Native mass spectrometry-based metabolomics identifies metal-binding compounds

Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data

Feature-based molecular networking (FBMN) is a popular analysis approach for liquid chromatography-tandem mass spectrometry-based non-targeted metabolomics data. While processing liquid chromatography-tandem mass spectrometry data through FBMN is fairly streamlined, downstream data handling and statistical interrogation are often a key bottleneck. Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices. Here we provide a comprehensive guide for the statistical analysis of FBMN results, focusing on the downstream analysis of the FBMN output table. We explain the data structure and principles of data cleanup and normalization, as well as uni- and multivariate statistical analysis of FBMN results. We provide explanations and code in two scripting languages (R and Python) as well as the QIIME2 framework for all protocol steps, from data clean-up to statistical analysis. All code is shared in the form of Jupyter Notebooks ( https://github.com/Functional-Metabolomics-Lab/FBMN-STATS ). Additionally, the protocol is accompanied by a web application with a graphical user interface ( https://fbmn-statsguide.gnps2.org/ ) to lower the barrier of entry for new users and for educational purposes. Finally, we also show users how to integrate their statistical results into the molecular network using the Cytoscape visualization tool. Throughout the protocol, we use a previously published environmental metabolomics dataset for demonstration purposes. Together, the protocol, code and web application provide a complete guide and toolbox for FBMN data integration, cleanup and advanced statistical analysis, enabling new users to uncover molecular insights from their non-targeted metabolomics data. Our protocol is tailored for the seamless analysis of FBMN results from Global Natural Products Social Molecular Networking and can be easily adapted to other mass spectrometry feature detection, annotation and networking tools.

High-Resolution Tandem Mass Spectrometry-Based Analysis of Model Lignin-Iron Complexes: Novel Pipeline and Complex Structures

Understanding the chemical nature of soil organic carbon (SOC) with great potential to bind iron (Fe) minerals is critical for predicting the stability of SOC. Organic ligands of Fe are among the top candidates for SOCs able to strongly sorb on Fe minerals, but most of them are still molecularly uncharacterized. To shed insights into the chemical nature of organic ligands in soil and their fate, this study developed a protocol for identifying organic ligands using ultrahigh-performance liquid chromatography-high-resolution tandem mass spectrometry (UHPLC-HRMS/MS) and metabolomic tools. The protocol was used for investigating the Fe complexes formed by model compounds of lignin-derived organic ligands, namely, caffeic acid (CA), p-coumaric acid (CMA), vanillin (VNL), and cinnamic acid (CNA). Isotopologue analysis of 54/56Fe was used to screen out the potential UHPLC-HRMS (m/z) features for complexes formed between organic ligands and Fe, with multiple features captured for CA, CMA, VNL, and CNA when 35/37Cl isotopologue analysis was used as supplementary evidence for the complexes with Cl. MS/MS spectra, fragment analysis, and structure prediction with SIRIUS were used to annotate the structures of mono/bidentate mono/biligand complexes. The analysis determined the structures of monodentate and bidentate complexes of FeLxCly (L: organic ligand, x = 1-4, y = 0-3) formed by model compounds. The protocol developed in this study can be used to identify unknown organic ligands occurring in complex environmental samples and shed light on the molecular-level processes governing the stability of the SOC.

Native metabolomics for mass spectrometry-based siderophore discovery

Microorganisms, plants, and animals alike have specialized acquisition pathways for obtaining metals, with microorganisms and plants biosynthesizing and secreting small molecule natural products called siderophores and metallophores with high affinities and specificities for iron or other non-iron metals, respectively. This chapter details a novel approach to discovering metal-binding molecules, including siderophores and metallophores, from complex samples ranging from microbial supernatants to biological tissue to environmental samples. This approach, called Native Metabolomics, is a mass spectrometry method in which pH adjustment and metal infusion post-liquid chromatography are interfaced with ion identity molecular networking (IIMN). This rule-based data analysis workflow that enables the identification of metal-binding species based on defined mass (m/z) offsets with the same chromatographic profiles and retention times. Ion identity molecular networking connects compounds that are structurally similar by their fragmentation pattern and species that are ion adducts of the same compound by chromatographic shape correlations. This approach has previously revealed new insights into metal binding metabolites, including that yersiniabactin can act as a biological zincophore (in addition to its known role as a siderophore), that the recently elucidated lepotchelin natural products are cyanobacterial metallophores, and that antioxidants in traditional medicine bind iron. Native metabolomics can be conducted on any liquid chromatography-mass spectrometry system to explore the binding of any metal or multiple metals simultaneously, underscoring the potential for this method to become an essential strategy for elucidating biological metal-binding molecules.

