Regulation of Phosphoribosyl-Linked Serine Ubiquitination by Deubiquitinases DupA and DupB.

The family of bacterial SidE enzymes catalyzes non-canonical phosphoribosyl-linked (PR) serine ubiquitination and promotes infectivity of Legionella pneumophila. Here, we describe identification of two bacterial effectors that reverse PR ubiquitination and are thus named deubiquitinases for PR ubiquitination (DUPs; DupA and DupB). Structural analyses revealed that DupA and SidE ubiquitin ligases harbor a highly homologous catalytic phosphodiesterase (PDE) domain. However, unlike SidE ubiquitin ligases, DupA displays increased affinity to PR-ubiquitinated substrates, which allows DupA to cleave PR ubiquitin from substrates. Interfering with DupA-ubiquitin binding switches its activity toward SidE-type ligase. Given the high affinity of DupA to PR-ubiquitinated substrates, we exploited a catalytically inactive DupA mutant to trap and identify more than 180 PR-ubiquitinated host proteins in Legionella-infected cells. Proteins involved in endoplasmic reticulum (ER) fragmentation and membrane recruitment to Legionella-containing vacuoles (LCV) emerged as major SidE targets. The global map of PR-ubiquitinated substrates provides critical insights into host-pathogen interactions during Legionella infection.

Landscape fragmentation overturns classical metapopulation thinking.

Habitat loss and isolation caused by landscape fragmentation represent a growing threat to global biodiversity. Existing theory suggests that the process will lead to a decline in metapopulation viability. However, since most metapopulation models are restricted to simple networks of discrete habitat patches, the effects of real landscape fragmentation, particularly in stochastic environments, are not well understood. To close this major gap in ecological theory, we developed a spatially explicit, individual-based model applicable to realistic landscape structures, bridging metapopulation ecology and landscape ecology. This model reproduced classical metapopulation dynamics under conventional model assumptions, but on fragmented landscapes, it uncovered general dynamics that are in stark contradiction to the prevailing views in the ecological and conservation literature. Notably, fragmentation can give rise to a series of dualities: a) positive and negative responses to environmental noise, b) relative slowdown and acceleration in density decline, and c) synchronization and desynchronization of local population dynamics. Furthermore, counter to common intuition, species that interact locally ("residents") were often more resilient to fragmentation than long-ranging "migrants." This set of findings signals a need to fundamentally reconsider our approach to ecosystem management in a noisy and fragmented world.

Elucidating the impact of Y chromosome microdeletions and altered gene expression on male fertility in assisted reproduction.

BACKGROUND: Genetic abnormalities like Y chromosome microdeletions are implicated in male infertility. This study investigated the association of azoospermia factor (AZF) region microdeletions with unsuccessful assisted reproductive techniques (ART), including in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI). METHODS: This cross-sectional analysis study examined 80 Iranian oligospermic men (mean age 34 years) with prior failed ICSI and IVF cycles (IR.IAU.TNB.REC.1401.041). Semen analysis evaluated quantity/quality parameters based on World Health Organization guidelines. Participants were stratified by sperm DNA fragmentation (SDF) levels into: control (SDF < 15%, n = 20), mild elevation (15% ≤ SDF ≤ 30%, n = 60), and high (SDF > 30%, n = 20). Multiplex PCR mapped AZF microdeletions in the high SDF group. The AZF-associated genes were selected by RNA Seq analysis, and the candidate genes were checked for expression level by real-time PCR. RESULTS: High SDF individuals exhibited poorer semen metrics, including 69% lower sperm concentration (P = 0.04) than those without SDF. Of this subset, 45% (9/20 men) harboured predominately AZF microdeletions. Men with AZF microdeletions showed higher SDF (32% vs 21%, P = 0.02) and altered AZF-associated genes expression. As USP9Y 3-fold, UTY 1.3-fold, and BPY2 1-fold revealed up-regulation, while IQCF1 8-fold, CDY 6.5-fold, DAZ 6-fold, and DDX3Y 1-fold underwent down-regulation. The PAWP gene was also down-regulated (5.7-fold, P = 0.029) in the IVF/ICSI failure group. CONCLUSION: AZF microdeletions significantly impact male infertility and ART outcomes. High SDF individuals exhibited poorer semen metrics, with 45% AZF microdeletions. These microdeletions altered AZF-associated genes expression, affecting fertility mediator PAWP independently. Dual AZF and SDF screening enables personalized management in severe male infertility, potentially explaining IVF/ICSI failures.

