Exploring new frontiers in type 1 diabetes through advanced mass-spectrometry-based molecular measurements.

Type 1 diabetes (T1D) is a devastating autoimmune disease for which advanced mass spectrometry (MS) methods are increasingly used to identify new biomarkers and better understand underlying mechanisms. For example, integration of MS analysis and machine learning has identified multimolecular biomarker panels. In mechanistic studies, MS has contributed to the discovery of neoepitopes, and pathways involved in disease development and identifying therapeutic targets. However, challenges remain in understanding the role of tissue microenvironments, spatial heterogeneity, and environmental factors in disease pathogenesis. Recent advancements in MS, such as ultra-fast ion-mobility separations, and single-cell and spatial omics, can play a central role in addressing these challenges. Here, we review recent advancements in MS-based molecular measurements and their role in understanding T1D.

A deep learning-guided automated workflow in LipidOz for detailed characterization of fungal fatty acid unsaturation by ozonolysis.

Understanding fungal lipid biology and metabolism is critical for antifungal target discovery as lipids play central roles in cellular processes. Nuances in lipid structural differences can significantly impact their functions, making it necessary to characterize lipids in detail to understand their roles in these complex systems. In particular, lipid double bond (DB) locations are an important component of lipid structure that can only be determined using a few specialized analytical techniques. Ozone-induced dissociation mass spectrometry (OzID-MS) is one such technique that uses ozone to break lipid DBs, producing pairs of characteristic fragments that allow the determination of DB positions. In this work, we apply OzID-MS and LipidOz software to analyze the complex lipids of Saccharomyces cerevisiae yeast strains transformed with different fatty acid desaturases from Histoplasma capsulatum to determine the specific unsaturated lipids produced. The automated data analysis in LipidOz made the determination of DB positions from this large dataset more practical, but manual verification for all targets was still time-consuming. The DL model reduces manual involvement in data analysis, but since it was trained using mammalian lipid extracts, the prediction accuracy on yeast-derived data was reduced. We addressed both shortcomings by retraining the DL model to act as a pre-filter to prioritize targets for automated analysis, providing confident manually verified results but requiring less computational time and manual effort. Our workflow resulted in the determination of detailed DB positions and enzymatic specificity.

PeakQC: A Software Tool for Omics-Agnostic Automated Quality Control of Mass Spectrometry Data.

Mass spectrometry is broadly employed to study complex molecular mechanisms in various biological and environmental fields, enabling 'omics' research such as proteomics, metabolomics, and lipidomics. As study cohorts grow larger and more complex with dozens to hundreds of samples, the need for robust quality control (QC) measures through automated software tools becomes paramount to ensure the integrity, high quality, and validity of scientific conclusions from downstream analyses and minimize the waste of resources. Since existing QC tools are mostly dedicated to proteomics, automated solutions supporting metabolomics are needed. To address this need, we developed the software PeakQC, a tool for automated QC of MS data that is independent of omics molecular types (i.e., omics-agnostic). It allows automated extraction and inspection of peak metrics of precursor ions (e.g., errors in mass, retention time, arrival time) and supports various instrumentations and acquisition types, from infusion experiments or using liquid chromatography and/or ion mobility spectrometry front-end separations and with/without fragmentation spectra from data-dependent or independent acquisition analyses. Diagnostic plots for fragmentation spectra are also generated. Here, we describe and illustrate PeakQC's functionalities using different representative data sets, demonstrating its utility as a valuable tool for enhancing the quality and reliability of omics mass spectrometry analyses.

Evaluation of a Reference-Free Collision Cross Section Calibration Strategy for Proteomics Using SLIM-Based High-Resolution Ion Mobility Spectrometry-Mass Spectrometry.

