Phosphoproteome profiling uncovers a key role for CDKs in TNF signaling

µPhos: a scalable and sensitive platform for high-dimensional phosphoproteomics

Mass spectrometry has revolutionized cell signaling research by vastly simplifying the analysis of many thousands of phosphorylation sites in the human proteome. Defining the cellular response to perturbations is crucial for further illuminating the functionality of the phosphoproteome. Here we describe µPhos ('microPhos'), an accessible phosphoproteomics platform that permits phosphopeptide enrichment from 96-well cell culture and small tissue amounts in <8 h total processing time. By greatly minimizing transfer steps and liquid volumes, we demonstrate increased sensitivity, >90% selectivity, and excellent quantitative reproducibility. Employing highly sensitive trapped ion mobility mass spectrometry, we quantify ~17,000 Class I phosphosites in a human cancer cell line using 20 µg starting material, and confidently localize ~6200 phosphosites from 1 µg. This depth covers key signaling pathways, rendering sample-limited applications and perturbation experiments with hundreds of samples viable. We employ µPhos to study drug- and time-dependent response signatures in a leukemia cell line, and by quantifying 30,000 Class I phosphosites in the mouse brain we reveal distinct spatial kinase activities in subregions of the hippocampal formation.

NFκB dynamics-dependent epigenetic changes modulate inflammatory gene expression and induce cellular senescence

Upregulation of nuclear factor κB (NFκB) signaling is a hallmark of aging and a major cause of age-related chronic inflammation. However, its effect on cellular senescence remains unclear. Here, we show that alteration of NFκB nuclear dynamics from oscillatory to sustained by depleting a negative feedback regulator of NFκB pathway, NFκB inhibitor alpha (IκBα), in the presence of tumor necrosis factor α (TNFα) promotes cellular senescence. Sustained NFκB activity enhanced inflammatory gene expression through increased NFκB-DNA binding and slowed the cell cycle. IκBα protein was decreased under replicative or oxidative stress in vitro. Furthermore, a decrease in IκBα protein and an increase in DNA-NFκB binding at the transcription start sites of age-associated genes in aged mouse hearts suggested that nuclear NFκB dynamics may play a critical role in the progression of aging. Our study suggests that nuclear NFκB dynamics-dependent epigenetic changes regulated over time in a living system, possibly through a decrease in IκBα, enhance the expression of inflammatory genes to advance the cells to a senescent state.

Deciphering the tumor immune microenvironment from a multidimensional omics perspective: insight into next-generation CAR-T cell immunotherapy and beyond

Tumor immune microenvironment (TIME) consists of intra-tumor immunological components and plays a significant role in tumor initiation, progression, metastasis, and response to therapy. Chimeric antigen receptor (CAR)-T cell immunotherapy has revolutionized the cancer treatment paradigm. Although CAR-T cell immunotherapy has emerged as a successful treatment for hematologic malignancies, it remains a conundrum for solid tumors. The heterogeneity of TIME is responsible for poor outcomes in CAR-T cell immunotherapy against solid tumors. The advancement of highly sophisticated technology enhances our exploration in TIME from a multi-omics perspective. In the era of machine learning, multi-omics studies could reveal the characteristics of TIME and its immune resistance mechanism. Therefore, the clinical efficacy of CAR-T cell immunotherapy in solid tumors could be further improved with strategies that target unfavorable conditions in TIME. Herein, this review seeks to investigate the factors influencing TIME formation and propose strategies for improving the effectiveness of CAR-T cell immunotherapy through a multi-omics perspective, with the ultimate goal of developing personalized therapeutic approaches.