Frugivore-mediated seed dispersal in fragmented landscapes: Compositional and functional turnover from forest to matrix.

Seed dispersal by frugivores is a fundamental function for plant community dynamics in fragmented landscapes, where forest remnants are typically embedded in a matrix of anthropogenic habitats. Frugivores can mediate both connectivity among forest remnants and plant colonization of the matrix. However, it remains poorly understood how frugivore communities change from forest to matrix due to the loss or replacement of species with traits that are less advantageous in open habitats and whether such changes ultimately influence the composition and traits of dispersed plants via species interactions. Here, we close this gap by using a unique dataset of seed-dispersal networks that were sampled in forest patches and adjacent matrix habitats of seven fragmented landscapes across Europe. We found a similar diversity of frugivores, plants, and interactions contributing to seed dispersal in forest and matrix, but a high turnover (replacement) in all these components. The turnover of dispersed seeds was smaller than that of frugivore communities because different frugivore species provided complementary seed dispersal in forest and matrix. Importantly, the turnover involved functional changes toward larger and more mobile frugivores in the matrix, which dispersed taller, larger-seeded plants with later fruiting periods. Our study provides a trait-based understanding of frugivore-mediated seed dispersal through fragmented landscapes, uncovering nonrandom shifts that can have cascading consequences for the composition of regenerating plant communities. Our findings also highlight the importance of forest remnants and frugivore faunas for ecosystem resilience, demonstrating a high potential for passive forest restoration of unmanaged lands in the matrix.

Spread of networked populations is determined by the interplay between dispersal behavior and habitat configuration.

Predicting the spread of populations across fragmented habitats is vital if we are to manage their persistence in the long term. We applied network theory with a model and an experiment to show that spread rate is jointly defined by the configuration of habitat networks (i.e., the arrangement and length of connections between habitat fragments) and the movement behavior of individuals. We found that population spread rate in the model was well predicted by algebraic connectivity of the habitat network. A multigeneration experiment with the microarthropod Folsomia candida validated this model prediction. The realized habitat connectivity and spread rate were determined by the interaction between dispersal behavior and habitat configuration, such that the network configurations that facilitated the fastest spread changed depending on the shape of the species' dispersal kernel. Predicting the spread rate of populations in fragmented landscapes requires combining knowledge of species-specific dispersal kernels and the spatial configuration of habitat networks. This information can be used to design landscapes to manage the spread and persistence of species in fragmented habitats.

Emergent encoding of dispersal network topologies in spatial metapopulation models.

We address a generalization of the concept of metapopulation capacity for trees and networks acting as the template for ecological interactions. The original measure had been derived from an insightful phenomenological model and is based on the leading eigenvalue of a suitable landscape matrix. It yields a versatile predictor of metapopulation persistence through a threshold value of the eigenvalue determined by ecological features of the focal species. Here, we present an analytical solution to a fundamental microscopic model that incorporates key ingredients of metapopulation dynamics and explicitly distinguishes between individuals comprising the "settled population" and "explorers" seeking colonization. Our approach accounts for general network characteristics (in particular graph-driven directional dispersal which is known to significantly constrain many ecological estimates) and yields a generalized version of the original model, to which it reduces for particular cases. Through examples, including real landscapes used as the template, we compare the predictions from our approach with those of the standard model. Results suggest that in several cases of practical interest, differences are significant. We also examine, with both models, how changes in habitat fragmentation, including removal, addition, or alteration in size, affect metapopulation persistence. The current approach demonstrates a high level of flexibility, enabling the incorporation of diverse "microscopic" elements and their impact on the resulting biodiversity landscape pattern.

Origins and evolving functionalities of tRNA-derived small RNAs.

Transfer RNA (tRNA)-derived small RNAs (tsRNAs) are among the most ancient small RNAs in all domains of life and are generated by the cleavage of tRNAs. Emerging studies have begun to reveal the versatile roles of tsRNAs in fundamental biological processes, including gene silencing, ribosome biogenesis, retrotransposition, and epigenetic inheritance, which are rooted in tsRNA sequence conservation, RNA modifications, and protein-binding abilities. We summarize the mechanisms of tsRNA biogenesis and the impact of RNA modifications, and propose how thinking of tsRNA functionality from an evolutionary perspective urges the expansion of tsRNA research into a wider spectrum, including cross-tissue/cross-species regulation and harnessing of the 'tsRNA code' for precision medicine.

