PhosR enables processing and functional analysis of phosphoproteomic data

Oligodendrocyte myelin glycoprotein impairs dendritic arbors via schizophrenia risk gene Trio

During adolescence, a critical developmental epoch coincident with the emergence of clinical symptoms of schizophrenia, cerebral cortical dendritic growth shifts from a rapid phase, reaching equilibrium. Oligodendrocyte Myelin Glycoprotein (OMGp) expression peaks during adolescence and has a known role in regulating dendritic stabilization. However, the precise signaling pathways transduced by OMGp are unknown. To identify these pathways, we performed unbiased phospho-proteomic analysis after OMGp stimulation, revealing 2991 phosphorylated proteins. Interestingly, several schizophrenia risk genes were identified as phospho-targets, including the potent risk factor Trio, which has a known role in regulating neurite outgrowth and the cytoskeleton through its dual Rac/RhoA catalytic domains. Phosphomimetic and phosphonull Trio9 constructs were employed to assess the functional role of OMGp-mediated phosphorylation at a novel phosphosite - Ser1258. Phosphomimetic Trio9 was deficient in Rac1 catalytic activity and induced loss of dendritic length and complexity compared to wild type protein. Moreover, phosphonull constructs blocked the OMGp-induced impairments in dendritic length and complexity. Together, these results highlight the ability of OMGp to regulate dendritic architecture by potently inhibiting the Rac1 catalytic activity of Trio through phosphorylation. These results provide a potential mechanism contributing to the emergence of neuronal structural dysfunction and schizophrenia symptomology during adolescence.

Identification of a resistance-exercise-specific signalling pathway that drives skeletal muscle growth

Endurance and resistance exercise lead to distinct functional adaptations: the former increases aerobic capacity and the latter increases muscle mass. However, the signalling pathways that drive these adaptations are not well understood. Here we identify phosphorylation events that are differentially regulated by endurance and resistance exercise. Using a model of unilateral exercise in male participants and deep phosphoproteomic analyses, we find that a prolonged activation of a signalling pathway involving MKK3b/6, p38, MK2 and mTORC1 occurs specifically in response to resistance exercise. Follow-up studies in both male and female participants reveal that the resistance-exercise-induced activation of MKK3b is highly correlated with the induction of protein synthesis (R = 0.87). Additionally, we show that in mice, genetic activation of MKK3b is sufficient to induce signalling through p38, MK2 and mTORC1, along with an increase in protein synthesis and muscle fibre size. Overall, we identify core components of a signalling pathway that drives the growth-promoting effects of resistance exercise.

Leveraging HILIC/ERLIC separations for online nanoscale LC-MS/MS analysis of phosphopeptide isoforms from RNA polymerase II C-terminal domain

The eukaryotic RNA polymerase II (Pol II) multi-protein complex transcribes mRNA and coordinates several steps of co-transcriptional mRNA processing and chromatin modification. The largest Pol II subunit, Rpb1, has a C-terminal domain (CTD) comprising dozens of repeated heptad sequences (Tyr1-Ser2-Pro3-Thr4-Ser5-Pro6-Ser7), each containing five phospho-accepting amino acids. The CTD heptads are dynamically phosphorylated, creating specific patterns correlated with steps of transcription initiation, elongation, and termination. This CTD phosphorylation 'code' choreographs dynamic recruitment of important co-regulatory proteins during gene transcription. Genetic tools were used to engineer protease cleavage sites across the CTD (msCTD), creating tryptic peptides with unique sequences amenable to mass spectrometry analysis. However, phosphorylation isoforms within each msCTD sequence are difficult to resolve by standard reversed phase chromatography typically used for LC-MS/MS applications. Here, we use a panel of synthetic CTD phosphopeptides to explore the potential of hydrophilic interaction and electrostatic repulsion hydrophilic interaction (HILIC and ERLIC) chromatography as alternatives to reversed phase separation for CTD phosphopeptide analysis. Our results demonstrate that ERLIC provides improved performance for separation of singly- and doubly-phosphorylated CTD peptides for sequence analysis by LC-MS/MS. Analysis of native yeast msCTD confirms that phosphorylation on Ser5 and Ser2 represents the major endogenous phosphoisoforms. We expect this methodology will be especially useful in the investigation of pathways where multiple protein phosphorylation events converge in close proximity.

Maternal Adiponectin Decreases Placenta Nutrient Transport in Mice

Women with obesity who develop gestational diabetes have lower serum adiponectin throughout pregnancy, suggesting that low levels impair the ability to handle metabolic challenges during pregnancy. The placenta expresses adiponectin receptors, and adiponectin could therefore indirectly affect the developing fetus. Here, we aimed to investigate how elevated maternal and fetal adiponectin affect placental function, fetal growth, and metabolism during pregnancy in normal-weight and obese mice. Wild-type (wt) and adiponectin-overexpressing (APNtg) mice were fed normal chow or a high fat/high sucrose (HF/HS) diet 8 weeks before and during pregnancy to induce obesity. Mice were euthanized and dissected on gestational day 18.5. Lipid, glucose, and amino acid tracers were administered to the obese pregnant dams to study nutrient uptake. The effects of elevated adiponectin on fetal liver and placental function were further investigated using global proteomics. A 40%-50% increase in serum adiponectin reduced fetal growth in dams fed a HF/HS diet, but not a normal chow diet. The uptake of glucose, lipid, and amino acid tracer was lower, along with decreased expression of several amino acid transporters in the placenta of APNtg dams on HF/HS diet. This suggests that adiponectin decreases placental transfer of nutrients. Livers of fetuses from APNtg dams showed downregulated lipid and amino acid metabolic pathways possibly reflecting an energy deficit. In conclusion, elevated serum adiponectin in obese dams reduced the placental transfer of nutrients, resulting in fetal growth restriction and altered fetal liver function. Maternal adiponectin levels were the main driver of placenta function. While this could be beneficial for pregnancy-related complications like babies born large for their gestation age, our study indicates that adiponectin should be in an optimal concentration range, neither too low nor too high, to prevent these complications.

Phosphoproteomics Uncovers Exercise Intensity-Specific Skeletal Muscle Signaling Networks Underlying High-Intensity Interval Training in Healthy Male Participants

BACKGROUND

In response to exercise, protein kinases and signaling networks are engaged to blunt homeostatic threats generated by acute contraction-induced increases in skeletal muscle energy and oxygen demand, as well as serving roles in the adaptive response to chronic exercise training to blunt future disruptions to homeostasis. High-intensity interval training (HIIT) is a time-efficient exercise modality that induces superior or similar health-promoting skeletal muscle and whole-body adaptations compared with prolonged, moderate-intensity continuous training (MICT). However, the skeletal muscle signaling pathways underlying HIIT's exercise intensity-specific adaptive responses are unknown.

