diaPASEF: parallel accumulation–serial fragmentation combined with data-independent acquisition

Succinate mediates inflammation-induced adrenocortical dysfunction

The hypothalamus-pituitary-adrenal (HPA) axis is activated in response to inflammation leading to increased production of anti-inflammatory glucocorticoids by the adrenal cortex, thereby representing an endogenous feedback loop. However, severe inflammation reduces the responsiveness of the adrenal gland to adrenocorticotropic hormone (ACTH), although the underlying mechanisms are poorly understood. Here, we show by transcriptomic, proteomic, and metabolomic analyses that LPS-induced systemic inflammation triggers profound metabolic changes in steroidogenic adrenocortical cells, including downregulation of the TCA cycle and oxidative phosphorylation, in mice. Inflammation disrupts the TCA cycle at the level of succinate dehydrogenase (SDH), leading to succinate accumulation and disturbed steroidogenesis. Mechanistically, IL-1β reduces SDHB expression through upregulation of DNA methyltransferase 1 (DNMT1) and methylation of the SDHB promoter. Consequently, increased succinate levels impair oxidative phosphorylation and ATP synthesis and enhance ROS production, leading to reduced steroidogenesis. Together, we demonstrate that the IL-1β-DNMT1-SDHB-succinate axis disrupts steroidogenesis. Our findings not only provide a mechanistic explanation for adrenal dysfunction in severe inflammation, but also offer a potential target for therapeutic intervention.

Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform

Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.

Combined proteomics and CRISPR‒Cas9 screens in PDX identify ADAM10 as essential for leukemia in vivo

BACKGROUND

Acute leukemias represent deadly malignancies that require better treatment. As a challenge, treatment is counteracted by a microenvironment protecting dormant leukemia stem cells.

METHODS

To identify responsible surface proteins, we performed deep proteome profiling on minute numbers of dormant patient-derived xenograft (PDX) leukemia stem cells isolated from mice. Candidates were functionally screened by establishing a comprehensive CRISPR‒Cas9 pipeline in PDX models in vivo.

RESULTS

A disintegrin and metalloproteinase domain-containing protein 10 (ADAM10) was identified as an essential vulnerability required for the survival and growth of different types of acute leukemias in vivo, and reconstitution assays in PDX models confirmed the relevance of its sheddase activity. Of translational importance, molecular or pharmacological targeting of ADAM10 reduced PDX leukemia burden, cell homing to the murine bone marrow and stem cell frequency, and increased leukemia response to conventional chemotherapy in vivo.

CONCLUSIONS

These findings identify ADAM10 as an attractive therapeutic target for the future treatment of acute leukemias.

Next-generation proteomics technologies in Alzheimer's disease: from clinical research to routine diagnostics

INTRODUCTION

Clinical proteomics studies of Alzheimer's disease (AD) research aim to identify biomarkers useful for clinical research, diagnostics, and improve our understanding of the pathological processes involved in the disease. The rapidly increasing performance of proteomics technologies is likely to have great impact on AD research.

AREAS COVERED

We review recent proteomics approaches that have advanced the field of clinical AD research. Specifically, we discuss the application of targeted mass spectrometry (MS), labeling-based and label-free MS-based as well as affinity-based proteomics to AD biomarker development, underpinning their importance with the latest impactful clinical studies. We evaluate how proteomics technologies have been adapted to meet current challenges. Finally, we discuss the limitations and potential of proteomics techniques and whether their scope might extend beyond current research-based applications.

EXPERT OPINION

To date, proteomics technologies in the AD field have been largely limited to AD biomarker discovery. The recent development of the first successful disease-modifying treatments of AD will further increase the need for blood biomarkers for early, accurate diagnosis, and CSF biomarkers that reflect specific pathological processes. Proteomics has the potential to meet these requirements and to progress into clinical routine practice, provided that current limitations are overcome.

Daytime-restricted feeding enhances running endurance without prior exercise in mice

Physical endurance and energy conservation are essential for survival in the wild. However, it remains unknown whether and how meal timing regulates physical endurance and muscle diurnal rhythms. Here, we show that day/sleep time-restricted feeding (DRF) enhances running endurance by 100% throughout the circadian cycle in both male and female mice, compared to either ad libitum feeding or night/wake time-restricted feeding. Ablation of the circadian clock in the whole body or the muscle abolished the exercise regulatory effect of DRF. Multi-omics analysis revealed that DRF robustly entrains diurnal rhythms of a mitochondrial oxidative metabolism-centric network, compared to night/wake time-restricted feeding. Remarkably, muscle-specific knockdown of the myocyte lipid droplet protein perilipin-5 completely mimics DRF in enhancing endurance, enhancing oxidative bioenergetics and outputting rhythmicity to circulating energy substrates, including acylcarnitine. Together, our work identifies a potent dietary regimen to enhance running endurance without prior exercise, as well as providing a multi-omics atlas of muscle circadian biology regulated by meal timing.

Paving the path toward multi-omics approaches in the diagnostic challenges faced in thyroid pathology

INTRODUCTION

Despite advancements in diagnostic methods, the classification of indeterminate thyroid nodules still poses diagnostic challenges not only in pre-surgical evaluation but even after histological evaluation of surgical specimens. Proteomics, aided by mass spectrometry and integrated with artificial intelligence and machine learning algorithms, shows great promise in identifying diagnostic markers for thyroid lesions.

AREAS COVERED

This review provides in-depth exploration of how proteomics has contributed to the understanding of thyroid pathology. It discusses the technical advancements related to immunohistochemistry, genetic and proteomic techniques, such as mass spectrometry, which have greatly improved sensitivity and spatial resolution up to single-cell level. These improvements allowed the identification of specific protein signatures associated with different types of thyroid lesions.

EXPERT COMMENTARY

Among all the proteomics approaches, spatial proteomics stands out due to its unique ability to capture the spatial context of proteins in both cytological and tissue thyroid samples. The integration of multi-layers of molecular information combining spatial proteomics, genomics, immunohistochemistry or metabolomics and the implementation of artificial intelligence and machine learning approaches, represent hugely promising steps forward toward the possibility to uncover intricate relationships and interactions among various molecular components, providing a complete picture of the biological landscape whilst fostering thyroid nodule diagnosis.

A Galaxy of informatics resources for MS-based proteomics

INTRODUCTION

Continuous advances in mass spectrometry (MS) technologies have enabled deeper and more reproducible proteome characterization and a better understanding of biological systems when integrated with other 'omics data. Bioinformatic resources meeting the analysis requirements of increasingly complex MS-based proteomic data and associated multi-omic data are critically needed. These requirements included availability of software that would span diverse types of analyses, scalability for large-scale, compute-intensive applications, and mechanisms to ease adoption of the software.

AREAS COVERED

The Galaxy ecosystem meets these requirements by offering a multitude of open-source tools for MS-based proteomics analyses and applications, all in an adaptable, scalable, and accessible computing environment. A thriving global community maintains these software and associated training resources to empower researcher-driven analyses.

EXPERT OPINION

The community-supported Galaxy ecosystem remains a crucial contributor to basic biological and clinical studies using MS-based proteomics. In addition to the current status of Galaxy-based resources, we describe ongoing developments for meeting emerging challenges in MS-based proteomic informatics. We hope this review will catalyze increased use of Galaxy by researchers employing MS-based proteomics and inspire software developers to join the community and implement new tools, workflows, and associated training content that will add further value to this already rich ecosystem.