Porcine reproductive and respiratory syndrome virus triggers Golgi apparatus fragmentation-mediated autophagy to facilitate viral self-replication.

Macroautophagy/autophagy is a cellular degradation and recycling process that maintains the homeostasis of organisms. A growing number of studies have reported that autophagy participates in infection by a variety of viruses. Porcine reproductive and respiratory syndrome virus (PRRSV) causes severe financial losses to the global swine industry. Although much research has shown that PRRSV triggers autophagy for its own benefits, the exact molecular mechanisms involved in PRRSV-triggered autophagy remain to be fully elucidated. In the current study, we demonstrated that PRRSV infection significantly induced Golgi apparatus (GA) fragmentation, which promoted autophagy to facilitate viral self-replication. Mechanistically, PRRSV nonstructural protein 2 was identified to interact with and degrade the Golgi reassembly and stacking protein 65 dependent on its papain-like cysteine protease 2 activity, resulting in GA fragmentation. Upon GA fragmentation, GA-resident Ras-like protein in brain 2 was disassociated from Golgi matrix protein 130 and subsequently bound to unc-51 like autophagy activating kinase 1 (ULK1), which enhanced phosphorylation of ULK1 and promoted autophagy. Taken together, all these results expand the knowledge of PRRSV-triggered autophagy as well as PRRSV pathogenesis to support novel potential avenues for prevention and control of the virus. More importantly, these results provide the detailed mechanism of GA fragmentation-mediated autophagy, deepening the understanding of autophagic processes.IMPORTANCEPorcine reproductive and respiratory syndrome virus (PRRSV) infection results in a serious swine disease affecting pig farming worldwide. Despite that numerous studies have shown that PRRSV triggers autophagy for its self-replication, how PRRSV induces autophagy is incompletely understood. Here, we identify that PRRSV Nsp2 degrades GRASP65 to induce GA fragmentation, which dissociates RAB2 from GM130 and activates RAB2-ULK1-mediated autophagy to enhance viral replication. This work expands our understanding of PRRSV-induced autophagy and PRRSV replication, which is beneficial for anti-viral drug development.

Self-supervised learning with chemistry-aware fragmentation for effective molecular property prediction.

Molecular property prediction (MPP) is a crucial and fundamental task for AI-aided drug discovery (AIDD). Recent studies have shown great promise of applying self-supervised learning (SSL) to producing molecular representations to cope with the widely-concerned data scarcity problem in AIDD. As some specific substructures of molecules play important roles in determining molecular properties, molecular representations learned by deep learning models are expected to attach more importance to such substructures implicitly or explicitly to achieve better predictive performance. However, few SSL pre-trained models for MPP in the literature have ever focused on such substructures. To challenge this situation, this paper presents a Chemistry-Aware Fragmentation for Effective MPP (CAFE-MPP in short) under the self-supervised contrastive learning framework. First, a novel fragment-based molecular graph (FMG) is designed to represent the topological relationship between chemistry-aware substructures that constitute a molecule. Then, with well-designed hard negative pairs, a is pre-trained on fragment-level by contrastive learning to extract representations for the nodes in FMGs. Finally, a Graphormer model is leveraged to produce molecular representations for MPP based on the embeddings of fragments. Experiments on 11 benchmark datasets show that the proposed CAFE-MPP method achieves state-of-the-art performance on 7 of the 11 datasets and the second-best performance on 3 datasets, compared with six remarkable self-supervised methods. Further investigations also demonstrate that CAFE-MPP can learn to embed molecules into representations implicitly containing the information of fragments highly correlated to molecular properties, and can alleviate the over-smoothing problem of graph neural networks.

A deep transfer learning-based protocol accelerates full quantum mechanics calculation of protein.

Effective full quantum mechanics (FQM) calculation of protein remains a grand challenge and of great interest in computational biology with substantial applications in drug discovery, protein dynamic simulation and protein folding. However, the huge computational complexity of the existing QM methods impends their applications in large systems. Here, we design a transfer-learning-based deep learning (TDL) protocol for effective FQM calculations (TDL-FQM) on proteins. By incorporating a transfer-learning algorithm into deep neural network (DNN), the TDL-FQM protocol is capable of performing calculations at any given accuracy using models trained from small datasets with high-precision and knowledge learned from large amount of low-level calculations. The high-level double-hybrid DFT functional and high-level quality of basis set is used in this work as a case study to evaluate the performance of TDL-FQM, where the selected 15 proteins are predicted to have a mean absolute error of 0.01 kcal/mol/atom for potential energy and an average root mean square error of 1.47 kcal/mol/$ {\rm A^{^{ \!\!\!o}}} $ for atomic forces. The proposed TDL-FQM approach accelerates the FQM calculation more than thirty thousand times faster in average and presents more significant benefits in efficiency as the size of protein increases. The ability to learn knowledge from one task to solve related problems demonstrates that the proposed TDL-FQM overcomes the limitation of standard DNN and has a strong power to predict proteins with high precision, which solves the challenge of high precision prediction in large chemical and biological systems.