OBJECTIVE

We mapped human muscle kinases, substrates, and signaling pathways activated/deactivated by an acute bout of HIIT versus work-matched MICT.

METHODS

In a randomized crossover trial design (Australian New Zealand Clinical Trials Registry number ACTRN12619000819123; prospectively registered 6 June 2019), ten healthy male participants (age 25.4 ± 3.2 years; BMI 23.5 ± 1.6 kg/m2;V ˙ O 2 max 37.9 ± 5.2 ml/kg/min, mean values ± SD) completed a single bout of HIIT and MICT cycling separated by ≥ 10 days and matched for total work (67.9 ± 10.2 kJ) and duration (10 min). Mass spectrometry-based phosphoproteomic analysis of muscle biopsy samples collected before, during (5 min), and immediately following (10 min) each exercise bout, to map acute temporal signaling responses to HIIT and MICT, identified and quantified 14,931 total phosphopeptides, corresponding to 8509 phosphorylation sites.

RESULTS

Bioinformatic analyses uncovered exercise intensity-specific signaling networks, including > 1000 differentially phosphorylated sites (± 1.5-fold change; adjusted P < 0.05; ≥ 3 participants) after 5 min and 10 min HIIT and/or MICT relative to rest. After 5 and 10 min, 92 and 348 sites were differentially phosphorylated by HIIT, respectively, versus MICT. Plasma lactate concentrations throughout HIIT were higher than MICT (P < 0.05), and correlation analyses identified > 3000 phosphosites significantly correlated with lactate (q < 0.05) including top functional phosphosites underlying metabolic regulation.

CONCLUSIONS

Collectively, this first global map of the work-matched HIIT versus MICT signaling networks has revealed rapid exercise intensity-specific regulation of kinases, substrates, and pathways in human skeletal muscle that may contribute to HIIT's skeletal muscle adaptations and health-promoting effects.  Preprint: The preprint version of this work is available on medRxiv, https://doi.org/10:1101/2024.07.11.24310302 .

Small molecule inhibitors of hnRNPA2B1-RNA interactions reveal a predictable sorting of RNA subsets into extracellular vesicles

Extracellular vesicles (EVs) are cell-secreted membranous particles contributing to intercellular communication. Coding and noncoding RNAs can be detected as EV cargo, and RNA-binding proteins (RBPs), such as hnRNPA2B1, have been circumstantially implicated in EV-RNA sorting mechanisms. However, the contribution of competitive RBP-RNA interactions responsible for RNA-sorting outcomes is still unclear, especially for predicting the EV-RNA content. We designed a reverse proteomic analysis exploiting the EV-RNA to identify intracellular protein binders in vitro. Using cells expressing a recombinant hnRNPA2B1 to normalize competitive interactions, we prioritized a network of heterogeneous nuclear ribonucleoproteins and purine-rich RNA sequences subsequently validated in secreted EV-RNA through short fluorescent RNA oligos. Then, we designed a GGGAG-enriched RNA probe that efficiently interacted with a full-length human hnRNPA2B1 protein. We exploited the interaction to conduct a pharmacological screening and identify inhibitors of the protein-RNA binding. Small molecules were orthogonally validated through biochemical and cell-based approaches. Selected drugs remarkably impacted secreted EV-RNAs and reduced an RNA-dependent, EV-mediated paracrine activation of NF-kB in recipient cells. These results demonstrate the relevance of post-transcriptional mechanisms for EV-RNA sorting and the possibility of predicting the EV-RNA quality for developing innovative strategies targeting discrete paracrine functions.

Human skeletal muscle fiber heterogeneity beyond myosin heavy chains

Skeletal muscle is a heterogenous tissue comprised primarily of myofibers, commonly classified into three fiber types in humans: one "slow" (type 1) and two "fast" (type 2A and type 2X). However, heterogeneity between and within traditional fiber types remains underexplored. We applied transcriptomic and proteomic workflows to 1050 and 1038 single myofibers from human vastus lateralis, respectively. Proteomics was conducted in males, while transcriptomics included ten males and two females. We identify metabolic, ribosomal, and cell junction proteins, in addition to myosin heavy chain isoforms, as sources of multi-dimensional variation between myofibers. Furthermore, whilst slow and fast fiber clusters are identified, our data suggests that type 2X fibers are not phenotypically distinct to other fast fibers. Moreover, myosin heavy chain-based classifications do not adequately describe the phenotype of myofibers in nemaline myopathy. Overall, our data indicates that myofiber heterogeneity is multi-dimensional with sources of variation beyond myosin heavy chain isoforms.

Temporal phosphoproteomics reveals circuitry of phased propagation in insulin signaling

Insulin is a pleiotropic hormone that elicits its metabolic and mitogenic actions through numerous rapid and reversible protein phosphorylations. The temporal regulation of insulin's intracellular signaling cascade is highly complex and insufficiently understood. We conduct a time-resolved analysis of the global insulin-regulated phosphoproteome of differentiated human primary myotubes derived from satellite cells of healthy donors using high-resolution mass spectrometry. Identification and tracking of ~13,000 phosphopeptides over time reveal a highly complex and coordinated network of transient phosphorylation and dephosphorylation events that can be allocated to time-phased regulation of distinct and non-overlapping subcellular pathways. Advanced network analysis combining protein-protein-interaction (PPI) resources and investigation of donor variability in relative phosphosite occupancy over time identifies novel putative candidates in non-canonical insulin signaling and key regulatory nodes that are likely essential for signal propagation. Lastly, we find that insulin-regulated phosphorylation of the pre-catalytic spliceosome complex is associated with acute alternative splicing events in the transcriptome of human skeletal muscle. Our findings highlight the temporal relevance of protein phosphorylations and suggest that synchronized contributions of multiple signaling pathways form part of the circuitry for propagating information to insulin effector sites.

