A combinatorial droplet microfluidic device integrated with mass spectrometry for enzyme screening.

Mass spectrometry (MS) enables detection of different chemical species with a very high specificity; however, it can be limited by its throughput. Integrating MS with microfluidics has a tremendous potential to improve throughput and accelerate biochemical research. In this work, we introduce Drop-NIMS, a combination of a passive droplet loading microfluidic device and a matrix-free MS laser desorption ionization technique called nanostructure-initiator mass spectrometry (NIMS). This platform combines different droplets at random to generate a combinatorial library of enzymatic reactions that are deposited directly on the NIMS surface without requiring additional sample handling. The enzyme reaction products are then detected with MS. Drop-NIMS was used to rapidly screen enzymatic reactions containing low (on the order of nL) volumes of glycoside reactants and glycoside hydrolase enzymes per reaction. MS "barcodes" (small compounds with unique masses) were added to the droplets to identify different combinations of substrates and enzymes created by the device. We assigned xylanase activities to several putative glycoside hydrolases, making them relevant to food and biofuel industrial applications. Overall, Drop-NIMS is simple to fabricate, assemble, and operate and it has potential to be used with many other small molecule metabolites.

Alkaline-SDS cell lysis of microbes with acetone protein precipitation for proteomic sample preparation in 96-well plate format.

Plate-based proteomic sample preparation offers a solution to the large sample throughput demands in the biotechnology field where hundreds or thousands of engineered microbes are constructed for testing is routine. Meanwhile, sample preparation methods that work efficiently on broader microbial groups are desirable for new applications of proteomics in other fields, such as microbial communities. Here, we detail a step-by-step protocol that consists of cell lysis in an alkaline chemical buffer (NaOH/SDS) followed by protein precipitation with high-ionic strength acetone in 96-well format. The protocol works for a broad range of microbes (e.g., Gram-negative bacteria, Gram-positive bacteria, non-filamentous fungi) and the resulting proteins are ready for tryptic digestion for bottom-up quantitative proteomic analysis without the need for desalting column cleanup. The yield of protein using this protocol increases linearly with respect to the amount of starting biomass from 0.5-2.0 OD*mL of cells. By using a bench-top automated liquid dispenser, a cost-effective and environmentally-friendly option to eliminating pipette tips and reducing reagent waste, the protocol takes approximately 30 minutes to extract protein from 96 samples. Tests on mock mixtures showed expected results that the biomass composition structure is in close agreement with the experimental design. Lastly, we applied the protocol for the composition analysis of a synthetic community of environmental isolates grown on two different media. This protocol has been developed to facilitate rapid, low-variance sample preparation of hundreds of samples and allow flexibility for future protocol development.

Mass spectrometry imaging-based assays for aminotransferase activity reveal a broad substrate spectrum for a previously uncharacterized enzyme.

Aminotransferases (ATs) catalyze pyridoxal 5'-phosphate-dependent transamination reactions between amino donor and keto acceptor substrates and play central roles in nitrogen metabolism of all organisms. ATs are involved in the biosynthesis and degradation of both proteinogenic and nonproteinogenic amino acids and also carry out a wide variety of functions in photorespiration, detoxification, and secondary metabolism. Despite the importance of ATs, their functionality is poorly understood as only a small fraction of putative ATs, predicted from DNA sequences, are associated with experimental data. Even for characterized ATs, the full spectrum of substrate specificity, among many potential substrates, has not been explored in most cases. This is largely due to the lack of suitable high-throughput assays that can screen for AT activity and specificity at scale. Here we present a new high-throughput platform for screening AT activity using bioconjugate chemistry and mass spectrometry imaging-based analysis. Detection of AT reaction products is achieved by forming an oxime linkage between the ketone groups of transaminated amino donors and a probe molecule that facilitates mass spectrometry-based analysis using nanostructure-initiator mass spectrometry or MALDI-mass spectrometry. As a proof-of-principle, we applied the newly established method and found that a previously uncharacterized Arabidopsis thaliana tryptophan AT-related protein 1 is a highly promiscuous enzyme that can utilize 13 amino acid donors and three keto acid acceptors. These results demonstrate that this oxime-mass spectrometry imaging AT assay enables high-throughput discovery and comprehensive characterization of AT enzymes, leading to an accurate understanding of the nitrogen metabolic network.

