acquire, traffic, deploy, and store these elements. Here, we examined the distribution of homologs of proteins mediating key steps in Fe/S metabolism in model microorganisms, including iron(II) sensing/uptake (FeoAB), sulfide extraction from cysteine (SufS), and the biosynthesis of iron-sulfur [Fe-S] clusters (SufBCDE), siroheme (Pch2 dehydrogenase), protoheme (AhbABCD), cytochrome c (Cyt c) (CcmCF), and iron storage/detoxification (Bfr, FtrA, and IssA), among 326 publicly available, complete or metagenome-assembled genomes of archaeal methanogens/methanotrophs/alkanotrophs. The results indicate several prevalent but nonuniversal features, including FeoB, SufBC, and the biosynthetic apparatus for the basic tetrapyrrole scaffold, as well as its siroheme (and F₄₃₀) derivatives. However, several early-diverging genomes lacked SufS and pathways to synthesize and deploy heme. Genomes encoding complete versus incomplete heme biosynthetic pathways exhibited equivalent prevalences of [Fe-S] cluster binding proteins, suggesting an expansion of catalytic capabilities rather than substitution of heme for [Fe-S] in the former group. Several strains with heme binding proteins lacked heme biosynthesis capabilities, while other strains with siroheme biosynthesis capability lacked homologs of known siroheme binding proteins, indicating heme auxotrophy and unknown siroheme biochemistry, respectively. While ferritin proteins involved in ferric oxide storage were widespread, those involved in storing Fe as thioferrate were unevenly distributed. Collectively, the results suggest that differences in the mechanisms of Fe and S acquisition, deployment, and storage have accompanied the diversification of methanogens/methanotrophs/alkanotrophs, possibly in response to differential availability of these elements as these organisms evolved.

IMPORTANCE Archaeal methanogens, methanotrophs, and alkanotrophs, argued to be among the most ancient forms of life, have a high demand for iron (Fe) and sulfur (S) for cofactor biosynthesis, among other uses. Here, using comparative bioinformatic approaches applied to 326 genomes, we show that major differences in Fe/S acquisition, trafficking, deployment, and storage exist in this group. Variation in these characters was generally congruent with the phylogenetic placement of these genomes, indicating that variation in Fe/S usage and deployment has contributed to the diversification and ecology of these organisms. However, incongruency was observed among the distribution of cofactor biosynthesis pathways and known protein destinations for those cofactors, suggesting auxotrophy or yet-to-be-discovered pathways for cofactor biosynthesis.

Identification of Disease-Related 2-Oxoglutarate/Fe (II)-Dependent Oxygenase Based on Reduced Amino Acid Cluster Strategy

The 2-oxoglutarate/Fe (II)-dependent (2OG) oxygenase superfamily is mainly responsible for protein modification, nucleic acid repair and/or modification, and fatty acid metabolism and plays important roles in cancer, cardiovascular disease, and other diseases. They are likely to become new targets for the treatment of cancer and other diseases, so the accurate identification of 2OG oxygenases is of great significance. Many computational methods have been proposed to predict functional proteins to compensate for the time-consuming and expensive experimental identification. However, machine learning has not been applied to the study of 2OG oxygenases. In this study, we developed OGFE_RAAC, a prediction model to identify whether a protein is a 2OG oxygenase. To improve the performance of OGFE_RAAC, 673 amino acid reduction alphabets were used to determine the optimal feature representation scheme by recoding the protein sequence. The 10-fold cross-validation test showed that the accuracy of the model in identifying 2OG oxygenases is 91.04%. Besides, the independent dataset results also proved that the model has excellent generalization and robustness. It is expected to become an effective tool for the identification of 2OG oxygenases. With further research, we have also found that the function of 2OG oxygenases may be related to their polarity and hydrophobicity, which will help the follow-up study on the catalytic mechanism of 2OG oxygenases and the way they interact with the substrate. Based on the model we built, a user-friendly web server was established and can be friendly accessed at http://bioinfor.imu.edu.cn/ogferaac.