Label-free single cell proteomics utilizing ultrafast LC and MS instrumentation: A valuable complementary technique to multiplexing

The ability to map a proteomic fingerprint to transcriptomic data would master the understanding of how gene expression translates into actual phenotype. In contrast to nucleic acid sequencing, in vitro protein amplification is impossible and no single cell proteomic workflow has been established as gold standard yet. Advances in microfluidic sample preparation, multi-dimensional sample separation, sophisticated data acquisition strategies, and intelligent data analysis algorithms have resulted in major improvements to successfully analyze such tiny sample amounts with steadily boosted performance. However, among the broad variation of published approaches, it is commonly accepted that highest possible sensitivity, robustness, and throughput are still the most urgent needs for the field. While many labs have focused on multiplexing to achieve these goals, label-free SCP is a highly promising strategy as well whenever high dynamic range and unbiased accurate quantification are needed. We here focus on recent advances in label-free single-cell mass spectrometry workflows and try to guide our readers to choose the best method or combinations of methods for their specific applications. We further highlight which techniques are most propitious in the future and which applications but also limitations we foresee for the field.

Simultaneous Multi-Omics Analysis by Direct Infusion Mass Spectrometry (SMAD-MS)

Combined multi-omics analysis of proteomics, polar metabolomics, and lipidomics requires separate liquid chromatography-mass spectrometry (LC-MS) platforms for each omics layer. This requirement for different platforms limits throughput and increases costs, preventing the application of mass spectrometry-based multi-omics to large scale drug discovery or clinical cohorts. Here, we present an innovative strategy for simultaneous multi-omics analysis by direct infusion (SMAD) using one single injection without liquid chromatography. SMAD allows quantification of over 9,000 metabolite m/z features and over 1,300 proteins from the same sample in less than five minutes. We validated the efficiency and reliability of this method and then present two practical applications: mouse macrophage M1/M2 polarization and high throughput drug screening in human 293T cells. Finally, we demonstrate relationships between proteomic and metabolomic data are discovered by machine learning.

Dear-DIAXMBD: Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition Proteomics

Data-independent acquisition (DIA) technology for protein identification from mass spectrometry and related algorithms is developing rapidly. The spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition data represents a promising direction. In this paper, we proposed an untargeted analysis method, Dear-DIA^XMBD, for direct analysis of DIA data. Dear-DIA^XMBD first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms, then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes, and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides and between fragments and peptides. We show that Dear-DIA^XMBD performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms. Dear-DIA^XMBD is publicly available at https://github.com/jianweishuai/Dear-DIA-XMBD.

Bromodomain-containing Protein 4 regulates innate inflammation via modulation of alternative splicing

Introduction

Bromodomain-containing Protein 4 (BRD4) is a transcriptional regulator which coordinates gene expression programs controlling cancer biology, inflammation, and fibrosis. In the context of airway viral infection, BRD4-specific inhibitors (BRD4i) block the release of pro-inflammatory cytokines and prevent downstream epithelial plasticity. Although the chromatin modifying functions of BRD4 in inducible gene expression have been extensively investigated, its roles in post-transcriptional regulation are not well understood. Given BRD4's interaction with the transcriptional elongation complex and spliceosome, we hypothesize that BRD4 is a functional regulator of mRNA processing.

Methods

To address this question, we combine data-independent analysis - parallel accumulation-serial fragmentation (diaPASEF) with RNA-sequencing to achieve deep and integrated coverage of the proteomic and transcriptomic landscapes of human small airway epithelial cells exposed to viral challenge and treated with BRD4i.

Results

We discover that BRD4 regulates alternative splicing of key genes, including Interferon-related Developmental Regulator 1 (IFRD1) and X-Box Binding Protein 1 (XBP1), related to the innate immune response and the unfolded protein response (UPR). We identify requirement of BRD4 for expression of serine-arginine splicing factors, splicosome components and the Inositol-Requiring Enzyme 1 IREα affecting immediate early innate response and the UPR.

Discussion

These findings extend the transcriptional elongation-facilitating actions of BRD4 in control of post-transcriptional RNA processing via modulating splicing factor expression in virus-induced innate signaling.

Structure Relaxation Approximation (SRA) for Elucidation of Protein Structures from Ion Mobility Measurements (II). Protein Complexes

Characterizing structures of protein complexes and their disease-related aberrations is essential to understanding molecular mechanisms of many biological processes. Electrospray ionization coupled with hybrid ion mobility/mass spectrometry (ESI-IM/MS) methods offer sufficient sensitivity, sample throughput, and dynamic range to enable systematic structural characterization of proteomes. However, because ESI-IM/MS characterizes ionized protein systems in the gas phase, it generally remains unclear to what extent the protein ions characterized by IM/MS have retained their solution structures. Here, we discuss the first application of our computational structure relaxation approximation [Bleiholder, C.; et al. J. Phys. Chem. B 2019, 123 (13), 2756-2769] to assign structures of protein complexes in the range from ∼16 to ∼60 kDa from their "native" IM/MS spectra. Our analysis shows that the computed IM/MS spectra agree with the experimental spectra within the errors of the methods. The structure relaxation approximation (SRA) indicates that native backbone contacts appear largely retained in the absence of solvent for the investigated protein complexes and charge states. Native contacts between polypeptide chains of the protein complex appear to be retained to a comparable extent as contacts within a folded polypeptide chain. Our computations also indicate that the hallmark "compaction" often observed for protein systems in native IM/MS measurements appears to be a poor indicator of the extent to which native residue-residue interactions are lost in the absence of solvent. Further, the SRA indicates that structural reorganization of the protein systems in IM/MS measurements appears driven largely by remodeling of the protein surface that increases its hydrophobic content by approximately 10%. For the systems studied here, this remodeling of the protein surface appears to occur mainly by structural reorganization of surface-associated hydrophilic amino acid residues not associated with β-strand secondary structure elements. Properties related to the internal protein structure, as assessed by void volume or packing density, appear unaffected by remodeling of the surface. Taken together, the structural reorganization of the protein surface appears to be generic in nature and to sufficiently stabilize protein structures to render them metastable on the time scale of IM/MS measurements.

Affi-BAMS™: A Robust Targeted Proteomics Microarray Platform to Measure Histone Post-Translational Modifications

For targeted protein panels, the ability to specifically assay post-translational modifications (PTMs) in a quantitative, sensitive, and straightforward manner would substantially advance biological and pharmacological studies. The present study highlights the effectiveness of the Affi-BAMS™ epitope-directed affinity bead capture/MALDI MS platform for quantitatively defining complex PTM marks of H3 and H4 histones. Using H3 and H4 histone peptides and isotopically labelled derivatives, this affinity bead and MALDI MS platform achieves a range of >3 orders of magnitude with a technical precision CV of <5%. Using nuclear cellular lysates, Affi-BAMS PTM-peptide capture resolves heterogeneous histone N-terminal PTMs with as little as 100 µg of starting material. In an HDAC inhibitor and MCF7 cell line model, the ability to monitor dynamic histone H3 acetylation and methylation events is further demonstrated (including SILAC quantification). Affi-BAMS (and its capacity for the multiplexing of samples and target PTM-proteins) thus provides a uniquely efficient and effective approach for analyzing dynamic epigenetic histone marks, which is critical for the regulation of chromatin structure and gene expression.