The systemic complexity of a monogenic disease: the molecular network of spinal muscular atrophy

Monogenic diseases are well-suited paradigms for the causal analysis of disease-driving molecular patterns. Spinal muscular atrophy (SMA) is one such monogenic model, caused by mutation or deletion of the survival of motor neuron 1 (SMN1) gene. Although several functions of the SMN protein have been studied, single functions and pathways alone do not allow the identification of crucial disease-driving molecules. Here, we analysed the systemic characteristics of SMA, using proteomics, phosphoproteomics, translatomics and interactomics, from two mouse models with different disease severities and genetics. This systems approach revealed subnetworks and proteins characterizing commonalities and differences of both models. To link the identified molecular networks with the disease-causing SMN protein, we combined SMN-interactome data with both proteomes, creating a comprehensive representation of SMA. By this approach, disease hubs and bottlenecks between SMN and downstream pathways could be identified. Linking a disease-causing molecule with widespread molecular dysregulations via multiomics is a concept for analyses of monogenic diseases.

Proteomic and phosphoproteomic analyses reveal the biological perturbations caused by capsaicin treatment

Capsaicin, the primary active ingredient and irritant in chili peppers, has been utilized across multiple fields as a food adductive or because of its potential anticancer, antioxidant, anti-inflammatory and metabolic regulatory properties. Despite its diverse uses, the mechanism of action of capsaicin has not been fully revealed. Here, we investigated the changes in the proteome and phosphoproteome of A549 cells upon treatment with capsaicin for different durations and at different doses. Pressure cycling technology (PCT) was applied for rapid sample preparation and digestion, significantly improving the stability of phosphorylated proteins and allowing in-depth phosphoproteome analysis within 6 h with protein inputs of 100 μg. Proteomic and phosphoproteomic alterations can be used to accurately identify perturbations caused by various capsaicin doses and exposure durations. Proteomic analysis revealed that capsaicin administration affected the cell cycle and DNA damage pathways in a time- and dose-dependent manner. Compared with the proteomic changes, more sensitive and rapid alterations were observed in the phosphoproteome, a finding further supported by posttranslational modification (PTM) set enrichment analysis (PTM-SEA) of the phosphoproteomic data. The phosphorylation status of serine protein kinase, Aurora kinase A, and Aurora kinase B changed faster than their protein expression. Overall, the findings here identify the proteomic and phosphoproteomic alterations caused by capsaicin, providing new insights for multiomics analysis to elucidate chemical perturbations.

Computational Methods for Data Integration and Imputation of Missing Values in Omics Datasets

Molecular profiling of different omic-modalities (e.g., DNA methylomics, transcriptomics, proteomics) in biological systems represents the basis for research and clinical decision-making. Measurement-specific biases, so-called batch effects, often hinder the integration of independently acquired datasets, and missing values further hamper the applicability of typical data processing algorithms. In addition to careful experimental design, well-defined standards in data acquisition and data exchange, the alleviation of these phenomena particularly requires a dedicated data integration and preprocessing pipeline. This review aims to give a comprehensive overview of computational methods for data integration and missing value imputation for omic data analyses. We provide formal definitions for missing value mechanisms and propose a novel statistical taxonomy for batch effects, especially in the presence of missing data. Based on an automated document search and systematic literature review, we describe 32 distinct data integration methods from five main methodological categories, as well as 37 algorithms for missing value imputation from five separate categories. Additionally, this review highlights multiple quantitative evaluation methods to aid researchers in selecting a suitable set of methods for their work. Finally, this work provides an integrated discussion of the relevance of batch effects and missing values in omics with corresponding method recommendations. We then propose a comprehensive three-step workflow from the study conception to final data analysis and deduce perspectives for future research. Eventually, we present a comprehensive flow chart as well as exemplary decision trees to aid practitioners in the selection of specific approaches for imputation and data integration in their studies.

Biased signaling by mutant EGFR underlies dependence on PKCα in lung adenocarcinoma

Activating mutations in the epidermal growth factor receptor (EGFR) promote ligand-independent signaling; however, the mechanisms involved are poorly defined, and it is unknown whether this generates specific vulnerabilities. We previously observed robust expression of protein kinase Cα (PKCα) in lung adenocarcinoma (LUAD) with mutant EGFR (mEGFR), which, unlike the activation of PKCα, is independent of mEGFR activity. Here, we identify a critical role for PKCα in anchorage-independent growth and survival of lung cancer cells with mEGFR. Mechanistically, signaling pathways initiated by mEGFR show a high preference for ligand-independent phosphorylation on Y992, resulting in biased activation and dependence on phospholipase-Cγ and PKCα. Moreover, through bioinformatic approaches, we find that mEGFR LUAD demonstrates a transcriptomic profile most similar to lung basal cells, which exhibit elevated levels of PKCα, suggesting that mEGFR tumors arise in cell types with high intrinsic levels of PKCα. Taken together, these findings explain the dependence of mEGFR on PKCα.

Comparative proteomic analysis of human vitreous in rhegmatogenous retinal detachment and diabetic retinopathy reveals a common pathway and potential therapeutic target

BACKGROUND

The vitreous humor serves as a window into the physiological and pathological processes of the eye, particularly the retina. Diabetic retinopathy (DR), a leading cause of blindness, involves hyperglycemia-induced damage to retinal cells, leading to ischemia and elevated nitric oxide levels, culminating in vascular proliferation. Rhegmatogenous retinal detachment (RD) results from a break in the neuroretina, triggering ischemia, photoreceptor death, and cellular proliferation. Proliferative vitreoretinopathy (PVR) further complicates these conditions through fibrous proliferation. Despite their prevalence and potential for blindness, our understanding of the molecular mechanisms underlying these vitreoretinal diseases is incomplete.

METHODS AND RESULTS

To elucidate disease mechanisms and identify potential therapeutic targets, we conducted a comparative proteomic analysis of vitreous samples from DR, RD, and macular pucker (P) patients, which were chosen as controls. LC-MS analysis identified 988 quantifiable proteins, with distinct clustering observed among disease groups. Differential expression analysis revealed 202 proteins in RD vs. P and 167 in DR vs. P, highlighting distinct proteomic signatures. Enrichment analysis identified glucose metabolism as an altered process in both diseases, suggesting common pathways despite differing etiologies. Notably, aldo-keto reductase family 1 member B1 (AKR1B1) has emerged as a potential key player in both DR and RD, indicating its role in glucose metabolism and inflammation. In silico drug screening identified diclofenac, an approved ophthalmic non-steroidal anti-inflammatory drug (NSAID), as a potential therapeutic agent targeting AKR1B1.

CONCLUSION

Our study revealed distinct proteomic signatures and common pathways in vitreoretinal diseases, highlighting AKR1B1 as a potential therapeutic target. Using diclofenac during diagnosis and postoperative care for diabetic retinopathy or rhegmatogenous retinal detachment may reduce complications, lower costs, and improve quality of life. Future research will focus on confirming AKR1B1's role in vitreoretinal diseases and understanding diclofenac's mechanism of action.