Leptochelins A-C, Cytotoxic Metallophores Produced by Geographically Dispersed Leptothoe Strains of Marine Cyanobacteria

Metals are important cofactors in the metabolic processes of cyanobacteria, including photosynthesis, cellular respiration, DNA replication, and the biosynthesis of primary and secondary metabolites. In adaptation to the marine environment, cyanobacteria use metallophores to acquire trace metals when necessary as well as to reduce potential toxicity from excessive metal concentrations. Leptochelins A-C were identified as structurally novel metallophores from three geographically dispersed cyanobacteria of the genus Leptothoe. Determination of the complex structures of these metabolites presented numerous challenges, but they were ultimately solved using integrated data from NMR, mass spectrometry and deductions from the biosynthetic gene cluster. The leptochelins are comprised of halogenated linear NRPS-PKS hybrid products with multiple heterocycles that have potential for hexadentate and tetradentate coordination with metal ions. The genomes of the three leptochelin producers were sequenced, and retrobiosynthetic analysis revealed one candidate biosynthetic gene cluster (BGC) consistent with the structure of leptochelin. The putative BGC is highly homologous in all three Leptothoe strains, and all possess genetic signatures associated with metallophores. Postcolumn infusion of metals using an LC-MS metabolomics workflow performed with leptochelins A and B revealed promiscuous binding of iron, copper, cobalt, and zinc, with greatest preference for copper. Iron depletion and copper toxicity experiments support the hypothesis that leptochelin metallophores may play key ecological roles in iron acquisition and in copper detoxification. In addition, the leptochelins possess significant cytotoxicity against several cancer cell lines.

Supramolecular self-assembly strategies of natural-based β-lactoglobulin modulating bitter perception of goat milk-derived bioactive peptides

Complete self-assembly and reassembly behavior of bitter peptide-protein necessitates multilevel theories that encompass phenomena ranging from the self-assembly of recombinant complex to atomic trajectories. An extension to the level of mechanism method was put forth, involves limited enzymatic digestion and bottom-up proteomics to dissect inherent heterogeneity within β-LG and β-LG-PPGLPDKY complex and uncover conformational and dynamic alterations occurring in specific local regions of the model protein. Bitter peptide PPGLPDKY spontaneously bound to IIAEKTK, IDALNENK, and YLLFCMENSAEPEQSLACQCLVR regions of β-LG in a 1:1 stoichiometric ratio to mask bitterness perception. Molecular dynamic simulation and free energy calculation provided time-varying atomic trajectories of the recombinant complex and found that a peptide was stabilized in the upper region of the hydrophobic cavity with the binding free energy of -30.56 kJ mol-1 through 4 hydrogen bonds (Glu74, Glu55, Lys69, and Ser116) and hydrophobic interactions (Asn88, Asn90, and Glu112). Current research aims to provide valuable physical insights into the macroscopic self-assembly behavior between proteins and bitter peptides, and the meticulous design of highly acceptable taste characteristics in goat milk products.

[Applications of native mass spectrometry and ultraviolet photodissociation in protein structure and interaction analysis]