MIRO-1 interacts with VDAC-1 to regulate mitochondrial membrane potential in Caenorhabditis elegans.

Precise regulation of mitochondrial fusion and fission is essential for cellular activity and animal development. Imbalances between these processes can lead to fragmentation and loss of normal membrane potential in individual mitochondria. In this study, we show that MIRO-1 is stochastically elevated in individual fragmented mitochondria and is required for maintaining mitochondrial membrane potential. We further observe a higher level of membrane potential in fragmented mitochondria in fzo-1 mutants and wounded animals. Moreover, MIRO-1 interacts with VDAC-1, a crucial mitochondrial ion channel located in the outer mitochondrial membrane, and this interaction depends on the residues E473 of MIRO-1 and K163 of VDAC-1. The E473G point mutation disrupts their interaction, resulting in a reduction of the mitochondrial membrane potential. Our findings suggest that MIRO-1 regulates membrane potential and maintains mitochondrial activity and animal health by interacting with VDAC-1. This study provides insight into the mechanisms underlying the stochastic maintenance of membrane potential in fragmented mitochondria.

MSFragger-Labile: A Flexible Method to Improve Labile PTM Analysis in Proteomics.

Posttranslational modifications of proteins play essential roles in defining and regulating the functions of the proteins they decorate, making identification of these modifications critical to understanding biology and disease. Methods for enriching and analyzing a wide variety of biological and chemical modifications of proteins have been developed using mass spectrometry-based proteomics, largely relying on traditional database search methods to identify the resulting mass spectra of modified peptides. These database search methods treat modifications as static attachments of a mass to particular position in the peptide sequence, but many modifications undergo fragmentation in tandem mass spectrometry experiments alongside, or instead of, the peptide backbone. While this fragmentation can confound traditional search methods, it also offers unique opportunities for improved searches that incorporate modification-specific fragment ions. Here, we present a new labile mode in the MSFragger search engine that provides the flexibility to tailor modification-centric searches to the fragmentation observed. We show that labile mode can dramatically improve spectrum identification rates of phosphopeptides, RNA-crosslinked peptides, and ADP-ribosylated peptides. Each of these modifications presents distinct fragmentation characteristics, showcasing the flexibility of MSFragger labile mode to improve search for a wide variety of biological and chemical modifications.

Prince Rupert's Drops: An analysis of fragmentation by thermal stresses and quench granulation of glass and bubbly glass.

When volcanic eruptions involve interaction with external water (hydrovolcanism), the result is an ash-rich and energetic volcanic plume, as illustrated dramatically by the January 2022 Tonga eruption. The origin of the high explosive energy of these events remains an important question. We investigate this question by studying Prince Rupert's Drops (PRDs)-tadpole-shaped glass beads formed by dripping molten glass into water-which have long fascinated materials scientists because the great strength of the head contrasts with the explosivity of the metastable interior when the tail is broken. We show that the fragment size distribution (FSD) produced by explosive fragmentation changes systematically with PRD fragmentation in air, water, and syrup. Most FSDs are fractal over much of the size range, scaling that can be explained by the repeated fracture bifurcation observed in three-dimensional images from microcomputed tomography. The shapes of constituent fragments are determined by their position within the original PRD, with platey fragments formed from the outer (compressive) shell and blocky fragments formed by fractures perpendicular to interior voids. When molten drops fail to form PRDs, the glass disintegrates by quench granulation, a process that produces fractal FSDs but with a larger median size than explosively generated fragments. Critically, adding bubbles to the molten glass prevents PRD formation and promotes quench granulation, suggesting that granulation is modulated by heterogeneous stress fields formed around the bubbles during sudden cooling and contraction. Together, these observations provide insight into glass fragmentation and potentially, processes operating during hydrovolcanism.