A concise guide to essential R packages for analyses of DNA, RNA, and proteins

R is widely regarded as unrivaled by other high-level programming languages for its statistical functions. The popularity of R as a statistical language has led many to overlook its applications outside the statistical realm. In this brief review, we present a list of R packages for supporting projects that entail analyses of DNA, RNA, and proteins. These R packages span the gamut of important molecular techniques, from routine quantitative polymerase chain reaction (qPCR) and Western blotting to high-throughput sequencing and proteomics generating very large datasets. The text-mining power of R can also be harnessed to facilitate literature reviews and predict future research trends and avenues. We encourage researchers to make full use of R in their work, given the versatility of the language, as well as its straightforward syntax which eases the initial learning curve.

Asparagine614 Determines the Transport and Function of the Murine Anti-Aging Protein Klotho

Klotho is an anti-aging protein whose deletion significantly reduces lifespan in mice, while its over-expression increases lifespan. Klotho is a type-I transmembrane protein that is N-glycosylated at eight positions within its ectodomain. Our study demonstrates that N-glycosylation or mutation at position N614, but not at N161, N285, or N346 in mouse Klotho, is critically involved in the transport of Klotho out of the endoplasmic reticulum (ER). Consequently, while wild-type Klotho-EGFP as well as the N-glycosylation mutants N161Q, N285Q, and N346Q were present at the plasma membrane (PM), only small amounts of the N614Q Klotho-EGFP were present at the PM, with most of the protein accumulating in the ER. Protein interactome analysis of Klotho-EGFP N614Q revealed increased interactions with proteasome-related proteins and proteins involved in ER protein processing, like heat shock proteins and protein disulfide isomerases, indicative of impaired protein folding. Co-immunoprecipitation experiments confirmed the interaction of Klotho-EGFP N614Q with ER chaperons. Interestingly, despite the low amounts of Klotho-EGFP N614Q at the PM, it efficiently induced FGF receptor-mediated ERK activation in the presence of FGF23, highlighting its efficacy in triggering downstream signaling, even in limited quantities at the PM.

Leveraging HILIC/ERLIC Separations for Online Nanoscale LC-MS/MS Analysis of Phosphopeptide Isoforms from RNA Polymerase II C-terminal Domain

The eukaryotic RNA polymerase II (Pol II) multi-protein complex transcribes mRNA and coordinates several steps of co-transcriptional mRNA processing and chromatin modification. The largest Pol II subunit, Rpb1, has a C-terminal domain (CTD) comprising dozens of repeated heptad sequences (Tyr1-Ser2-Pro3-Thr4-Ser5-Pro6-Ser7), each containing five phospho-accepting amino acids. The CTD heptads are dynamically phosphorylated, creating specific patterns correlated with steps of transcription initiation, elongation, and termination. This CTD phosphorylation 'code' choreographs dynamic recruitment of important co-regulatory proteins during gene transcription. Genetic tools were used to engineer protease cleavage sites across the CTD (msCTD), creating tryptic peptides with unique sequences amenable to mass spectrometry analysis. However, phosphorylation isoforms within each msCTD sequence are difficult to resolve by standard reversed phase chromatography typically used for LC-MS/MS applications. Here, we use a panel of synthetic CTD phosphopeptides to explore the potential of hydrophilic interaction and electrostatic repulsion hydrophilic interaction (HILIC and ERLIC) chromatography as alternatives to reversed phase separation for CTD phosphopeptide analysis. Our results demonstrate that ERLIC provides improved performance for separation of singly- and doubly-phosphorylated CTD peptides for sequence analysis by LC-MS/MS. Analysis of native yeast msCTD confirms that phosphorylation on Ser5 and Ser2 represents the major endogenous phosphoisoforms. We expect this methodology will be especially useful in the investigation of pathways where multiple protein phosphorylation events converge in close proximity.

IL15/IL15Rα complex induces an anti-tumor immune response following radiation therapy only in the absence of Tregs and fails to induce expansion of progenitor TCF1+ CD8 T cells

Background

This work seeks to understand whether IL15-incorporating treatments improve response to radiotherapy and uncover mechanistic rationale for overcoming resistance to IL15 agonism using novel therapeutic combinations.

Experimental Design

Orthotopic tumor models of PDAC were used to determine response to treatment. IL15-/- and Rag1-/- mouse models were employed to determine dependence on IL15 and CTLs, respectively. Flow cytometry was used to assess immune cell frequency and activation state. Phospho-proteomic analyses were used to characterize intracellular signaling pathways.

Results

We show that the combination of radiation therapy (RT) and an IL15/IL15Ra fusion complex (denoted IL15c) fails to confer anti-tumor efficacy; however, a CD8-driven anti-tumor immune response is elicited with the concurrent administration of an aCD25 Treg-depleting antibody. Using IL15-/- and Rag1-/- mice, we demonstrate that response to RT + IL15c + aCD25 is dependent on both IL15 and CTLs. Furthermore, despite an equivalent survival benefit following treatment with RT + IL15c + aCD25 and combination RT + PD1-IL2v, a novel immunocytokine with PD-1 and IL2Rβγ binding domains, CTL immunophenotyping and phospho-proteomic analysis of intracellular metabolites showed significant upregulation of activation and functionality in CD8 T cells treated with RT + PD1-IL2v. Finally, we show the immunostimulatory response to RT + PD1-IL2v is significantly diminished with a concurrent lack of TCF+ CD8 T cell generation in the absence of functional IL15 signaling.

Conclusions

Our results are illustrative of a mechanism wherein unimpeded effector T cell activation through IL2Rβ signaling and Treg inhibition are necessary in mediating an anti-tumor immune response.