Dynamic changes in the structures and interactions of proteins are closely correlated with their biological functions. However, the precise detection and analysis of these molecules are challenging. Native mass spectrometry (nMS) introduces proteins or protein complexes into the gas phase by electrospray ionization, and then performs MS analysis under near-physiological conditions that preserve the folded state of proteins and their complexes in solution. nMS can provide information on stoichiometry, assembly, and dissociation constants by directly determining the relative molecular masses of protein complexes through high-resolution MS. It can also integrate various MS dissociation technologies, such as collision-induced dissociation (CID), surface-induced dissociation (SID), and ultraviolet photodissociation (UVPD), to analyze the conformational changes, binding interfaces, and active sites of protein complexes, thereby revealing the relationship between their interactions and biological functions. UVPD, especially 193 nm excimer laser UVPD, is a rapidly evolving MS dissociation method that can directly dissociate the covalent bonds of protein backbones with a single pulse. It can generate different types of fragment ions, while preserving noncovalent interactions such as hydrogen bonds within these ions, thereby enabling the MS analysis of protein structures with single-amino-acid-site resolution. This review outlines the applications and recent progress of nMS and UVPD in protein dynamic structure and interaction analyses. It covers the nMS techniques used to analyze protein-small-molecule ligand interactions, the structures of membrane proteins and their complexes, and protein-protein interactions. The discussion on UVPD includes the analysis of gas-phase protein structures and interactions, as well as alterations in protein dynamic structures, and interactions resulting from mutations and ligand binding. Finally, this review describes the future development prospects for protein analysis by nMS and new-generation advanced extreme UV light sources with higher brightness and shorter pulses.

Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products

Covering: 1995 to 2023Advances in bioanalytical methods, particularly mass spectrometry, have provided valuable molecular insights into the mechanisms of life. Non-targeted metabolomics aims to detect and (relatively) quantify all observable small molecules present in a biological system. By comparing small molecule abundances between different conditions or timepoints in a biological system, researchers can generate new hypotheses and begin to understand causes of observed phenotypes. Functional metabolomics aims to investigate the functional roles of metabolites at the scale of the metabolome. However, most functional metabolomics studies rely on indirect measurements and correlation analyses, which leads to ambiguity in the precise definition of functional metabolomics. In contrast, the field of natural products has a history of identifying the structures and bioactivities of primary and specialized metabolites. Here, we propose to expand and reframe functional metabolomics by integrating concepts from the fields of natural products and chemical biology. We highlight emerging functional metabolomics approaches that shift the focus from correlation to physical interactions, and we discuss how this allows researchers to uncover causal relationships between molecules and phenotypes.

Accessing the specialized metabolome of actinobacteria from the bulk soil of Paullinia cupana Mart. on the Brazilian Amazon: a promising source of bioactive compounds against soybean phytopathogens

The Amazon rainforest, an incredibly biodiverse ecosystem, has been increasingly vulnerable to deforestation. Despite its undeniable importance and potential, the Amazonian microbiome has historically received limited study, particularly in relation to its unique arsenal of specialized metabolites. Therefore, in this study our aim was to assess the metabolic diversity and the antifungal activity of actinobacterial strains isolated from the bulk soil of Paullinia cupana, a native crop, in the Brazilian Amazon Rainforest. Extracts from 24 strains were subjected to UPLC-MS/MS analysis using an integrative approach that relied on the Chemical Structural and Compositional Similarity (CSCS) metric, GNPS molecular networking, and in silico dereplication tools. This procedure allowed the comprehensive understanding of the chemical space encompassed by these actinobacteria, which consists of features belonging to known bioactive metabolite classes and several unannotated molecular families. Among the evaluated strains, five isolates exhibited bioactivity against a panel of soybean fungal phytopathogens (Rhizoctonia solani, Macrophomina phaseolina, and Sclerotinia sclerotiorum). A focused inspection led to the annotation of pepstatins, oligomycins, hydroxamate siderophores and dorrigocins as metabolites produced by these bioactive strains, with potentially unknown compounds also comprising their metabolomes. This study introduces a pragmatic protocol grounded in established and readily available tools for the annotation of metabolites and the prioritization of strains to optimize further isolation of specialized metabolites. Conclusively, we demonstrate the relevance of the Amazonian actinobacteria as sources for bioactive metabolites useful for agriculture. We also emphasize the importance of preserving this biome and conducting more in-depth studies on its microbiota.