Examining pathways of iron and sulfur acquisition, trafficking, deployment, and storage in mineral-grown methanogen cells

Methanogens have a high demand for iron (Fe) and sulfur (S); however, little is known of how they acquire, deploy, and store these elements and how this, in turn, affects their physiology. Methanogens were recently shown to reduce pyrite (FeS₂) generating aqueous iron-sulfide (FeS_(aq)) clusters that are likely assimilated as a source of Fe and S. Here, we compare the phenotype of Methanococcus voltae when grown with FeS₂ or ferrous iron (Fe(II)) and sulfide (HS^-). FeS₂-grown cells are 33% smaller yet have 193% more Fe than Fe(II)/HS^--grown cells. Whole cell EPR revealed similar distributions of paramagnetic Fe, although FeS₂-grown cells showed a broad spectral feature attributed to intracellular thioferrate-like nanoparticles. Differential proteomic analyses showed similar expression of core methanogenesis enzymes, indicating that Fe and S source does not substantively alter the energy metabolism of cells. However, a homolog of the Fe(II) transporter FeoB and its putative transcriptional regulator DtxR were up-expressed in FeS₂-grown cells, suggesting that cells sense Fe(II) limitation. Two homologs of IssA, a protein putatively involved in coordinating thioferrate nanoparticles, were also up-expressed in FeS₂-grown cells. We interpret these data to indicate that, in FeS₂-grown cells, DtxR cannot sense Fe(II) and therefore cannot down-regulate FeoB. We suggest this is due to the transport of Fe(II) complexed with sulfide (FeS_(aq)) leading to excess Fe that is sequestered by IssA as a thioferrate-like species. This model provides a framework for the design of targeted experiments aimed at further characterizing Fe acquisition and homeostasis in M. voltae and other methanogens. IMPORTANCE FeS₂ is the most abundant sulfide mineral in the Earth's crust and is common in environments inhabited by methanogenic archaea. FeS₂ can be reduced by methanogens, yielding aqueous FeS_(aq) clusters that are thought to be a source of Fe and S. Here, we show that growth of Methanococcus voltae on FeS₂ results in smaller cell size and higher Fe content per cell, with Fe likely stored intracellularly as thioferrate-like nanoparticles. Fe(II) transporters and storage proteins were up-regulated in FeS₂-grown cells. These responses are interpreted to result from cells incorrectly sensing Fe(II) limitation due to assimilation of Fe(II) as FeS_(aq). These findings have implications for our understanding of how Fe/S availability influences methanogen physiology and the biogeochemical cycling of these elements.

Substrate Inhibition of 5β-Δ4-3-Ketosteroid Dehydrogenase in Sphingobium sp. Strain Chol11 Acts as Circuit Breaker During Growth With Toxic Bile Salts

In contrast to many steroid hormones and cholesterol, mammalian bile salts are 5β-steroids, which leads to a bent structure of the steroid core. Bile salts are surface-active steroids excreted into the environment in large amounts, where they are subject to bacterial degradation. Bacterial steroid degradation is initiated by the oxidation of the A-ring leading to canonical Δ⁴-3-keto steroids with a double bond in the A-ring. For 5β-bile salts, this Δ⁴-double bond is introduced into 3-keto-bile salts by a 5β-Δ⁴-ketosteroid dehydrogenase (5β-Δ⁴-KSTD). With the Nov2c019 protein from bile-salt degrading Sphingobium sp. strain Chol11, a novel 5β-Δ⁴-KSTD for bile-salt degradation belonging to the Old Yellow Enzyme family was identified and named 5β-Δ⁴-KSTD1. By heterologous production in Escherichia coli, 5β-Δ⁴-KSTD function could be shown for 5β-Δ⁴-KSTD1 as well as the homolog CasH from bile-salt degrading Rhodococcus jostii RHA1. The deletion mutant of 5β-Δ⁴-kstd1 had a prolonged lag-phase with cholate as sole carbon source and, in accordance with the function of 5β-Δ⁴-KSTD1, showed delayed 3-ketocholate transformation. Purified 5β-Δ⁴-KSTD1 was specific for 5β-steroids in contrast to 5α-steroids and converted steroids with a variety of hydroxy groups regardless of the presence of a side chain. 5β-Δ⁴-KSTD1 showed a relatively low K _m for 3-ketocholate, a very high specific activity and pronounced substrate inhibition. With respect to the toxicity of bile salts, these kinetic properties indicate that 5β-Δ⁴-KSTD1 can achieve fast detoxification of the detergent character as well as prevention of an overflow of the catabolic pathway in presence of increased bile-salt concentrations.