In-Depth Endogenous Phosphopeptidomics of Serum with Zirconium(IV)-Grafted Mesoporous Silica Enrichment

Detection of endogenous peptides, especially those with modifications (such as phosphorylation) in biofluids, can serve as an indicator of intracellular pathophysiology. Although great progress has been made in phosphoproteomics in recent years, endogenous phosphopeptidomics has largely lagged behind. One main hurdle in endogenous phosphopeptidomics analysis is the coexistence of proteins and highly abundant nonmodified peptides in complex matrices. In this study, we developed an approach using zirconium(IV)-grafted mesoporous beads to enrich phosphopeptides, followed by analysis with a high resolution nanoRPLC-MS/MS system. The bifunctional material was first tested with digests of standard phosphoproteins and HeLa cell lysates, with excellent enrichment performance achieved. Given the size exclusion nature, the beads were directly applied for endogenous phosphopeptidomic analysis of serum samples from pancreatic ductal adenocarcinoma (PDAC) patients and controls. In total, 329 endogenous phosphopeptides (containing 113 high confidence sites) were identified across samples, by far the largest endogenous phosphopeptide data set cataloged to date. In addition, the method was readily applied for phosphoproteomics of the same set of samples, with 172 phosphopeptides identified and significant changes in dozens of phosphopeptides observed. Given the simplicity and robustness of the proposed method, we envision that it can be readily used for comprehensive phosphorylation studies of serum and other biofluid samples.

Targeted delivery of NO donor and ROS scavenger for synergistic treatment of rheumatoid arthritis

Rheumatoid arthritis (RA) is characterized by high level of reactive oxygen species (ROS) and proinflammatory cytokines, which facilitate the activation of the inflammatory signaling such as NF-κB pathway and exacerbate the development of inflammation. Herein, we designed a nanodrug by encapsulating the NO donor S-nitrosoglutathione (GSNO) into an emulsion and coating the surface with a polydopamine (PDA) layer to yield GSNO@PDA, which simultaneously scavenged the extra ROS and suppressed NF-κB signaling for potent RA treatment. To enhance the cellular uptake and NO generation efficiency, dextran sulfate (DS) and Cu2+ were anchored on the surface of GSNO@PDA to obtain the final formulation GSNO@PDA@DS. Our results demonstrated that GSNO@PDA@DS were successfully prepared and the modification of DS effectively boosted the cellular uptake of GSNO@PDA@DS. Moreover, GSNO@PDA@DS lowered cellular ROS and elevated intracellular NO, resulting in a decrease of M1 phenotype, inhibition of NF-κB pathway and down-regulation of proinflammatory cytokine tumor necrosis factor-α (TNF-α). Further in vivo studies confirmed that GSNO@PDA@DS significantly relieved symptoms and bone erosion by regulating the microenvironment of RA, highlighting the potential of GSNO@PDA@DS for RA therapy through ROS scavenging and NO-mediated suppression of inflammatory signaling.

Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape

The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.

Phosphoproteome reveals long-term potentiation deficit following treatment of ultra-low dose soman exposure in mice

Soman, a warfare nerve agent, poses a significant threat by inducing severe brain damage that often results in death. Nonetheless, our understanding of the biological changes underlying persistent neurocognitive dysfunction caused by low dosage of soman remains limited. This study used mice to examine the effects of different doses of soman over time. Phosphoproteomic analysis of the mouse brain is the first time to be used to detect toxic effects of soman at such low or ultra-low doses, which were undetectable based on measuring the activity of acetylcholinesterase at the whole-animal level. We also found that phosphoproteome alterations could accurately track the soman dose, irrespective of the sampling time. Moreover, phosphoproteome revealed a rapid and adaptive cellular response to soman exposure, with the points of departure 8-38 times lower than that of acetylcholinesterase activity. Impaired long-term potentiation was identified in phosphoproteomic studies, which was further validated by targeted quantitative proteomics, immunohistochemistry, and immunofluorescence analyses, with significantly increased levels of phosphorylation of protein phosphatase 1 in the hippocampus following soman exposure. This increase in phosphorylation inhibits long-term potentiation, ultimately leading to long-term memory dysfunction in mice.

