A basal-level activity of ATR links replication fork surveillance and stress response.

Mammalian cells use diverse pathways to prevent deleterious consequences during DNA replication, yet the mechanism by which cells survey individual replisomes to detect spontaneous replication impediments at the basal level, and their accumulation during replication stress, remain undefined. Here, we used single-molecule localization microscopy coupled with high-order-correlation image-mining algorithms to quantify the composition of individual replisomes in single cells during unperturbed replication and under replicative stress. We identified a basal-level activity of ATR that monitors and regulates the amounts of RPA at forks during normal replication. Replication-stress amplifies the basal activity through the increased volume of ATR-RPA interaction and diffusion-driven enrichment of ATR at forks. This localized crowding of ATR enhances its collision probability, stimulating the activation of its replication-stress response. Finally, we provide a computational model describing how the basal activity of ATR is amplified to produce its canonical replication stress response.

Sequence-Independent Self-Assembly of Germ Granule mRNAs into Homotypic Clusters.

mRNAs enriched in membraneless condensates provide functional compartmentalization within cells. The mechanisms that recruit transcripts to condensates are under intense study; however, how mRNAs organize once they reach a granule remains poorly understood. Here, we report on a self-sorting mechanism by which multiple mRNAs derived from the same gene assemble into discrete homotypic clusters. We demonstrate that in vivo mRNA localization to granules and self-assembly within granules are governed by different mRNA features: localization is encoded by specific RNA regions, whereas self-assembly involves the entire mRNA, does not involve sequence-specific, ordered intermolecular RNA:RNA interactions, and is thus RNA sequence independent. We propose that the ability of mRNAs to self-sort into homotypic assemblies is an inherent property of an messenger ribonucleoprotein (mRNP) that is augmented under conditions that increase RNA concentration, such as upon enrichment in RNA-protein granules, a process that appears conserved in diverse cellular contexts and organisms.

Encoding LC-MS-Based Untargeted Metabolomics Data into Images toward AI-Based Clinical Diagnosis.

Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics provides comprehensive and quantitative profiling of metabolites in clinical investigations. The use of whole metabolome profiles is a promising strategy for disease diagnosis but technically challenging. Here, we developed an approach, namely MetImage, to encode LC-MS-based untargeted metabolomics data into multi-channel digital images. Then, the images that represent the comprehensive metabolome profiles can be employed for developing deep learning-based AI models toward clinical diagnosis. In this work, we demonstrated the application of MetImage for clinical screening of esophageal squamous cell carcinoma (ESCC) in a clinical cohort with 1104 participants. A convolutional neuronal network-based AI model was trained to distinguish ESCC screening positive and negative subjects using their serum metabolomics data. Superior performances such as sensitivity (85%), specificity (92%), and area under curve (0.95) were validated in an independent testing cohort (N = 442). Importantly, we demonstrated that our AI-based ESCC screening model is not a "black box". The encoded images reserved the characteristics of mass spectra from the raw LC-MS data; therefore, metabolite identifications in key image features were readily achieved. Altogether, MetImage is a unique approach that encodes raw LC-MS-based untargeted metabolomics data into images and facilitates the utilization of whole metabolome profiles for AI-based clinical applications with improved interpretability.

AllCCS2: Curation of Ion Mobility Collision Cross-Section Atlas for Small Molecules Using Comprehensive Molecular Representations.

The development of ion mobility-mass spectrometry (IM-MS) has revolutionized the analysis of small molecules, such as metabolomics, lipidomics, and exposome studies. The curation of comprehensive reference collision cross-section (CCS) databases plays a pivotal role in the successful application of IM-MS for small-molecule analysis. In this study, we presented AllCCS2, an enhanced version of AllCCS, designed for the universal prediction of the ion mobility CCS values of small molecules. AllCCS2 incorporated newly available experimental CCS data, including 10,384 records and 7713 unified values, as training data. By leveraging a neural network trained on diverse molecular representations encompassing mass spectrometry features, molecular descriptors, and graph features extracted using a graph convolutional network, AllCCS2 achieved exceptional prediction accuracy. AllCCS2 achieved median relative error (MedRE) values of 0.31, 0.72, and 1.64% in the training, validation, and testing sets, respectively, surpassing existing CCS prediction tools in terms of accuracy and coverage. Furthermore, AllCCS2 exhibited excellent compatibility with different instrument platforms (DTIMS, TWIMS, and TIMS). The prediction uncertainties in AllCCS2 from the training data and the prediction model were comprehensively investigated by using representative structure similarity and model prediction variation. Notably, small molecules with high structural similarities to the training set and lower model prediction variation exhibited improved accuracy and lower relative errors. In summary, AllCCS2 serves as a valuable resource to support applications of IM-MS technologies. The AllCCS2 database and tools are freely accessible at http://allccs.zhulab.cn/.