Roles of Ferredoxin-Dependent Proteins in the Apicoplast of Plasmodium falciparum Parasites

Ferredoxin (Fd) and ferredoxin-NADP+ reductase (FNR) form a redox system that is hypothesized to play a central role in the maintenance and function of the apicoplast organelle of malaria parasites. The Fd/FNR system provides reducing power to various iron-sulfur cluster (FeS)-dependent proteins in the apicoplast and is believed to help to maintain redox balance in the organelle. While the Fd/FNR system has been pursued as a target for antimalarial drug discovery, Fd, FNR, and the FeS proteins presumably reliant on their reducing power play an unknown role in parasite survival and apicoplast maintenance. To address these questions, we generated genetic deletions of these proteins in a parasite line containing an apicoplast bypass system. Through these deletions, we discovered that Fd, FNR, and certain FeS proteins are essential for parasite survival but found that none are required for apicoplast maintenance. Additionally, we addressed the question of how Fd and its downstream FeS proteins obtain FeS cofactors by deleting the FeS transfer proteins SufA and NfuApi. While individual deletions of these proteins revealed their dispensability, double deletion resulted in synthetic lethality, demonstrating a redundant role in providing FeS clusters to Fd and other essential FeS proteins. Our data support a model in which the reducing power from the Fd/FNR system to certain downstream FeS proteins is essential for the survival of blood-stage malaria parasites but not for organelle maintenance, while other FeS proteins are dispensable for this stage of parasite development. IMPORTANCE Ferredoxin (Fd) and ferredoxin-NADP+ reductase (FNR) form one of the few known redox systems in the apicoplast of malaria parasites and provide reducing power to iron-sulfur (FeS) cluster proteins within the organelle. While the Fd/FNR system has been explored as a drug target, the essentiality and roles of this system and the identity of its downstream FeS proteins have not been determined. To answer these questions, we generated deletions of these proteins in an apicoplast metabolic bypass line (PfMev) and determined the minimal set of proteins required for parasite survival. Moving upstream of this pathway, we also generated individual and dual deletions of the two FeS transfer proteins that deliver FeS clusters to Fd and downstream FeS proteins. We found that both transfer proteins are dispensable, but double deletion displayed a synthetic lethal phenotype, demonstrating their functional redundancy. These findings provide important insights into apicoplast biochemistry and drug development.

Integrated Metabolomics and Targeted Gene Transcription Analysis Reveal Global Bacterial Antimonite Resistance Mechanisms

Antimony (Sb)-resistant bacteria have potential applications in the remediation of Sb-contaminated sites. However, the effect of Sb(III) exposure on whole-cell metabolic change has not been studied. Herein, we combined untargeted metabolomics with a previous proteomics dataset and confirmatory gene transcription analysis to identify metabolic responses to Sb(III) exposure in Agrobacterium tumefaciens GW4. Dynamic changes in metabolism between control and Sb(III)-exposed groups were clearly shown. KEGG pathway analysis suggested that with Sb(III) exposure: (1) the branching pathway of gluconeogenesis is down-regulated, resulting in the up-regulation of pentose phosphate pathway to provide precursors of anabolism and NADPH; (2) glycerophospholipid and arachidonic acid metabolisms are down-regulated, resulting in more acetyl-CoA entry into the TCA cycle and increased capacity to produce energy and macromolecular synthesis; (3) nucleotide and fatty acid synthesis pathways are all increased perhaps to protect cells from DNA and lipid peroxidation; (4) nicotinate metabolism increases which likely leads to increased production of co-enzymes (e.g., NAD⁺ and NADP⁺) for the maintenance of cellular redox and Sb(III) oxidation. Expectedly, the total NADP⁺/NADPH content, total glutathione, and reduced glutathione contents were all increased after Sb(III) exposure in strain GW4, which contribute to maintaining the reduced state of the cytoplasm. Our results provide novel information regarding global bacterial responses to Sb(III) exposure from a single gene level to the entire metabolome and provide specific hypotheses regarding the metabolic change to be addressed in future research.

DeepCys: Structure-based multiple cysteine function prediction method trained on deep neural network: Case study on domains of unknown functions belonging to COX2 domains

Cysteine (Cys) is the most reactive amino acid participating in a wide range of biological functions. In-silico predictions complement the experiments to meet the need of functional characterization. Multiple Cys function prediction algorithm is scarce, in contrast to specific function prediction algorithms. Here we present a deep neural network-based multiple Cys function prediction, available on web-server (DeepCys) (https://deepcys.herokuapp.com/). DeepCys model was trained and tested on two independent datasets curated from protein crystal structures. This prediction method requires three inputs, namely, PDB identifier (ID), chain ID and residue ID for a given Cys and outputs the probabilities of four cysteine functions, namely, disulphide, metal-binding, thioether and sulphenylation and predicts the most probable Cys function. The algorithm exploits the local and global protein properties, like, sequence and secondary structure motifs, buried fractions, microenvironments and protein/enzyme class. DeepCys outperformed most of the multiple and specific Cys function algorithms. This method can predict maximum number of cysteine functions. Moreover, for the first time, explicitly predicts thioether function. This tool was used to elucidate the cysteine functions on domains of unknown functions belonging to cytochrome C oxidase subunit-II like transmembrane domains. Apart from the web-server, a standalone program is also available on GitHub (https://github.com/vam-sin/deepcys).