Identification and validation of serum metabolite biomarkers for endometrial cancer diagnosis

Endometrial cancer (EC) stands as the most prevalent gynecological tumor in women worldwide. Notably, differentiation diagnosis of abnormity detected by ultrasound findings (e.g., thickened endometrium or mass in the uterine cavity) is essential and remains challenging in clinical practice. Herein, we identified a metabolic biomarker panel for differentiation diagnosis of EC using machine learning of high-performance serum metabolic fingerprints (SMFs) and validated the biological function. We first recorded the high-performance SMFs of 191 EC and 204 Non-EC subjects via particle-enhanced laser desorption/ionization mass spectrometry (PELDI-MS). Then, we achieved an area-under-the-curve (AUC) of 0.957-0.968 for EC diagnosis through machine learning of high-performance SMFs, outperforming the clinical biomarker of cancer antigen 125 (CA-125, AUC of 0.610-0.684, p < 0.05). Finally, we identified a metabolic biomarker panel of glutamine, glucose, and cholesterol linoleate with an AUC of 0.901-0.902 and validated the biological function in vitro. Therefore, our work would facilitate the development of novel diagnostic biomarkers for EC in clinics.

Metabolomics-guided utilization of beneficial microbes for climate-resilient crops

In the rhizosphere, plants and microbes communicate chemically, especially under environmental stress. Over millions of years, plants and their microbiome have coevolved, sharing various chemicals, including signaling molecules. This mutual exchange impacts bacterial communication and influences plant metabolism. Inter-kingdom signal crosstalk affects bacterial colonization and plant fitness. Beneficial microbes and their metabolomes offer eco-friendly ways to enhance plant resilience and agriculture. Plant metabolites are pivotal in this dynamic interaction between host plants and their interacting beneficial microbes. Understanding these associations is key to engineering a robust microbiome for stress mitigation and improved plant growth. This review explores mechanisms behind plant-microbe interactions, the role of beneficial microbes and metabolomics, and the practical applications for addressing climate change's impact on agriculture. Integrating beneficial microbes' activities and metabolomics' application to study metabolome-driven interaction between host plants and their corresponding beneficial microbes holds promise for enhancing crop resilience and productivity.

Human brains preserve in diverse environments for at least 12 000 years

The brain is thought to be among the first human organs to decompose after death. The discovery of brains preserved in the archaeological record is therefore regarded as unusual. Although mechanisms such as dehydration, freezing, saponification, and tanning are known to allow for the preservation of the brain on short time scales in association with other soft tissues (≲4000 years), discoveries of older brains, especially in the absence of other soft tissues, are rare. Here, we collated an archive of more than 4400 human brains preserved in the archaeological record across approximately 12 000 years, more than 1300 of which constitute the only soft tissue preserved amongst otherwise skeletonized remains. We found that brains of this type persist on time scales exceeding those preserved by other means, which suggests an unknown mechanism may be responsible for preservation particular to the central nervous system. The untapped archive of preserved ancient brains represents an opportunity for bioarchaeological studies of human evolution, health and disease.

Biophysical Characterization of RAS-SOS Complexes by Native Mass Spectrometry

RAS is regulated by specific guanine nucleotide exchange factors, such as Son of Sevenless (SOS), that activates RAS by facilitating the exchange of inactive, GDP-bound RAS with GTP. The catalytic activity of SOS is known to be allosterically modulated by an active, GTP-bound RAS. However, it remains poorly understood how oncogenic RAS mutants interact with SOS and modulate its activity. In this chapter, we describe the application of native mass spectrometry (MS) to monitor the assembly of the catalytic domain of SOS (SOScat) with RAS and cancer-associated mutants. Results from this approach have led to the discovery of different molecular assemblies and distinct conformers of SOScat engaging KRAS. It was also found that KRASG13D exhibits high affinity for SOScat and is a potent allosteric modulator of its SOScat activity. KRASG13D-GTP can allosterically increase the nucleotide exchange rate of KRAS at the active site by more than twofold compared to the wild-type protein. Furthermore, small-molecule RAS•SOS disruptors fail to dissociate KRASG13D•SOScat complexes, underscoring the need for more potent disruptors targeting oncogenic RAS mutants. Taken together, native MS will be instrumental in better understanding the interaction between oncogenic RAS mutants and SOS, which is of crucial importance for development of improved therapeutics.

