High throughput screening of enzyme activity with mass spectrometry imaging

Image to insight: exploring natural products through mass spectrometry imaging

Covering: 2017 to 2022Mass spectrometry imaging (MSI) has become a mature molecular imaging technique that is well-matched for natural product (NP) discovery. Here we present a brief overview of MSI, followed by a thorough discussion of different MSI applications in NP research. This review will mainly focus on the recent progress of MSI in plants and microorganisms as they are the main producers of NPs. Specifically, the opportunity and potential of combining MSI with other imaging modalities and stable isotope labeling are discussed. Throughout, we focus on both the strengths and weaknesses of MSI, with an eye on future improvements that are necessary for the progression of MSI toward routine NP studies. Finally, we discuss new areas of research, future perspectives, and the overall direction that the field may take in the years to come.

Towards one sample per second for mass spectrometric screening of engineered microbial strains

Microbial cell factories convert renewable feedstocks into desirable chemicals and materials. Due to the lack of predictive modeling, high-throughput screening remains essential for microbial strain engineering. Mass spectrometry (MS) is a label-free modality with superior sensitivity and chemical specificity. Critical advances in improving the throughput of MS assays on complex microbial samples include massively parallel cultivation, robotic sample preparation, and chromatography-free instrumentation. Here, we review the recent development and application of rapid MS assays in screening microbial libraries, achieving or approaching a rate of one sample per second. We conclude with unique challenges associated with MS screening of strain libraries and discuss future solutions.

Fluorescence-activated droplet sorting of PET degrading microorganisms

Enzymes that can decompose synthetic plastics such as polyethylene terephthalate (PET) are urgently needed. Still, a bottleneck remains due to a lack of techniques for detecting and sorting environmental microorganisms with vast diversity and abundance. Here, we developed a fluorescence-activated droplet sorting (FADS) pipeline for high-throughput screening of PET-degrading microorganisms or enzymes (PETases). The pipeline comprises three steps: generation and incubation of droplets encapsulating single cells, picoinjection of fluorescein dibenzoate (FDBz) as the fluorogenic probe, and screening of droplets to obtain PET-degrading cells. We characterized critical factors associated with this method, including specificity and sensitivity for discriminating PETase from other enzymes. We then optimized its performance and compatibility with environmental samples. The system was used to screen a wastewater sample from a PET textile mill. We successfully obtained PET-degrading species from nine different genera. Moreover, two putative PETases from isolates Kineococcus endophyticus Un-5 and Staphylococcus epidermidis Un-C2-8 were genetically derived, heterologously expressed, and preliminarily validated for PET-degrading activities. We speculate that the FADS pipeline can be widely adopted to discover new plastic-degrading microorganisms and enzymes in various environments and may be utilized in the directed evolution of degrading enzymes using synthetic biology.

A roadmap for metagenomic enzyme discovery

Covering: up to 2021Metagenomics has yielded massive amounts of sequencing data offering a glimpse into the biosynthetic potential of the uncultivated microbial majority. While genome-resolved information about microbial communities from nearly every environment on earth is now available, the ability to accurately predict biocatalytic functions directly from sequencing data remains challenging. Compared to primary metabolic pathways, enzymes involved in secondary metabolism often catalyze specialized reactions with diverse substrates, making these pathways rich resources for the discovery of new enzymology. To date, functional insights gained from studies on environmental DNA (eDNA) have largely relied on PCR- or activity-based screening of eDNA fragments cloned in fosmid or cosmid libraries. As an alternative, shotgun metagenomics holds underexplored potential for the discovery of new enzymes directly from eDNA by avoiding common biases introduced through PCR- or activity-guided functional metagenomics workflows. However, inferring new enzyme functions directly from eDNA is similar to searching for a 'needle in a haystack' without direct links between genotype and phenotype. The goal of this review is to provide a roadmap to navigate shotgun metagenomic sequencing data and identify new candidate biosynthetic enzymes. We cover both computational and experimental strategies to mine metagenomes and explore protein sequence space with a spotlight on natural product biosynthesis. Specifically, we compare in silico methods for enzyme discovery including phylogenetics, sequence similarity networks, genomic context, 3D structure-based approaches, and machine learning techniques. We also discuss various experimental strategies to test computational predictions including heterologous expression and screening. Finally, we provide an outlook for future directions in the field with an emphasis on meta-omics, single-cell genomics, cell-free expression systems, and sequence-independent methods.