BioMetAll: Identifying Metal-Binding Sites in Proteins from Backbone Preorganization

With a large amount of research dedicated to decoding how metallic species bind to proteins, in silico methods are interesting allies for experimental procedures. To date, computational predictors mostly work by identifying the best possible sequence or structural match of the target protein with metal-binding templates. These approaches are fundamentally focused on the first coordination sphere of the metal. Here, we present the BioMetAll predictor that is based on a different postulate: the formation of a potential metal-binding site is related to the geometric organization of the protein backbone. We first report the set of convenient geometric descriptors of the backbone needed for the algorithm and their parameterization from a statistical analysis. Then, the successful benchmark of BioMetAll on a set of more than 90 metal-binding X-ray structures is presented. Because BioMetAll allows structural predictions regardless of the exact geometry of the side chains, it appears extremely valuable for systems whose structures (either experimental or theoretical) are not optimal for metal-binding sites. We report here its application on three different challenging cases: (i) the modulation of metal-binding sites during conformational transition in human serum albumin, (ii) the identification of possible routes of metal migration in hemocyanins, and (iii) the prediction of mutations to generate convenient metal-binding sites for de novo biocatalysts. This study shows that BioMetAll offers a versatile platform for numerous fields of research at the interface between inorganic chemistry and biology and allows to highlight the role of the preorganization of the protein backbone as a marker for metal binding. BioMetAll is an open-source application available at https://github.com/insilichem/biometall.

Leveraging computational genomics to understand the molecular basis of metal homeostasis

Genome-based data is helping to reveal the diverse strategies plants and algae use to maintain metal homeostasis. In addition to acquisition, distribution and storage of metals, acclimating to feast or famine can involve a wealth of genes that we are just now starting to understand. The fast-paced acquisition of genome-based data, however, is far outpacing our ability to experimentally characterize protein function. Computational genomic approaches are needed to fill the gap between what is known and unknown. To avoid misconstruing bioinformatically derived data, which is the root cause of the inaccurate functional annotations that plague databases, functional inferences from diverse sources and contextualization of that evidence with a robust understanding of protein family evolution is needed. Phylogenomic- and comparative-genomic-based studies can aid in the interpretation of experimental data or provide a spark for the discovery of a new function. These analyses not only lead to novel insight into a target protein's function but can generate thought-provoking insights across protein families.

Bioinformatics of Metalloproteins and Metalloproteomes

Trace metals are inorganic elements that are required for all organisms in very low quantities. They serve as cofactors and activators of metalloproteins involved in a variety of key cellular processes. While substantial effort has been made in experimental characterization of metalloproteins and their functions, the application of bioinformatics in the research of metalloproteins and metalloproteomes is still limited. In the last few years, computational prediction and comparative genomics of metalloprotein genes have arisen, which provide significant insights into their distribution, function, and evolution in nature. This review aims to offer an overview of recent advances in bioinformatic analysis of metalloproteins, mainly focusing on metalloprotein prediction and the use of different metals across the tree of life. We describe current computational approaches for the identification of metalloprotein genes and metal-binding sites/patterns in proteins, and then introduce a set of related databases. Furthermore, we discuss the latest research progress in comparative genomics of several important metals in both prokaryotes and eukaryotes, which demonstrates divergent and dynamic evolutionary patterns of different metalloprotein families and metalloproteomes. Overall, bioinformatic studies of metalloproteins provide a foundation for systematic understanding of trace metal utilization in all three domains of life.

Genomic mosaicism underlies the adaptation of marine Synechococcus ecotypes to distinct oceanic iron niches

Phytoplankton are limited by iron (Fe) in ~40% of the world's oceans including high-nutrient low-chlorophyll (HNLC) regions. While low-Fe adaptation has been well-studied in large eukaryotic diatoms, less is known for small, prokaryotic marine picocyanobacteria. This study reveals key physiological and genomic differences underlying Fe adaptation in marine picocyanobacteria. HNLC ecotype CRD1 strains have greater physiological tolerance to low Fe congruent with their expanded repertoire of Fe transporter, storage and regulatory genes compared to other ecotypes. From metagenomic analysis, genes encoding ferritin, flavodoxin, Fe transporters and siderophore uptake genes were more abundant in low-Fe waters, mirroring paradigms of low-Fe adaptation in diatoms. Distinct Fe-related gene repertories of HNLC ecotypes CRD1 and CRD2 also highlight how coexisting ecotypes have evolved independent approaches to life in low-Fe habitats. Synechococcus and Prochlorococcus HNLC ecotypes likewise exhibit independent, genome-wide reductions of predicted Fe-requiring genes. HNLC ecotype CRD1 interestingly was most similar to coastal ecotype I in Fe physiology and Fe-related gene content, suggesting populations from these different biomes experience similar Fe-selective conditions. This work supports an improved perspective that phytoplankton are shaped by more nuanced Fe niches in the oceans than previously implied from mostly binary comparisons of low- versus high-Fe habitats and populations.