Comparison of Data-Dependent Acquisition, Data-Independent Acquisition, and Parallel Reaction Monitoring in Trapped Ion Mobility Spectrometry-Time-of-Flight Tandem Mass Spectrometry-Based Lipidomics

The parallel accumulation-serial fragmentation (PASEF) approach based on trapped ion mobility spectrometry (TIMS) enables mobility-resolved fragmentation and a higher number of fragments in the same time period compared to conventional MS/MS experiments. Furthermore, the ion mobility dimension offers novel approaches for fragmentation. Using parallel reaction monitoring (prm), the ion mobility dimension allows a more accurate selection of precursor windows, while using data-independent aquisition (dia) spectral quality is improved through ion-mobility filtering. Owing to favorable implementation in proteomics, the transferability of these PASEF modes to lipidomics is of great interest, especially as a result of the high complexity of analytes with similar fragments. However, these novel PASEF modes have not yet been thoroughly evaluated for lipidomics applications. Therefore, data-dependent acquisition (dda)-, dia-, and prm-PASEF were compared using hydrophilic interaction liquid chromatography (HILIC) for phospholipid class separation in human plasma samples. Results show that all three PASEF modes are generally suitable for usage in lipidomics. Although dia-PASEF achieves a high sensitivity in generating MS/MS spectra, the fragment-to-precursor assignment for lipids with both, similar retention time as well as ion mobility, was difficult in HILIC-MS/MS. Therefore, dda-PASEF is the method of choice to investigate unknown samples. However, the best data quality was achieved by prm-PASEF, owing to the focus on fragmentation of specified targets. The high selectivity and sensitivity in generating MS/MS spectra of prm-PASEF could be a potential alternative for targeted lipidomics, e.g., in clinical applications.

The protein and miRNA profile of plasma extracellular vesicles (EVs) can distinguish feline mammary adenocarcinoma patients from healthy feline controls

Feline mammary adenocarcinomas (FMA) are aggressive tumours with metastatic capability and limited treatment options. This study aims to investigate whether miRNAs associated with FMA tumours are secreted in extracellular vesicles (EVs) and whether they can potentially be used as a cancer biomarker in EVs from feline plasma. Tumours and matched tumour free margins from 10 felines with FMA were selected. Following a detailed literature search, RT-qPCR analyses of 90 miRNAs identified 8 miRNAs of interest for further investigation. Tumour tissue, margins and plasma were subsequently collected from a further 10 felines with FMA. EVs were isolated from the plasma. RT-qPCR expression analyses of the 8 miRNAs of interest were carried out in tumour tissue, margins, FMA EVs and control EVs. Additionally, proteomic analysis of both control and FMA plasma derived EVs was undertaken. RT-qPCR revealed significantly increased miR-20a and miR-15b in tumours compared to margins. A significant decrease in miR-15b and miR-20a was detected in EVs from FMAs compared to healthy feline EVs. The proteomic content of EVs distinguished FMAs from controls, with the protein targets of miR-20a and miR-15b also displaying lower levels in the EVs from patients with FMA. This study has demonstrated that miRNAs are readily detectable in both the tissue and plasma derived EVs from patients with FMA. These miRNAs and their protein targets are a detectable panel of markers in circulating plasma EVs that may inform future diagnostic tests for FMA in a non-invasive manner. Moreover, the clinical relevance of miR-20a and miR-15b warrants further investigation.

Proteomic approaches to study ubiquitinomics

As originally described some 40 years ago, protein ubiquitination was thought to serve primarily as a static mark for protein degradation. In the ensuing years, it has become clear that 'ubiquitination' is a structurally diverse and dynamic post-translational modification and is intricately involved in a myriad of signaling pathways in all eukaryote cells. And like other key pathways in the functional proteome, ubiquitin signaling is often disrupted, sometimes severely so, in human pathophysiology. As a result of its central role in normal physiology and human disease, the ubiquitination field is now represented across the full landscape of biomedical research from fundamental structural and biochemical studies to translational and clinical research. In recent years, mass spectrometry has emerged as a powerful technology for the detection and characterization of protein ubiquitination. Herein we detail qualitative and quantitative proteomic methods using a compare/contrast approach to highlight their strengths and weaknesses.

Juvenile CLN3 disease is a lysosomal cholesterol storage disorder: similarities with Niemann-Pick type C disease

BACKGROUND

The most common form of neuronal ceroid lipofuscinosis (NCL) is juvenile CLN3 disease (JNCL), a currently incurable neurodegenerative disorder caused by mutations in the CLN3 gene. Based on our previous work and on the premise that CLN3 affects the trafficking of the cation-independent mannose-6 phosphate receptor and its ligand NPC2, we hypothesised that dysfunction of CLN3 leads to the aberrant accumulation of cholesterol in the late endosomes/lysosomes (LE/Lys) of JNCL patients' brains.

METHODS

An immunopurification strategy was used to isolate intact LE/Lys from frozen autopsy brain samples. LE/Lys isolated from samples of JNCL patients were compared with age-matched unaffected controls and Niemann-Pick Type C (NPC) disease patients. Indeed, mutations in NPC1 or NPC2 result in the accumulation of cholesterol in LE/Lys of NPC disease samples, thus providing a positive control. The lipid and protein content of LE/Lys was then analysed using lipidomics and proteomics, respectively.

FINDINGS

Lipid and protein profiles of LE/Lys isolated from JNCL patients were profoundly altered compared to controls. Importantly, cholesterol accumulated in LE/Lys of JNCL samples to a comparable extent than in NPC samples. Lipid profiles of LE/Lys were similar in JNCL and NPC patients, except for levels of bis(monoacylglycero)phosphate (BMP). Protein profiles detected in LE/Lys of JNCL and NPC patients appeared identical, except for levels of NPC1.

INTERPRETATION

Our results support that JNCL is a lysosomal cholesterol storage disorder. Our findings also support that JNCL and NPC disease share pathogenic pathways leading to aberrant lysosomal accumulation of lipids and proteins, and thus suggest that the treatments available for NPC disease may be beneficial to JNCL patients. This work opens new avenues for further mechanistic studies in model systems of JNCL and possible therapeutic interventions for this disorder.

FUNDING

San Francisco Foundation.

HUWE1 Amplifies Ubiquitin Modifications to Broadly Stimulate Clearance of Proteins and Aggregates

Selective breakdown of proteins and aggregates is crucial for maintaining normal cellular activities and is involved in the pathogenesis of diverse diseases. How the cell recognizes and tags these targets in different structural states for degradation by the proteasome and autophagy pathways has not been well understood. Here, we discovered that a HECT-family ubiquitin ligase HUWE1 is broadly required for the efficient degradation of soluble factors and for the clearance of protein aggregates/condensates. Underlying this capacity of HUWE1 is a novel Ubiquitin-Directed ubiquitin Ligase (UDL) activity which recognizes both soluble substrates and aggregates that carry a high density of ubiquitin chains and rapidly expand the ubiquitin modifications on these targets. Ubiquitin signal amplification by HUWE1 recruits the ubiquitin-dependent segregase p97/VCP to process these targets for subsequent degradation or clearance. HUWE1 controls the cytotoxicity of protein aggregates, mediates Targeted Protein Degradation and regulates cell-cycle transitions with its UDL activity.