Ion mobility spectrometry (IMS) is a gas-phase analytical technique that separates ions with different sizes and shapes and is compatible with mass spectrometry (MS) to provide an additional separation dimension. The rapid nature of the IMS separation combined with the high sensitivity of MS-based detection and the ability to derive structural information on analytes in the form of the property collision cross section (CCS) makes IMS particularly well-suited for characterizing complex samples in -omics applications. In such applications, the quality of CCS from IMS measurements is critical to confident annotation of the detected components in the complex -omics samples. However, most IMS instrumentation in mainstream use requires calibration to calculate CCS from measured arrival times, with the most notable exception being drift tube IMS measurements using multifield methods. The strategy for calibrating CCS values, particularly selection of appropriate calibrants, has important implications for CCS accuracy, reproducibility, and transferability between laboratories. The conventional approach to CCS calibration involves explicitly defining calibrants ahead of data acquisition and crucially relies upon availability of reference CCS values. In this work, we present a novel reference-free approach to CCS calibration which leverages trends among putatively identified features and computational CCS prediction to conduct calibrations post-data acquisition and without relying on explicitly defined calibrants. We demonstrated the utility of this reference-free CCS calibration strategy for proteomics application using high-resolution structures for lossless ion manipulations (SLIM)-based IMS-MS. We first validated the accuracy of CCS values using a set of synthetic peptides and then demonstrated using a complex peptide sample from cell lysate.

Expanding the CRISPR Toolbox for Engineering Lycopene Biosynthesis in Corynebacterium glutamicum.

Lycopene represents one of the central compounds in the carotenoid pathway and it exhibits a potent antioxidant ability with wide potential applications in medicine, food, and cosmetics. The microbial production of lycopene has received increasing concern in recent years. Corynebacterium glutamicum (C. glutamicum) is considered to be a safe and beneficial industrial production platform, naturally endowed with the ability to produce lycopene. However, the scarcity of efficient genetic tools and the challenge of identifying crucial metabolic genes impede further research on C. glutamicum for achieving high-yield lycopene production. To address these challenges, a novel genetic editing toolkit, CRISPR/MAD7 system, was established and developed. By optimizing the promoter, ORI and PAM sequences, the CRISPR/MAD7 system facilitated highly efficient gene deletion and exhibited a broad spectrum of PAM sites. Notably, 25 kb of DNA from the genome was successfully deleted. In addition, the CRISPR/MAD7 system was effectively utilized in the metabolic engineering of C. glutamicum, allowing for the simultaneous knockout of crtEb and crtR genes in one step to enhance the accumulation of lycopene by blocking the branching pathway. Through screening crucial genes such as crtE, crtB, crtI, idsA, idi, and cg0722, an optimal carotenogenic gene combination was obtained. Particularly, cg0722, a membrane protein gene, was found to play a vital role in lycopene production. Therefore, the CBIEbR strain was obtained by overexpressing cg0722, crtB, and crtI while strategically blocking the by-products of the lycopene pathway. As a result, the final engineered strain produced lycopene at 405.02 mg/L (9.52 mg/g dry cell weight, DCW) in fed-batch fermentation, representing the highest reported lycopene yield in C. glutamicum to date. In this study, a powerful and precise genetic tool was used to engineer C. glutamicum for lycopene production. Through the modifications between the host cell and the carotenogenic pathway, the lycopene yield was stepwise improved by 102-fold as compared to the starting strain. This study highlights the usefulness of the CRISPR/MAD7 toolbox, demonstrating its practical applications in the metabolic engineering of industrially robust C. glutamicum.

Self-adjuvanting polymeric nanovaccines enhance IFN production and cytotoxic T cell response.