ATR-mediated phosphorylation of FANCI regulates dormant origin firing in response to replication stress.

Excess dormant origins bound by the minichromosome maintenance (MCM) replicative helicase complex play a critical role in preventing replication stress, chromosome instability, and tumorigenesis. In response to DNA damage, replicating cells must coordinate DNA repair and dormant origin firing to ensure complete and timely replication of the genome; how cells regulate this process remains elusive. Herein, we identify a member of the Fanconi anemia (FA) DNA repair pathway, FANCI, as a key effector of dormant origin firing in response to replication stress. Cells lacking FANCI have reduced number of origins, increased inter-origin distances, and slowed proliferation rates. Intriguingly, ATR-mediated FANCI phosphorylation inhibits dormant origin firing while promoting replication fork restart/DNA repair. Using super-resolution microscopy, we show that FANCI co-localizes with MCM-bound chromatin in response to replication stress. These data reveal a unique role for FANCI as a modulator of dormant origin firing and link timely genome replication to DNA repair.

Super-resolution visualization of distinct stalled and broken replication fork structures.

Endogenous genotoxic stress occurs in healthy cells due to competition between DNA replication machinery, and transcription and topographic relaxation processes. This causes replication fork stalling and regression, which can further collapse to form single-ended double strand breaks (seDSBs). Super-resolution microscopy has made it possible to directly observe replication stress and DNA damage inside cells, however new approaches to sample preparation and analysis are required. Here we develop and apply multicolor single molecule microscopy to visualize individual replication forks under mild stress from the trapping of Topoisomerase I cleavage complexes, a damage induction which closely mimics endogenous replicative stress. We observe RAD51 and RAD52, alongside RECQ1, as the first responder proteins to stalled but unbroken forks, whereas Ku and MRE11 are initially recruited to seDSBs. By implementing novel super-resolution imaging assays, we are thus able to discern closely related replication fork stress motifs and their repair pathways.

An ATR and CHK1 kinase signaling mechanism that limits origin firing during unperturbed DNA replication.

DNA damage-induced signaling by ATR and CHK1 inhibits DNA replication, stabilizes stalled and collapsed replication forks, and mediates the repair of multiple classes of DNA lesions. We and others have shown that ATR kinase inhibitors, three of which are currently undergoing clinical trials, induce excessive origin firing during unperturbed DNA replication, indicating that ATR kinase activity limits replication initiation in the absence of damage. However, the origins impacted and the underlying mechanism(s) have not been described. Here, we show that unperturbed DNA replication is associated with a low level of ATR and CHK1 kinase signaling and that inhibition of this signaling induces dormant origin firing at sites of ongoing replication throughout the S phase. We show that ATR and CHK1 kinase inhibitors induce RIF1 Ser2205 phosphorylation in a CDK1-dependent manner, which disrupts an interaction between RIF1 and PP1 phosphatase. Thus, ATR and CHK1 signaling suppresses CDK1 kinase activity throughout the S phase and stabilizes an interaction between RIF1 and PP1 in replicating cells. PP1 dephosphorylates key CDC7 and CDK2 kinase substrates to inhibit the assembly and activation of the replicative helicase. This mechanism limits origin firing during unperturbed DNA replication in human cells.

Disruption of Ca2+i Homeostasis and Connexin 43 Hemichannel Function in the Right Ventricle Precedes Overt Arrhythmogenic Cardiomyopathy in Plakophilin-2-Deficient Mice.