Directed differentiation of human iPSCs into mesenchymal lineages by optogenetic control of TGF-β signaling

In tissue development and homeostasis, transforming growth factor (TGF)-β signaling is finely coordinated by latent forms and matrix sequestration. Optogenetics can offer precise and dynamic control of cell signaling. We report the development of an optogenetic human induced pluripotent stem cell system for TGF-β signaling and demonstrate its utility in directing differentiation into the smooth muscle, tenogenic, and chondrogenic lineages. Light-activated TGF-β signaling resulted in expression of differentiation markers at levels close to those in soluble factor-treated cultures, with minimal phototoxicity. In a cartilage-bone model, light-patterned TGF-β gradients allowed the establishment of hyaline-like layer of cartilage tissue at the articular surface while attenuating with depth to enable hypertrophic induction at the osteochondral interface. By selectively activating TGF-β signaling in co-cultures of light-responsive and non-responsive cells, undifferentiated and differentiated cells were simultaneously maintained in a single culture with shared medium. This platform can enable patient-specific and spatiotemporally precise studies of cellular decision making.

Targeted delivery of tumor necrosis factor in combination with CCNU induces a T cell-dependent regression of glioblastoma

Glioblastoma is the most aggressive primary brain tumor with an unmet need for more effective therapies. Here, we investigated combination therapies based on L19TNF, an antibody-cytokine fusion protein based on tumor necrosis factor that selectively localizes to cancer neovasculature. Using immunocompetent orthotopic glioma mouse models, we identified strong anti-glioma activity of L19TNF in combination with the alkylating agent CCNU, which cured the majority of tumor-bearing mice, whereas monotherapies only had limited efficacy. In situ and ex vivo immunophenotypic and molecular profiling in the mouse models revealed that L19TNF and CCNU induced tumor DNA damage and treatment-associated tumor necrosis. In addition, this combination also up-regulated tumor endothelial cell adhesion molecules, promoted the infiltration of immune cells into the tumor, induced immunostimulatory pathways, and decreased immunosuppression pathways. MHC immunopeptidomics demonstrated that L19TNF and CCNU increased antigen presentation on MHC class I molecules. The antitumor activity was T cell dependent and completely abrogated in immunodeficient mouse models. On the basis of these encouraging results, we translated this treatment combination to patients with glioblastoma. The clinical translation is ongoing but already shows objective responses in three of five patients in the first recurrent glioblastoma patient cohort treated with L19TNF in combination with CCNU (NCT04573192).

MS-based proteomics of body fluids: The end of the beginning

Accurate biomarkers are a crucial and necessary precondition for precision medicine, yet existing ones are often unspecific and new ones have been very slow to enter the clinic. Mass spectrometry (MS)-based proteomics excels by its untargeted nature, specificity of identification and quantification making it an ideal technology for biomarker discovery and routine measurement. It has unique attributes compared to affinity binder technologies, such as OLINK Proximity Extension Assay and SOMAscan. In a previous review we described technological and conceptual limitations that had held back success (Geyer et al., 2017). We proposed a 'rectangular strategy' to better separate true biomarkers by minimizing cohort-specific effects. Today, this has converged with advances in MS-based proteomics technology, such as increased sample throughput, depth of identification and quantification. As a result, biomarker discovery studies have become more successful, producing biomarker candidates that withstand independent verification and, in some cases, already outperform state-of-the-art clinical assays. We summarize developments over the last years, including the benefits of large and independent cohorts, which are necessary for clinical acceptance. They are also required for machine learning or deep learning. Shorter gradients, new scan modes and multiplexing are about to drastically increase throughput, cross-study integration, and quantification, including proxies for absolute levels. We have found that multi-protein panels are inherently more robust than current single analyte tests and better capture the complexity of human phenotypes. Routine MS measurement in the clinic is fast becoming a viable option. The full set of proteins in a body fluid (global proteome) is the most important reference and the best process control. Additionally, it increasingly has all the information that could be obtained from targeted analysis although the latter may be the most straightforward way to enter into regular use. Many challenges remain, not least of a regulatory and ethical nature, but the outlook for MS-based clinical applications has never been brighter.

Cardiovascular Protective Effects of NP-6A4, a Drug with the FDA Designation for Pediatric Cardiomyopathy, in Female Rats with Obesity and Pre-Diabetes

BACKGROUND

Obese and pre-diabetic women have a higher risk for cardiovascular death than age-matched men with the same symptoms, and there are no effective treatments. We reported that obese and pre-diabetic female Zucker Diabetic Fatty (ZDF-F) rats recapitulate metabolic and cardiac pathology of young obese and pre-diabetic women and exhibit suppression of cardio-reparative AT2R. Here, we investigated whether NP-6A4, a new AT2R agonist with the FDA designation for pediatric cardiomyopathy, mitigate heart disease in ZDF-F rats by restoring AT2R expression.

METHODS

ZDF-F rats on a high-fat diet (to induce hyperglycemia) were treated with saline, NP-6A4 (10 mg/kg/day), or NP-6A4 + PD123319 (AT2R-specific antagonist, 5 mg/kg/day) for 4 weeks (n = 21). Cardiac functions, structure, and signaling were assessed by echocardiography, histology, immunohistochemistry, immunoblotting, and cardiac proteome analysis.

RESULTS

NP-6A4 treatment attenuated cardiac dysfunction, microvascular damage (-625%) and cardiomyocyte hypertrophy (-263%), and increased capillary density (200%) and AT2R expression (240%) (p < 0.05). NP-6A4 activated a new 8-protein autophagy network and increased autophagy marker LC3-II but suppressed autophagy receptor p62 and autophagy inhibitor Rubicon. Co-treatment with AT2R antagonist PD123319 suppressed NP-6A4's protective effects, confirming that NP-6A4 acts through AT2R. NP-6A4-AT2R-induced cardioprotection was independent of changes in body weight, hyperglycemia, hyperinsulinemia, or blood pressure.

CONCLUSIONS

Cardiac autophagy impairment underlies heart disease induced by obesity and pre-diabetes, and there are no drugs to re-activate autophagy. We propose that NP-6A4 can be an effective drug to reactivate cardiac autophagy and treat obesity- and pre-diabetes-induced heart disease, particularly for young and obese women.

Collision Cross Section Prediction Based on Machine Learning

Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected.

HAPSTR1 localizes HUWE1 to the nucleus to limit stress signaling pathways

HUWE1 is a large, enigmatic HECT-domain ubiquitin ligase implicated in the regulation of diverse pathways, including DNA repair, apoptosis, and differentiation. How HUWE1 engages its structurally diverse substrates and how HUWE1 activity is regulated are unknown. Using unbiased quantitative proteomics, we find that HUWE1 targets substrates in a largely cell-type-specific manner. However, we identify C16orf72/HAPSTR1 as a robust HUWE1 substrate in multiple cell lines. Previously established physical and genetic interactions between HUWE1 and HAPSTR1 suggest that HAPSTR1 positively regulates HUWE1 function. Here, we show that HAPSTR1 is required for HUWE1 nuclear localization and nuclear substrate targeting. Nuclear HUWE1 is required for both cell proliferation and modulation of stress signaling pathways, including p53 and nuclear factor κB (NF-κB)-mediated signaling. Combined, our results define a role for HAPSTR1 in gating critical nuclear HUWE1 functions.