Evidence for adaptive morphological plasticity in the Caribbean coral, Acropora cervicornis.

Genotype-by-environment interactions (GxE) indicate that variation in organismal traits cannot be explained by fixed effects of genetics or site-specific plastic responses alone. For tropical coral reefs experiencing dramatic environmental change, identifying the contributions of genotype, environment, and GxE on coral performance will be vital for both predicting persistence and developing restoration strategies. We quantified the impacts of G, E, and GxE on the morphology and survival of the endangered coral, Acropora cervicornis, through an in situ transplant experiment exposing common garden (nursery)-raised clones of ten genotypes to nine reef sites in the Florida Keys. By fate-tracking outplants over one year with colony-level 3D photogrammetry, we uncovered significant GxE on coral size, shape, and survivorship, indicating that no universal winner exists in terms of colony performance. Rather than differences in mean trait values, we found that individual-level morphological plasticity is adaptive in that the most plastic individuals also exhibited the fastest growth and highest survival. This indicates that adaptive morphological plasticity may continue to evolve, influencing the success of A. cervicornis and resulting reef communities in a changing climate. As focal reefs are active restoration sites, the knowledge that variation in phenotype is an important predictor of performance can be directly applied to restoration planning. Taken together, these results establish A. cervicornis as a system for studying the ecoevolutionary dynamics of phenotypic plasticity that also can inform genetic- and environment-based strategies for coral restoration.

Early detection and stratification of colorectal cancer using plasma cell-free DNA fragmentomic profiling.

Timely accurate and cost-efficient detection of colorectal cancer (CRC) is of great clinical importance. This study aims to establish prediction models for detecting CRC using plasma cell-free DNA (cfDNA) fragmentomic features. Whole-genome sequencing (WGS) was performed on cfDNA from 620 participants, including healthy individuals, patients with benign colorectal diseases and CRC patients. Using WGS data, three machine learning methods were compared to build prediction models for the stratification of CRC patients. The optimal model to discriminate CRC patients of all stages from healthy individuals achieved a sensitivity of 92.31% and a specificity of 91.14%, while the model to separate early-stage CRC patients (stage 0-II) from healthy individuals achieved a sensitivity of 88.8% and a specificity of 96.2%. Additionally, the cfDNA fragmentation profiles reflected disease-specific genomic alterations in CRC. Overall, this study suggests that cfDNA fragmentation profiles may potentially become a noninvasive approach for the detection and stratification of CRC.

Reciprocal Regulation of Mitochondrial Fission and Fusion.

The dynamic processes of mitochondrial fission and fusion are tightly regulated, determine mitochondrial shape, and influence mitochondrial functions. For example, fission and fusion mediate energy output, production of reactive oxygen species (ROS), and mitochondrial quality control. As our understanding of the molecular machinery and mechanisms regulating dynamic changes in the mitochondrial network continues to grow, we are beginning to unravel important signaling pathways that integrate physiological cues to modulate mitochondrial morphology and function. Here, we highlight reciprocal regulation of mitochondrial fusion and fission as an emerging trend in the regulation of mitochondrial function.

A semi-automated workflow for DIA-based global discovery to pathway-driven PRM analysis.

Targeted proteomics, which includes parallel reaction monitoring (PRM), is typically utilized for more precise detection and quantitation of key proteins and/or pathways derived from complex discovery proteomics datasets. Initial discovery-based analysis using data independent acquisition (DIA) can obtain deep proteome coverage with low data missingness while targeted PRM assays can provide additional benefits in further eliminating missing data and optimizing measurement precision. However, PRM method development from bioinformatic predictions can be tedious and time-consuming because of the DIA output complexity. We address this limitation with a Python script that rapidly generates a PRM method for the TIMS-TOF platform using DIA data and a user-defined target list. To evaluate the script, DIA data obtained from HeLa cell lysate (200 ng, 45-min gradient method) as well as canonical pathway information from Ingenuity Pathway Analysis was utilized to generate a pathway-driven PRM method. Subsequent PRM analysis of targets within the example pathway, regulation of apoptosis, resulted in improved chromatographic data and enhanced quantitation precision (100% peptides below 10% CV with a median CV of 2.9%, n = 3 technical replicates). The script is freely available at https://github.com/StevensOmicsLab/PRM-script and provides a framework that can be adapted to multiple DDA/DIA data outputs and instrument-specific PRM method types.

Four-dimensional lipidomics profiling in X-linked adrenoleukodystrophy using trapped ion mobility mass spectrometry.