Uncovering the Achilles heel of genetic heterogeneity: machine learning-based classification and immunological properties of necroptosis clusters in Alzheimer's disease

Background

Alzheimer's disease (AD) is an age-associated neurodegenerative disease, and the currently available diagnostic modalities and therapeutic agents are unsatisfactory due to its high clinical heterogeneity. Necroptosis is a common type of programmed cell death that has been shown to be activated in AD.

Methods

In this study, we first investigated the expression profiles of necroptosis-related genes (NRGs) and the immune landscape of AD based on GSE33000 dataset. Next, the AD samples in the GSE33000 dataset were extracted and subjected to consensus clustering based upon the differentially expressed NRGs. Key genes associated with necroptosis clusters were identified using Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm, and then intersected with the key gene related to AD. Finally, we developed a diagnostic model for AD by comparing four different machine learning approaches. The discrimination performance and clinical relevance of the diagnostic model were assessed using various evaluation metrics, including the nomogram, calibration plot, decision curve analysis (DCA), and independent validation datasets.

Results

Aberrant expression patterns of NRGs and specific immune landscape were identified in the AD samples. Consensus clustering revealed that patients in the GSE33000 dataset could be classified into two necroptosis clusters, each with distinct immune landscapes and enriched pathways. The Extreme Gradient Boosting (XGB) was found to be the most optimal diagnostic model for the AD based on the predictive ability and reliability of the models constructed by four machine learning approaches. The five most important variables, including ACAA2, BHLHB4, CACNA2D3, NRN1, and TAC1, were used to construct a five-gene diagnostic model. The constructed nomogram, calibration plot, DCA, and external independent validation datasets exhibited outstanding diagnostic performance for AD and were closely related with the pathologic hallmarks of AD.

Conclusion

This work presents a novel diagnostic model that may serve as a framework to study disease heterogeneity and provide a plausible mechanism underlying neuronal loss in AD.

A TNF-α blocking peptide that reduces NF-κB and MAPK activity for attenuating inflammation

Overexpression of tumor necrosis factor-α (TNF-α) is implicated in many inflammatory diseases, including septic shock, hepatitis, asthma, insulin resistance and autoimmune diseases, such as rheumatoid arthritis and Crohn's disease. The TNF-α signaling pathway is a valuable target, and anti-TNF-α drugs are successfully used to treat autoimmune and inflammatory diseases. Here, we study anti-inflammatory activity of an anti-TNF-α peptide (SN1-13, DEFHLELHLYQSW). In the cellular level assessment, SN1-13 inhibited TNF-α-induced cytotoxicity and blocks TNF-α-triggered signaling activities (IC50 = 15.40 μM). Moreover, the potential binding model between SN1-13 and TNF-α/TNFRs conducted through molecular docking revealed that SN1-13 could stunt TNF-α mediated signaling thought blocking TNF-α and its receptor TNFR1 and TNFR2. These results suggest that SN1-13 would be a potential lead peptide to treat TNF-α-mediated inflammatory diseases.

Hybrid-DIA: intelligent data acquisition integrates targeted and discovery proteomics to analyze phospho-signaling in single spheroids

Achieving sufficient coverage of regulatory phosphorylation sites by mass spectrometry (MS)-based phosphoproteomics for signaling pathway reconstitution is challenging, especially when analyzing tiny sample amounts. To address this, we present a hybrid data-independent acquisition (DIA) strategy (hybrid-DIA) that combines targeted and discovery proteomics through an Application Programming Interface (API) to dynamically intercalate DIA scans with accurate triggering of multiplexed tandem mass spectrometry (MSx) scans of predefined (phospho)peptide targets. By spiking-in heavy stable isotope labeled phosphopeptide standards covering seven major signaling pathways, we benchmark hybrid-DIA against state-of-the-art targeted MS methods (i.e., SureQuant) using EGF-stimulated HeLa cells and find the quantitative accuracy and sensitivity to be comparable while hybrid-DIA also profiles the global phosphoproteome. To demonstrate the robustness, sensitivity, and biomedical potential of hybrid-DIA, we profile chemotherapeutic agents in single colon carcinoma multicellular spheroids and evaluate the phospho-signaling difference of cancer cells in 2D vs 3D culture.

