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/.

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.

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.

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.

Serum metabolic traits reveal therapeutic toxicities and responses of neoadjuvant chemoradiotherapy in patients with rectal cancer.

Neoadjuvant chemoradiotherapy (nCRT) has become the standard treatment for patients with locally advanced rectal cancer (LARC). Therapeutic efficacy of nCRT is significantly affected by treatment-induced diarrhea and hematologic toxicities. Metabolic alternations in cancer therapy are key determinants to therapeutic toxicities and responses, but exploration in large-scale clinical studies remains limited. Here, we analyze 743 serum samples from 165 LARC patients recruited in a phase III clinical study using untargeted metabolomics and identify responsive metabolic traits over the course of nCRT. Pre-therapeutic serum metabolites successfully predict the chances of diarrhea and hematologic toxicities during nCRT. Particularly, levels of acyl carnitines are linked to sex disparity in nCRT-induced diarrhea. Finally, we show that differences in phenylalanine metabolism and essential amino acid metabolism may underlie distinct therapeutic responses of nCRT. This study illustrates the metabolic dynamics over the course of nCRT and provides potential to guide personalized nCRT treatment using responsive metabolic traits.

Single-virus tracking reveals variant SARS-CoV-2 spike proteins induce ACE2-independent membrane interactions.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) became a global health crisis after its emergence in 2019. Replication of the virus is initiated by binding of the viral spike (S) protein to human angiotensin-converting enzyme 2 (ACE2) on the target cell surface. Mutations acquired by SARS-CoV-2 S variants likely influence virus-target cell interaction. Here, using single-virus tracking to capture these initial steps, we observe how viruses carrying variant S interact with target cells. Specificity for ACE2 occurs for viruses with the reference sequence or D614G mutation. Analysis of the Alpha, Beta, and Delta SARS-CoV-2 variant S proteins revealed a progressive altered cell interaction with a reduced dependence on ACE2. Notably, the Delta variant S affinity was independent of ACE2. These enhanced interactions may account for the increased transmissibility of variants. Knowledge of how mutations influence cell interaction is essential for vaccine development against emerging variants of SARS-CoV-2.

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.

A two-step mechanism governing PARP1-DNA retention by PARP inhibitors.

PARP inhibitors (PARPi) have emerged as promising cancer therapeutics capable of targeting specific DNA repair pathways, but their mechanism of action with respect to PARP1-DNA retention remains unclear. Here, we developed single-molecule assays to directly monitor the retention of PARP1 on DNA lesions in real time. Our study reveals a two-step mechanism by which PARPi modulate the retention of PARP1 on DNA lesions, consisting of a primary step of catalytic inhibition via binding competition with NAD+ followed by an allosteric modulation of bound PARPi. While clinically relevant PARPi exhibit distinct allosteric modulation activities that can either increase retention of PARP1 on DNA or induce its release, their retention potencies are predominantly determined by their ability to outcompete NAD+ binding. These findings provide a mechanistic basis for improved PARPi selection according to their characteristic activities and enable further development of more potent inhibitors.

Global stable-isotope tracing metabolomics reveals system-wide metabolic alternations in aging Drosophila.

System-wide metabolic homeostasis is crucial for maintaining physiological functions of living organisms. Stable-isotope tracing metabolomics allows to unravel metabolic activity quantitatively by measuring the isotopically labeled metabolites, but has been largely restricted by coverage. Delineating system-wide metabolic homeostasis at the whole-organism level remains challenging. Here, we develop a global isotope tracing metabolomics technology to measure labeled metabolites with a metabolome-wide coverage. Using Drosophila as an aging model organism, we probe the in vivo tracing kinetics with quantitative information on labeling patterns, extents and rates on a metabolome-wide scale. We curate a system-wide metabolic network to characterize metabolic homeostasis and disclose a system-wide loss of metabolic coordinations that impacts both intra- and inter-tissue metabolic homeostasis significantly during Drosophila aging. Importantly, we reveal an unappreciated metabolic diversion from glycolysis to serine metabolism and purine metabolism as Drosophila aging. The developed technology facilitates a system-level understanding of metabolic regulation in living organisms.

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.

USP1-trapping lesions as a source of DNA replication stress and genomic instability.

The deubiquitinase USP1 is a critical regulator of genome integrity through the deubiquitylation of Fanconi Anemia proteins and the DNA replication processivity factor, proliferating cell nuclear antigen (PCNA). Uniquely, following UV irradiation, USP1 self-inactivates through autocleavage, which enables its own degradation and in turn, upregulates PCNA monoubiquitylation. However, the functional role for this autocleavage event during physiological conditions remains elusive. Herein, we discover that cells harboring an autocleavage-defective USP1 mutant, while still able to robustly deubiquitylate PCNA, experience more replication fork-stalling and premature fork termination events. Using super-resolution microscopy and live-cell single-molecule tracking, we show that these defects are related to the inability of this USP1 mutant to be properly recycled from sites of active DNA synthesis, resulting in replication-associated lesions. Furthermore, we find that the removal of USP1 molecules from DNA is facilitated by the DNA-dependent metalloprotease Spartan to counteract the cytotoxicity caused by "USP1-trapping". We propose a utility of USP1 inhibitors in cancer therapy based on their ability to induce USP1-trapping lesions and consequent replication stress and genomic instability in cancer cells, similar to how non-covalent DNA-protein crosslinks cause cytotoxicity by imposing steric hindrances upon proteins involved in DNA transactions.

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.

Ion mobility-based sterolomics reveals spatially and temporally distinctive sterol lipids in the mouse brain.