Mass Spectrometric Biosensing: A Powerful Approach for Multiplexed Analysis of Clinical Biomolecules

Rapid and sensitive detection of clinical biomolecules in a multiplexed fashion is of great importance for accurate diagnosis of diseases. Mass spectrometric (MS) approaches are exceptionally suitable for clinical analysis due to its high throughput, high sensitivity, and reliable qualitative and quantitative capabilities. To break through the bottleneck of MS technique for detecting high-molecular-weight substances with low ionization efficiency, the concept of mass spectrometric biosensing has been put forward by adopting mass spectrometric chips to recognize the targets and mass spectrometry to detect the signals switched by the recognition. In this review, the principle of mass spectrometric sensing, the construction of different mass tags used for biosensing, and the typical combination mode of mass spectrometric imaging (MSI) technique are summarized. Future perspectives including the design of portable matching platforms, exploitation of novel mass tags, development of effective signal amplification strategies, and standardization of MSI methodologies are proposed to promote the advancements and practical applications of mass spectrometric biosensing.

Directed Evolution: Methodologies and Applications

Directed evolution aims to expedite the natural evolution process of biological molecules and systems in a test tube through iterative rounds of gene diversifications and library screening/selection. It has become one of the most powerful and widespread tools for engineering improved or novel functions in proteins, metabolic pathways, and even whole genomes. This review describes the commonly used gene diversification strategies, screening/selection methods, and recently developed continuous evolution strategies for directed evolution. Moreover, we highlight some representative applications of directed evolution in engineering nucleic acids, proteins, pathways, genetic circuits, viruses, and whole cells. Finally, we discuss the challenges and future perspectives in directed evolution.

Mass Spectrometric Enzyme Histochemistry for Choline Acetyltransferase Reveals De Novo Acetylcholine Synthesis in Rodent Brain and Spinal Cord

Choline acetyltransferase (ChAT), responsible for the synthesis of acetylcholine, plays an important role in neurotransmission. However, no method to visualize the ChAT activity in tissues has been reported to date. In this study, mass spectrometry imaging (MSI) was used to visualize ChAT activity in situ, which is difficult with conventional enzyme histochemistry. By using choline chloride-trimethyl-d9 (choline-d9) as a substrate and simultaneously supplying an inhibitor of cholinesterase to tissues, we succeeded in directly visualizing the ChAT activity in the rodent brain and spinal cord. The findings revealed heterogeneous ChAT activity in the striatum of the mouse brain and in the spinal lower motor neurons that connect the anterior horn to the ventral root. Furthermore, extending the developed method to spinal cord injury (SCI) model mice revealed the site-specific effect of primary and secondary injury on ChAT activity. This study shows that the MSI-based enzyme histochemistry of ChAT could be a useful tool for studying cholinergic neurons.

macroMS: Image-Guided Analysis of Random Objects by Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry

Mass spectrometry imaging is well-suited to characterizing sample surfaces for their chemical content in a spatially resolved manner. However, when the surface contains small objects with significant empty spaces between them, more efficient approaches to sample acquisition are possible. Image-guided mass spectrometry (MS) enables high-throughput analysis of a diverse range of sample types, such as microbial colonies, liquid microdroplets, and others, by recognizing and analyzing selected location targets in an image. Here, we describe an imaging protocol and macroMS, an online software suite that can be used to enhance MS measurements of macroscopic samples that are imaged by a camera or a flatbed scanner. The web-based tool enables users to find and filter targets from the optical images, correct optical distortion issues for improved spatial location of selected targets, input the custom geometry files into an MS device to acquire spectra at the selected locations, and finally, perform limited data analysis and use visualization tools to aid locating samples containing compounds of interest. Using the macroMS suite, an enzyme mutant library of Saccharomyces cerevisiae and nL droplet arrays of Escherichia coli and Pseudomonas fluorescens have been assayed at a rate of ∼2 s/sample.