The Oxymonad Genome Displays Canonical Eukaryotic Complexity in the Absence of a Mitochondrion

The discovery that the protist Monocercomonoides exilis completely lacks mitochondria demonstrates that these organelles are not absolutely essential to eukaryotic cells. However, the degree to which the metabolism and cellular systems of this organism have adapted to the loss of mitochondria is unknown. Here, we report an extensive analysis of the M. exilis genome to address this question. Unexpectedly, we find that M. exilis genome structure and content is similar in complexity to other eukaryotes and less "reduced" than genomes of some other protists from the Metamonada group to which it belongs. Furthermore, the predicted cytoskeletal systems, the organization of endomembrane systems, and biosynthetic pathways also display canonical eukaryotic complexity. The only apparent preadaptation that permitted the loss of mitochondria was the acquisition of the SUF system for Fe-S cluster assembly and the loss of glycine cleavage system. Changes in other systems, including in amino acid metabolism and oxidative stress response, were coincident with the loss of mitochondria but are likely adaptations to the microaerophilic and endobiotic niche rather than the mitochondrial loss per se. Apart from the lack of mitochondria and peroxisomes, we show that M. exilis is a fully elaborated eukaryotic cell that is a promising model system in which eukaryotic cell biology can be investigated in the absence of mitochondria.

Multi-metal Restriction by Calprotectin Impacts De Novo Flavin Biosynthesis in Acinetobacter baumannii

Calprotectin (CP) inhibits bacterial viability through extracellular chelation of transition metals. However, how CP influences general metabolism remains largely unexplored. We show here that CP restricts bioavailable Zn and Fe to the pathogen Acinetobacter baumannii, inducing an extensive multi-metal perturbation of cellular physiology. Proteomics reveals severe metal starvation, and a strain lacking the candidate ZnII metallochaperone ZigA possesses altered cellular abundance of multiple essential Zn-dependent enzymes and enzymes in de novo flavin biosynthesis. The ΔzigA strain exhibits decreased cellular flavin levels during metal starvation. Flavin mononucleotide provides regulation of this biosynthesis pathway, via a 3,4-dihydroxy-2-butanone 4-phosphate synthase (RibB) fusion protein, RibBX, and authentic RibB. We propose that RibBX ensures flavin sufficiency under CP-induced Fe limitation, allowing flavodoxins to substitute for Fe-ferredoxins as cell reductants. These studies elucidate adaptation to nutritional immunity and define an intersection between metallostasis and cellular metabolism in A. baumannii.

Prediction of Protein Metal Binding Sites Using Deep Neural Networks

Metals have crucial roles for many physiological, pathological and diagnostic processes. Metal binding proteins or metalloproteins are important for metabolism functions. The proteins that reach the three-dimensional structure by folding show which vital function is fulfilled. The prediction of metal-binding in proteins will be considered as a step-in function assignment for new proteins, which helps to obtain functional proteins in genomic studies, is critical to protein function annotation and drug discovery. Computational predictions made by using machine learning methods from the data obtained from amino acid sequences are widely used in the protein metal-binding and various bioinformatics fields. In this work, we present three different deep learning architectures for prediction of metal-binding of Histidines (HIS) and Cysteines (CYS) amino acids. These architectures are as follows: 2D Convolutional Neural Network, Long-Short Term Memory and Recurrent Neural Network. Their comparison is carried out on the three different sets of attributes derived from a public dataset of protein sequences. These three sets of features extracted from the protein sequence were obtained using the PAM scoring matrix, protein composition server, and binary representation methods. The results show that a better performance for prediction of protein metal- binding sites is obtained through Convolutional Neural Network architecture.