Vaccines represent one of the most powerful and cost-effective innovations for controlling a wide range of infectious diseases caused by various viruses and bacteria. Unlike mRNA and DNA-based vaccines, subunit vaccines carry no risk of insertional mutagenesis and can be lyophilized for convenient transportation and long-term storage. However, existing adjuvants are often associated with toxic effect and reactogenicity, necessitating expanding the repertoire of adjuvants with better biocompatibility, for instance, designing self-adjuvating polymeric carriers. We herein report a novel subunit vaccine delivery platform constructed via in situ free radical polymerization of C7A (2-(Hexamethyleneimino) ethyl methacrylate) and acrylamide around the surface of individual protein antigens. Using ovalbumin (OVA) as a model antigen, we observed substantial increases in both diameter (∼70 nm) and surface potential (-1.18 mV) following encapsulation, referred to as n(OVA)C7A. C7A's ultra pH sensitivity with a transition pH around 6.9 allows for rapid protonation in acidic environments. This property facilitates crucial processes such as endosomal escape and major histocompatibility complex (MHC)-I-mediated antigen presentation, culminating in the substantial CD8+ T cell activation. Additionally, compared to OVA nanocapsules without the C7A components and native OVA without modifications, we observed heightened B cell activation within the germinal center, along with remarkable increases in serum antibody and cytokine production. It's important to note that mounting evidence suggests that adjuvant effects, particularly its targeted stimulation of type I interferons (IFNs), can contribute to advantageous adaptive immune responses. Beyond its exceptional potency, the nanovaccine also demonstrated robust formation of immune memory and exhibited a favorable biosafety profile. These findings collectively underscore the promising potential of our nanovaccine in the realm of immunotherapy and vaccine development.

Computational tools and algorithms for ion mobility spectrometry-mass spectrometry.

Ion mobility spectrometry-mass spectrometry (IMS-MS or IM-MS) is a powerful analytical technique that combines the gas-phase separation capabilities of IM with the identification and quantification capabilities of MS. IM-MS can differentiate molecules with indistinguishable masses but different structures (e.g., isomers, isobars, molecular classes, and contaminant ions). The importance of this analytical technique is reflected by a staged increase in the number of applications for molecular characterization across a variety of fields, from different MS-based omics (proteomics, metabolomics, lipidomics, etc.) to the structural characterization of glycans, organic matter, proteins, and macromolecular complexes. With the increasing application of IM-MS there is a pressing need for effective and accessible computational tools. This article presents an overview of the most recent free and open-source software tools specifically tailored for the analysis and interpretation of data derived from IM-MS instrumentation. This review enumerates these tools and outlines their main algorithmic approaches, while highlighting representative applications across different fields. Finally, a discussion of current limitations and expectable improvements is presented.

Accumulation of Large Ion Populations with High Ion Densities and Effects Due to Space Charge in Traveling Wave-Based Structures for Lossless Ion Manipulations (SLIM) IMS-MS.

The accumulation of very large ion populations in traveling wave (TW)-based Structures for Lossless ion Manipulations (SLIM) has been studied to better understand aspects of "in-SLIM" ion accumulation, and particularly its use in conjunction with ion mobility spectrometry (IMS). A linear SLIM ion path was implemented that had a "gate" for blocking and accumulating ions for arbitrary time periods. Removing the gate potential caused ions to exit, and the spatial distributions of accumulated ions examined. The ion populations for a set of peptides increased approximately linearly with increased accumulation times until space change effects became significant, after which the peptide precursor ion populations decreased due to growing space charge-related ion activation, reactions, and losses. Ion activation increased with added storage times and the TW amplitude. Lower amplitude TWs in the accumulation/storage region prevented or minimized ion losses or ion heating effects that can also lead to fragmentation. Our results supported the use of an accumulation region close to the SLIM entrance for speeding accumulation, minimizing ion heating, and avoiding ion population profiles that result in IMS peak tailing. Importantly, space charge-driven separations were observed for large populations of accumulated species and attributed to the opposing effects of space charge and the TW. In these separations, ion species form distributions or peaks, sometimes moving against the TW, and are ordered in the SLIM based on their mobilities. Only the highest mobility ions located closest to the gate in the trapped ion population (and where the highest ion densities were achieved) were significantly activated. The observed separations may offer utility for ion prefractionation of ions and increasing the dynamic range measurements, increasing the resolving power of IMS separations by decreasing peak widths for accumulated ion populations, and other purposes benefiting from separations of extremely large ion populations.

Deciphering ApoE Genotype-Driven Proteomic and Lipidomic Alterations in Alzheimer's Disease Across Distinct Brain Regions.