Development of MALDI MS peptide array for thrombin inhibitor screening

The development of in situ methods for the analysis and visualization of enzyme activity is of paramount importance in drug discovery, research, and development. In this work, the functionalized and array patterned indium tin oxide (ITO) glass slides were fabricated by non-covalent immobilization of amphipathic phospholipid-tagged peptides encompassing the thrombin cleavage site on steric acid-modified ITO slides. The fabricated peptide arrays provide 60 spots per slide, and are compatible with matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS) measurement, free matrix peak interference, and tolerance to repeated aqueous washing. The peptide arrays were used for the investigation of thrombin activity and screening for its potential inhibitors. The thrombin activity and its Michaelis-Menten constant (K_m) for immobilized peptide substrate was determined using developed MALDI MS peptide array. To investigate the applicability and effectiveness of peptide arrays, the anti-thrombin activity of grape seed proanthocyanidins with different degrees of polymerization (DP) was monitored and visualized. MALDI MS imaging results showed that the fractions of proanthocyanidins with the mean DP of 4.61-6.82 had good thrombin inhibitory activity and their half-maximal inhibitory concentration (IC₅₀) were below 10 μg/mL. Therefore, the developed peptide array is a reliable platform for the discovery of natural thrombin inhibitors.

The Use of Acoustic Mist Ionization Mass Spectrometry for High-Throughput Screening

It is clear from the analysis of the distribution of approved drug targets that enzymes continue to be a major target class for the pharmaceutical industry. The application of high-throughput screens designed to monitor the activity of these enzyme targets, and the ability of test compounds to modulate this activity, is still the predominant hit finding approach in the industry. The widespread use of enzyme activity-based screens has led to the development of several useful guidelines for the development and validation of robust and reliable assays. Key learnings for the development, validation, and implementation of acoustic mist ionization mass spectrometry for high-throughput enzyme assays are described.

High Throughput Screening with SAMDI Mass Spectrometry for Directed Evolution

Advances in directed evolution have led to an exploration of new and important chemical transformations; however, many of these efforts still rely on the use of low-throughput chromatography-based screening methods. We present a high-throughput strategy for screening libraries of enzyme variants for improved activity. Unpurified reaction products are immobilized to a self-assembled monolayer and analyzed by mass spectrometry, allowing for direct evaluation of thousands of variants in under an hour. The method was demonstrated with libraries of randomly mutated cytochrome P411 variants to identify improved catalysts for C-H alkylation. The technique may be tailored to evolve enzymatic activity for a variety of transformations where higher throughput is needed.

High-Throughput Amenable MALDI-MS Detection of RNA and DNA with On-Surface Analyte Enrichment Using Fluorous Partitioning

High-throughput matrix-assisted laser desorption/ionization mass spectrometry (HT-MALDI-MS) has garnered considerable attention within the drug discovery industry as an information-rich alternative to assays using light-based detection methods. To date, these efforts have been primarily focused on assays using protein or peptide substrates. Methods for RNA or DNA analysis by HT-MALDI-MS have not been extensively reported due to the challenges associated with MALDI-MS of oligonucleotides, including the propensity to form multiple salt adducts, low ionization potential, and ease of fragmentation. The objective of this work was to develop a platform suitable for HT-MS analysis of RNA and DNA substrates that overcomes these hurdles by combining on-surface sample preparation with soft ionization. This has been accomplished through the selective immobilization of fluorous-tagged oligonucleotides on a fluorous-modified MS target plate, followed by on-surface enrichment, matrix addition, and direct laser desorption/ionization, a process dubbed fluorous HT-MS (F-HT-MS). The work has resulted in methods by which RNA and DNA substrates can be detected at nanomolar concentrations from a typical assay buffer system using procedures that are amenable to full automation. The protocols were applied to an miRNA biogenesis assay, demonstrating its potential for RNA processes and thereby filling a prominent gap in RNA drug discovery: the paucity of in vitro functional assays.

Optically guided mass spectrometry to screen microbial colonies for directed enzyme evolution

Directed evolution is a well-established and powerful tool for enzyme engineering, which consists of iterative rounds of creating and screening a library of variants. In many cases, the ability to characterize these variants in high-throughput remains a bottleneck. In addition, profiling of desired candidates becomes even more challenging when engineering multiple enzymes in a biochemical pathway. In this chapter, we describe a label-free, high-throughput method for the engineering of multistep enzymatic reactions in bacterial colonies via optically guided matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) mass spectrometry (MS). This method is able to detect products, reactants, and byproducts with high sensitivity and accuracy. We demonstrate its effectiveness in two applications related to natural product biosynthesis, including facile creation of analog of the peptidic antibiotic plantazolicin and rapid profiling of congeners of rhamnolipid. Computational algorithms were developed to process and visualize the resulting mass spectral data sets. In both cases, improved MS acquisition efficiency and information-rich insights were obtained through this technique on large populations of colonies at a rate of 1-2.5s per colony. This method should be generally applicable to high-throughput phenotyping of microbial libraries from a wide range of enzymatic reactions.