Rapid quantification of alcohol production in microorganisms based on nanostructure-initiator mass spectrometry (NIMS).

We described a mass spectrometry-based assay to rapidly quantify the production of primary alcohols directly from cell cultures. This novel assay used the combination of TEMPO-based oxidation chemistry and oxime ligation, followed by product analysis based on Nanostructure-Initiator Mass Spectrometry. This assay enables quantitative monitor both C5 to C18 alcohols as well as glucose and gluconate in the growth medium to support strain characterization and optimization. We find that this assay yields similar results to gas chromatography for isoprenol production but required much less acquisition time per sample. We applied this assay to gain new insights into P. Putida's utilization of alcohols and find that this strain largely could not grow on heptanol and octanol.

A Defined Medium for Cultivation and Exometabolite Profiling of Soil Bacteria.

Exometabolomics is an approach to assess how microorganisms alter, or react to their environments through the depletion and production of metabolites. It allows the examination of how soil microbes transform the small molecule metabolites within their environment, which can be used to study resource competition and cross-feeding. This approach is most powerful when used with defined media that enable tracking of all metabolites. However, microbial growth media have traditionally been developed for the isolation and growth of microorganisms but not metabolite utilization profiling through Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS). Here, we describe the construction of a defined medium, the Northen Lab Defined Medium (NLDM), that not only supports the growth of diverse soil bacteria but also is defined and therefore suited for exometabolomic experiments. Metabolites included in NLDM were selected based on their presence in R2A medium and soil, elemental stoichiometry requirements, as well as knowledge of metabolite usage by different bacteria. We found that NLDM supported the growth of 108 of the 110 phylogenetically diverse (spanning 36 different families) soil bacterial isolates tested and all of its metabolites were trackable through LC-MS/MS analysis. These results demonstrate the viability and utility of the constructed NLDM medium for growing and characterizing diverse microbial isolates and communities.

Biofilm Interaction Mapping and Analysis (BIMA) of Interspecific Interactions in Pseudomonas Co-culture Biofilms.

Pseudomonas species are ubiquitous in nature and include numerous medically, agriculturally and technologically beneficial strains of which the interspecific interactions are of great interest for biotechnologies. Specifically, co-cultures containing Pseudomonas stutzeri have been used for bioremediation, biocontrol, aquaculture management and wastewater denitrification. Furthermore, the use of P. stutzeri biofilms, in combination with consortia-based approaches, may offer advantages for these processes. Understanding the interspecific interaction within biofilm co-cultures or consortia provides a means for improvement of current technologies. However, the investigation of biofilm-based consortia has been limited. We present an adaptable and scalable method for the analysis of macroscopic interactions (colony morphology, inhibition, and invasion) between colony-forming bacterial strains using an automated printing method followed by analysis of the genes and metabolites involved in the interactions. Using Biofilm Interaction Mapping and Analysis (BIMA), these interactions were investigated between P. stutzeri strain RCH2, a denitrifier isolated from chromium (VI) contaminated soil, and 13 other species of pseudomonas isolated from non-contaminated soil. One interaction partner, Pseudomonas fluorescens N1B4 was selected for mutant fitness profiling of a DNA-barcoded mutant library; with this approach four genes of importance were identified and the effects on interactions were evaluated with deletion mutants and mass spectrometry based metabolomics.

Faster, better, and cheaper: harnessing microfluidics and mass spectrometry for biotechnology.