Evaluation of DDA Library-Free Strategies for Phosphoproteomics and Ubiquitinomics Data-Independent Acquisition Data

Phosphoproteomics and ubiquitinomics data-independent acquisition (DIA) mass spectrometry (MS) data is typically analyzed by using a data-dependent acquisition (DDA) spectral library. The performance of various library-free strategies for analyzing phosphoproteomics and ubiquitinomics DIA MS data has not been evaluated. In this study, we systematically compare four commonly used DDA library-free approaches including Spectronaut's directDIA, DIA-Umpire, DIA-MSFragger, and in silico-predicted library for analysis of phosphoproteomics SWATH, DIA, and diaPASEF data as well as ubiquitinomics diaPASEF data. Spectronaut's directDIA shows the highest sensitivity for phosphopeptide detection not only in synthetic phosphopeptide samples but also in phosphoproteomics SWATH-MS and DIA data from real biological samples, when compared to the other three library-free strategies. For phosphoproteomics diaPASEF data, Spectronaut's directDIA and the in silico-predicted library based on DIA-NN identify almost the same number of phosphopeptides as a project-specific DDA spectral library. However, only about 30% of the total phosphopeptides are commonly identified, suggesting that the library-free strategies for phospho-diaPASEF data need further improvement in terms of sensitivity. For ubiquitinomics diaPASEF data, the in silico-predicted library performs the best among the four workflows and detects ∼50% more K-GG peptides than a project-specific DDA spectral library. Our results demonstrate that Spectronaut's directDIA is suitable for the analysis of phosphoproteomics SWATH-MS and DIA MS data, while the in silico-predicted library based on DIA-NN shows substantial advantages for ubiquitinomics diaPASEF MS data.

Orchestration of Mitochondrial Function and Remodeling by Post-Translational Modifications Provide Insight into Mechanisms of Viral Infection

The regulation of mitochondria structure and function is at the core of numerous viral infections. Acting in support of the host or of virus replication, mitochondria regulation facilitates control of energy metabolism, apoptosis, and immune signaling. Accumulating studies have pointed to post-translational modification (PTM) of mitochondrial proteins as a critical component of such regulatory mechanisms. Mitochondrial PTMs have been implicated in the pathology of several diseases and emerging evidence is starting to highlight essential roles in the context of viral infections. Here, we provide an overview of the growing arsenal of PTMs decorating mitochondrial proteins and their possible contribution to the infection-induced modulation of bioenergetics, apoptosis, and immune responses. We further consider links between PTM changes and mitochondrial structure remodeling, as well as the enzymatic and non-enzymatic mechanisms underlying mitochondrial PTM regulation. Finally, we highlight some of the methods, including mass spectrometry-based analyses, available for the identification, prioritization, and mechanistic interrogation of PTMs.

Cell-selective proteomics segregates pancreatic cancer subtypes by extracellular proteins in tumors and circulation

Cell-selective proteomics is a powerful emerging concept to study heterocellular processes in tissues. However, its high potential to identify non-cell-autonomous disease mechanisms and biomarkers has been hindered by low proteome coverage. Here, we address this limitation and devise a comprehensive azidonorleucine labeling, click chemistry enrichment, and mass spectrometry-based proteomics and secretomics strategy to dissect aberrant signals in pancreatic ductal adenocarcinoma (PDAC). Our in-depth co-culture and in vivo analyses cover more than 10,000 cancer cell-derived proteins and reveal systematic differences between molecular PDAC subtypes. Secreted proteins, such as chemokines and EMT-promoting matrisome proteins, associated with distinct macrophage polarization and tumor stromal composition, differentiate classical and mesenchymal PDAC. Intriguingly, more than 1,600 cancer cell-derived proteins including cytokines and pre-metastatic niche formation-associated factors in mouse serum reflect tumor activity in circulation. Our findings highlight how cell-selective proteomics can accelerate the discovery of diagnostic markers and therapeutic targets in cancer.