A mass spectrometry-based high-throughput screening method for engineering fatty acid synthases with improved production of medium-chain fatty acids

Microbial cell factories have been extensively engineered to produce free fatty acids (FFAs) as key components of crucial nutrients, soaps, industrial chemicals, and fuels. However, our ability to control the composition of microbially synthesized FFAs is still limited, particularly, for producing medium-chain fatty acids (MCFAs). This is mainly due to the lack of high-throughput approaches for FFA analysis to engineer enzymes with desirable product specificity. Here we report a mass spectrometry (MS)-based method for rapid profiling of MCFAs in Saccharomyces cerevisiae by using membrane lipids as a proxy. In particular, matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) MS was used to detect shorter acyl chain phosphatidylcholines from membrane lipids and a higher m/z peak ratio at 730 and 758 was used as an indication for improved MCFA production. This colony-based method can be performed at a rate of ~2 s per sample, representing a substantial improvement over gas chromatography-MS (typically >30 min per sample) as the gold standard method for FFA detection. To demonstrate the power of this method, we performed site-saturation mutagenesis of the yeast fatty acid synthase and identified nine missense mutations that resulted in improved MCFA production relative to the wild-type strain. Colony-based MALDI-ToF MS screening provides an effective approach for engineering microbial fatty acid compositions in a high-throughput manner.

Recent advances in high-throughput mass spectrometry that accelerates enzyme engineering for biofuel research

Enzymes play indispensable roles in producing biofuels, a sustainable and renewable source of transportation fuels. Lacking rational design rules, the development of industrially relevant enzyme catalysts relies heavily on high-throughput screening. However, few universal methods exist to rapidly characterize large-scale enzyme libraries. Therefore, assay development is necessary on an ad hoc basis to link enzyme properties to spectrophotometric signals and often requires the use of surrogate, optically active substrates. On the other hand, mass spectrometry (MS) performs label-free enzyme assays that utilize native substrates and is therefore generally applicable. But the analytical speed of MS is considered rate limiting, mainly due to the use of time-consuming chromatographic separation in traditional MS analysis. Thanks to new instrumentation and sample preparation methods, direct analyte introduction into a mass spectrometer without a prior chromatographic step can be achieved by laser, microfluidics, and acoustics, so that each sample can be analyzed within seconds. Here we review recent advances in MS platforms that improve the throughput of enzyme library screening and discuss how these advances can potentially facilitate biofuel research by providing high sensitivity, selectivity and quantitation that are difficult to obtain using traditional assays. We also highlight the limitations of current MS assays in studying biofuel-related enzymes and propose possible solutions.

A High-Throughput Mass Spectrometric Enzyme Activity Assay Enabling the Discovery of Cytochrome P450 Biocatalysts

Assaying for enzymatic activity is a persistent bottleneck in biocatalyst and drug development. Existing high-throughput assays for enzyme activity tend to be applicable only to a narrow range of biochemical transformations, whereas universal enzyme characterization methods usually require chromatography to determine substrate turnover, greatly diminishing throughput. We present an enzyme activity assay that allows the high-throughput mass-spectrometric detection of enzyme activity in complex matrices without the need for a chromatographic step. This technology, which we call probing enzymes with click-assisted NIMS (PECAN), can detect the activity of medically and biocatalytically significant cytochrome P450s in cell lysate, microsomes, and bacteria. Using this approach, a cytochrome P450_BM3 mutant library was successfully screened for the ability to catalyze the oxidation of the sesquiterpene valencene.

Biochemical control systems for small molecule damage in plants

As a system, plant metabolism is far from perfect: small molecules (metabolites, cofactors, coenzymes, and inorganic molecules) are frequently damaged by unwanted enzymatic or spontaneous reactions. Here, we discuss the emerging principles in small molecule damage biology. We propose that plants evolved at least three distinct systems to control small molecule damage: (i) repair, which returns a damaged molecule to its original state; (ii) scavenging, which converts reactive molecules to harmless products; and (iii) steering, in which the possible formation of a damaged molecule is suppressed. We illustrate the concept of small molecule damage control in plants by describing specific examples for each of these three categories. We highlight interesting insights that we expect future research will provide on those systems, and we discuss promising strategies to discover new small molecule damage-control systems in plants.