Progress and challenges in exploring aquatic microbial communities using non-targeted metabolomics

Advances in bioanalytical technologies are constantly expanding our insights into complex ecosystems. Here, we highlight strategies and applications that make use of non-targeted metabolomics methods in aquatic chemical ecology research and discuss opportunities and remaining challenges of mass spectrometry-based methods to broaden our understanding of environmental systems.

Illuminating the oral microbiome and its host interactions: recent advancements in omics and bioinformatics technologies in the context of oral microbiome research

The oral microbiota has an enormous impact on human health, with oral dysbiosis now linked to many oral and systemic diseases. Recent advancements in sequencing, mass spectrometry, bioinformatics, computational biology, and machine learning are revolutionizing oral microbiome research, enabling analysis at an unprecedented scale and level of resolution using omics approaches. This review contains a comprehensive perspective of the current state-of-the-art tools available to perform genomics, metagenomics, phylogenomics, pangenomics, transcriptomics, proteomics, metabolomics, lipidomics, and multi-omics analysis on (all) microbiomes, and then provides examples of how the techniques have been applied to research of the oral microbiome, specifically. Key findings of these studies and remaining challenges for the field are highlighted. Although the methods discussed here are placed in the context of their contributions to oral microbiome research specifically, they are pertinent to the study of any microbiome, and the intended audience of this includes researchers would simply like to get an introduction to microbial omics and/or an update on the latest omics methods. Continued research of the oral microbiota using omics approaches is crucial and will lead to dramatic improvements in human health, longevity, and quality of life.

Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation

Natural Products (NP) are essential for the discovery of novel drugs and products for numerous biotechnological applications. The NP discovery process is expensive and time-consuming, having as major hurdles dereplication (early identification of known compounds) and structure elucidation, particularly the determination of the absolute configuration of metabolites with stereogenic centers. This review comprehensively focuses on recent technological and instrumental advances, highlighting the development of methods that alleviate these obstacles, paving the way for accelerating NP discovery towards biotechnological applications. Herein, we emphasize the most innovative high-throughput tools and methods for advancing bioactivity screening, NP chemical analysis, dereplication, metabolite profiling, metabolomics, genome sequencing and/or genomics approaches, databases, bioinformatics, chemoinformatics, and three-dimensional NP structure elucidation.

Surface manipulation for prevention of migratory viscous crude oil fouling in superhydrophilic membranes

Here, we present a proactive fouling prevention mechanism that endows superhydrophilic membranes with antifouling capability against migratory viscous crude oil fouling. By simulating the hierarchical architecture/chemical composition of a dahlia leaf, a membrane surface is decorated with wrinkled-pattern microparticles, exhibiting a unique proactive fouling prevention mechanism based on a synergistic hydration layer/steric hindrance. The density functional theory and physicochemical characterizations demonstrate that the main chains of the microparticles are bent towards Fe³⁺ through coordination interactions to create nanoscale wrinkled patterns on smooth microparticle surfaces. Nanoscale wrinkled patterns reduce the surface roughness and increase the contact area between the membrane surface and water molecules, expanding the steric hindrance between the oil molecules and membrane surface. Molecular dynamic simulations reveal that the water-molecule densities and strengths of the hydrogen bonds are higher near the resultant membrane surface. With this concept, we can successfully inhibit the initial adhesion, migration, and deposition of oil, regardless of the viscosity, on the membrane surface and achieve migratory viscous crude oil antifouling. This research on the PFP mechanism opens pathways to realize superwettable materials for diverse applications in fields related to the environment, energy, health, and beyond.

Mechanism of Cytotoxic Action of Gold Nanorods Photothermal Therapy for A549 Cell

Photothermal therapy has developed into an important field of tumor treatment research, and numerous studies have focused on the preparation of photothermal therapeutic agents, tumor targeting, diagnosis, and treatment integration. However, there are few studies on the mechanism of photothermal therapy acting on cancer cells. Here we investigated the metabolomics of lung cancer cell A549 during gold nanorod (GNR) photothermal treatment by high-resolution LC/MS, and several differential metabolites and corresponding metabolic pathways during photothermal therapy were found. The main differential metabolites contained 18-hydroxyoleate, beta-alanopine and cis-9,10-epoxystearic acid, and phosphorylcholine. Pathway analysis also showed metabolic changes involving cutin, suberine, and wax biosynthesis, pyruvate and glutamic acid synthesis, and choline metabolism. Analysis also showed that the photothermal process of GNRs may induce cytotoxicity by affecting pyruvate and glutamate synthesis, normal choline metabolism, and ultimately apoptosis.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Don't let go: co-fractionation mass spectrometry for untargeted mapping of protein-metabolite interactomes