Oxygen induces the expression of invasion and stress response genes in the anaerobic salmon parasite Spironucleus salmonicida

Background

Spironucleus salmonicida is an anaerobic parasite that can cause systemic infections in Atlantic salmon. Unlike other diplomonad parasites, such as the human pathogen Giardia intestinalis, Spironucleus species can infiltrate the blood stream of their hosts eventually colonizing organs, skin and gills. How this presumed anaerobe can persist and invade oxygenated tissues, despite having a strictly anaerobic metabolism, remains elusive.

Results

To investigate how S. salmonicida response to oxygen stress, we performed RNAseq transcriptomic analyses of cells grown in the presence of oxygen or antioxidant-free medium. We found that over 20% of the transcriptome is differentially regulated in oxygen (1705 genes) and antioxidant-depleted (2280 genes) conditions. These differentially regulated transcripts encode proteins related to anaerobic metabolism, cysteine and Fe-S cluster biosynthesis, as well as a large number of proteins of unknown function. S. salmonicida does not encode genes involved in the classical elements of oxygen metabolism (e.g., catalases, superoxide dismutase, glutathione biosynthesis, oxidative phosphorylation). Instead, we found that genes encoding bacterial-like oxidoreductases were upregulated in response to oxygen stress. Phylogenetic analysis revealed some of these oxygen-responsive genes (e.g., nadh oxidase, rubrerythrin, superoxide reductase) are rare in eukaryotes and likely derived from lateral gene transfer (LGT) events into diplomonads from prokaryotes. Unexpectedly, we observed that many host evasion- and invasion-related genes were also upregulated under oxidative stress suggesting that oxygen might be an important signal for pathogenesis.

Conclusion

While oxygen is toxic for related organisms, such as G. intestinalis, we find that oxygen is likely a gene induction signal for host invasion- and evasion-related pathways in S. salmonicida. These data provide the first molecular evidence for how S. salmonicida could tolerate oxic host environments and demonstrate how LGT can have a profound impact on the biology of anaerobic parasites.

Electronic supplementary material

The online version of this article (10.1186/s12915-019-0634-8) contains supplementary material, which is available to authorized users.

Simple Coordination Geometry Descriptors Allow to Accurately Predict Metal-Binding Sites in Proteins

With more than a third of the genome encoding for metal-containing biomolecules, the in silico prediction of how metal ions bind to proteins is crucial in chemistry, biology, and medicine. To date, algorithms for metal-binding site prediction are mainly based on sequence analysis. Those methods have reached enough quality to predict the correct region of the protein and the coordinating residues involved in metal-binding, but they do not provide three-dimensional (3D) models. On the contrary, the prediction of accurate 3D models for protein-metal adducts by structural bioinformatics and molecular modeling techniques is still a challenge. Here, we present an update of our multipurpose molecular modeling suite, GaudiMM, to locate metal-binding sites in proteins. The approach is benchmarked on 105 X-ray structures with resolution lower than 2.0 Å. Results predict the correct binding site of the metal in the biological scaffold for all the entries in the data set. Generated 3D models of the protein-metal coordination complexes reach root-mean-square deviation values under 1.0 Å between calculated and experimental structures. The whole process is purely based on finding poses that satisfy metal-derived geometrical rules without needing sequence or fine electronic inputs. Additional post-optimizations, including receptor flexibility, have been tested and suggest that more extensive searches, required when the host structures present a low level of pre-organization, are also possible. With this new update, GaudiMM is now able to look for metal-binding sites in biological scaffolds and clearly shows how explicitly considering the geometric particularities of the first coordination sphere of the metal in a docking process provides excellent results.

The cellular economy of the Saccharomyces cerevisiae zinc proteome

Zinc is an essential cofactor for many proteins. A key mechanism of zinc homeostasis during deficiency is "zinc sparing" in which specific zinc-binding proteins are repressed to reduce the cellular requirement. In this report, we evaluated zinc sparing across the zinc proteome of Saccharomyces cerevisiae. The yeast zinc proteome of 582 known or potential zinc-binding proteins was identified using a bioinformatics analysis that combined global domain searches with local motif searches. Protein abundance was determined by mass spectrometry. In zinc-replete cells, we detected over 2500 proteins among which 229 were zinc proteins. Based on copy number estimates and binding stoichiometries, a replete cell contains ∼9 million zinc-binding sites on proteins. During zinc deficiency, many zinc proteins decreased in abundance and the zinc-binding requirement decreased to ∼5 million zinc atoms per cell. Many of these effects were due at least in part to changes in mRNA levels rather than simply protein degradation. Measurements of cellular zinc content showed that the level of zinc atoms per cell dropped from over 20 million in replete cells to only 1.7 million in deficient cells. These results confirmed the ability of replete cells to store excess zinc and suggested that the majority of zinc-binding sites on proteins in deficient cells are either unmetalated or mismetalated. Our analysis of two abundant zinc proteins, Fba1 aldolase and Met6 methionine synthetase, supported that hypothesis. Thus, we have discovered widespread zinc sparing mechanisms and obtained evidence of a high accumulation of zinc proteins that lack their cofactor during deficiency.