RNA-seq reveals the role of miR-223 in alleviating inflammation of bovine mammary epithelial cells

Bovine mammary epithelial cells (bMECs) are involved in the early defense against the invasion of intramammary pathogens and are essential for the health of bovine mammary gland. MicroRNA (MiRNA) is a key factor that regulates cell state and physiological function. In the present study, the transcriptome profiles of miR-223 inhibitor transfection group (miR-223_Inhibitor) and negative control inhibitor transfection group (NC_Inhibitor) within bMECs were detected via the RNA sequencing (RNA-seq) platform. Based on these experiments, the differentially expressed mRNAs (DE-mRNAs) of the miR-223_Inhibitor transfection group were screened, and the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional analyses of DE-mRNAs were performed. The results revealed that compared with the NC_Inhibitor, 224 differentially expressed genes (DEGs) were identified in the miR-223_Inhibitor, including 184 upregulated and 40 downregulated genes. The functional annotation of the above DEGs indicated that some of these genes are involved in the immune response generated by extracellular substance stimulation, regulation of the activity of cytokines and chemokines, and the immune signaling pathways of NF-κB and TNF. Meanwhile, miR-223_inhibitor upregulated the immune key genes IRF1 and NFκBIA, cytokines IL-6 and IL-24, as well as chemokines CXCL3, CXCL5, and CCR6, triggering a signaling cascade response that exacerbated inflammation in bMECs. These results suggested that miR-223 plays an important role in inhibiting the inflammatory response and maintaining the stability of bMECs, and is a potential target for treating mastitis in dairy cows.

Experimental study on the optimization of ANM33 release in foam cells

Given the miR-33's mechanistic relationships with multiple etiological factors in the pathogenesis of atherosclerosis (AS), we investigated the therapeutic potentials of dual-targeted microbubbles (HA-PANBs) in foam cell-specific release of anti-miR-33 (ANM33) oligonucleotides, resulting in the early prevention of AS progression and severity. The intracellular localization, loading optimization, and therapeutic effects of HA-PANBs were examined in detail in a co-cultured cell model of phagocytosis. Compared with non-targeting nanobubbles (NBs) and single-targeted microbubbles as controls, HA-PANBs efficiently delivered the ANM33 specifically to foam cells via sustained release, exhibiting its clinical value in mediating RNA silencing. Moreover, when used at a dose of 12 µg/mL HA-PANBs per 10⁷ cells for 48 h, a higher release rate and drug efficacy were observed. Therefore, HA-PANBs, effectively targeting early AS foam cells, may represent a novel and optimal gene therapy approach for AS management.

You are what you eat and how you digest it! A discussion on inflammatory efferocytosis

Efferocytosis is a process by which phagocytes remove dead or dying cells. It is considered anti-inflammatory, as the removal process reduces potential inflammatory molecules originating from dead cells and results in the reprogramming of macrophages to an anti-inflammatory state. However, engulfment of infected dead cells, deregulated phagocytosis and perturbed digestion of apoptotic bodies induce inflammatory signalling pathways during efferocytosis. The affected inflammatory signalling molecules and the mechanism of activation are largely unknown. I discuss how the choice of dead cell cargo, the type of ingestion, and the digestion efficiency can influence phagocyte programming in the context of disease. I also present the latest findings, highlight knowledge gaps, and propose selected experimental approaches to fill them.

The web of death: the expanding complexity of necroptotic signaling

The past decade has seen the emergence of the necroptosis programmed cell death pathway as an important contributor to the pathophysiology of myriad diseases. The receptor interacting protein kinase (RIPK)1 and RIPK3, and the pseudokinase executioner protein, mixed lineage kinase domain-like (MLKL), have grown to prominence as the core pathway components. Depending on cellular context, these proteins also serve as integrators of signals, such as post-translational modifications and protein or metabolite interactions, adding layers of complexity to pathway regulation. Here, we describe the emerging picture of the web of proteins that tune necroptotic signal transduction and how these events have diverged across species, presumably owing to selective pressures of pathogens upon the RIPK3-MLKL protein pair.