High-throughput screening technologies are widely used for elucidating biological activities. These typically require trade-offs in assay specificity and sensitivity to achieve higher throughput. Microfluidic approaches enable rapid manipulation of small volumes and have found a wide range of applications in biotechnology providing improved control of reaction conditions, faster assays, and reduced reagent consumption. The integration of mass spectrometry with microfluidics has the potential to create high-throughput, sensitivity, and specificity assays. This review introduces the widely-used mass spectrometry ionization techniques that have been successfully integrated with microfluidics approaches such as continuous-flow system, microchip electrophoresis, droplet microfluidics, digital microfluidics, centrifugal microfluidics, and paper microfluidics. In addition, we discuss recent applications of microfluidics integrated with mass spectrometry in single-cell analysis, compound screening, and the study of microorganisms. Lastly, we provide future outlooks towards online coupling, improving the sensitivity and integration of multi-omics into a single platform.

Cervicovaginal Microbiome Composition Is Associated with Metabolic Profiles in Healthy Pregnancy.

Microbes and their metabolic products influence early-life immune and microbiome development, yet remain understudied during pregnancy. Vaginal microbial communities are typically dominated by one or a few well-adapted microbes which are able to survive in a narrow pH range and are adapted to live on host-derived carbon sources, likely sourced from glycogen and mucin present in the vaginal environment. We characterized the cervicovaginal microbiomes of 16 healthy women throughout the three trimesters of pregnancy. Additionally, we analyzed saliva and urine metabolomes using gas chromatography-time of flight mass spectrometry (GC-TOF MS) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) lipidomics approaches for samples from mothers and their infants through the first year of life. Amplicon sequencing revealed most women had either a simple community with one highly abundant species of Lactobacillus or a more diverse community characterized by a high abundance of Gardnerella, as has also been previously described in several independent cohorts. Integrating GC-TOF MS and lipidomics data with amplicon sequencing, we found metabolites that distinctly associate with particular communities. For example, cervicovaginal microbial communities dominated by Lactobacillus crispatus have high mannitol levels, which is unexpected given the characterization of L. crispatus as a homofermentative Lactobacillus species. It may be that fluctuations in which Lactobacillus dominate a particular vaginal microbiome are dictated by the availability of host sugars, such as fructose, which is the most likely substrate being converted to mannitol. Overall, using a multi-"omic" approach, we begin to address the genetic and molecular means by which a particular vaginal microbiome becomes vulnerable to large changes in composition.IMPORTANCE Humans have a unique vaginal microbiome compared to other mammals, characterized by low diversity and often dominated by Lactobacillus spp. Dramatic shifts in vaginal microbial communities sometimes contribute to the presence of a polymicrobial overgrowth condition called bacterial vaginosis (BV). However, many healthy women lacking BV symptoms have vaginal microbiomes dominated by microbes associated with BV, resulting in debate about the definition of a healthy vaginal microbiome. Despite substantial evidence that the reproductive health of a woman depends on the vaginal microbiota, future therapies that may improve reproductive health outcomes are stalled due to limited understanding surrounding the ecology of the vaginal microbiome. Here, we use sequencing and metabolomic techniques to show novel associations between vaginal microbes and metabolites during healthy pregnancy. We speculate these associations underlie microbiome dynamics and may contribute to a better understanding of transitions between alternative vaginal microbiome compositions.

Root morphology and exudate availability are shaped by particle size and chemistry in Brachypodium distachyon.