The chemical complexity of metabolomes goes hand in hand with their functional diversity. Small molecules have many essential roles, many of which are executed by binding and modulating the function of a protein partner. The complex and dynamic protein-metabolite interaction (PMI) network underlies most if not all biological processes, but remains under-characterized. Herein, we highlight how co-fractionation mass spectrometry (CF-MS), a well-established approach to map protein assemblies, can be used for proteome and metabolome identification of the PMIs. We will review recent CF-MS studies, discuss the main advantages and limitations, summarize the available CF-MS guidelines, and outline future challenges and opportunities.

Microbial Metabolites Annotation by Mass Spectrometry-Based Metabolomics

Since the discovery of penicillin, microbial metabolites have been extensively investigated for drug discovery purposes. In the last decades, microbial derived compounds have gained increasing attention in different fields from pharmacognosy to industry and agriculture. Microbial metabolites in microbiomes present specific functions and can be associated with the maintenance of the natural ecosystems. These metabolites may exhibit a broad range of biological activities of great interest to human purposes. Samples from either microbial isolated cultures or microbiomes consist of complex mixtures of metabolites and their analysis are not a simple process. Mass spectrometry-based metabolomics encompass a set of analytical methods that have brought several improvements to the microbial natural products field. This analytical tool allows the comprehensively detection of metabolites, and therefore, the access of the chemical profile from those biological samples. These analyses generate thousands of mass spectra which is challenging to analyse. In this context, bioinformatic metabolomics tools have been successfully employed to accelerate and facilitate the investigation of specialized microbial metabolites. Herein, we describe metabolomics tools used to provide chemical information for the metabolites, and furthermore, we discuss how they can improve investigation of microbial cultures and interactions.

Determination of ultra-trace metal-protein interactions in co-formulated monoclonal antibody drug product by SEC-ICP-MS

Transition metals can be introduced in therapeutic protein drugs at various steps of the manufacturing process (e.g. manufacturing raw materials, formulation, storage), and can cause a variety of modifications on the protein. These modifications can potentially influence the efficacy, safety, and stability of the therapeutic protein, especially if critical quality attributes (CQAs) are affected. Therefore, it is meaningful to understand the interactions between proteins and metals that can occur during the manufacturing process, formulation, and storage of biotherapeutics. Here, we describe a novel strategy to differentiate between ultra-trace levels of transition metals (cobalt, chromium, copper, iron, and nickel) interacting with therapeutic proteins and free metal in solution in the drug formulation using size exclusion chromatography coupled to inductively coupled plasma mass spectrometry (SEC-ICP-MS). Two monoclonal antibodies (mAbs) were coformulated and stored up to nine days in a scaled down model to mimic metal exposure from manufacturing tanks. The samples containing the mAbs were first analyzed by ICP-MS for bulk metal analysis, then studied using SEC-ICP-MS to measure the extent of metal-protein interactions. The SEC separation was used to differentiate metal associated with the mAbs from free metal in solution. Relative quantitation of metal-protein interaction was then calculated using the relative peak areas of protein-associated metal to free metal in solution and weighting it to the total metal concentration in the mixture as measured by bulk metal analysis by ICP-MS. The SEC-ICP-MS method offers an informative means of measuring metal-protein interactions during drug development.

Linking research of biomedical datasets

Biomedical data preprocessing and efficient computing can be as important as the statistical methods used to fit the data; data processing needs to consider application scenarios, data acquisition and individual rights and interests. We review common principles, knowledge and methods of integrated research according to the whole-pipeline processing mechanism diverse, coherent, sharing, auditable and ecological. First, neuromorphic and native algorithms integrate diverse datasets, providing linear scalability and high visualization. Second, the choice mechanism of different preprocessing, analysis and transaction methods from raw to neuromorphic was summarized on the node and coordinator platforms. Third, combination of node, network, cloud, edge, swarm and graph builds an ecosystem of cohort integrated research and clinical diagnosis and treatment. Looking forward, it is vital to simultaneously combine deep computing, mass data storage and massively parallel communication.