Traceless Immobilization of Analytes for High-Throughput Experiments with SAMDI Mass Spectrometry

Label-free assays, and particularly those based on the combination of mass spectroscopy with surface chemistries, enable high-throughput experiments of a broad range of reactions. However, these methods can still require the incorporation of functional groups that allow immobilization of reactants and products to surfaces prior to analysis. In this paper, we report a traceless method for attaching molecules to a self-assembled monolayer for matrix-assisted laser desorption and ionization (SAMDI) mass spectrometry. This method uses monolayers that are functionalized with a 3-trifluoromethyl-3-phenyl-diazirine group that liberates nitrogen when irradiated and gives a carbene that inserts into a wide range of bonds to covalently immobilize molecules. Analysis of the monolayer with SAMDI then reveals peaks for each of the adducts formed from molecules in the sample. This method is applied to characterize a P450 drug metabolizing enzyme and to monitor a Suzuki–Miyaura coupling chemical reaction and is important because modification of the substrates with a functional group would alter their activities. This method will be important for high-throughput experiments in many areas, including reaction discovery and optimization.

Methods of Measuring Enzyme Activity Ex Vivo and In Vivo

Enzymes catalyze a variety of biochemical reactions in the body and, in conjunction with transporters and receptors, control virtually all physiological processes. There is great value in measuring enzyme activity ex vivo and in vivo. Spatial and temporal differences or changes in enzyme activity can be related to a variety of natural and pathological processes. Several analytical approaches have been developed to meet this need. They can be classified broadly as methods either based on artificial substrates, with the goal of creating images of diseased tissue, or based on natural substrates, with the goal of understanding natural processes. This review covers a selection of these methods, including optical, magnetic resonance, mass spectrometry, and physical sampling approaches, with a focus on creative chemistry and method development that make ex vivo and in vivo measurements of enzyme activity possible.

Profiling of Microbial Colonies for High-Throughput Engineering of Multistep Enzymatic Reactions via Optically Guided Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry

Matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) mass spectrometry (MS) imaging has been used for rapid phenotyping of enzymatic activities, but is mainly limited to single-step conversions. Herein we report a label-free method for high-throughput engineering of multistep biochemical reactions based on optically guided MALDI-ToF MS analysis of bacterial colonies. The bacterial cells provide containment of multiple enzymes and access to substrates and cofactors via metabolism. Automated MALDI-ToF MS acquisition from randomly distributed colonies simplifies procedures to prepare strain libraries without liquid handling. MALDI-ToF MS profiling was utilized to screen both substrate and enzyme libraries for natural product biosynthesis. Computational algorithms were developed to process and visualize the resulting mass spectral data sets. For analogues of the peptidic antibiotic plantazolicin, multivariate analyses by t-distributed stochastic neighbor embedding were used to group similar spectra for rapid identification of nonisobaric variants. After MALDI-ToF MS screening, follow-up analyses using high-resolution MS and tandem MS were readily performed on the same sample target. Separately, relative ion intensities of rhamnolipid congeners with various lipid moieties were evaluated to engineer enzymatic specificity. The glycolipid profiles of each colony were overlaid with optical images to facilitate the recovery of desirable mutants. For both the antibiotic and rhamnolipid cases, large populations of colonies were rapidly surveyed at the molecular level, providing information-rich insights not easily obtained with traditional screening assays. Utilizing standard microbiological techniques with routine microscopy and MALDI-ToF MS instruments, this simple yet effective workflow is applicable for a wide range of screening campaigns targeting multistep enzymatic reactions.

OpenMSI Arrayed Analysis Toolkit: Analyzing Spatially Defined Samples Using Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) has primarily been applied in localizing biomolecules within biological matrices. Although well-suited, the application of MSI for comparing thousands of spatially defined spotted samples has been limited. One reason for this is a lack of suitable and accessible data processing tools for the analysis of large arrayed MSI sample sets. The OpenMSI Arrayed Analysis Toolkit (OMAAT) is a software package that addresses the challenges of analyzing spatially defined samples in MSI data sets. OMAAT is written in Python and is integrated with OpenMSI ( http://openmsi.nersc.gov ), a platform for storing, sharing, and analyzing MSI data. By using a web-based python notebook (Jupyter), OMAAT is accessible to anyone without programming experience yet allows experienced users to leverage all features. OMAAT was evaluated by analyzing an MSI data set of a high-throughput glycoside hydrolase activity screen comprising 384 samples arrayed onto a NIMS surface at a 450 μm spacing, decreasing analysis time >100-fold while maintaining robust spot-finding. The utility of OMAAT was demonstrated for screening metabolic activities of different sized soil particles, including hydrolysis of sugars, revealing a pattern of size dependent activities. These results introduce OMAAT as an effective toolkit for analyzing spatially defined samples in MSI. OMAAT runs on all major operating systems, and the source code can be obtained from the following GitHub repository: https://github.com/biorack/omaat .