Integrative multi-omics deciphers the spatial characteristics of host-gut microbiota interactions in Crohn's disease

Dysregulated host-microbial interactions play critical roles in initiation and perpetuation of gut inflammation in Crohn's disease (CD). However, the spatial distribution and interaction network across the intestine and its accessory tissues are still elusive. Here, we profile the host proteins and tissue microbes in 540 samples from the intestinal mucosa, submucosa-muscularis-serosa, mesenteric adipose tissues, mesentery, and mesenteric lymph nodes of 30 CD patients and spatially decipher the host-microbial interactions. We observe aberrant antimicrobial immunity and metabolic processes across multi-tissues during CD and determine bacterial transmission along with altered microbial communities and ecological patterns. Moreover, we identify several candidate interaction pairs between host proteins and microbes associated with perpetuation of gut inflammation and bacterial transmigration across multi-tissues in CD. Signature alterations in host proteins (e.g., SAA2 and GOLM1) and microbes (e.g., Alistipes and Streptococcus) are further imprinted in serum and fecal samples as potential diagnostic biomarkers, thus providing a rationale for precision diagnosis.

Robust and High-Throughput Analytical Flow Proteomics Analysis of Cynomolgus Monkey and Human Matrices with Zeno SWATH Data Independent Acquisition

Modern mass spectrometers routinely allow deep proteome coverage in a single experiment. These methods are typically operated at nano and micro flow regimes, but they often lack throughput and chromatographic robustness, which is critical for large-scale studies. In this context, we have developed, optimized and benchmarked LC-MS methods combining the robustness and throughput of analytical flow chromatography with the added sensitivity provided by the Zeno trap across a wide range of cynomolgus monkey and human matrices of interest for toxicological studies and clinical biomarker discovery. SWATH data independent acquisition (DIA) experiments with Zeno trap activated (Zeno SWATH DIA) provided a clear advantage over conventional SWATH DIA in all sample types tested with improved sensitivity, quantitative robustness and signal linearity as well as increased protein coverage by up to 9-fold. Using a 10-min gradient chromatography, up to 3,300 proteins were identified in tissues at 2 μg peptide load. Importantly, the performance gains with Zeno SWATH translated into better biological pathway representation and improved the ability to identify dysregulated proteins and pathways associated with two metabolic diseases in human plasma. Finally, we demonstrate that this method is highly stable over time with the acquisition of reliable data over the injection of 1,000+ samples (14.2 days of uninterrupted acquisition) without the need for human intervention or normalization. Altogether, Zeno SWATH DIA methodology allows fast, sensitive and robust proteomic workflows using analytical flow and is amenable to large-scale studies. This work provides detailed method performance assessment on a variety of relevant biological matrices and serves as a valuable resource for the proteomics community.

Suspension TRAPping Filter (sTRAP) Sample Preparation for Quantitative Proteomics in the Low µg Input Range Using a Plasmid DNA Micro-Spin Column: Analysis of the Hippocampus from the 5xFAD Alzheimer's Disease Mouse Model

Suspension TRAPping filter (sTRAP) is an attractive sample preparation method for proteomics studies. The sTRAP protocol uses 5% SDS that maximizes protein solubilization. Proteins are trapped on a borosilicate glass membrane filter, where SDS is subsequently removed from the filter. After trypsin digestion, peptides are analyzed directly by LC-MS. Here, we demonstrated the use of a low-cost plasmid DNA micro-spin column for the sTRAP sample preparation of a dilution series of a synapse-enriched sample with a range of 10-0.3 µg. With 120 ng tryptic peptides loaded onto the Evosep LC system coupled to timsTOF Pro 2 mass spectrometer, we identified 5700 protein groups with 4% coefficient of variation (CoV). Comparing other sample preparation protocols, such as the in-gel digestion and the commercial Protifi S-TRAP with the plasmid DNA micro-spin column, the last is superior in both protein and peptide identification numbers and CoV. We applied sTRAP for the analysis of the hippocampal proteome from the 5xFAD mouse model of Alzheimer's disease and their wildtype littermates, and revealed 121 up- and 54 down-regulated proteins. Protein changes in the mutant mice point to the alteration of processes related to the immune system and Amyloid aggregation, which correlates well with the known major Alzheimer's-disease-related pathology. Data are available via ProteomeXchange with the identifier PXD041045.

AKT activity orchestrates marginal zone B cell development in mice and humans

The signals controlling marginal zone (MZ) and follicular (FO) B cell development remain incompletely understood. Here, we show that AKT orchestrates MZ B cell formation in mice and humans. Genetic models that increase AKT signaling in B cells or abolish its impact on FoxO transcription factors highlight the AKT-FoxO axis as an on-off switch for MZ B cell formation in mice. In humans, splenic immunoglobulin (Ig) D+CD27+ B cells, proposed as an MZ B cell equivalent, display higher AKT signaling than naive IgD+CD27- and memory IgD-CD27+ B cells and develop in an AKT-dependent manner from their precursors in vitro, underlining the conservation of this developmental pathway. Consistently, CD148 is identified as a receptor indicative of the level of AKT signaling in B cells, expressed at a higher level in MZ B cells than FO B cells in mice as well as humans.

A rapid and sensitive single-cell proteomic method based on fast liquid-chromatography separation, retention time prediction and MS1-only acquisition

Single-cell analysis has received much attention in recent years for elucidating the widely existing cellular heterogeneity in biological systems. However, the ability to measure the proteome in single cells is still far behind that of transcriptomics due to the lack of sensitive and high-throughput mass spectrometry methods. Herein, we report an integrated strategy termed "SCP-MS1" that combines fast liquid chromatography (LC) separation, deep learning-based retention time (RT) prediction and MS1-only acquisition for rapid and sensitive single-cell proteome analysis. In SCP-MS1, the peptides were identified via four-dimensional MS1 feature (m/z, RT, charge and FAIMS CV) matching, therefore relieving MS acquisition from the time consuming and information losing MS2 step and making this method particularly compatible with fast LC separation. By completely omitting the MS2 step, all the MS analysis time was utilized for MS1 acquisition in SCP-MS1 and therefore led to 65%-138% increased MS1 feature collection. Unlike "match between run" methods that still needed MS2 information for RT alignment, SCP-MS1 used deep learning-based RT prediction to transfer the measured RTs in long gradient bulk analyses to short gradient single cell analyses, which was the key step to enhance both identification scale and matching accuracy. Using this strategy, more than 2000 proteins were obtained from 0.2 ng of peptides with a 14-min active gradient at a false discovery rate (FDR) of 0.8%. Comparing with the DDA method, improved quantitative performance was also observed for SCP-MS1 with approximately 50% decreased median coefficient of variation of quantified proteins. For single-cell analysis, 1715 ± 204 and 1604 ± 224 proteins were quantified in single 293T and HeLa cells, respectively. Finally, SCP-MS1 was applied to single-cell proteome analysis of sorafenib resistant and non-resistant HepG2 cells and revealed clear cellular heterogeneity in the resistant population that may be masked in bulk studies.