Root morphology and exudation define a plants' sphere of influence in soils. In turn, soil characteristics influence plant growth, morphology, root microbiome, and rhizosphere chemistry. Collectively, all these parameters have significant implications on the major biogeochemical cycles, crop yield, and ecosystem health. However, how plants are shaped by the physiochemistry of soil particles is still not well understood. We explored how particle size and chemistry of growth substrates affect root morphology and exudation of a model grass. We grew Brachypodium distachyon in glass beads with various sizes (0.5, 1, 2, 3 mm), as well as in sand (0.005, 0.25, 4 mm) and in clay (4 mm) particles and in particle-free hydroponic medium. Plant morphology, root weight, and shoot weight were measured. We found that particle size significantly influenced root fresh weight and root length, whereas root number and shoot weight remained constant. Next, plant exudation profiles were analyzed with mass spectrometry imaging and liquid chromatography-mass spectrometry. Mass spectrometry imaging suggested that both, root length and number shape root exudation. Exudate profiles were comparable for plants growing in glass beads or sand with various particles sizes, but distinct for plants growing in clay for in situ exudate collection. Clay particles were found to sorb 20% of compounds exuded by clay-grown plants, and 70% of compounds from a defined exudate medium. The sorbed compounds belonged to a range of chemical classes, among them nucleosides, organic acids, sugars, and amino acids. Some of the sorbed compounds could be desorbed by a rhizobacterium (Pseudomonas fluorescens WCS415), supporting its growth. This study demonstrates the effect of different characteristics of particles on root morphology, plant exudation and availability of nutrients to microorganisms. These findings further support the critical importance of the physiochemical properties of soils when investigating plant morphology, plant chemistry, and plant-microbe interactions.

Function-driven single-cell genomics uncovers cellulose-degrading bacteria from the rare biosphere.

Assigning a functional role to a microorganism has historically relied on cultivation of isolates or detection of environmental genome-based biomarkers using a posteriori knowledge of function. However, the emerging field of function-driven single-cell genomics aims to expand this paradigm by identifying and capturing individual microbes based on their in situ functions or traits. To identify and characterize yet uncultivated microbial taxa involved in cellulose degradation, we developed and benchmarked a function-driven single-cell screen, which we applied to a microbial community inhabiting the Great Boiling Spring (GBS) Geothermal Field, northwest Nevada. Our approach involved recruiting microbes to fluorescently labeled cellulose particles, and then isolating single microbe-bound particles via fluorescence-activated cell sorting. The microbial community profiles prior to sorting were determined via bulk sample 16S rRNA gene amplicon sequencing. The flow-sorted cellulose-bound microbes were subjected to whole genome amplification and shotgun sequencing, followed by phylogenetic placement. Next, putative cellulase genes were identified, expressed and tested for activity against derivatives of cellulose and xylose. Alongside typical cellulose degraders, including members of the Actinobacteria, Bacteroidetes, and Chloroflexi, we found divergent cellulases encoded in the genome of a recently described candidate phylum from the rare biosphere, Goldbacteria, and validated their cellulase activity. As this genome represents a species-level organism with novel and phylogenetically distinct cellulolytic activity, we propose the name Candidatus 'Cellulosimonas argentiregionis'. We expect that this function-driven single-cell approach can be extended to a broad range of substrates, linking microbial taxonomy directly to in situ function.

CRAGE enables rapid activation of biosynthetic gene clusters in undomesticated bacteria.

It is generally believed that exchange of secondary metabolite biosynthetic gene clusters (BGCs) among closely related bacteria is an important driver of BGC evolution and diversification. Applying this idea may help researchers efficiently connect many BGCs to their products and characterize the products' roles in various environments. However, existing genetic tools support only a small fraction of these efforts. Here, we present the development of chassis-independent recombinase-assisted genome engineering (CRAGE), which enables single-step integration of large, complex BGC constructs directly into the chromosomes of diverse bacteria with high accuracy and efficiency. To demonstrate the efficacy of CRAGE, we expressed three known and six previously identified but experimentally elusive non-ribosomal peptide synthetase (NRPS) and NRPS-polyketide synthase (PKS) hybrid BGCs from Photorhabdus luminescens in 25 diverse γ-Proteobacteria species. Successful activation of six BGCs identified 22 products for which diversity and yield were greater when the BGCs were expressed in strains closely related to the native strain than when they were expressed in either native or more distantly related strains. Activation of these BGCs demonstrates the feasibility of exploiting their underlying catalytic activity and plasticity, and provides evidence that systematic approaches based on CRAGE will be useful for discovering and identifying previously uncharacterized metabolites.