BACKGROUND: Plakophilin-2 (PKP2) is classically defined as a desmosomal protein. Mutations in PKP2 associate with most cases of gene-positive arrhythmogenic right ventricular cardiomyopathy. A better understanding of PKP2 cardiac biology can help elucidate the mechanisms underlying arrhythmic and cardiomyopathic events consequent to PKP2 deficiency. Here, we sought to capture early molecular/cellular events that can act as nascent arrhythmic/cardiomyopathic substrates. METHODS: We used multiple imaging, biochemical and high-resolution mass spectrometry methods to study functional/structural properties of cells/tissues derived from cardiomyocyte-specific, tamoxifen-activated, PKP2 knockout mice (PKP2cKO) 14 days post-tamoxifen injection, a time point preceding overt electrical or structural phenotypes. Myocytes from right or left ventricular free wall were studied separately. RESULTS: Most properties of PKP2cKO left ventricular myocytes were not different from control; in contrast, PKP2cKO right ventricular (RV) myocytes showed increased amplitude and duration of Ca2+ transients, increased Ca2+ in the cytoplasm and sarcoplasmic reticulum, increased frequency of spontaneous Ca2+ release events (sparks) even at comparable sarcoplasmic reticulum load, and dynamic Ca2+ accumulation in mitochondria. We also observed early- and delayed-after transients in RV myocytes and heightened susceptibility to arrhythmias in Langendorff-perfused hearts. In addition, ryanodine receptor 2 in PKP2cKO-RV cells presented enhanced Ca2+ sensitivity and preferential phosphorylation in a domain known to modulate Ca2+ gating. RNAseq at 14 days post-tamoxifen showed no relevant difference in transcript abundance between RV and left ventricle, neither in control nor in PKP2cKO cells. Instead, we found an RV-predominant increase in membrane permeability that can permit Ca2+ entry into the cell. Connexin 43 ablation mitigated the membrane permeability increase, accumulation of cytoplasmic Ca2+, increased frequency of sparks and early stages of RV dysfunction. Connexin 43 hemichannel block with GAP19 normalized [Ca2+]i homeostasis. Similarly, protein kinase C inhibition normalized spark frequency at comparable sarcoplasmic reticulum load levels. CONCLUSIONS: Loss of PKP2 creates an RV-predominant arrhythmogenic substrate (Ca2+ dysregulation) that precedes the cardiomyopathy; this is, at least in part, mediated by a Connexin 43-dependent membrane conduit and repressed by protein kinase C inhibitors. Given that asymmetric Ca2+ dysregulation precedes the cardiomyopathic stage, we speculate that abnormal Ca2+ handling in RV myocytes can be a trigger for gross structural changes observed at a later stage.

DecoMetDIA: Deconvolution of Multiplexed MS/MS Spectra for Metabolite Identification in SWATH-MS-Based Untargeted Metabolomics.

SWATH-MS-based data-independent acquisition mass spectrometry (DIA-MS) technology has been recently developed for untargeted metabolomics due to its capability to acquire all MS2 spectra with high quantitative accuracy. However, software tools for deconvolving multiplexed MS/MS spectra from SWATH-MS with high efficiency and high quality are still lacking in untargeted metabolomics. Here, we developed a new software tool, namely, DecoMetDIA, to deconvolve multiplexed MS/MS spectra for metabolite identification and support the SWATH-based untargeted metabolomics. In DecoMetDIA, multiple model peaks are selected to model the coeluted and unresolved chromatographic peaks of fragment ions in multiplexed spectra and decompose them into a linear combination of the model peaks. DecoMetDIA enabled us to reconstruct the MS2 spectra of metabolites from a variety of different biological samples with high coverages. We also demonstrated that the deconvolved MS2 spectra from DecoMetDIA were of high accuracy through comparison to the experimental MS2 spectra from data-dependent acquisition (DDA). Finally, about 90% of deconvolved MS2 spectra in various biological samples were successfully annotated using software tools such as MetDNA and Sirius. The results demonstrated that the deconvolved MS2 spectra obtained from DecoMetDIA were accurate and valid for metabolite identification and structural elucidation. The comparison of DecoMetDIA to other deconvolution software such as MS-DIAL demonstrated that it performs very well for small polar metabolites. DecoMetDIA software is freely available at https://github.com/ZhuMSLab/DecoMetDIA .