Blue Native PAGE-Antibody Shift in Conjunction with Mass Spectrometry to Reveal Protein Subcomplexes: Detection of a Cerebellar α1/α6-Subunits Containing γ-Aminobutyric Acid Type A Receptor Subtype

The pentameric γ-Aminobutyric acid type A receptors (GABA_ARs) are ligand-gated ion channels that mediate the majority of inhibitory neurotransmission in the brain. In the cerebellum, the two main receptor subtypes are the 2α1/2β/γ and 2α6/2β/δ subunits. In the present study, an interaction proteomics workflow was used to reveal additional subtypes that contain both α1 and α6 subunits. Immunoprecipitation of the α6 subunit from mouse brain cerebellar extract co-purified the α1 subunit. In line with this, pre-incubation of the cerebellar extract with anti-α6 antibodies and analysis by blue native gel electrophoresis mass-shifted part of the α1 complexes, indicative of the existence of an α1α6-containing receptor. Subsequent mass spectrometry of the blue native gel showed the α1α6-containing receptor subtype to exist in two main forms, i.e., with or without Neuroligin-2. Immunocytochemistry on a cerebellar granule cell culture revealed co-localization of α6 and α1 in post-synaptic puncta that apposed the presynaptic marker protein Vesicular GABA transporter, indicative of the presence of this synaptic GABA_AR subtype.

Heterobifunctional Ligase Recruiters Enable pan-Degradation of Inhibitor of Apoptosis Proteins

Proteolysis targeting chimeras (PROTACs) represent a new pharmacological modality to inactivate disease-causing proteins. PROTACs operate via recruiting E3 ubiquitin ligases, which enable the transfer of ubiquitin tags onto their target proteins, leading to proteasomal degradation. However, several E3 ligases are validated pharmacological targets themselves, of which inhibitor of apoptosis (IAP) proteins are considered druggable in cancer. Here, we report three series of heterobifunctional PROTACs, which consist of an IAP antagonist linked to either von Hippel-Lindau- or cereblon-recruiting ligands. Hijacking E3 ligases against each other led to potent, rapid, and preferential depletion of cellular IAPs. In addition, these compounds caused complete X-chromosome-linked IAP knockdown, which was rarely observed for monovalent and homobivalent IAP antagonists. In cellular assays, hit degrader 9 outperformed antagonists and showed potent inhibition of cancer cell viability. The hetero-PROTACs disclosed herein are valuable tools to facilitate studies of the biological roles of IAPs and will stimulate further efforts toward E3-targeting therapies.

Secreted HLA Fc-Fusion Profiles Immunopeptidome in Hypoxic PDAC and Cellular Senescence

Human leukocyte antigens (HLA) display peptides largely from intracellular proteins on the surface of cells in major histocompatibility complex (MHC)-peptide complexes. These complexes provide a biological window into the cell, and peptides derived from disease-associated antigens can serve as biomarkers and therapeutic targets. Thus, proper identification of peptides and the corresponding presenting HLA allele in disease phenotypes is important for the design and execution of therapeutic strategies using engineered T-cell receptors or antibodies. Yet, current mass spectrometry methods for profiling the immunopeptidome typically require large and complex sample inputs, complicating the study of several disease phenotypes and lowering the confidence of both peptide and allele identification. Here, we describe a novel secreted HLA (sHLA) Fc-fusion construct that allows for simple peptide identification from single HLA alleles in two important disease models: hypoxic pancreatic ductal adenocarcinoma (PDAC) and cellular senescence. We identify hypoxia and senescence-associated peptides that could act as future targets for immunotherapy. More generally, the method streamlines the time between sample preparation and injection from days to hours, yielding allele-restricted target identification in a temporally controlled manner. Overall, this method identified >30,000 unique HLA-associated peptides across two different HLA alleles and seven cell lines. Notably, ∼9,300 of these unique HLA-associated peptides had previously not been identified in the Immune Epitope Database. We believe the sHLA Fc-fusion capture technology will accelerate the study of the immunopeptidome as therapeutic interest in HLA-peptide complexes increases in cancer and beyond.

Reading and writing of mRNA m6A modification orchestrate maternal-to-zygotic transition in mice

N6-methyladenosine (m6A) modification has been shown to regulate RNA metabolism. Here, we investigate m6A dynamics during maternal-to-zygotic transition (MZT) in mice through multi-omic analysis. Our results show that m6A can be maternally inherited or de novo gained after fertilization. Interestingly, m6A modification on maternal mRNAs not only correlates with mRNA degradation, but also maintains the stability of a small group of mRNAs thereby promoting their translation after fertilization. We identify Ythdc1 and Ythdf2 as key m6A readers for mouse preimplantation development. Our study reveals a key role of m6A mediated RNA metabolism during MZT in mammals.

Quantification of 782 Plasma Peptides by Multiplexed Targeted Proteomics

Blood analysis is one of the foundations of clinical diagnostics. In recent years, the analysis of proteins in blood samples by mass spectrometry has taken a jump forward in terms of sensitivity and the number of identified proteins. The recent development of parallel reaction monitoring with parallel accumulation and serial fragmentation (prm-PASEF) combines ion mobility as an additional separation dimension. This increases the proteome coverage while allowing the use of shorter chromatographic gradients. To demonstrate the method's full potential, we used an isotope-labeled synthetic peptide mix of 782 peptides, derived from 579 plasma proteins, spiked into blood plasma samples with a prm-PASEF measurement allowing the quantification of 565 plasma proteins by targeted proteomics. As a less time-consuming alternative to the prm-PASEF method, we describe guided data independent acquisition (dia)-PASEF (g-dia-PASEF) and compare its application to prm-PASEF for measuring blood plasma. To demonstrate both methods' performance in clinical samples, 20 patient plasma samples from a colorectal cancer (CRC) cohort were analyzed. The analysis identified 14 differentially regulated proteins between the CRC patient and control individual plasma samples. This shows the technique's potential for the rapid and unbiased screening of blood proteins, abolishing the need for the preselection of potential biomarker proteins.

A high-throughput approach reveals distinct peptide charging behaviors in electrospray ionization mass spectrometry

Electrospray ionization is a powerful and prevalent technique used to ionize analytes in mass spectrometry. The distribution of charges that an analyte receives (charge state distribution, CSD) is an important consideration for interpreting mass spectra. However, due to an incomplete understanding of the ionization mechanism, the analyte properties that influence CSDs are not fully understood. Here, we employ a machine learning-based high-throughput approach and analyze CSDs of hundreds of thousands of peptides. Interestingly, half of the peptides exhibit charges that differ from what one would naively expect (number of basic sites). We find that these peptides can be classified into two regimes-undercharging and overcharging-and that these two regimes display markedly different charging characteristics. Strikingly, peptides in the overcharging regime show minimal dependence on basic site count, and more generally, the two regimes exhibit distinct sequence determinants. These findings highlight the rich ionization behavior of peptides and the potential of CSDs for enhancing peptide identification.

Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology

There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.

Data-independent acquisition and quantification of extracellular matrix from human lung in chronic inflammation-associated carcinomas

Early events associated with chronic inflammation and cancer involve significant remodeling of the extracellular matrix (ECM), which greatly affects its composition and functional properties. Using lung squamous cell carcinoma (LSCC), a chronic inflammation-associated cancer (CIAC), we optimized a robust proteomic pipeline to discover potential biomarker signatures and protein changes specifically in the stroma. We combined ECM enrichment from fresh human tissues, data-independent acquisition (DIA) strategies, and stringent statistical processing to analyze "Tumor" and matched adjacent histologically normal ("Matched Normal") tissues from patients with LSCC. Overall, 1802 protein groups were quantified with at least two unique peptides, and 56% of those proteins were annotated as "extracellular." Confirming dramatic ECM remodeling during CIAC progression, 529 proteins were significantly altered in the "Tumor" compared to "Matched Normal" tissues. The signature was typified by a coordinated loss of basement membrane proteins and small leucine-rich proteins. The dramatic increase in the stromal levels of SERPINH1/heat shock protein 47, that was discovered using our ECM proteomic pipeline, was validated by immunohistochemistry (IHC) of "Tumor" and "Matched Normal" tissues, obtained from an independent cohort of LSCC patients. This integrated workflow provided novel insights into ECM remodeling during CIAC progression, and identified potential biomarker signatures and future therapeutic targets.