Lessons from Two Design-Build-Test-Learn Cycles of Dodecanol Production in Escherichia coli Aided by Machine Learning.

The Design-Build-Test-Learn (DBTL) cycle, facilitated by exponentially improving capabilities in synthetic biology, is an increasingly adopted metabolic engineering framework that represents a more systematic and efficient approach to strain development than historical efforts in biofuels and biobased products. Here, we report on implementation of two DBTL cycles to optimize 1-dodecanol production from glucose using 60 engineered Escherichia coli MG1655 strains. The first DBTL cycle employed a simple strategy to learn efficiently from a relatively small number of strains (36), wherein only the choice of ribosome-binding sites and an acyl-ACP/acyl-CoA reductase were modulated in a single pathway operon including genes encoding a thioesterase (UcFatB1), an acyl-ACP/acyl-CoA reductase (Maqu_2507, Maqu_2220, or Acr1), and an acyl-CoA synthetase (FadD). Measured variables included concentrations of dodecanol and all proteins in the engineered pathway. We used the data produced in the first DBTL cycle to train several machine-learning algorithms and to suggest protein profiles for the second DBTL cycle that would increase production. These strategies resulted in a 21% increase in dodecanol titer in Cycle 2 (up to 0.83 g/L, which is more than 6-fold greater than previously reported batch values for minimal medium). Beyond specific lessons learned about optimizing dodecanol titer in E. coli, this study had findings of broader relevance across synthetic biology applications, such as the importance of sequencing checks on plasmids in production strains as well as in cloning strains, and the critical need for more accurate protein expression predictive tools.

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 P450BM3 mutant library was successfully screened for the ability to catalyze the oxidation of the sesquiterpene valencene.

Enzyme promiscuity shapes adaptation to novel growth substrates.

Evidence suggests that novel enzyme functions evolved from low-level promiscuous activities in ancestral enzymes. Yet, the evolutionary dynamics and physiological mechanisms of how such side activities contribute to systems-level adaptations are not well characterized. Furthermore, it remains untested whether knowledge of an organism's promiscuous reaction set, or underground metabolism, can aid in forecasting the genetic basis of metabolic adaptations. Here, we employ a computational model of underground metabolism and laboratory evolution experiments to examine the role of enzyme promiscuity in the acquisition and optimization of growth on predicted non-native substrates in Escherichia coli K-12 MG1655. After as few as approximately 20 generations, evolved populations repeatedly acquired the capacity to grow on five predicted non-native substrates-D-lyxose, D-2-deoxyribose, D-arabinose, m-tartrate, and monomethyl succinate. Altered promiscuous activities were shown to be directly involved in establishing high-efficiency pathways. Structural mutations shifted enzyme substrate turnover rates toward the new substrate while retaining a preference for the primary substrate. Finally, genes underlying the phenotypic innovations were accurately predicted by genome-scale model simulations of metabolism with enzyme promiscuity.

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 .

Application of Black Silicon for Nanostructure-Initiator Mass Spectrometry.

Nanostructure-initiator mass spectrometry (NIMS) is a matrix-free desorption/ionization technique with high sensitivity for small molecules. Surface preparation has relied on hydrofluoric acid (HF) electrochemical etching which is undesirable given the significant safety controls required in this specialized process. In this study, we examine a conventional and widely used process for producing black silicon based on sulfur hexafluoride/oxygen (SF6/O2) inductively coupled plasma (ICP) etching at cryogenic temperatures and we find it to be suitable for NIMS. A systematic study varying parameters in the plasma etching process was performed to understand the relationship of black silicon morphology and its sensitivity as a NIMS substrate. The results suggest that a combination of higher silicon temperature and oxygen flow rate gives rise to the formation of black silicon with fine pillar structures, whose aspect ratio are ∼ 8.7 and depth are <1 µm resulting in higher NIMS sensitivity which is attributed to surface restructuring caused by their low melting point upon laser irradiation. Interestingly, we find selectivity of these black silicon substrates to different analytes depending on the etching parameters. Though, the sensitivity of the dry etching process is lower than the traditional "wet" electrochemical etching process, it is suitable for many applications and is prepared using conventional equipment without the use of HF.