Advancing untargeted metabolomics using data-independent acquisition mass spectrometry technology.

Metabolomics quantitatively measures metabolites in a given biological system and facilitates the understanding of physiological and pathological activities. With the recent advancement of mass spectrometry (MS) technology, liquid chromatography-mass spectrometry (LC-MS) with data-independent acquisition (DIA) has been emerged as a powerful technology for untargeted metabolomics due to its capability to acquire all MS2 spectra and high quantitative accuracy. In this trend article, we first introduced the basic principles of several common DIA techniques including MSE, all ion fragmentation (AIF), SWATH, and MSX. Then, we summarized and compared the data analysis strategies to process DIA-based untargeted metabolomics data, including metabolite identification and quantification. We think the advantages of the DIA technique will enable its broad application in untargeted metabolomics.

Bridging of double-stranded breaks by the nonhomologous end-joining ligation complex is modulated by DNA end chemistry.

The nonhomologous end-joining (NHEJ) pathway is the primary repair pathway for DNA double strand breaks (DSBs) in humans. Repair is mediated by a core complex of NHEJ factors that includes a ligase (DNA Ligase IV; L4) that relies on juxtaposition of 3΄ hydroxyl and 5΄ phosphate termini of the strand breaks for catalysis. However, chromosome breaks arising from biological sources often have different end chemistries, and how these different end chemistries impact the way in which the core complex directs the necessary transitions from end pairing to ligation is not known. Here, using single-molecule FRET (smFRET), we show that prior to ligation, differences in end chemistry strongly modulate the bridging of broken ends by the NHEJ core complex. In particular, the 5΄ phosphate group is a recognition element for L4 and is critical for the ability of NHEJ factors to promote stable pairing of ends. Moreover, other chemical incompatibilities, including products of aborted ligation, are sufficient to disrupt end pairing. Based on these observations, we propose a mechanism for iterative repair of DSBs by NHEJ.

SWATHtoMRM: Development of High-Coverage Targeted Metabolomics Method Using SWATH Technology for Biomarker Discovery.

The complexity of metabolome presents a great analytical challenge for quantitative metabolite profiling, and restricts the application of metabolomics in biomarker discovery. Targeted metabolomics using multiple-reaction monitoring (MRM) technique has excellent capability for quantitative analysis, but suffers from the limited metabolite coverage. To address this challenge, we developed a new strategy, namely, SWATHtoMRM, which utilizes the broad coverage of SWATH-MS technology to develop high-coverage targeted metabolomics method. Specifically, SWATH-MS technique was first utilized to untargeted profile one pooled biological sample and to acquire the MS2 spectra for all metabolites. Then, SWATHtoMRM was used to extract the large-scale MRM transitions for targeted analysis with coverage as high as 1000-2000 metabolites. Then, we demonstrated the advantages of SWATHtoMRM method in quantitative analysis such as coverage, reproducibility, sensitivity, and dynamic range. Finally, we applied our SWATHtoMRM approach to discover potential metabolite biomarkers for colorectal cancer (CRC) diagnosis. A high-coverage targeted metabolomics method with 1303 metabolites in one injection was developed to profile colorectal cancer tissues from CRC patients. A total of 20 potential metabolite biomarkers were discovered and validated for CRC diagnosis. In plasma samples from CRC patients, 17 out of 20 potential biomarkers were further validated to be associated with tumor resection, which may have a great potential in assessing the prognosis of CRC patients after tumor resection. Together, the SWATHtoMRM strategy provides a new way to develop high-coverage targeted metabolomics method, and facilitates the application of targeted metabolomics in disease biomarker discovery. The SWATHtoMRM program is freely available on the Internet ( http://www.zhulab.cn/software.php ).

Absolute quantitative lipidomics reveals lipidome-wide alterations in aging brain.