Benchmarking commonly used software suites and analysis workflows for DIA proteomics and phosphoproteomics

A plethora of software suites and multiple classes of spectral libraries have been developed to enhance the depth and robustness of data-independent acquisition (DIA) data processing. However, how the combination of a DIA software tool and a spectral library impacts the outcome of DIA proteomics and phosphoproteomics data analysis has been rarely investigated using benchmark data that mimics biological complexity. In this study, we create DIA benchmark data sets simulating the regulation of thousands of proteins in a complex background, which are collected on both an Orbitrap and a timsTOF instruments. We evaluate four commonly used software suites (DIA-NN, Spectronaut, MaxDIA and Skyline) combined with seven different spectral libraries in global proteome analysis. Moreover, we assess their performances in analyzing phosphopeptide standards and TNF-α-induced phosphoproteome regulation. Our study provides a practical guidance on how to construct a robust data analysis pipeline for different proteomics studies implementing the DIA technique.

Proteomics to study cancer immunity and improve treatment

Cancer survival and progression depend on the ability of tumor cells to avoid immune recognition. Advances in the understanding of cancer immunity and tumor immune escape mechanisms enabled the development of immunotherapeutic approaches. In patients with otherwise incurable metastatic cancers, immunotherapy resulted in unprecedented response rates with the potential for durable complete responses. However, primary and acquired resistance mechanisms limit the efficacy of immunotherapy. Further therapeutic advances require a deeper understanding of the interplay between immune cells and tumors. Most high-throughput studies within the past decade focused on an omics characterization at DNA and RNA level. However, proteins are the molecular effectors of genomic information; therefore, the study of proteins provides deeper understanding of cellular functions. Recent advances in mass spectrometry (MS)-based proteomics at a system-wide scale may allow translational and clinical discoveries by enabling the analysis of understudied post-translational modifications, subcellular protein localization, cell signaling, and protein-protein interactions. In this review, we discuss the potential contribution of MS-based proteomics to preclinical and clinical research findings in the context of tumor immunity and cancer immunotherapies.

Role of necroptosis in kidney health and disease

Cell death, particularly that of tubule epithelial cells, contributes critically to the pathophysiology of kidney disease. A body of evidence accumulated over the past 15 years has ascribed a central pathophysiological role to a particular form of regulated necrosis, termed necroptosis, to acute tubular necrosis, nephron loss and maladaptive renal fibrogenesis. Unlike apoptosis, which is a non-immunogenic process, necroptosis results in the release of cellular contents and cytokines, which triggers an inflammatory response in neighbouring tissue. This necroinflammatory environment can lead to severe organ dysfunction and cause lasting tissue injury in the kidney. Despite evidence of a link between necroptosis and various kidney diseases, there are no available therapeutic options to target this process. Greater understanding of the molecular mechanisms, triggers and regulators of necroptosis in acute and chronic kidney diseases may identify shortcomings in current approaches to therapeutically target necroptosis regulators and lead to the development of innovative therapeutic approaches.

Neuroinflammation in retinitis pigmentosa: Therapies targeting the innate immune system

Retinitis pigmentosa (RP) is an important cause of irreversible blindness worldwide and lacks effective treatment strategies. Although mutations are the primary cause of RP, research over the past decades has shown that neuroinflammation is an important cause of RP progression. Due to the abnormal activation of immunity, continuous sterile inflammation results in neuron loss and structural destruction. Therapies targeting inflammation have shown their potential to attenuate photoreceptor degeneration in preclinical models. Regardless of variations in genetic background, inflammatory modulation is emerging as an important role in the treatment of RP. We summarize the evidence for the role of inflammation in RP and mention therapeutic strategies where available, focusing on the modulation of innate immune signals, including TNFα signaling, TLR signaling, NLRP3 inflammasome activation, chemokine signaling and JAK/STAT signaling. In addition, we describe epigenetic regulation, the gut microbiome and herbal agents as prospective treatment strategies for RP in recent advances.