ProInfer: An interpretable protein inference tool leveraging on biological networks

In mass spectrometry (MS)-based proteomics, protein inference from identified peptides (protein fragments) is a critical step. We present ProInfer (Protein Inference), a novel protein assembly method that takes advantage of information in biological networks. ProInfer assists recovery of proteins supported only by ambiguous peptides (a peptide which maps to more than one candidate protein) and enhances the statistical confidence for proteins supported by both unique and ambiguous peptides. Consequently, ProInfer rescues weakly supported proteins thereby improving proteome coverage. Evaluated across THP1 cell line, lung cancer and RAW267.4 datasets, ProInfer always infers the most numbers of true positives, in comparison to mainstream protein inference tools Fido, EPIFANY and PIA. ProInfer is also adept at retrieving differentially expressed proteins, signifying its usefulness for functional analysis and phenotype profiling. Source codes of ProInfer are available at https://github.com/PennHui2016/ProInfer.

MUG CCArly: A Novel Autologous 3D Cholangiocarcinoma Model Presents an Increased Angiogenic Potential

Cholangiocarcinoma (CCA) are characterized by their desmoplastic and hypervascularized tumor microenvironment (TME), which is mainly composed of tumor cells and cancer-associated fibroblasts (CAFs). CAFs play a pivotal role in general and CCA tumor progression, angiogenesis, metastasis, and the development of treatment resistance. To our knowledge, no continuous human in vivo-like co-culture model is available for research. Therefore, we aimed to establish a new model system (called MUG CCArly) that mimics the desmoplastic microenvironment typically seen in CCA. Proteomic data comparing the new CCA tumor cell line with our co-culture tumor model (CCTM) indicated a higher gene expression correlation of the CCTM with physiological CCA characteristics. A pro-angiogenic TME that is typically observed in CCA could also be better simulated in the CCTM group. Further analysis of secreted proteins revealed CAFs to be the main source of these angiogenic factors. Our CCTM MUG CCArly represents a new, reproducible, and easy-to-handle 3D CCA model for preclinical studies focusing on CCA-stromal crosstalk, tumor angiogenesis, and invasion, as well as the immunosuppressive microenvironment and the involvement of CAFs in the way that drug resistance develops.

Ribosome-rescuer PELO catalyzes the oligomeric assembly of NOD-like receptor family proteins via activating their ATPase enzymatic activity

NOD-like receptors (NLRs) are pattern recognition receptors for diverse innate immune responses. Self-oligomerization after engagement with a ligand is a generally accepted model for the activation of each NLR. We report here that a catalyzer was required for NLR self-oligomerization. PELO, a well-known surveillance factor in translational quality control and/or ribosome rescue, interacted with all cytosolic NLRs and activated their ATPase activity. In the case of flagellin-initiated NLRC4 inflammasome activation, flagellin-bound NAIP5 recruited the first NLRC4 and then PELO was required for correctly assembling the rest of NLRC4s into the NLRC4 complex, one by one, by activating the NLRC4 ATPase activity. Stoichiometric and functional data revealed that PELO was not a structural constituent of the NLRC4 inflammasome but a powerful catalyzer for its assembly. The catalytic role of PELO in the activation of cytosolic NLRs provides insight into NLR activation and provides a direction for future studies of NLR family members.

Accessing three-branched high-affinity cereblon ligands for molecular glue and protein degrader design

The Petasis borono-Mannich reaction was employed for an alternative entry towards three-branched cereblon ligands. Such compounds are capabable of making multiple interactions with the protein surface and possess a suitable linker exit vector. The high-affinity ligands were used to assemble prototypic new molecular glues and proteolysis targeting chimeras (PROTACs) targeting BRD4 for degradation. Our results highlight the importance of multicomponent reactions (MCRs) in drug discovery and add new insights into the rapidly growing field of protein degraders.

Advances in Mass Spectrometry-Based Single Cell Analysis

Technological developments and improvements in single-cell isolation and analytical platforms allow for advanced molecular profiling at the single-cell level, which reveals cell-to-cell variation within the admixture cells in complex biological or clinical systems. This helps to understand the cellular heterogeneity of normal or diseased tissues and organs. However, most studies focused on the analysis of nucleic acids (e.g., DNA and RNA) and mass spectrometry (MS)-based analysis for proteins and metabolites of a single cell lagged until recently. Undoubtedly, MS-based single-cell analysis will provide a deeper insight into cellular mechanisms related to health and disease. This review summarizes recent advances in MS-based single-cell analysis methods and their applications in biology and medicine.

Metabolic heterogeneity and cross-feeding within isogenic yeast populations captured by DILAC

Genetically identical cells are known to differ in many physiological parameters such as growth rate and drug tolerance. Metabolic specialization is believed to be a cause of such phenotypic heterogeneity, but detection of metabolically divergent subpopulations remains technically challenging. We developed a proteomics-based technology, termed differential isotope labelling by amino acids (DILAC), that can detect producer and consumer subpopulations of a particular amino acid within an isogenic cell population by monitoring peptides with multiple occurrences of the amino acid. We reveal that young, morphologically undifferentiated yeast colonies contain subpopulations of lysine producers and consumers that emerge due to nutrient gradients. Deconvoluting their proteomes using DILAC, we find evidence for in situ cross-feeding where rapidly growing cells ferment and provide the more slowly growing, respiring cells with ethanol. Finally, by combining DILAC with fluorescence-activated cell sorting, we show that the metabolic subpopulations diverge phenotypically, as exemplified by a different tolerance to the antifungal drug amphotericin B. Overall, DILAC captures previously unnoticed metabolic heterogeneity and provides experimental evidence for the role of metabolic specialization and cross-feeding interactions as a source of phenotypic heterogeneity in isogenic cell populations.

Applying genetic technologies to combat infectious diseases in aquaculture

Disease and parasitism cause major welfare, environmental and economic concerns for global aquaculture. In this review, we examine the status and potential of technologies that exploit genetic variation in host resistance to tackle this problem. We argue that there is an urgent need to improve understanding of the genetic mechanisms involved, leading to the development of tools that can be applied to boost host resistance and reduce the disease burden. We draw on two pressing global disease problems as case studies-sea lice infestations in salmonids and white spot syndrome in shrimp. We review how the latest genetic technologies can be capitalised upon to determine the mechanisms underlying inter- and intra-species variation in pathogen/parasite resistance, and how the derived knowledge could be applied to boost disease resistance using selective breeding, gene editing and/or with targeted feed treatments and vaccines. Gene editing brings novel opportunities, but also implementation and dissemination challenges, and necessitates new protocols to integrate the technology into aquaculture breeding programmes. There is also an ongoing need to minimise risks of disease agents evolving to overcome genetic improvements to host resistance, and insights from epidemiological and evolutionary models of pathogen infestation in wild and cultured host populations are explored. Ethical issues around the different approaches for achieving genetic resistance are discussed. Application of genetic technologies and approaches has potential to improve fundamental knowledge of mechanisms affecting genetic resistance and provide effective pathways for implementation that could lead to more resistant aquaculture stocks, transforming global aquaculture.