Alzheimer's disease (AD) is a neurodegenerative disease with a complex etiology influenced by confounding factors such as genetic polymorphisms, age, sex, and race. Traditionally, AD research has not prioritized these influences, resulting in dramatically skewed cohorts such as three times the number of Apolipoprotein E (APOE) ε4-allele carriers in AD relative to healthy cohorts. Thus, the resulting molecular changes in AD have previously been complicated by the influence of apolipoprotein E disparities. To explore how apolipoprotein E polymorphism influences AD progression, 62 post-mortem patients consisting of 33 AD and 29 controls (Ctrl) were studied to balance the number of ε4-allele carriers and facilitate a molecular comparison of the apolipoprotein E genotype. Lipid and protein perturbations were assessed across AD diagnosed brains compared to Ctrl brains, ε4 allele carriers (APOE4+ for those carrying 1 or 2 ε4s and APOE4- for non-ε4 carriers), and differences in ε3ε3 and ε3ε4 Ctrl brains across two brain regions (frontal cortex (FCX) and cerebellum (CBM)). The region-specific influences of apolipoprotein E on AD mechanisms showcased mitochondrial dysfunction and cell proteostasis at the core of AD pathophysiology in the post-mortem brains, indicating these two processes may be influenced by genotypic differences and brain morphology.

Magnetic nanoparticle-based immunosensors and aptasensors for mycotoxin detection in foodstuffs: An update.

Mycotoxin contamination of food crops is a global challenge due to their unpredictable occurrence and severe adverse health effects on humans. Therefore, it is of great importance to develop effective tools to prevent the accumulation of mycotoxins through the food chain. The use of magnetic nanoparticle (MNP)-assisted biosensors for detecting mycotoxin in complex foodstuffs has garnered great interest due to the significantly enhanced sensitivity and accuracy. Within such a context, this review includes the fundamentals and recent advances (2020-2023) in the area of mycotoxin monitoring in food matrices using MNP-based aptasensors and immunosensors. In this review, we start by providing a comprehensive introduction to the design of immunosensors (natural antibody or nanobody, random or site-oriented immobilization) and aptasensors (techniques for aptamer selection, characterization, and truncation). Meanwhile, special attention is paid to the multifunctionalities of MNPs (recoverable adsorbent, versatile carrier, and signal indicator) in preparing mycotoxin-specific biosensors. Further, the contribution of MNPs to the multiplexing determination of various mycotoxins is summarized. Finally, challenges and future perspectives for the practical applications of MNP-assisted biosensors are also discussed. The progress and updates of MNP-based biosensors shown in this review are expected to offer readers valuable insights about the design of MNP-based tools for the effective detection of mycotoxins in practical applications.

Significance of the antibody orientation for the lateral flow immunoassays: A mini-review.

Lateral flow immunoassays (LFIAs) are well-established and broadly commercialized tools in the field of point-of-care testing due to their simplicity, rapidity, cost-effectiveness, and low requirements for users and equipment. However, the insensitivity and the possibility of producing inaccurate results associated with conventional LFIAs have impeded their wide-ranging implementation, especially for monitoring ultra-trace level of analytes. Moreover, the heterogeneous distribution of amino acids on the surface of antibody (Ab) results in a lack of precise control over their orientation, which ultimately leads to unsatisfactory detection performance. To address those concerns, herein we provide an overview of the emerging efforts to prepare well-established LFIAs from the perspective of orientation manipulation of immobilized Abs on the nanoprobes or membranes. The preparation of excellent nanoprobes with Abs being oriented immobilized, consisting of the nanoprobe types, Ab types, and their conjugation chemistries, are reviewed. Followed by the introduction of efforts highlight the importance of directionally immobilized Ab on the membrane. The effects of Ab orientation on the analytical performance of LFIA platforms in terms of sensitivity, specificity, rapidity, reliability, cost-effectiveness, and stability are also summarized. Finally, the future development and challenges of Ab-oriented immobilization-assisted LFIAs are also discussed.