Region-specific bioconversion of dynorphin neuropeptide detected by in situ histochemistry and MALDI imaging mass spectrometry

Brain region-specific expression of proteolytic enzymes can control the biological activity of endogenous neuropeptides and has recently been targeted for the development of novel drugs, for neuropathic pain, cancer, and Parkinson's disease. Rapid and sensitive analytical methods to profile modulators of enzymatic activity are important for finding effective inhibitors with high therapeutic value. Combination of in situ enzyme histochemistry with MALDI imaging mass spectrometry allowed developing a highly sensitive method for analysis of brain-area specific neuropeptide conversion of synthetic and endogenous neuropeptides, and for selection of peptidase inhibitors that differentially target conversion enzymes at specific anatomical sites. Conversion and degradation products of Dynorphin B as model neuropeptide and effects of peptidase inhibitors applied to native brain tissue sections were analyzed at different brain locations. Synthetic dynorphin B (2pmol) was found to be converted to the N-terminal fragments on brain sections whereas fewer C-terminal fragments were detected. N-ethylmaleimide (NEM), a non-selective inhibitor of cysteine peptidases, almost completely blocked the conversion of dynorphin B to dynorphin B(1-6; Leu-Enk-Arg), (1-9), (2-13), and (7-13). Proteinase inhibitor cocktail, and also incubation with acetic acid displayed similar results. Bioconversion of synthetic dynorphin B was region-specific producing dynorphin B(1-7) in the cortex and dynorphin B (2-13) in the striatum. Enzyme inhibitors showed region- and enzyme-specific inhibition of dynorphin bioconversion. Both phosphoramidon (inhibitor of the known dynorphin converting enzyme neprilysin) and opiorphin (inhibitor of neprilysin and aminopeptidase N) blocked cortical bioconversion to dynorphin B(1-7), wheras only opiorphin blocked striatal bioconversion to dynorphin B(2-13). This method may impact the development of novel therapies with aim to strengthen the effects of endogenous neuropeptides under pathological conditions such as chronic pain. Combining histochemistry and MALDI imaging MS is a powerful and sensitive tool for the study of inhibition of enzyme activity directly in native tissue sections.

The application of microfluidic-based technologies in the cycle of metabolic engineering

The process of metabolic engineering consists of multiple cycles of design, build, test and learn, which is typically laborious and time-consuming. To increase the efficiency and the rate of success of strain engineering, novel instrumentation must be applied. Microfluidics, the control of liquid flow in microstructures, has enabled flexible, accurate, automatic, and high-throughput manipulation of cells in liquid at picoliter to nanoliter scale. These attributes hold great promise in advancing metabolic engineering in terms of the phases of design, build, test and learn. To promote the application of microfluidic-based technologies in strain improvement, this review addressed the potentials of microfluidics and the related approaches in DNA assembly, transformation, strain screening, genotyping and phenotyping, and highlighted their adaptations for single-cell analysis. As a result, this facilitates in-depth understanding of the metabolic network, which in turn promote efficient optimization in the following cycles of strain engineering. Taken together, microfluidic-based technologies enable on-chip workflow, and could greatly accelerate the turnaround of metabolic engineering.

Comparative Community Proteomics Demonstrates the Unexpected Importance of Actinobacterial Glycoside Hydrolase Family 12 Protein for Crystalline Cellulose Hydrolysis

Glycoside hydrolases (GHs) are key enzymes in the depolymerization of plant-derived cellulose, a process central to the global carbon cycle and the conversion of plant biomass to fuels and chemicals. A limited number of GH families hydrolyze crystalline cellulose, often by a processive mechanism along the cellulose chain. During cultivation of thermophilic cellulolytic microbial communities, substantial differences were observed in the crystalline cellulose saccharification activities of supernatants recovered from divergent lineages. Comparative community proteomics identified a set of cellulases from a population closely related to actinobacterium Thermobispora bispora that were highly abundant in the most active consortium. Among the cellulases from T. bispora, the abundance of a GH family 12 (GH12) protein correlated most closely with the changes in crystalline cellulose hydrolysis activity. This result was surprising since GH12 proteins have been predominantly characterized as enzymes active on soluble polysaccharide substrates. Heterologous expression and biochemical characterization of the suite of T. bispora hydrolytic cellulases confirmed that the GH12 protein possessed the highest activity on multiple crystalline cellulose substrates and demonstrated that it hydrolyzes cellulose chains by a predominantly random mechanism. This work suggests that the role of GH12 proteins in crystalline cellulose hydrolysis by cellulolytic microbes should be reconsidered.