Complete Workflow for High Throughput Human Single Skeletal Muscle Fiber Proteomics

Skeletal muscle is a major regulatory tissue of whole-body metabolism and is composed of a diverse mixture of cell (fiber) types. Aging and several diseases differentially affect the various fiber types, and therefore, investigating the changes in the proteome in a fiber-type specific manner is essential. Recent breakthroughs in isolated single muscle fiber proteomics have started to reveal heterogeneity among fibers. However, existing procedures are slow and laborious requiring two hours of mass spectrometry time per single muscle fiber; 50 fibers would take approximately four days to analyze. Thus, to capture the high variability in fibers both within and between individuals requires advancements in high throughput single muscle fiber proteomics. Here we use a single cell proteomics method to enable quantification of single muscle fiber proteomes in 15 minutes total instrument time. As proof of concept, we present data from 53 isolated skeletal muscle fibers obtained from two healthy individuals analyzed in 13.25 hours. Adapting single cell data analysis techniques to integrate the data, we can reliably separate type 1 and 2A fibers. Sixty-five proteins were statistically different between clusters indicating alteration of proteins involved in fatty acid oxidation, muscle structure and regulation. Our results indicate that this method is significantly faster than prior single fiber methods in both data collection and sample preparation while maintaining sufficient proteome depth. We anticipate this assay will enable future studies of single muscle fibers across hundreds of individuals, which has not been possible previously due to limitations in throughput.

Next-generation interaction proteomics for quantitative Jumbophage-bacteria interaction mapping

Host-pathogen interactions (HPIs) are pivotal in regulating establishment, progression, and outcome of an infection. Affinity-purification mass spectrometry has become instrumental for the characterization of HPIs, however the targeted nature of exogenously expressing individual viral proteins has limited its utility to the analysis of relatively small pathogens. Here we present the use of co-fractionation mass spectrometry (SEC-MS) for the high-throughput analysis of HPIs from native viral infections of two jumbophages ( ϕ KZ and ϕ PA3) in Pseudomonas aeruginosa . This enabled the detection > 6000 unique host-pathogen and > 200 pathogen-pathogen interactions for each phage, encompassing > 50% of the phage proteome. Interactome-wide comparison across phages showed similar perturbed protein interactions suggesting fundamentally conserved mechanisms of phage predation within the KZ-like phage family. Prediction of novel ORFs revealed a ϕ PA3 complex showing strong structural and sequence similarity to ϕ KZ nvRNAp, suggesting ϕ PA3 also possesses two RNA polymerases acting at different stages of the infection cycle. We further expanded our understanding on the molecular organization of the virion packaged and injected proteome by identifying 23 novel virion components and 5 novel injected proteins, as well as providing the first evidence for interactions between KZ-like phage proteins and the host ribosome. To enable accessibility to this data, we developed PhageMAP, an online resource for network query, visualization, and interaction prediction ( https://phagemap.ucsf.edu/ ). We anticipate this study will lay the foundation for the application of co-fractionation mass spectrometry for the scalable profiling of hostpathogen interactomes and protein complex dynamics upon infection.

Four-dimensional trapped ion mobility spectrometry lipidomics for high throughput clinical profiling of human blood samples

Lipidomics encompassing automated lipid extraction, a four-dimensional (4D) feature selection strategy for confident lipid annotation as well as reproducible and cross-validated quantification can expedite clinical profiling. Here, we determine 4D descriptors (mass to charge, retention time, collision cross section, and fragmentation spectra) of 200 lipid standards and 493 lipids from reference plasma via trapped ion mobility mass spectrometry to enable the implementation of stringent criteria for lipid annotation. We use 4D lipidomics to confidently annotate 370 lipids in reference plasma samples and 364 lipids in serum samples, and reproducibly quantify 359 lipids using level-3 internal standards. We show the utility of our 4D lipidomics workflow for high-throughput applications by reliable profiling of intra-individual lipidome phenotypes in plasma, serum, whole blood, venous and finger-prick dried blood spots.

Mobility-resolved broadband dissociation and parallel reaction monitoring for laser desorption/ionization-mass spectrometry - Tattoo pigment identification supported by trapped ion mobility spectrometry

In this work, trapped ion mobility spectrometry (TIMS) was introduced to facilitate tandem mass spectrometry (MS²) experiments for laser desorption/ionization-mass spectrometry (LDI-MS) as mobility-resolved fragmentation. The mobility separation of desorbed ions was followed by subsequent fragmentation using data-independent broadband collision-induced dissociation (bbCID) or targeted fragmentation through a prototypic version of parallel reaction monitoring-parallel accumulation serial fragmentation (prm-PASEF) for LDI. Both mobility-resolved fragmentation options, TIMS-bbCID and prm-PASEF, were applied to LDI point measurements to identify organic pigments in tattoo inks. Furthermore, the prototypic prm-PASEF algorithm was used in imaging applications to increase confidence in annotating organic tattoo pigments in skin samples with adverse reactions. Due to less complex spectra in matrix-free LDI, both fragmentation methods yielded fast and reliable MS² identification workflows. TIMS-bbCID was especially beneficial for the rapid acquisition of multiple fragment spectra. For the targeted prm-PASEF approach, analytes' mobilities needed to be collected prior to simplified fragmentation. Therefore, a reference list for 14 pigments was created. The possible number of experiments per thin section and the associated savings in analysis time compared to conventional MS² were particularly suitable for the imaging application. Furthermore, the mobility dimension enabled a new orthogonal identification parameter, increasing the annotation confidence of tattoo pigments through compound specific mobilities.

Template-assisted covalent modification of DCAF16 underlies activity of BRD4 molecular glue degraders

Small molecules that induce protein-protein interactions to exert proximity-driven pharmacology such as targeted protein degradation are a powerful class of therapeutics1-3. Molecular glues are of particular interest given their favorable size and chemical properties and represent the only clinically approved degrader drugs4-6. The discovery and development of molecular glues for novel targets, however, remains challenging. Covalent strategies could in principle facilitate molecular glue discovery by stabilizing the neo-protein interfaces. Here, we present structural and mechanistic studies that define a trans-labeling covalent molecular glue mechanism, which we term "template-assisted covalent modification". We found that a novel series of BRD4 molecular glue degraders act by recruiting the CUL4DCAF16 ligase to the second bromodomain of BRD4 (BRD4BD2). BRD4BD2, in complex with DCAF16, serves as a structural template to facilitate covalent modification of DCAF16, which stabilizes the BRD4-degrader-DCAF16 ternary complex formation and facilitates BRD4 degradation. A 2.2 Å cryo-electron microscopy structure of the ternary complex demonstrates that DCAF16 and BRD4BD2 have pre-existing structural complementarity which optimally orients the reactive moiety of the degrader for DCAF16Cys58 covalent modification. Systematic mutagenesis of both DCAF16 and BRD4BD2 revealed that the loop conformation around BRD4His437, rather than specific side chains, is critical for stable interaction with DCAF16 and BD2 selectivity. Together our work establishes "template-assisted covalent modification" as a mechanism for covalent molecular glues, which opens a new path to proximity driven pharmacology.