The Role of Ion Rotation in Ion Mobility: Ultrahigh-Precision Prediction of Ion Mobility Dependence on Ion Mass Distribution and Translational to Rotational Energy Transfer.

The role of ion rotation in determining ion mobilities is explored using the subtle gas phase ion mobility shifts based on differences in ion mass distributions between isotopomer ions that have been observed with ion mobility spectrometry (IMS) measurements. These mobility shifts become apparent for IMS resolving powers on the order of ∼1500 where relative mobilities (or alternatively momentum transfer collision cross sections; Ω) can be measured with a precision of ∼10 ppm. The isotopomer ions have identical structures and masses, differing only in their internal mass distributions, and their Ω differences cannot be predicted by widely used computational approaches, which ignore the dependence of Ω on the ion's rotational properties. Here, we investigate the rotational dependence of Ω, which includes changes to its collision frequency due to thermal rotation as well as the coupling of translational to rotational energy transfer. We show that differences in rotational energy transfer during ion-molecule collisions provide the major contribution to isotopomer ion separations, with only a minor contribution due to an increase in collision frequency due to ion rotation. Modeling including these factors allowed for differences in Ω to be calculated that precisely mirror the experimental separations. These findings also highlight the promise of pairing high-resolution IMS measurements with theory and computation for improved elucidation of subtle structural differences between ions.

mzapy: An Open-Source Python Library Enabling Efficient Extraction and Processing of Ion Mobility Spectrometry-Mass Spectrometry Data in the MZA File Format.

Analysis of ion mobility spectrometry (IMS) data has been challenging and limited the full utility of these measurements. Unlike liquid chromatography-mass spectrometry, where a plethora of tools with well-established algorithms exist, the incorporation of the additional IMS dimension requires upgrading existing computational pipelines and developing new algorithms to fully exploit the advantages of the technology. We have recently reported MZA, a new and simple mass spectrometry data structure based on the broadly supported HDF5 format and created to facilitate software development. While this format is inherently supportive of application development, the availability of core libraries in popular programming languages with standard mass spectrometry utilities will facilitate fast software development and broader adoption of the format. To this end, we present a Python package, mzapy, for efficient extraction and processing of mass spectrometry data in the MZA format, especially for complex data containing ion mobility spectrometry dimension. In addition to raw data extraction, mzapy contains supporting utilities enabling tasks including calibration, signal processing, peak finding, and generating plots. Being implemented in pure Python and having minimal and largely standardized dependencies makes mzapy uniquely suited to application development in the multiomics domain. The mzapy package is free and open-source, includes comprehensive documentation, and is structured to support future extension to meet the evolving needs of the MS community. The software source code is freely available at https://github.com/PNNL-m-q/mzapy.

Heterogeneity changes of active bacterial community on cigar filler leaves after fermentation based on metagenome.

Microorganisms play an important role in cigar fermentation. To further explore the dynamic changes of bacterial community composition, the changes of surface bacterial diversity of cigar filler leaves were investigated in the present study by high-throughput sequencing technology. It was found that the surface bacterial richness was declined after fermentation, and the dominant microorganisms on the surface of cigar filler leaves evolved from Pseudomonas spp. and Sphingomonas spp. before fermentation to Staphylococcus spp. after fermentation. The chemical composition and sensory quality evaluation of cigar filler leaves were closely related to the changes of surface bacterial community. The changes of the dominant surface bacterial community led to the differences of metabolic functions, among which the metabolic pathways such as the synthesis of secondary metabolites, carbon metabolism, and amino acid biosynthesis were significantly different. The results provide a basis for clarifying the roles of bacteria in fermentation of cigar filler leaves.

Evaluating Software Tools for Lipid Identification from Ion Mobility Spectrometry-Mass Spectrometry Lipidomics Data.