Lipids play pivotal roles in an extensive range of metabolic and physiological processes. In recent years, the convergence of trapped ion mobility spectrometry and MS has enabled 4D-lipidomics, a highly promising technology for comprehensive lipid analysis. 4D-lipidomics assesses lipid annotations across four distinct dimensions-retention time, collisional cross section, m/z (mass-to-charge ratio), and MS/MS spectra-providing a heightened level of confidence in lipid annotation. These advantages prove particularly valuable when investigating complex disorders involving lipid metabolism, such as adrenoleukodystrophy (ALD). ALD is characterized by the accumulation of very-long-chain fatty acids (VLCFAs) due to pathogenic variants in the ABCD1 gene. A comprehensive 4D-lipidomics strategy of ALD fibroblasts demonstrated significant elevations of various lipids from multiple classes. This indicates that the changes observed in ALD are not confined to a single lipid class and likely impacts a broad spectrum of lipid-mediated physiological processes. Our findings highlight the incorporation of mainly saturated and monounsaturated VLCFA variants into a range of lipid classes, encompassing phosphatidylcholines, triacylglycerols, and cholesterol esters. These include ultra-long-chain fatty acids with a length of up to thirty carbon atoms. Lipid species containing C26:0 and C26:1 were the most frequently detected VLCFA lipids in our study. Furthermore, we report a panel of 121 new candidate biomarkers in fibroblasts, exhibiting significant differentiation between controls and individuals with ALD. In summary, this study demonstrates the capabilities of a 4D-lipid profiling workflow in unraveling novel insights into the intricate lipid modifications associated with metabolic disorders like ALD.

Quantification and Site-Specific Analysis of Co-occupied N- and O-Glycopeptides.

Protein glycosylation is a complex post-translational modification that is generally classified as N- or O-linked. Site-specific analysis of glycopeptides is accomplished with a variety of fragmentation methods, depending on the type of glycosylation being investigated and the instrumentation available. For instance, collisional dissociation methods are frequently used for N-glycoproteomic analysis with the assumption that one N-sequon exists per tryptic peptide. Alternatively, electron-based methods are preferable for O-glycosite localization. However, the presence of simultaneously N- and O-glycosylated peptides could suggest the necessity of electron-based fragmentation methods for N-glycoproteomics, which is not commonly performed. Thus, we quantified the prevalence of N- and O-glycopeptides in mucins and other glycoproteins. A much higher frequency of co-occupancy within mucins was detected whereas only a negligible occurrence occurred within nonmucin glycoproteins. This was demonstrated from analyses of recombinant and/or purified proteins, as well as more complex samples. Where co-occupancy occurred, O-glycosites were frequently localized to the Ser/Thr within the N-sequon. Additionally, we found that O-glycans in close proximity to the occupied Asn were predominantly unelaborated core 1 structures, while those further away were more extended. Overall, we demonstrate electron-based methods are required for robust site-specific analysis of mucins, wherein co-occupancy is more prevalent. Conversely, collisional methods are generally sufficient for analyses of other types of glycoproteins.

High-Throughput Glycan Profiling of Human Serum IgG Subclasses Using Parallel Reaction Monitoring Peptide Bond Fragmentation of Glycopeptides and Microflow LC-MS.

LC-MS-based N-glycosylation profiling in four human serum IgG subclasses (IgG1, IgG2, IgG3, and IgG4) often requires additional affinity-based enrichment of specific IgG subclasses, owing to the high amino acid sequence similarity of Fc glycopeptides among subclasses. Notably, for IgG4 and the major allotype of IgG3, the glycopeptide precursors share identical retention time and mass and therefore cannot be distinguished based on precursor or glycan fragmentation. Here, we developed a parallel reaction monitoring (PRM)-based method for quantifying Fc glycopeptides through combined transitions generated from both glycosidic and peptide bond fragmentation. The latter enables the subpopulation of IgG3 and IgG4 to be directly distinguished according to mass differences without requiring further enrichment of specific IgG subclasses. In addition, a multinozzle electrospray emitter coupled to a capillary flow liquid chromatograph was used to increase the robustness and detection sensitivity of the method for low-yield peptide backbone fragment ions. The gradient was optimized to decrease the overall run time and make the method compatible with high-throughput analysis. We demonstrated that this method can be used to effectively monitor the relative levels of 13 representative glycoforms, with a good limit of detection for individual IgG subclasses.