Cellulose is the most abundant organic polymer on earth, and its enzymatic hydrolysis is a key reaction in the global carbon cycle and the conversion of plant biomass to biofuels. The glycoside hydrolases that depolymerize crystalline cellulose have been primarily characterized from isolates. In this study, we demonstrate that adapting microbial consortia from compost to grow on crystalline cellulose generated communities whose soluble enzymes exhibit differential abilities to hydrolyze crystalline cellulose. Comparative proteomics of these communities identified a protein of glycoside hydrolase family 12 (GH12), a family of proteins previously observed to primarily hydrolyze soluble substrates, as a candidate that accounted for some of the differences in hydrolytic activities. Heterologous expression confirmed that the GH12 protein identified by proteomics was active on crystalline cellulose and hydrolyzed cellulose by a random mechanism, in contrast to most cellulases that act on the crystalline polymer in a processive mechanism.

Advanced Multidimensional Separations in Mass Spectrometry: Navigating the Big Data Deluge

Hybrid analytical instrumentation constructed around mass spectrometry (MS) is becoming the preferred technique for addressing many grand challenges in science and medicine. From the omics sciences to drug discovery and synthetic biology, multidimensional separations based on MS provide the high peak capacity and high measurement throughput necessary to obtain large-scale measurements used to infer systems-level information. In this article, we describe multidimensional MS configurations as technologies that are big data drivers and review some new and emerging strategies for mining information from large-scale datasets. We discuss the information content that can be obtained from individual dimensions, as well as the unique information that can be derived by comparing different levels of data. Finally, we summarize some emerging data visualization strategies that seek to make highly dimensional datasets both accessible and comprehensible.

The Evolution of MALDI-TOF Mass Spectrometry toward Ultra-High-Throughput Screening: 1536-Well Format and Beyond

Mass spectrometry (MS) offers a label-free, direct-detection method, in contrast to fluorescent or colorimetric methodologies. Over recent years, solid-phase extraction–based techniques, such as the Agilent RapidFire system, have emerged that are capable of analyzing samples in <10 s. While dramatically faster than liquid chromatography–coupled MS, an analysis time of 8–10 s is still considered relatively slow for full-diversity high-throughput screening (HTS). Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF) offers an alternative for high-throughput MS detection. However, sample preparation and deposition onto the MALDI target, as well as interference from matrix ions, have been considered limitations for the use of MALDI for screening assays. Here we describe the development and validation of assays for both small-molecule and peptide analytes using MALDI-TOF coupled with nanoliter liquid handling. Using the JMJD2c histone demethylase and acetylcholinesterase as model systems, we have generated robust data in a 1536 format and also increased sample deposition to 6144 samples per target. Using these methods, we demonstrate that this technology can deliver fast sample analysis time with low sample volume, and data comparable to that of current RapidFire assays.

The proportion of genes in a functional category is linked to mass-specific metabolic rate and lifespan

Metabolic rate and lifespan are important biological parameters that are studied in a wide range of research fields. They are known to correlate with body mass, but their association with gene (protein) functions is poorly understood. In this study, we collected data on the metabolic rate and lifespan of various organisms and investigated the relationship of these parameters with their genomes. We showed that the proportion of genes in a functional category, but not genome size, was correlated with mass-specific metabolic rate and maximal lifespan. In particular, the proportion of genes in oxic reactions (which occur in the presence of oxygen) was significantly associated with these two biological parameters. Additionally, we found that temperature, taxonomy, and mode-of-life traits had little effect on the observed associations. Our findings emphasize the importance of considering the biological functions of genes when investigating the relationships between genome, metabolic rate, and lifespan. Moreover, this provides further insights into these relationships, and may be useful for estimating metabolic rate and lifespan in individuals and the ecosystem using a combination of body mass measurements and genomic data.