Molecular Signatures of Neurodegenerative Diseases Identified by Proteomic and Phosphoproteomic Analyses in Aging Mouse Brain

A central hallmark of neurodegenerative diseases is the irreversible accumulation of misfolded proteins in the brain by aberrant phosphorylation. Understanding the mechanisms underlying protein phosphorylation and its role in pathological protein aggregation within the context of aging is crucial for developing therapeutic strategies aimed at preventing or reversing such diseases. Here, we applied multi-protease digestion and quantitative mass spectrometry to compare and characterize dysregulated proteins and phosphosites in the mouse brain proteome using three different age groups: young-adult (3-4 months), middle-age (10 months), and old mice (19-21 months). Proteins associated with senescence, neurodegeneration, inflammation, cell cycle regulation, the p53 hallmark pathway, and cytokine signaling showed significant age-dependent changes in abundances and level of phosphorylation. Several proteins implicated in Alzheimer's disease (AD) and Parkinson's disease (PD) including tau (Mapt), Nefh, and Dpysl2 (also known as Crmp2) were hyperphosphorylated in old mice brain suggesting their susceptibility to the diseases. Cdk5 and Gsk3b, which are known to phosphorylate Dpysl2 at multiple specific sites, had also increased phosphorylation levels in old mice suggesting a potential crosstalk between them to contribute to AD. Hapln2, which promotes α-synuclein aggregation in patients with PD, was one of the proteins with highest abundance in old mice. CD9, which regulates senescence through the PI3K-AKT-mTOR-p53 signaling was upregulated in old mice and its regulation was correlated with the activation of phosphorylated AKT1. Overall, the findings identify a significant association between aging and the dysregulation of proteins involved in various pathways linked to neurodegenerative diseases with potential therapeutic implications.

Memory effects of prior subculture may impact the quality of multiomic perturbation profiles

Mass spectrometry-based omics technologies are increasingly used in perturbation studies to map drug effects to biological pathways by identifying significant molecular events. Significance is influenced by fold change and variation of each molecular parameter, but also by multiple testing corrections. While the fold change is largely determined by the biological system, the variation is determined by experimental workflows. Here, it is shown that memory effects of prior subculture can influence the variation of perturbation profiles using the two colon carcinoma cell lines SW480 and HCT116. These memory effects are largely driven by differences in growth states that persist into the perturbation experiment. In SW480 cells, memory effects combined with moderate treatment effects amplify the variation in multiple omics levels, including eicosadomics, proteomics, and phosphoproteomics. With stronger treatment effects, the memory effect was less pronounced, as demonstrated in HCT116 cells. Subculture homogeneity was controlled by real-time monitoring of cell growth. Controlled homogeneous subculture resulted in a perturbation network of 321 causal conjectures based on combined proteomic and phosphoproteomic data, compared to only 58 causal conjectures without controlling subculture homogeneity in SW480 cells. Some cellular responses and regulatory events were identified that extend the mode of action of arsenic trioxide (ATO) only when accounting for these memory effects. Controlled prior subculture led to the finding of a synergistic combination treatment of ATO with the thioredoxin reductase 1 inhibitor auranofin, which may prove useful in the management of NRF2-mediated resistance mechanisms.

The ribotoxic stress response drives UV-mediated cell death

While ultraviolet (UV) radiation damages DNA, eliciting the DNA damage response (DDR), it also damages RNA, triggering transcriptome-wide ribosomal collisions and eliciting a ribotoxic stress response (RSR). However, the relative contributions, timing, and regulation of these pathways in determining cell fate is unclear. Here we use time-resolved phosphoproteomic, chemical-genetic, single-cell imaging, and biochemical approaches to create a chronological atlas of signaling events activated in cells responding to UV damage. We discover that UV-induced apoptosis is mediated by the RSR kinase ZAK and not through the DDR. We identify two negative-feedback modules that regulate ZAK-mediated apoptosis: (1) GCN2 activation limits ribosomal collisions and attenuates ZAK-mediated RSR and (2) ZAK activity leads to phosphodegron autophosphorylation and its subsequent degradation. These events tune ZAK's activity to collision levels to establish regimes of homeostasis, tolerance, and death, revealing its key role as the cellular sentinel for nucleic acid damage.

Nanoparticles Dysregulate the Human Placental Secretome with Consequences on Angiogenesis and Vascularization

Exposure to nanoparticles (NPs) in pregnancy is increasingly linked to adverse effects on embryo-fetal development and health later in life. However, the developmental toxicity mechanisms of NPs are largely unknown, in particular potential effects on the placental secretome, which orchestrates many developmental processes pivotal for pregnancy success. This study demonstrates extensive material- and pregnancy stage-specific deregulation of placental signaling from a single exposure of human placental explants to physiologically relevant concentrations of engineered (silica (SiO2) and titanium dioxide (TiO2) NPs) and environmental NPs (diesel exhaust particles, DEPs). This includes a multitude of secreted inflammatory, vascular, and endocrine placental factors as well as extracellular vesicle (EV)-associated proteins. Moreover, conditioned media (CM) from NP-exposed explants induce pronounced anti-angiogenic and anti-vasculogenic effects, while early neurodevelopmental processes are only marginally affected. These findings underscore the potential of metal oxide NPs and DEPs for widespread interference with the placental secretome and identify vascular morphogenesis as a sensitive outcome for the indirect developmental toxicity of different NPs. Overall, this work has profound implications for the future safety assessment of NPs for industrial, commercial, or medical applications in pregnancy, which should consider placenta-mediated toxicity by holistic secretomics approaches to ensure the development of safe nanotechnologies.

Making proteomics accessible: RokaiXplorer for interactive analysis of phospho-proteomic data

Summary

We present RokaiXplorer, an intuitive web tool designed to address the scarcity of user-friendly solutions for proteomics and phospho-proteomics data analysis and visualization. RokaiXplorer streamlines data processing, analysis, and visualization through an interactive online interface, making it accessible to researchers without specialized training in proteomics or data science. With its comprehensive suite of modules, RokaiXplorer facilitates phospho-proteomic analysis at the level of phosphosites, proteins, kinases, biological processes, and pathways. The tool offers functionalities such as data normalization, statistical testing, activity inference, pathway enrichment, subgroup analysis, automated report generation, and multiple visualizations, including volcano plots, bar plots, heat maps, and network views. As a unique feature, RokaiXplorer allows researchers to effortlessly deploy their own data browsers, enabling interactive sharing of research data and findings. Overall, RokaiXplorer fills an important gap in phospho-proteomic data analysis by providing the ability to comprehensively analyze data at multiple levels within a single application.

Availability and implementation

Access RokaiXplorer at: http://explorer.rokai.io.