INTRODUCTION: The absolute quantitation of lipids at the lipidome-wide scale is a challenge but plays an important role in the comprehensive study of lipid metabolism. OBJECTIVES: We aim to develop a high-throughput quantitative lipidomics approach to enable the simultaneous identification and absolute quantification of hundreds of lipids in a single experiment. Then, we will systematically characterize lipidome-wide changes in the aging mouse brain and provide a link between aging and disordered lipid homeostasis. METHODS: We created an in-house lipid spectral library, containing 76,361 lipids and 181,300 MS/MS spectra in total, to support accurate lipid identification. Then, we developed a response factor-based approach for the large-scale absolute quantifications of lipids. RESULTS: Using the lipidomics approach, we absolutely quantified 1212 and 864 lipids in human cells and mouse brains, respectively. The quantification accuracy was validated using the traditional approach with a median relative error of 12.6%. We further characterized the lipidome-wide changes in aging mouse brains, and dramatic changes were observed in both glycerophospholipids and sphingolipids. Sphingolipids with longer acyl chains tend to accumulate in aging brains. Membrane-esterified fatty acids demonstrated diverse changes with aging, while most polyunsaturated fatty acids consistently decreased. CONCLUSION: We developed a high-throughput quantitative lipidomics approach and systematically characterized the lipidome-wide changes in aging mouse brains. The results proved a link between aging and disordered lipid homeostasis.

Dynamics of spontaneous flipping of a mismatched base in DNA duplex.

DNA base flipping is a fundamental theme in DNA biophysics. The dynamics for a B-DNA base to spontaneously flip out of the double helix has significant implications in various DNA-protein interactions but are still poorly understood. The spontaneous base-flipping rate obtained previously via the imino proton exchange assay is most likely the rate of base wobbling instead of flipping. Using the diffusion-decelerated fluorescence correlation spectroscopy together with molecular dynamics simulations, we show that a base of a single mismatched base pair (T-G, T-T, or T-C) in a double-stranded DNA can spontaneously flip out of the DNA duplex. The extrahelical lifetimes are on the order of 10 ms, whereas the intrahelical lifetimes range from 0.3 to 20 s depending on the stability of the base pairs. These findings provide detailed understanding on the dynamics of DNA base flipping and lay down foundation to fully understand how exactly the repair proteins search and locate the target mismatched base among a vast excess of matched DNA bases.

Lysophosphatidylcholine binds α-synuclein and prevents its pathological aggregation.

Accumulation of aggregated α-synuclein (α-syn) in Lewy bodies is the pathological hallmark of Parkinson's disease (PD). Genetic mutations in lipid metabolism are causative for a subset of patients with Parkinsonism. The role of α-syn's lipid interactions in its function and aggregation is recognized, yet the specific lipids involved and how lipid metabolism issues trigger α-syn aggregation and neurodegeneration remain unclear. Here, we found that α-syn shows a preference for binding to lysophospholipids (LPLs), particularly targeting lysophosphatidylcholine (LPC) without relying on electrostatic interactions. LPC is capable of maintaining α-syn in a compact conformation, significantly reducing its propensity to aggregate both in vitro and within cellular environments. Conversely, a reduction in the production of cellular LPLs is associated with an increase in α-syn accumulation. Our work underscores the critical role of LPLs in preserving the natural conformation of α-syn to inhibit improper aggregation, and establishes a potential connection between lipid metabolic dysfunction and α-syn aggregation in PD.

A mass spectrum-oriented computational method for ion mobility-resolved untargeted metabolomics.

Ion mobility (IM) adds a new dimension to liquid chromatography-mass spectrometry-based untargeted metabolomics which significantly enhances coverage, sensitivity, and resolving power for analyzing the metabolome, particularly metabolite isomers. However, the high dimensionality of IM-resolved metabolomics data presents a great challenge to data processing, restricting its widespread applications. Here, we develop a mass spectrum-oriented bottom-up assembly algorithm for IM-resolved metabolomics that utilizes mass spectra to assemble four-dimensional peaks in a reverse order of multidimensional separation. We further develop the end-to-end computational framework Met4DX for peak detection, quantification and identification of metabolites in IM-resolved metabolomics. Benchmarking and validation of Met4DX demonstrates superior performance compared to existing tools with regard to coverage, sensitivity, peak fidelity and quantification precision. Importantly, Met4DX successfully detects and differentiates co-eluted metabolite isomers with small differences in the chromatographic and IM dimensions. Together, Met4DX advances metabolite discovery in biological organisms by deciphering the complex 4D metabolomics data.