Novel biochemical, structural, and systems insights into inflammatory signaling revealed by contextual interaction proteomics

Protein-protein interactions (PPIs) represent the main mode of the proteome organization in the cell. In the last decade, several large-scale representations of PPI networks have captured generic aspects of the functional organization of network components but mostly lack the context of cellular states. However, the generation of context-dependent PPI networks is essential for structural and systems-level modeling of biological processes-a goal that remains an unsolved challenge. Here we describe an experimental/computational strategy to achieve a modeling of PPIs that considers contextual information. This strategy defines the composition, stoichiometry, temporal organization, and cellular requirements for the formation of target assemblies. We used this approach to generate an integrated model of the formation principles and architecture of a large signalosome, the TNF-receptor signaling complex (TNF-RSC). Overall, we show that the integration of systems- and structure-level information provides a generic, largely unexplored link between the modular proteome and cellular function.

Targeting the Heterogeneous Genomic Landscape in Triple-Negative Breast Cancer through Inhibitors of the Transcriptional Machinery

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer defined by lack of the estrogen, progesterone and human epidermal growth factor receptor 2. Although TNBC tumors contain a wide variety of oncogenic mutations and copy number alterations, the direct targeting of these alterations has failed to substantially improve therapeutic efficacy. This efficacy is strongly limited by interpatient and intratumor heterogeneity, and thereby a lack in uniformity of targetable drivers. Most of these genetic abnormalities eventually drive specific transcriptional programs, which may be a general underlying vulnerability. Currently, there are multiple selective inhibitors, which target the transcriptional machinery through transcriptional cyclin-dependent kinases (CDKs) 7, 8, 9, 12 and 13 and bromodomain extra-terminal motif (BET) proteins, including BRD4. In this review, we discuss how inhibitors of the transcriptional machinery can effectively target genetic abnormalities in TNBC, and how these abnormalities can influence sensitivity to these inhibitors. These inhibitors target the genomic landscape in TNBC by specifically suppressing MYC-driven transcription, inducing further DNA damage, improving anti-cancer immunity, and preventing drug resistance against MAPK and PI3K-targeted therapies. Because the transcriptional machinery enables transcription and propagation of multiple cancer drivers, it may be a promising target for (combination) treatment, especially of heterogeneous malignancies, including TNBC.

A CRISPRi/a platform in human iPSC-derived microglia uncovers regulators of disease states

Microglia are emerging as key drivers of neurological diseases. However, we lack a systematic understanding of the underlying mechanisms. Here, we present a screening platform to systematically elucidate functional consequences of genetic perturbations in human induced pluripotent stem cell-derived microglia. We developed an efficient 8-day protocol for the generation of microglia-like cells based on the inducible expression of six transcription factors. We established inducible CRISPR interference and activation in this system and conducted three screens targeting the ‘druggable genome’. These screens uncovered genes controlling microglia survival, activation and phagocytosis, including neurodegeneration-associated genes. A screen with single-cell RNA sequencing as the readout revealed that these microglia adopt a spectrum of states mirroring those observed in human brains and identified regulators of these states. A disease-associated state characterized by osteopontin (SPP1) expression was selectively depleted by colony-stimulating factor-1 (CSF1R) inhibition. Thus, our platform can systematically uncover regulators of microglial states, enabling their functional characterization and therapeutic targeting.

Dräger et al. establish a rapid, scalable platform for iPSC-derived microglia. CRISPRi/a screens uncover roles of disease-associated genes in phagocytosis, and regulators of disease-relevant microglial states that can be targeted pharmacologically.