Photodegradation Kinetics and Deep Learning-Based Intelligent Colorimetric Method for Bioavailability-Based Dissolved Iron Speciation

Via the photodegradation of dissolved iron (dFe) complexes in the euphotic zone, released free Fe(III) is the most important source of bioavailable iron for eukaryotic phytoplankton. There is an urgent need to establish bioavailability-based dissolved iron speciation (BDIS) methods. Herein, an intelligent system with dFe pretreatment and a colorimetric sensor is developed for real-time monitoring of newly generated Fe(III) ions. According to the photodegradation kinetics of dFe, including kinetic constant and photogenerated time of free Fe(III) ions, 3 sources, 6 kinds, and 12 species of dFe are determined by our photocatalytic-assisted colorimetric sensor and deep learning model within 20.0 min. The algal dFe-uptake for 4 days can be predicted by BDIS with correlation coefficient 0.85, which could be explained by the hard and soft acids and bases theory (HSAB) and density functional theory (DFT). These results successfully demonstrate the proof-of-concept for photodegradation kinetics-based speciation and bioavailability assessments of dissolved metals.

Novel Insights on Pyoverdine: From Biosynthesis to Biotechnological Application

Pyoverdines (PVDs) are a class of siderophores produced mostly by members of the genus Pseudomonas. Their primary function is to accumulate, mobilize, and transport iron necessary for cell metabolism. Moreover, PVDs also play a crucial role in microbes’ survival by mediating biofilm formation and virulence. In this review, we reorganize the information produced in recent years regarding PVDs biosynthesis and pathogenic mechanisms, since PVDs are extremely valuable compounds. Additionally, we summarize the therapeutic applications deriving from the PVDs’ use and focus on their role as therapeutic target themselves. We assess the current biotechnological applications of different sectors and evaluate the state-of-the-art technology relating to the use of synthetic biology tools for pathway engineering. Finally, we review the most recent methods and techniques capable of identifying such molecules in complex matrices for drug-discovery purposes.

Native metabolomics identifies the rivulariapeptolide family of protease inhibitors

The identity and biological activity of most metabolites still remain unknown. A bottleneck in the exploration of metabolite structures and pharmaceutical activities is the compound purification needed for bioactivity assignments and downstream structure elucidation. To enable bioactivity-focused compound identification from complex mixtures, we develop a scalable native metabolomics approach that integrates non-targeted liquid chromatography tandem mass spectrometry and detection of protein binding via native mass spectrometry. A native metabolomics screen for protease inhibitors from an environmental cyanobacteria community reveals 30 chymotrypsin-binding cyclodepsipeptides. Guided by the native metabolomics results, we select and purify five of these compounds for full structure elucidation via tandem mass spectrometry, chemical derivatization, and nuclear magnetic resonance spectroscopy as well as evaluation of their biological activities. These results identify rivulariapeptolides as a family of serine protease inhibitors with nanomolar potency, highlighting native metabolomics as a promising approach for drug discovery, chemical ecology, and chemical biology studies.

Bioactivity-guided isolation of specialized metabolites is an iterative process. Here, the authors demonstrate a native metabolomics approach that allows for fast screening of complex metabolite extracts against a protein of interest and simultaneous structure annotation.