Proteome-based molecular subtyping and therapeutic target prediction in gastric cancer

Different molecular classifications for gastric cancer (GC) have been proposed based on multi-omics platforms with the long-term goal of improved precision treatment. However, the GC (phospho)proteome remains incompletely characterized, particularly at the level of tyrosine phosphorylation. In addition, previous multiomics-based stratification of patient cohorts has lacked identification of corresponding cell line models and comprehensive validation of broad or subgroup-selective therapeutic targets. To address these knowledge gaps, we applied a reverse approach, undertaking the most comprehensive (phospho)proteomic analysis of GC cell lines to date and cross-validating this using publicly available data. Mass spectrometry (MS)-based (phospho)proteomic and tyrosine phosphorylation datasets were subjected to individual or integrated clustering to identify subgroups that were subsequently characterized in terms of enriched molecular processes and pathways. Significant congruence was detected between cell line proteomic and specific patient-derived transcriptomic subclassifications. Many protein kinases exhibiting 'outlier' expression or phosphorylation in the cell line dataset exhibited genomic aberrations in patient samples and association with poor prognosis, with casein kinase I isoform delta/epsilon (CSNK1D/E) being experimentally validated as potential therapeutic targets. Src family kinases were predicted to be commonly hyperactivated in GC cell lines, consistent with broad sensitivity to the next-generation Src inhibitor eCF506. In addition, phosphoproteomic and integrative clustering segregated the cell lines into two subtypes, with epithelial-mesenchyme transition (EMT) and proliferation-associated processes enriched in one, designated the EMT subtype, and metabolic pathways, cell-cell junctions, and the immune response dominating the features of the other, designated the metabolism subtype. Application of kinase activity prediction algorithms and interrogation of gene dependency and drug sensitivity databases predicted that the mechanistic target of rapamycin kinase (mTOR) and dual specificity mitogen-activated protein kinase kinase 2 (MAP2K2) represented potential therapeutic targets for the EMT and metabolism subtypes, respectively, and this was confirmed using selective inhibitors. Overall, our study provides novel, in-depth insights into GC proteomics, kinomics, and molecular taxonomy and reveals potential therapeutic targets that could provide the basis for precision treatments.

Trans-omic profiling uncovers molecular controls of early human cerebral organoid formation

Defining the molecular networks orchestrating human brain formation is crucial for understanding neurodevelopment and neurological disorders. Challenges in acquiring early brain tissue have incentivized the use of three-dimensional human pluripotent stem cell (hPSC)-derived neural organoids to recapitulate neurodevelopment. To elucidate the molecular programs that drive this highly dynamic process, here, we generate a comprehensive trans-omic map of the phosphoproteome, proteome, and transcriptome of the exit of pluripotency and neural differentiation toward human cerebral organoids (hCOs). These data reveal key phospho-signaling events and their convergence on transcriptional factors to regulate hCO formation. Comparative analysis with developing human and mouse embryos demonstrates the fidelity of our hCOs in modeling embryonic brain development. Finally, we demonstrate that biochemical modulation of AKT signaling can control hCO differentiation. Together, our data provide a comprehensive resource to study molecular controls in human embryonic brain development and provide a guide for the future development of hCO differentiation protocols.

HSPA5 and FGFR1 genes in the mesenchymal subtype of glioblastoma can improve a treatment efficacy

Tyrosine kinase inhibitors (TKIs) have emerged as a potential treatment strategy for glioblastoma multiforme (GBM). However, their efficacy is limited by various drug resistance mechanisms. To devise more effective treatments for GBM, genetic characteristics must be considered in addition to pre-existing treatments. We performed an integrative analysis with heterogeneous GBM datasets of genomic, transcriptomic, and proteomic data from DepMap, TCGA and CPTAC. We found that poor prognosis was induced by co-upregulation of heat shock protein family A member 5 (HSPA5) and fibroblast growth factor receptor 1 (FGFR1). Co-up regulation of these two genes could regulate the PI3K/AKT pathway. GBM cell lines with co-upregulation of these two genes showed higher drug sensitivity to PI3K inhibitors. In the mesenchymal subtype, the co-upregulation of FGFR1 and HSPA5 resulted in the most malignant subtype of GBM. Furthermore, we found this newly discovered subtype was correlated with homologous recombination deficiency (HRD) In conclusion, we discovered novel druggable candidates within the group exhibiting co-upregulation of these two genes in GBM, suggest potential strategies for combination therapy.

PhosMap: An ensemble bioinformatic platform to empower interactive analysis of quantitative phosphoproteomics

BACKGROUND

Liquid chromatography-mass spectrometry (LC-MS)-based quantitative phosphoproteomics has been widely used to detect thousands of protein phosphorylation modifications simultaneously from the biological specimens. However, the complicated procedures for analyzing phosphoproteomics data has become a bottleneck to widening its application.

METHODS

Here, we develop PhosMap, a versatile and scalable tool to accomplish phosphoproteomics data analysis. A standardized phosphorylation data format was created for data analyses, from data preprocessing to downstream bioinformatic analyses such as dimension reduction, differential phosphorylation analysis, kinase activity, survival analysis, and so on. For better usability, we distribute PhosMap as a Docker image for easy local deployment upon any of Windows, Linux, and Mac system.

RESULTS

The source code is deposited at https://github.com/BADD-XMU/PhosMap. A free PhosMap webserver (https://huggingface.co/spaces/Bio-Add/PhosMap), with easy-to-follow fashion of dashboards, is curated for interactive data analysis.

CONCLUSIONS

PhosMap fills the technical gap of large-scale phosphorylation research by empowering researchers to process their own phosphoproteomics data expediently and efficiently, and facilitates better data interpretation.

Proteomic and phosphoproteomic characterization of cardiovascular tissues after long term exposure to simulated space radiation