Aberrant sterol lipid metabolism is associated with physiological dysfunctions in the aging brain and aging-dependent disorders such as neurodegenerative diseases. There is an unmet demand to comprehensively profile sterol lipids spatially and temporally in different brain regions during aging. Here, we develop an ion mobility-mass spectrometry based four-dimensional sterolomics technology leveraged by a machine learning-empowered high-coverage library (>2000 sterol lipids) for accurate identification. We apply this four-dimensional technology to profile the spatially resolved landscapes of sterol lipids in ten functional regions of the mouse brain, and quantitatively uncover ~200 sterol lipids uniquely distributed in specific regions with concentrations spanning up to 8 orders of magnitude. Further spatial analysis pinpoints age-associated differences in region-specific sterol lipid metabolism, revealing changes in the numbers of altered sterol lipids, concentration variations, and age-dependent coregulation networks. These findings will contribute to our understanding of abnormal sterol lipid metabolism and its role in brain diseases.

Single-molecule imaging reveals replication fork coupled formation of G-quadruplex structures hinders local replication stress signaling.

Guanine-rich DNA sequences occur throughout the human genome and can transiently form G-quadruplex (G4) structures that may obstruct DNA replication, leading to genomic instability. Here, we apply multi-color single-molecule localization microscopy (SMLM) coupled with robust data-mining algorithms to quantitatively visualize replication fork (RF)-coupled formation and spatial-association of endogenous G4s. Using this data, we investigate the effects of G4s on replisome dynamics and organization. We show that a small fraction of active replication forks spontaneously form G4s at newly unwound DNA immediately behind the MCM helicase and before nascent DNA synthesis. These G4s locally perturb replisome dynamics and organization by reducing DNA synthesis and limiting the binding of the single-strand DNA-binding protein RPA. We find that the resolution of RF-coupled G4s is mediated by an interplay between RPA and the FANCJ helicase. FANCJ deficiency leads to G4 accumulation, DNA damage at G4-associated replication forks, and silencing of the RPA-mediated replication stress response. Our study provides first-hand evidence of the intrinsic, RF-coupled formation of G4 structures, offering unique mechanistic insights into the interference and regulation of stable G4s at replication forks and their effect on RPA-associated fork signaling and genomic instability.

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.

Development of a combined strategy for accurate lipid structural identification and quantification in ion-mobility mass spectrometry based untargeted lipidomics.

Lipids are an important class of biomolecules, and play many essential functions in biology. Ion mobility-mass spectrometry (IM-MS) has emerged as a promising technology for lipidomics by providing a holistic and multi-dimensional characterization of lipid structures. However, the lipid identification using the multi-dimensional match (i.e., MS1, retention time, collision cross section, and MS/MS spectra) gives multiple lipid candidates, and often over-reports the structural information. Here, we developed a lipid identification strategy that integrated library-based match and rule-based refinement for accurate lipid structural elucidation in IM-MS based lipidomics. The new strategy took the advantage of multi-dimensional information for high-coverage identification, while it also utilized the fragmentation rules to determine the accurate structural information. We demonstrated that the combined strategy accurately determined the lipid structures as lipid species level, fatty acyl level, or fatty acyl position level for different lipid classes in the lipid standard mixture and various biological samples. The combined strategy efficiently reduced the redundancy and improved the accuracy for different lipid classes, and identified a total of 440-960 lipid species in various biological samples. Finally, we performed quantitative lipidomics analysis of NIST SRM 1950 human plasma using IM-MS technology. The measured concentrations of most quantified lipids (>80%) were highly consistent with values reported from other independent laboratories. In summary, the developed lipid identification strategy allowed for the accurate identification of lipid structures, and facilitated accurate lipid quantification in IM-MS based untargeted lipidomics.

Ion mobility collision cross-section atlas for known and unknown metabolite annotation in untargeted metabolomics.

The metabolome includes not just known but also unknown metabolites; however, metabolite annotation remains the bottleneck in untargeted metabolomics. Ion mobility - mass spectrometry (IM-MS) has emerged as a promising technology by providing multi-dimensional characterizations of metabolites. Here, we curate an ion mobility CCS atlas, namely AllCCS, and develop an integrated strategy for metabolite annotation using known or unknown chemical structures. The AllCCS atlas covers vast chemical structures with >5000 experimental CCS records and ~12 million calculated CCS values for >1.6 million small molecules. We demonstrate the high accuracy and wide applicability of AllCCS with medium relative errors of 0.5-2% for a broad spectrum of small molecules. AllCCS combined with in silico MS/MS spectra facilitates multi-dimensional match and substantially improves the accuracy and coverage of both known and unknown metabolite annotation from biological samples. Together, AllCCS is a versatile resource that enables confident metabolite annotation, revealing comprehensive chemical and metabolic insights towards biological processes.

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.

CDK7 Inhibition Potentiates Genome Instability Triggering Anti-tumor Immunity in Small Cell Lung Cancer.

Cyclin-dependent kinase 7 (CDK7) is a central regulator of the cell cycle and gene transcription. However, little is known about its impact on genomic instability and cancer immunity. Using a selective CDK7 inhibitor, YKL-5-124, we demonstrated that CDK7 inhibition predominately disrupts cell-cycle progression and induces DNA replication stress and genome instability in small cell lung cancer (SCLC) while simultaneously triggering immune-response signaling. These tumor-intrinsic events provoke a robust immune surveillance program elicited by T cells, which is further enhanced by the addition of immune-checkpoint blockade. Combining YKL-5-124 with anti-PD-1 offers significant survival benefit in multiple highly aggressive murine models of SCLC, providing a rationale for new combination regimens consisting of CDK7 inhibitors and immunotherapies.