Nontarget Approach to Identify Complexing Agents in Atmospheric Aerosol and Rainwater Samples

Atmospheric particles and droplets contain numerous organic substances, some of which form complexes with metal ions, significantly affecting bulk physicochemical properties and chemical reactivity. However, the detection and identification of complexing agents and their corresponding metal complexes remains an analytical challenge. In this study, we developed an LC/HRMS nontarget screening (NTS) approach which allows the selective detection of complexing agents in aerosol particle extracts and rainwater. To achieve this, a T-junction is installed between the LC outlet and the ion source, and a FeCl₃ solution is added for postcolumn complexation. The resulting mass spectra are screened for the three characteristic iron(III)-complexes [M - H + FeCl₃]^-, [M - 2H + FeCl₂]^-, and [M - 3H + FeCl]^- with mass differences (Δm/z) between the complexing agent and the iron complex of 160.8416, 124.8648, and 89.8959, respectively. Up to 29 di- or tricarboxylic acids were identified as complexing agents in aerosol particle samples from two different sites (Melpitz, Germany, and Wangdu, China) at concentrations as low as 50 nM. Thirteen complexing agents were detected even in measurements without postcolumn iron addition from complexation with background Fe³⁺ traces from the analytical system. At least for the highest concentrated complexing agents, the proposed screening approach can thus be exploited in a NTS approach without any device modification. Besides carboxylic acids, 4-nitrophenol and 4-nitrocatechol were identified as further complexing agents, demonstrating the applicability of the approach to other matrices and to a range of different complexing agents.

A Comprehensive Review of Computation-Based Metal-Binding Prediction Approaches at the Residue Level

Clear evidence has shown that metal ions strongly connect and delicately tune the dynamic homeostasis in living bodies. They have been proved to be associated with protein structure, stability, regulation, and function. Even small changes in the concentration of metal ions can shift their effects from natural beneficial functions to harmful. This leads to degenerative diseases, malignant tumors, and cancers. Accurate characterizations and predictions of metalloproteins at the residue level promise informative clues to the investigation of intrinsic mechanisms of protein-metal ion interactions. Compared to biophysical or biochemical wet-lab technologies, computational methods provide open web interfaces of high-resolution databases and high-throughput predictors for efficient investigation of metal-binding residues. This review surveys and details 18 public databases of metal-protein binding. We collect a comprehensive set of 44 computation-based methods and classify them into four categories, namely, learning-, docking-, template-, and meta-based methods. We analyze the benchmark datasets, assessment criteria, feature construction, and algorithms. We also compare several methods on two benchmark testing datasets and include a discussion about currently publicly available predictive tools. Finally, we summarize the challenges and underlying limitations of the current studies and propose several prospective directions concerning the future development of the related databases and methods.

Deciphering the evolution of metallo-β-lactamases: a journey from the test tube to the bacterial periplasm

Understanding the evolution of metallo-β-lactamases (MBLs) is fundamental to deciphering the mechanistic basis of resistance to carbapenems in pathogenic and opportunistic bacteria. Presently, these MBL producing pathogens are linked to high rates of morbidity and mortality worldwide. However, the study of the biochemical and biophysical features of MBLs in vitro provides an incomplete picture of their evolutionary potential, since this limited and artificial environment disregards the physiological context where evolution and selection take place. Herein, we describe recent efforts aimed to address the evolutionary traits acquired by different clinical variants of MBLs in conditions mimicking their native environment (the bacterial periplasm) and considering whether they are soluble or membrane-bound proteins. This includes addressing the metal content of MBLs within the cell under zinc starvation conditions, and the context provided by different bacterial hosts that result in particular resistance phenotypes. Our analysis highlights recent progress bridging the gap between in vitro and in-cell studies.

Siderophore-mediated zinc acquisition enhances enterobacterial colonization of the inflamed gut

Zinc is an essential cofactor for bacterial metabolism, and many Enterobacteriaceae express the zinc transporters ZnuABC and ZupT to acquire this metal in the host. However, the probiotic bacterium Escherichia coli Nissle 1917 (or "Nissle") exhibits appreciable growth in zinc-limited media even when these transporters are deleted. Here, we show that Nissle utilizes the siderophore yersiniabactin as a zincophore, enabling Nissle to grow in zinc-limited media, to tolerate calprotectin-mediated zinc sequestration, and to thrive in the inflamed gut. We also show that yersiniabactin's affinity for iron or zinc changes in a pH-dependent manner, with increased relative zinc binding as the pH increases. Thus, our results indicate that siderophore metal affinity can be influenced by the local environment and reveal a mechanism of zinc acquisition available to commensal and pathogenic Enterobacteriaceae.