Introduction: It may take decades to develop cardiovascular dysfunction following exposure to high doses of ionizing radiation from medical therapy or from nuclear accidents. Since astronauts may be exposed continually to a complex space radiation environment unlike that experienced on Earth, it is unresolved whether there is a risk to cardiovascular health during long-term space exploration missions. Previously, we have described that mice exposed to a single dose of simplified Galactic Cosmic Ray (GCR5-ion) develop cardiovascular dysfunction by 12 months post-radiation. Methods: To investigate the biological basis of this dysfunction, here we performed a quantitative mass spectrometry-based proteomics analysis of heart tissue (proteome and phosphoproteome) and plasma (proteome only) from these mice at 8 months post-radiation. Results: Differentially expressed proteins (DEPs) for irradiated versus sham irradiated samples (fold-change ≥1.2 and an adjusted p-value of ≤0.05) were identified for each proteomics data set. For the heart proteome, there were 87 significant DEPs (11 upregulated and 76 downregulated); for the heart phosphoproteome, there were 60 significant differentially phosphorylated peptides (17 upregulated and 43 downregulated); and for the plasma proteome, there was only one upregulated protein. A Gene Set Enrichment Analysis (GSEA) technique that assesses canonical pathways from BIOCARTA, KEGG, PID, REACTOME, and WikiPathways revealed significant perturbation in pathways in each data set. For the heart proteome, 166 pathways were significantly altered (36 upregulated and 130 downregulated); for the plasma proteome, there were 73 pathways significantly altered (25 upregulated and 48 downregulated); and for the phosphoproteome, there were 223 pathways significantly affected at 0.1 adjusted p-value cutoff. Pathways related to inflammation were the most highly perturbed in the heart and plasma. In line with sustained inflammation, neutrophil extracellular traps (NETs) were demonstrated to be increased in GCR5-ion irradiated hearts at 12-month post irradiation. NETs play a fundamental role in combating bacterial pathogens, modulating inflammatory responses, inflicting damage on healthy tissues, and escalating vascular thrombosis. Discussion: These findings suggest that a single exposure to GCR5-ion results in long-lasting changes in the proteome and that these proteomic changes can potentiate acute and chronic health issues for astronauts, such as what we have previously described with late cardiac dysfunction in these mice.

Tumor-Extrinsic Axl Expression Shapes an Inflammatory Microenvironment Independent of Tumor Cell Promoting Axl Signaling in Hepatocellular Carcinoma

The activation of the receptor tyrosine kinase Axl by Gas6 is a major driver of tumorigenesis. Despite recent insights, tumor cell-intrinsic and -extrinsic Axl functions are poorly understood in hepatocellular carcinoma (HCC). Thus, we analyzed the cell-specific aspects of Axl in liver cancer cells and in the tumor microenvironment. We show that tumor-intrinsic Axl expression decreased the survival of mice and elevated the number of pulmonary metastases in a model of resection-based tumor recurrence. Axl expression increased the invasion of hepatospheres by the activation of Akt signaling and a partial epithelial-to-mesenchymal transition (EMT). However, the liver tumor burden of Axl+/+ mice induced by diethylnitrosamine plus carbon tetrachloride was reduced compared to systemic Axl-/- mice. Tumors of Axl+/+ mice were highly infiltrated with cytotoxic cells, suggesting a key immune-modulatory role of Axl. Interestingly, hepatocyte-specific Axl deficiency did not alter T cell infiltration, indicating that these changes are independent of tumor cell-intrinsic Axl. In this context, we observed an upregulation of multiple chemokines in Axl+/+ compared to Axl-/- tumors, correlating with HCC patient data. In line with this, Axl is associated with a cytotoxic immune signature in HCC patients. Together these data show that tumor-intrinsic Axl expression fosters progression, while tumor-extrinsic Axl expression shapes an inflammatory microenvironment.

AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor

Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays or AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold-Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.

NAK-associated protein 1/NAP1 activates TBK1 to ensure accurate mitosis and cytokinesis

Subcellular location and activation of Tank Binding Kinase 1 (TBK1) govern precise progression through mitosis. Either loss of activated TBK1 or its sequestration from the centrosomes causes errors in mitosis and growth defects. Yet, what regulates its recruitment and activation on the centrosomes is unknown. We identified that NAK-associated protein 1 (NAP1) is essential for mitosis, binding to and activating TBK1, which both localize to centrosomes. Loss of NAP1 causes several mitotic and cytokinetic defects due to inactivation of TBK1. Our quantitative phosphoproteomics identified numerous TBK1 substrates that are not only confined to the centrosomes but are also associated with microtubules. Substrate motifs analysis indicates that TBK1 acts upstream of other essential cell cycle kinases like Aurora and PAK kinases. We also identified NAP1 as a TBK1 substrate phosphorylating NAP1 at S318 to promote its degradation by the ubiquitin proteasomal system. These data uncover an important distinct function for the NAP1-TBK1 complex during cell division.

Nuclear Phosphoproteome Reveals Prolyl Isomerase PIN1 as a Modulator of Oncogene-Induced Senescence

Mammalian cells possess intrinsic mechanisms to prevent tumorigenesis upon deleterious mutations, including oncogene-induced senescence (OIS). The molecular mechanisms underlying OIS are, however, complex and remain to be fully characterized. In this study, we analyzed the changes in the nuclear proteome and phosphoproteome of human lung fibroblast IMR90 cells during the progression of OIS induced by oncogenic RASG12V activation. We found that most of the differentially regulated phosphosites during OIS contained prolyl isomerase PIN1 target motifs, suggesting PIN1 is a key regulator of several promyelocytic leukemia nuclear body proteins, specifically regulating several proteins upon oncogenic Ras activation. We showed that PIN1 knockdown promotes cell proliferation, while diminishing the senescence phenotype and hallmarks of senescence, including p21, p16, and p53 with concomitant accumulation of the protein PML and the dysregulation of promyelocytic leukemia nuclear body formation. Collectively, our data demonstrate that PIN1 plays an important role as a tumor suppressor in response to oncogenic ER:RasG12V activation.

msqrob2PTM: Differential Abundance and Differential Usage Analysis of MS-Based Proteomics Data at the Posttranslational Modification and Peptidoform Level

In the era of open-modification search engines, more posttranslational modifications than ever can be detected by LC-MS/MS-based proteomics. This development can switch proteomics research into a higher gear, as PTMs are key in many cellular pathways important in cell proliferation, migration, metastasis, and aging. However, despite these advances in modification identification, statistical methods for PTM-level quantification and differential analysis have yet to catch up. This absence can partly be explained by statistical challenges inherent to the data, such as the confounding of PTM intensities with its parent protein abundance. Therefore, we have developed msqrob2PTM, a new workflow in the msqrob2 universe capable of differential abundance analysis at the PTM and at the peptidoform level. The latter is important for validating PTMs found as significantly differential. Indeed, as our method can deal with multiple PTMs per peptidoform, there is a possibility that significant PTMs stem from one significant peptidoform carrying another PTM, hinting that it might be the other PTM driving the perceived differential abundance. Our workflows can flag both differential peptidoform abundance (DPA) and differential peptidoform usage (DPU). This enables a distinction between direct assessment of differential abundance of peptidoforms (DPA) and differences in the relative usage of peptidoforms corrected for corresponding protein abundances (DPU). For DPA, we directly model the log2-transformed peptidoform intensities, while for DPU, we correct for parent protein abundance by an intermediate normalization step which calculates the log2-ratio of the peptidoform intensities to their summarized parent protein intensities. We demonstrated the utility and performance of msqrob2PTM by applying it to datasets with known ground truth, as well as to biological PTM-rich datasets. Our results show that msqrob2PTM is on par with, or surpassing the performance of, the current state-of-the-art methods. Moreover, msqrob2PTM is currently unique in providing output at the peptidoform level.