Broadly Effective ACE2 Decoy Proteins Protect Mice from Lethal SARS-CoV-2 Infection.

As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants have been causing increasingly serious drug resistance problem, development of broadly effective and hard-to-escape anti-SARS-CoV-2 agents is an urgent need. Here, we describe further development and characterization of two SARS-CoV-2 receptor decoy proteins, ACE2-Ig-95 and ACE2-Ig-105/106. We found that both proteins had potent and robust in vitro neutralization activities against diverse SARS-CoV-2 variants, including BQ.1 and XBB.1, that are resistant to most clinically used monoclonal antibodies. In a stringent lethal SARS-CoV-2 infection mouse model, both proteins lowered the lung viral load by up to ~1,000-fold, prevented the emergence of clinical signs in >75% animals, and increased the animal survival rate from 0% (untreated) to >87.5% (treated). These results demonstrate that both proteins are good drug candidates for protecting animals from severe COVID-19. In a head-to-head comparison of these two proteins with five previously described ACE2-Ig constructs, we found that two constructs, each carrying five surface mutations in the ACE2 region, had partial loss of neutralization potency against three SARS-CoV-2 variants. These data suggest that extensively mutating ACE2 residues near the receptor binding domain (RBD)-binding interface should be avoided or performed with extra caution. Furthermore, we found that both ACE2-Ig-95 and ACE2-Ig-105/106 could be produced to the level of grams per liter, demonstrating the developability of them as biologic drug candidates. Stress condition stability testing of them further suggests that more studies are required in the future to improve the stability of these proteins. These studies provide useful insight into critical factors for engineering and preclinical development of ACE2 decoys as broadly effective therapeutics against diverse ACE2-utilizing coronaviruses. IMPORTANCE Engineering soluble ACE2 proteins that function as a receptor decoy to block SARS-CoV-2 infection is a very attractive approach to creating broadly effective and hard-to-escape anti-SARS-CoV-2 agents. This article describes development of two antibody-like soluble ACE2 proteins that broadly block diverse SARS-CoV-2 variants, including Omicron. In a stringent COVID-19 mouse model, both proteins successfully protected >87.5% animals from lethal SARS-CoV-2 infection. In addition, a head-to-head comparison of the two constructs developed in this study with five previously described ACE2 decoy constructs was performed here. Two previously described constructs with relatively more ACE2 surface mutations were found with less robust neutralization activities against diverse SARS-CoV-2 variants. Furthermore, the developability of the two proteins as biologic drug candidates was also assessed here. This study provides two broad anti-SARS-CoV-2 drug candidates and useful insight into critical factors for engineering and preclinical development of ACE2 decoys as broadly effective therapeutics against diverse ACE2-utilizing coronaviruses.

Kinetics and dynamics of DNA hybridization.

