High throughput nanostructure-initiator mass spectrometry screening of microbial growth conditions for maximal β-glucosidase production

Mechanism Across Scales: A Holistic Modeling Framework Integrating Laboratory and Field Studies for Microbial Ecology

Over the last century, leaps in technology for imaging, sampling, detection, high-throughput sequencing, and -omics analyses have revolutionized microbial ecology to enable rapid acquisition of extensive datasets for microbial communities across the ever-increasing temporal and spatial scales. The present challenge is capitalizing on our enhanced abilities of observation and integrating diverse data types from different scales, resolutions, and disciplines to reach a causal and mechanistic understanding of how microbial communities transform and respond to perturbations in the environment. This type of causal and mechanistic understanding will make predictions of microbial community behavior more robust and actionable in addressing microbially mediated global problems. To discern drivers of microbial community assembly and function, we recognize the need for a conceptual, quantitative framework that connects measurements of genomic potential, the environment, and ecological and physical forces to rates of microbial growth at specific locations. We describe the Framework for Integrated, Conceptual, and Systematic Microbial Ecology (FICSME), an experimental design framework for conducting process-focused microbial ecology studies that incorporates biological, chemical, and physical drivers of a microbial system into a conceptual model. Through iterative cycles that advance our understanding of the coupling across scales and processes, we can reliably predict how perturbations to microbial systems impact ecosystem-scale processes or vice versa. We describe an approach and potential applications for using the FICSME to elucidate the mechanisms of globally important ecological and physical processes, toward attaining the goal of predicting the structure and function of microbial communities in chemically complex natural environments.

On-chip integration of droplet microfluidics and nanostructure-initiator mass spectrometry for enzyme screening

Biological assays often require expensive reagents and tedious manipulations. These shortcomings can be overcome using digitally operated microfluidic devices that require reduced sample volumes to automate assays. One particular challenge is integrating bioassays with mass spectrometry based analysis. Towards this goal we have developed μNIMS, a highly sensitive and high throughput technique that integrates droplet microfluidics with nanostructure-initiator mass spectrometry (NIMS). Enzyme reactions are carried out in droplets that can be arrayed on discrete NIMS elements at defined time intervals for subsequent mass spectrometry analysis, enabling time resolved enzyme activity assay. We apply the μNIMS platform for kinetic characterization of a glycoside hydrolase enzyme (CelE-CMB3A), a chimeric enzyme capable of deconstructing plant hemicellulose into monosaccharides for subsequent conversion to biofuel. This study reveals NIMS nanostructures can be fabricated into arrays for microfluidic droplet deposition, NIMS is compatible with droplet and digital microfluidics, and can be used on-chip to assay glycoside hydrolase enzyme in vitro.

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.

Profiling microbial lignocellulose degradation and utilization by emergent omics technologies

The use of plant materials to generate renewable biofuels and other high-value chemicals is the sustainable and preferable option, but will require considerable improvements to increase the rate and efficiency of lignocellulose depolymerization. This review highlights novel and emerging technologies that are being developed and deployed to characterize the process of lignocellulose degradation. The review will also illustrate how microbial communities deconstruct and metabolize lignocellulose by identifying the necessary genes and enzyme activities along with the reaction products. These technologies include multi-omic measurements, cell sorting and isolation, nuclear magnetic resonance spectroscopy (NMR), activity-based protein profiling, and direct measurement of enzyme activity. The recalcitrant nature of lignocellulose necessitates the need to characterize the methods microbes employ to deconstruct lignocellulose to inform new strategies on how to greatly improve biofuel conversion processes. New technologies are yielding important insights into microbial functions and strategies employed to degrade lignocellulose, providing a mechanistic blueprint in order to advance biofuel production.

Metagenomic analysis of microbial consortia enriched from compost: new insights into the role of Actinobacteria in lignocellulose decomposition

BACKGROUND

Compost habitats sustain a vast ensemble of microbes specializing in the degradation of lignocellulosic plant materials and are thus important both for their roles in the global carbon cycle and as potential sources of biochemical catalysts for advanced biofuels production. Studies have revealed substantial diversity in compost microbiomes, yet how this diversity relates to functions and even to the genes encoding lignocellulolytic enzymes remains obscure. Here, we used a metagenomic analysis of the rice straw-adapted (RSA) microbial consortia enriched from compost ecosystems to decipher the systematic and functional contexts within such a distinctive microbiome.

RESULTS

Analyses of the 16S pyrotag library and 5 Gbp of metagenomic sequence showed that the phylum Actinobacteria was the predominant group among the Bacteria in the RSA consortia, followed by Proteobacteria, Firmicutes, Chloroflexi, and Bacteroidetes. The CAZymes profile revealed that CAZyme genes in the RSA consortia were also widely distributed within these bacterial phyla. Strikingly, about 46.1 % of CAZyme genes were from actinomycetal communities, which harbored a substantially expanded catalog of the cellobiohydrolase, β-glucosidase, acetyl xylan esterase, arabinofuranosidase, pectin lyase, and ligninase genes. Among these communities, a variety of previously unrecognized species was found, which reveals a greater ecological functional diversity of thermophilic Actinobacteria than previously assumed.

CONCLUSION

These data underline the pivotal role of thermophilic Actinobacteria in lignocellulose biodegradation processes in the compost habitat. Besides revealing a new benchmark for microbial enzymatic deconstruction of lignocelluloses, the results suggest that actinomycetes found in compost ecosystems are potential candidates for mining efficient lignocellulosic enzymes in the biofuel industry.

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.

High throughput screening of enzyme activity with mass spectrometry imaging

Mass spectrometry imaging (MSI) has found a diversity of applications ranging from localizing metabolites and proteins in tissues to investigating microbial interactions, and as a result is perhaps the fastest growing subfield of mass spectrometry. Advances in surface mass spectrometry technologies are equally applicable to the analysis of arrayed samples. One promising field in which this capacity has been leveraged is the high-throughput analysis of enzyme activity, an important step in the development of a wide range of biotechnologies. This review article describes several emerging approaches that seek to improve the quality and scope of this application of MSI.

Application of stable isotope-assisted metabolomics for cell metabolism studies

The applications of stable isotopes in metabolomics have facilitated the study of cell metabolisms. Stable isotope-assisted metabolomics requires: (1) properly designed tracer experiments; (2) stringent sampling and quenching protocols to minimize isotopic alternations; (3) efficient metabolite separations; (4) high resolution mass spectrometry to resolve overlapping peaks and background noises; and (5) data analysis methods and databases to decipher isotopic clusters over a broad m/z range (mass-to-charge ratio). This paper overviews mass spectrometry based techniques for precise determination of metabolites and their isotopologues. It also discusses applications of isotopic approaches to track substrate utilization, identify unknown metabolites and their chemical formulas, measure metabolite concentrations, determine putative metabolic pathways, and investigate microbial community populations and their carbon assimilation patterns. In addition, 13C-metabolite fingerprinting and metabolic models can be integrated to quantify carbon fluxes (enzyme reaction rates). The fluxome, in combination with other "omics" analyses, may give systems-level insights into regulatory mechanisms underlying gene functions. More importantly, 13C-tracer experiments significantly improve the potential of low-resolution gas chromatography-mass spectrometry (GC-MS) for broad-scope metabolism studies. We foresee the isotope-assisted metabolomics to be an indispensable tool in industrial biotechnology, environmental microbiology, and medical research.