The unambiguous identification of lipids is a critical component of lipidomics studies and greatly impacts the interpretation and significance of analyses as well as the ultimate biological understandings derived from measurements. The level of structural detail that is available for lipid identifications is largely determined by the analytical platform being used. Mass spectrometry (MS) coupled with liquid chromatography (LC) is the predominant combination of analytical techniques used for lipidomics studies, and these methods can provide fairly detailed lipid identification. More recently, ion mobility spectrometry (IMS) has begun to see greater adoption in lipidomics studies thanks to the additional dimension of separation that it provides and the added structural information that can support lipid identification. At present, relatively few software tools are available for IMS-MS lipidomics data analysis, which reflects the still limited adoption of IMS as well as the limited software support. This fact is even more pronounced for isomer identifications, such as the determination of double bond positions or integration with MS-based imaging. In this review, we survey the landscape of software tools that are available for the analysis of IMS-MS-based lipidomics data and we evaluate lipid identifications produced by these tools using open-access data sourced from the peer-reviewed lipidomics literature.

LipidOz enables automated elucidation of lipid carbon-carbon double bond positions from ozone-induced dissociation mass spectrometry data.

Lipids play essential roles in many biological processes and disease pathology, but unambiguous identification of lipids is complicated by the presence of multiple isomeric species differing by fatty acyl chain length, stereospecifically numbered (sn) position, and position/stereochemistry of double bonds. Conventional liquid chromatography-mass spectrometry (LC-MS/MS) analyses enable the determination of fatty acyl chain lengths (and in some cases sn position) and number of double bonds, but not carbon-carbon double bond positions. Ozone-induced dissociation (OzID) is a gas-phase oxidation reaction that produces characteristic fragments from lipids containing double bonds. OzID can be incorporated into ion mobility spectrometry (IMS)-MS instruments for the structural characterization of lipids, including additional isomer separation and confident assignment of double bond positions. The complexity and repetitive nature of OzID data analysis and lack of software tool support have limited the application of OzID for routine lipidomics studies. Here, we present an open-source Python tool, LipidOz, for the automated determination of lipid double bond positions from OzID-IMS-MS data, which employs a combination of traditional automation and deep learning approaches. Our results demonstrate the ability of LipidOz to robustly assign double bond positions for lipid standard mixtures and complex lipid extracts, enabling practical application of OzID for future lipidomics.

Multistage-Responsive Dual-Enzyme Nanocascades for Synergistic Radiosensitization-Starvation Cancer Therapy.

Radiotherapy is a common cancer treatment approach in clinical practice, yet its efficacy has been restricted by tumor hypoxia. Nanomaterials-mediated systemic delivery of glucose oxidase (GOx) and catalase (CAT) or CAT-like nanoenzymes holds the potential to enhance tumor oxygenation. However, they face the challenge of intermediate (hydrogen peroxide [H2 O2 ]) escape during systemic circulation if the enzyme pair is not closely placed to largely decompose H2 O2 , leading to oxidative stress on normal tissues. In the present study, a oxygen-generating nanocascade, n(GOx-CAT)C7A , constructed by strategically placing an enzymatic cascade (GOx and CAT) within a polymeric coating rich in hexamethyleneimine (C7A) moieties, is reported. During blood circulation, C7A remains predominantly non-protonated , achieving prolonged blood circulation due to its low-fouling surface. Once n(GOx-CAT)C7A reaches the tumor site, the acidic tumor microenvironment (TME) induces protonation of C7A moieties, resulting in a positively charged surface for enhanced tumor transcytosis. Moreover, GOx and CAT are covalently conjugated into close spatial proximity (<10 nm) for effective H2 O2  elimination. As demonstrated by the in vivo results, n(GOx-CAT)C7A achieves effective tumor retention and oxygenation, potent radiosensitization and antitumor effects. Such a dual-enzyme nanocascade for smart O2  delivery holds great potential for enhancing the hypoxia-compromised cancer therapies.

MZA: A Data Conversion Tool to Facilitate Software Development and Artificial Intelligence Research in Multidimensional Mass Spectrometry.