DNA hybridization, wherein strands of DNA form duplex or larger hybrids through noncovalent, sequence-specific interactions, is one of the most fundamental processes in biology. Developing a better understanding of the kinetic and dynamic properties of DNA hybridization will thus help in the elucidation of molecular mechanisms involved in numerous biochemical processes. Moreover, because DNA hybridization has been widely adapted in biotechnology, its study is invaluable to the development of a range of commercially important processes. In this Account, we examine recent studies of the kinetics and dynamics of DNA hybridization, including (i) intramolecular collision of random coil, single-stranded DNA (ssDNA), (ii) nucleic acid hairpin folding, and (iii) considerations of DNA hybridization from both a global view and a detailed base-by-base view. We also examine the spontaneous single-base-pair flipping in duplex DNA because of its importance to both DNA hybridization and repair. Intramolecular collision of random coil ssDNA, with chemical relaxation times ranging from hundreds of nanoseconds to a few microseconds, is investigated both theoretically and experimentally. The first passage time theory of Szabo, Schulten, and Schulten, which determines the average reaction time of the intrachain collision, was tested. Although it was found to provide an acceptable approximation, a more sophisticated theoretical treatment is desirable. Nucleic acid hairpin folding has been extensively investigated as an important model system of DNA hybridization. The relaxation time of hairpin folding and unfolding strongly depends on the stem length, and it may range from hundreds of microseconds to hundreds of milliseconds. The traditional two-state model has been revised to a multistate model as a result of new experimental observations and theoretical study, and partially folded intermediate states have been introduced to the folding energy landscape. On the other hand, new techniques are needed to provide more accurate and detailed information on the dynamics of DNA hairpin folding in the time domain of sub-milliseconds to tens of milliseconds. From a global view, the hybridization of unstructured ssDNA goes through an entropy-controlled nucleation step, whereas the hybridization of ssDNA with a hairpin structure must overcome an extra, enthalpy-controlled energy barrier to eliminate the hairpin. From a detailed base-by-base view, however, there exist many intermediate states. The average single-base-pair hybridization and dehybridization rates in a duplex DNA formation have been determined to be on the order of a millisecond. Meanwhile, accurate information on the early stages of hybridization, such as the dynamics of nucleation, is still lacking. The investigation of spontaneous flipping of a single base in a mismatched base pair in a duplex DNA, although very important, has only recently been initiated because of the earlier lack of suitable probing tools. In sum, the study of DNA hybridization offers a rich range of research opportunities; recent progress is highlighting areas that are ripe for more detailed investigation.

Trapped ion mobility spectrometry-mass spectrometry improves the coverage and accuracy of four-dimensional untargeted lipidomics.

Lipids play vital roles in many physiological and pathological processes in living organisms. Due to the high structural diversity and the numerous isomers and isobars of lipids, high-coverage and high-accuracy lipidomic analysis of complex biological samples remain the bottleneck to investigate lipid metabolism. Here, we developed the trapped ion mobility spectrometry-mass spectrometry (TIMS-MS) based four-dimensional untargeted lipidomics to support accurate lipid identification and quantification in biological samples. We first demonstrated that the TIMS based multi-dimensional separation improved the differentiations of isomeric and isobaric lipids, and increased the purity of precursor ion isolation and the quality of MS/MS spectra. Hyphenation of TIMS and PASEF technologies significantly improved the coverages of MS/MS spectra. These technological advantages jointly improved the coverage and accuracy of lipid identification in untargeted lipidomics. We further demonstrated that the CCS values of lipids acquired using TIMS were highly consistent with those from drift tube ion mobility spectrometry (DTIMS). Lipid identification and quantification results of NIST human plasma samples were also verified with inter-laboratory reports. Finally, we applied the TIMS-MS based untargeted lipidomics to characterize the spatial distributions of 1393 distinctive lipids in the mouse brain, and demonstrated that diverse lipid distributions and compositions among brain regions contributed to different functions of brain regions. Altogether, TIMS-MS based four-dimensional untargeted lipidomics significantly improved the coverage and accuracy of untargeted metabolomics, thereby facilitating a system-level understanding of lipid metabolism in biological organisms.

Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking.

Liquid chromatography - mass spectrometry (LC-MS) based untargeted metabolomics allows to measure both known and unknown metabolites in the metabolome. However, unknown metabolite annotation is a major challenge in untargeted metabolomics. Here, we develop an approach, namely, knowledge-guided multi-layer network (KGMN), to enable global metabolite annotation from knowns to unknowns in untargeted metabolomics. The KGMN approach integrates three-layer networks, including knowledge-based metabolic reaction network, knowledge-guided MS/MS similarity network, and global peak correlation network. To demonstrate the principle, we apply KGMN in an in vitro enzymatic reaction system and different biological samples, with ~100-300 putative unknowns annotated in each data set. Among them, >80% unknown metabolites are corroborated with in silico MS/MS tools. Finally, we validate 5 metabolites that are absent in common MS/MS libraries through repository mining and synthesis of chemical standards. Together, the KGMN approach enables efficient unknown annotations, and substantially advances the discovery of recurrent unknown metabolites for common biological samples from model organisms, towards deciphering dark matter in untargeted metabolomics.