Branched late-steps of the cytosolic iron-sulphur cluster assembly machinery of Trypanosoma brucei

Fe-S clusters are ubiquitous cofactors of proteins involved in a variety of essential cellular processes. The biogenesis of Fe-S clusters in the cytosol and their insertion into proteins is accomplished through the cytosolic iron-sulphur protein assembly (CIA) machinery. The early- and middle-acting modules of the CIA pathway concerned with the assembly and trafficking of Fe-S clusters have been previously characterised in the parasitic protist Trypanosoma brucei. In this study, we applied proteomic and genetic approaches to gain insights into the network of protein-protein interactions of the late-acting CIA targeting complex in T. brucei. All components of the canonical CIA machinery are present in T. brucei including, as in humans, two distinct CIA2 homologues TbCIA2A and TbCIA2B. These two proteins are found interacting with TbCIA1, yet the interaction is mutually exclusive, as determined by mass spectrometry. Ablation of most of the components of the CIA targeting complex by RNAi led to impaired cell growth in vitro, with the exception of TbCIA2A in procyclic form (PCF) trypanosomes. Depletion of the CIA-targeting complex was accompanied by reduced levels of protein-bound cytosolic iron and decreased activity of an Fe-S dependent enzyme in PCF trypanosomes. We demonstrate that the C-terminal domain of TbMMS19 acts as a docking site for TbCIA2B and TbCIA1, forming a trimeric complex that also interacts with target Fe-S apo-proteins and the middle-acting CIA component TbNAR1.

Cytosolic and nuclear proteins containing iron-sulphur clusters (Fe-S) are essential for the survival of every extant eukaryotic cell. The biogenesis of Fe-S clusters and their insertion into proteins is accomplished through the cytosolic iron-sulphur protein assembly (CIA) machinery. Recently, the CIA factors that generate cytosolic Fe-S clusters were characterised in T. brucei, a unicellular parasite that causes diseases in humans and animals. However, an outstanding question in this organism is the way by which the CIA machinery directs and inserts newly formed Fe-S clusters into proteins. We found that the T. brucei proteins TbCIA2B and TbCIA1 assemble at a region of the C-terminal domain of a third protein, TbMMS19, to form a complex labelled the CIA targeting complex (CTC). The CTC interacts with TbNAR1 and with Fe-S proteins, meaning that the complex assists in the transfer of Fe-S clusters from the upstream members of the pathway into target Fe-S proteins. T. brucei cells depleted of CTC had decreased levels of protein-bound cytosolic iron, and lower activities of cytosolic aconitase, an enzyme that depends upon Fe-S clusters to function.

Single-Cell Genomics Reveals a Diverse Metabolic Potential of Uncultivated Desulfatiglans-Related Deltaproteobacteria Widely Distributed in Marine Sediment

Desulfatiglans-related organisms comprise one of the most abundant deltaproteobacterial lineages in marine sediments where they occur throughout the sediment column in a gradient of increasing sulfate and organic carbon limitation with depth. Characterized Desulfatiglans isolates are dissimilatory sulfate reducers able to grow by degrading aromatic hydrocarbons. The ecophysiology of environmental Desulfatiglans-populations is poorly understood, however, possibly utilization of aromatic compounds may explain their predominance in marine subsurface sediments. We sequenced and analyzed seven Desulfatiglans-related single-cell genomes (SAGs) from Aarhus Bay sediments to characterize their metabolic potential with regard to aromatic compound degradation and energy metabolism. The average genome assembly size was 1.3 Mbp and completeness estimates ranged between 20 and 50%. Five of the SAGs (group 1) originated from the sulfate-rich surface part of the sediment while two (group 2) originated from sulfate-depleted subsurface sediment. Based on 16S rRNA gene amplicon sequencing group 2 SAGs represent the more frequent types of Desulfatiglans-populations in Aarhus Bay sediments. Genes indicative of aromatic compound degradation could be identified in both groups, but the two groups were metabolically distinct with regard to energy conservation. Group 1 SAGs carry a full set of genes for dissimilatory sulfate reduction, whereas the group 2 SAGs lacked any genetic evidence for sulfate reduction. The latter may be due to incompleteness of the SAGs, but as alternative energy metabolisms group 2 SAGs carry the genetic potential for growth by acetogenesis and fermentation. Group 1 SAGs encoded reductive dehalogenase genes, allowing them to access organohalides and possibly conserve energy by their reduction. Both groups possess sulfatases unlike their cultured relatives allowing them to utilize sulfate esters as source of organic carbon and sulfate. In conclusion, the uncultivated marine Desulfatiglans populations are metabolically diverse, likely reflecting different strategies for coping with energy and sulfate limitation in the subsurface seabed.