Rapid and in-depth coverage of the (phospho-)proteome with deep libraries and optimal window design for dia-PASEF

Data-independent acquisition (DIA) methods have become increasingly attractive in mass spectrometry–based proteomics because they enable high data completeness and a wide dynamic range. Recently, we combined DIA with parallel accumulation–serial fragmentation (dia-PASEF) on a Bruker trapped ion mobility (IM) separated quadrupole time-of-flight mass spectrometer. This requires alignment of the IM separation with the downstream mass selective quadrupole, leading to a more complex scheme for dia-PASEF window placement compared with DIA. To achieve high data completeness and deep proteome coverage, here we employ variable isolation windows that are placed optimally depending on precursor density in the m/z and IM plane. This is implemented in the freely available py_diAID (Python package for DIA with an automated isolation design) package. In combination with in-depth project-specific proteomics libraries and the Evosep liquid chromatography system, we reproducibly identified over 7700 proteins in a human cancer cell line in 44 min with quadruplicate single-shot injections at high sensitivity. Even at a throughput of 100 samples per day (11 min liquid chromatography gradients), we consistently quantified more than 6000 proteins in mammalian cell lysates by injecting four replicates. We found that optimal dia-PASEF window placement facilitates in-depth phosphoproteomics with very high sensitivity, quantifying more than 35,000 phosphosites in a human cancer cell line stimulated with an epidermal growth factor in triplicate 21 min runs. This covers a substantial part of the regulated phosphoproteome with high sensitivity, opening up for extensive systems-biological studies.

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Optimal dia-PASEF window design with py_diAID combined with deep libraries.

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Quantification of the HeLa cell proteome to a depth of >7700 in only 44 min.

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Ion mobility–resolved phosphoproteomics determines >35,000 class I phosphosites.

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py_diAID is freely available as GUI, CLI, and Python modules.

A proteomic perspective on TNF-mediated signalling and cell death

The tumour necrosis factor (TNF) is the most potent inducer of cell death amongst cytokines. It is crucial for processes including homeostasis, the development of the immune system and fighting infections. However, high levels of TNF due to genetic disorders or persistent infections can contribute to autoinflammatory and autoimmune diseases or life-threatening conditions like sepsis. These diseases generally display increased levels of cell death, which, downstream of the TNF receptor, can either be caspase-dependent (apoptosis) or caspase-independent (necroptosis). Significant efforts have been invested in unravelling and manipulating signalling mechanisms regulating these two different types of cell death. Here I discuss how modern proteomic approaches like phosphoproteomics and secretomics provide a novel perspective on this central cytokine and its effect on inflammation and cell survival.

Gene-selective transcription promotes the inhibition of tissue reparative macrophages by TNF

Pro-inflammatory TNF is a highly gene-selective inhibitor of the gene expression program of tissue repair and wound healing macrophages.

Anti-TNF therapies are a core anti-inflammatory approach for chronic diseases such as rheumatoid arthritis and Crohn’s Disease. Previously, we and others found that TNF blocks the emergence and function of alternative-activated or M2 macrophages involved in wound healing and tissue-reparative functions. Conceivably, anti-TNF drugs could mediate their protective effects in part by an altered balance of macrophage activity. To understand the mechanistic basis of how TNF regulates tissue-reparative macrophages, we used RNAseq, scRNAseq, ATACseq, time-resolved phospho-proteomics, gene-specific approaches, metabolic analysis, and signaling pathway deconvolution. We found that TNF controls tissue-reparative macrophage gene expression in a highly gene-specific way, dependent on JNK signaling via the type 1 TNF receptor on specific populations of alternative-activated macrophages. We further determined that JNK signaling has a profound and broad effect on activated macrophage gene expression. Our findings suggest that TNF’s anti-M2 effects evolved to specifically modulate components of tissue and reparative M2 macrophages and TNF is therefore a context-specific modulator of M2 macrophages rather than a pan-M2 inhibitor.