High-throughput platforms for metabolomics.

Mass spectrometry has become a choice method for broad-spectrum metabolite analysis in both fundamental and applied research. This can range from comprehensive analysis achieved through time-consuming chromatography to the rapid analysis of a few target metabolites without chromatography. In this review article, we highlight current high-throughput MS-based platforms and their potential application in metabolomics. Although current MS platforms can reach throughputs up to 0.5 seconds per sample, the metabolite coverage of these platforms are low compared to low-throughput, separation-based MS methods. High-throughput comes at a cost, as it's a trade-off between sample throughput and metabolite coverage. As we will discuss, promising emerging technologies, including microfluidics and miniaturization of separation techniques, have the potential to achieve both rapid and more comprehensive metabolite analysis.

DNA-Linked Enzyme-Coupled Assay for Probing Glucosyltransferase Specificity.

Traditional enzyme characterization methods are low-throughput and therefore limit engineering efforts in synthetic biology and biotechnology. Here, we propose a DNA-linked enzyme-coupled assay (DLEnCA) to monitor enzyme reactions in a high-throughput manner. Throughput is improved by removing the need for protein purification and by limiting the need for liquid chromatography mass spectrometry (LCMS) product detection by linking enzymatic function to DNA modification. We demonstrate the DLEnCA methodology using glucosyltransferases as an illustration. The assay utilizes cell free transcription/translation systems to produce enzymes of interest, while UDP-glucose and T4-ß-glucosyltransferase are used to modify DNA, which is detected postreaction using qPCR or a similar means of DNA analysis. OleD and two glucosyltransferases from Arabidopsis were used to verify the assay's generality toward glucosyltransferases. We further show DLEnCA's utility by mapping out the substrate specificity for these enzymes.

Production and biomedical applications of virus-like particles derived from polyomaviruses.

Virus-like particles (VLPs), aggregates of capsid proteins devoid of viral genetic material, show great promise in the fields of vaccine development and gene therapy. These particles spontaneously self-assemble after heterologous expression of viral structural proteins. This review will focus on the use of virus-like particles derived from polyomavirus capsid proteins. Since their first recombinant production 27 years ago these particles have been investigated for a myriad of biomedical applications. These virus-like particles are safe, easy to produce, can be loaded with a broad range of diverse cargoes and can be tailored for specific delivery or epitope presentation. We will highlight the structural characteristics of polyomavirus-derived VLPs and give an overview of their applications in diagnostics, vaccine development and gene delivery.

A solid-phase platform for combinatorial and scarless multipart gene assembly.

With the emergence of standardized genetic modules as part of the synthetic biology toolbox, the need for universal and automatable assembly protocols increases. Although several methods and standards have been developed, these all suffer from drawbacks such as the introduction of scar sequences during ligation or the need for specific flanking sequences or a priori knowledge of the final sequence to be obtained. We have developed a method for scarless ligation of multipart gene segments in a truly sequence-independent fashion. The big advantage of this approach is that it is combinatorial, allowing the generation of all combinations of several variants of two or more modules to be ligated in less than a day. This method is based on the ligation of single-stranded or double-stranded oligodeoxynucleotides (ODN) and PCR products immobilized on a solid support. Different settings were tested to optimize the solid-support ligation. Finally, to show proof of concept for this novel multipart gene assembly platform a small library of all possible combinations of 4 BioBrick modules was generated and tested.