Modern mass spectrometry-based workflows employing hybrid instrumentation and orthogonal separations collect multidimensional data, potentially allowing deeper understanding in omics studies through adoption of artificial intelligence methods. However, the large volume of these rich spectra challenges existing data storage and access technologies, therefore precluding informatics advancements. We present MZA (pronounced m-za), the mass-to-charge (m/z) generic data storage and access tool designed to facilitate software development and artificial intelligence research in multidimensional mass spectrometry measurements. Composed of a data conversion tool and a simple file structure based on the HDF5 format, MZA provides easy, cross-platform and cross-programming language access to raw MS-data, enabling fast development of new tools in data science programming languages such as Python and R. The software executable, example MS-data and example Python and R scripts are freely available at https://github.com/PNNL-m-q/mza.

Enhanced Expression of Alcohol Dehydrogenase I in Pichia pastoris Reduces the Content of Acetaldehyde in Wines.

Acetaldehyde is an important carbonyl compound commonly detected in wines. A high concentration of acetaldehyde can affect the flavor of wines and result in adverse effects on human health. Alcohol dehydrogenase I (ADH1) in Saccharomyces cerevisiae catalyzes the reduction reaction of acetaldehyde into ethanol in the presence of cofactors, showing the potential to reduce the content of acetaldehyde in wines. In this study, ADH1 was successfully expressed in Pichia pastoris GS115 based on codon optimization. Then, the expression level of ADH1 was enhanced by replacing its promoter with optimized promoters and increasing the copy number of the expression cassette, with ADH1 being purified using nickel column affinity chromatography. The enzymatic activity of purified ADH1 reached 605.44 ± 44.30 U/mg. The results of the effect of ADH1 on the content of acetaldehyde in wine revealed that the acetaldehyde content of wine samples was reduced from 168.05 ± 0.55 to 113.17 ± 6.08 mg/L with the addition of 5 mM NADH and the catalysis of ADH1, and from 135.53 ± 4.08 to 52.89 ± 2.20 mg/L through cofactor regeneration. Our study provides a novel approach to reducing the content of acetaldehyde in wines through enzymatic catalysis.

Adaptive laboratory evolution to obtain furfural tolerant Saccharomyces cerevisiae for bioethanol production and the underlying mechanism.

Introduction: Furfural, a main inhibitor produced during pretreatment of lignocellulose, has shown inhibitory effects on S. cerevisiae. Method: In the present study, new strains named 12-1 with enhanced resistance to furfural were obtained through adaptive laboratory evolution, which exhibited a shortened lag phase by 36 h, and an increased ethanol conversion rate by 6.67% under 4 g/L furfural. Results and Discussion: To further explore the mechanism of enhanced furfural tolerance, ADR1_1802 mutant was constructed by CRISPR/Cas9 technology, based on whole genome re-sequencing data. The results indicated that the time when ADR1_1802 begin to grow was shortened by 20 h compared with reference strain (S. cerevisiae CEN.PK113-5D) when furfural was 4 g/L. Additionally, the transcription levels of GRE2 and ADH6 in ADR1_ 1802 mutant were increased by 53.69 and 44.95%, respectively, according to real-time fluorescence quantitative PCR analysis. These findings suggest that the enhanced furfural tolerance of mutant is due to accelerated furfural degradation. Importance: Renewable carbon worldwide is vital to achieve "zero carbon" target. Bioethanol obtained from biomass is one of them. To make bioethanol price competitive to fossil fuel, higher ethanol yield is necessary, therefore, monosaccharide produced during biomass pretreatment should be effectively converted to ethanol by Saccharomyces cerevisiae. However, inhibitors formed by glucose or xylose oxidation could make ethanol yield lower. Thus, inhibitor tolerant Saccharomyces cerevisiae is important to this process. As one of the main component of pretreatment hydrolysate, furfural shows obvious impact on growth and ethanol production of Saccharomyces cerevisiae. To get furfural tolerant Saccharomyces cerevisiae and find the underlying mechanism, adaptive laboratory evolution and CRISPR/Cas9 technology were applied in the present study.