Trace Elements and Healthcare: A Bioinformatics Perspective

Biological trace elements are essential for human health. Imbalance in trace element metabolism and homeostasis may play an important role in a variety of diseases and disorders. While the majority of previous researches focused on experimental verification of genes involved in trace element metabolism and those encoding trace element-dependent proteins, bioinformatics study on trace elements is relatively rare and still at the starting stage. This chapter offers an overview of recent progress in bioinformatics analyses of trace element utilization, metabolism, and function, especially comparative genomics of several important metals. The relationship between individual elements and several diseases based on recent large-scale systematic studies such as genome-wide association studies and case-control studies is discussed. Lastly, developments of ionomics and its recent application in human health are also introduced.

Differential Effects of Iron, Zinc, and Copper on Dictyostelium discoideum Cell Growth and Resistance to Legionella pneumophila

Iron, zinc, and copper play fundamental roles in eucaryotes and procaryotes, and their bioavailability regulates host-pathogen interactions. For intracellular pathogens, the source of metals is the cytoplasm of the host, which in turn manipulates intracellular metal traffic following pathogen recognition. It is established that iron is withheld from the pathogen-containing vacuole, whereas for copper and zinc the evidence is unclear. Most infection studies in mammals have concentrated on effects of metal deficiency/overloading at organismal level. Thus, zinc deficiency or supplementation correlate with high risk of respiratory tract infection or recovery from severe infection, respectively. Iron, zinc, and copper deficiency or overload affects lymphocyte proliferation/maturation, and thus the adaptive immune response. Whether they regulate innate immunity at macrophage level is open, except for iron. The early identification in a mouse mutant susceptible to mycobacterial infection of the iron transporter Nramp1 allowed dissecting Nramp1 role in phagocytes, from the social amoeba Dictyostelium to macrophages. Nramp1 regulates iron efflux from the phagosomes, thus starving pathogenic bacteria for iron. Similar studies for zinc or copper are scant, due to the large number of copper and zinc transporters. In Dictyostelium, zinc and copper transporters include 11 and 6 members, respectively. To assess the role of zinc or copper in Dictyostelium, cells were grown under conditions of metal depletion or excess and tested for resistance to Legionella pneumophila infection. Iron shortage or overload inhibited Dictyostelium cell growth within few generations. Surprisingly, zinc or copper depletion failed to affect growth. Zinc or copper overloading inhibited cell growth at, respectively, 50- or 500-fold the physiological concentration, suggesting very efficient control of their homeostasis, as confirmed by Inductively Coupled Plasma Mass Spectrometry quantification of cellular metals. Legionella infection was inhibited or enhanced in cells grown under iron shortage or overload, respectively, confirming a major role for iron in controlling resistance to pathogens. In contrast, zinc and copper depletion or excess during growth did not affect Legionella infection. Using Zinpyr-1 as fluorescent sensor, we show that zinc accumulates in endo-lysosomal vesicles, including phagosomes, and the contractile vacuole. Furthermore, we provide evidence for permeabilization of the Legionella-containing vacuole during bacterial proliferation.

Computational approaches for de novo design and redesign of metal-binding sites on proteins

Metal ions play pivotal roles in protein structure, function and stability. The functional and structural diversity of proteins in nature expanded with the incorporation of metal ions or clusters in proteins. Approximately one-third of these proteins in the databases contain metal ions. Many biological and chemical processes in nature involve metal ion-binding proteins, aka metalloproteins. Many cellular reactions that underpin life require metalloproteins. Most of the remarkable, complex chemical transformations are catalysed by metalloenzymes. Realization of the importance of metal-binding sites in a variety of cellular events led to the advancement of various computational methods for their prediction and characterization. Furthermore, as structural and functional knowledgebase about metalloproteins is expanding with advances in computational and experimental fields, the focus of the research is now shifting towards de novo design and redesign of metalloproteins to extend nature’s own diversity beyond its limits. In this review, we will focus on the computational toolbox for prediction of metal ion-binding sites, de novo metalloprotein design and redesign. We will also give examples of tailor-made artificial metalloproteins designed with the computational toolbox.