Disruption of lysosomal proteolysis in astrocytes facilitates midbrain organoid proteostasis failure in an early-onset Parkinson's disease model

Accumulation of advanced glycation end products (AGEs) on biopolymers accompanies cellular aging and drives poorly understood disease processes. Here, we studied how AGEs contribute to development of early onset Parkinson's Disease (PD) caused by loss-of-function of DJ1, a protein deglycase. In induced pluripotent stem cell (iPSC)-derived midbrain organoid models deficient for DJ1 activity, we find that lysosomal proteolysis is impaired, causing AGEs to accumulate, α-synuclein (α-syn) phosphorylation to increase, and proteins to aggregate. We demonstrated these processes are at least partly driven by astrocytes, as DJ1 loss reduces their capacity to provide metabolic support and triggers acquisition of a pro-inflammatory phenotype. Consistently, in co-cultures, we find that DJ1-expressing astrocytes are able to reverse the proteolysis deficits of DJ1 knockout midbrain neurons. In conclusion, astrocytes' capacity to clear toxic damaged proteins is critical to preserve neuronal function and their dysfunction contributes to the neurodegeneration observed in a DJ1 loss-of-function PD model.

Remodeling of the Cardiac Extracellular Matrix Proteome During Chronological and Pathological Aging

Impaired extracellular matrix (ECM) remodeling is a hallmark of many chronic inflammatory disorders that can lead to cellular dysfunction, aging, and disease progression. The ECM of the aged heart and its effects on cardiac cells during chronological and pathological aging are poorly understood across species. For this purpose, we first used mass spectrometry-based proteomics to quantitatively characterize age-related remodeling of the left ventricle (LV) of mice and humans during chronological and pathological (Hutchinson-Gilford progeria syndrome (HGPS)) aging. Of the approximately 300 ECM and ECM-associated proteins quantified (named as Matrisome), we identified 13 proteins that were increased during aging, including lactadherin (MFGE8), collagen VI α6 (COL6A6), vitronectin (VTN) and immunoglobulin heavy constant mu (IGHM), whereas fibulin-5 (FBLN5) was decreased in most of the data sets analyzed. We show that lactadherin accumulates with age in large cardiac blood vessels and when immobilized, triggers phosphorylation of several phosphosites of GSK3B, MAPK isoforms 1, 3, and 14, and MTOR kinases in aortic endothelial cells (ECs). In addition, immobilized lactadherin increased the expression of pro-inflammatory markers associated with an aging phenotype. These results extend our knowledge of the LV proteome remodeling induced by chronological and pathological aging in different species (mouse and human). The lactadherin-triggered changes in the proteome and phosphoproteome of ECs suggest a straight link between ECM component remodeling and the aging process of ECs, which may provide an additional layer to prevent cardiac aging.

NOC1 is a direct MYC target, and its protein interactome dissects its activity in controlling nucleolar function

The nucleolus is a subnuclear compartment critical in ribosome biogenesis and cellular stress responses. These mechanisms are governed by a complex interplay of proteins, including NOC1, a member of the NOC family of nucleolar proteins responsible for controlling rRNA processing and ribosomal maturation. This study reveals a novel relationship between NOC1 and MYC transcription factor, known for its crucial role in controlling ribosomal biogenesis, cell growth, and proliferation. Here, we demonstrate that NOC1 functions as a direct target of MYC, as it is transcriptionally induced through a functional MYC-binding E-box sequence in the NOC1 promoter region. Furthermore, protein interactome analysis reveals that NOC1-complex includes the nucleolar proteins NOC2 and NOC3 and other nucleolar components such as Nucleostemin1 Ns1 transporters of ribosomal subunits and components involved in rRNA processing and maturation. In response to MYC, NOC1 expression and localization within the nucleolus significantly increase, suggesting a direct functional link between MYC activity and NOC1 function. Notably, NOC1 over-expression leads to the formation of large nuclear granules and enlarged nucleoli, which co-localize with nucleolar fibrillarin and Ns1. Additionally, we demonstrate that NOC1 expression is necessary for Ns1 nucleolar localization, suggesting a role for NOC1 in maintaining nucleolar structure. Finally, the co-expression of NOC1 and MYC enhances nucleolus size and maintains their co-localization, outlining another aspect of the cooperation between NOC1 and MYC in nucleolar dynamics. This study also reveals an enrichment with NOC1 with few proteins involved in RNA processing, modification, and splicing. Moreover, proteins such as Ythdc1, Flacc, and splenito are known to mediate N6-methyladenosine (m6A) methylation of mRNAs in nuclear export, revealing NOC1's potential involvement in coordinating RNA splicing and nuclear mRNA export. In summary, we uncovered novel roles for NOC1 in nucleolar homeostasis and established its direct connection with MYC in the network governing nucleolar structure and function. These findings also highlight NOC1's interaction with proteins relevant to specific RNA functions, suggesting a broader role in addition to its control of nucleolar homeostasis and providing new insight that can be further investigated.

Clonal heterogeneity in ER+ breast cancer reveals the proteasome and PKC as potential therapeutic targets

Intratumoral heterogeneity impacts the success or failure of anti-cancer therapies. Here, we investigated the evolution and mechanistic heterogeneity in clonal populations of cell models for estrogen receptor positive breast cancer. To this end, we established barcoded models of luminal breast cancer and rendered them resistant to commonly applied first line endocrine therapies. By isolating single clones from the resistant cell pools and characterizing replicates of individual clones we observed inter- (between cell lines) and intra-tumor (between different clones from the same cell line) heterogeneity. Molecular characterization at RNA and phospho-proteomic levels revealed private clonal activation of the unfolded protein response and respective sensitivity to inhibition of the proteasome, and potentially shared sensitivities for repression of protein kinase C. Our in vitro findings are consistent with tumor-heterogeneity that is observed in breast cancer patients thus highlighting the need to uncover heterogeneity at an individual patient level and to adjust therapies accordingly.

SnapKin: a snapshot deep learning ensemble for kinase-substrate prediction from phosphoproteomics data

A major challenge in mass spectrometry-based phosphoproteomics lies in identifying the substrates of kinases, as currently only a small fraction of substrates identified can be confidently linked with a known kinase. Machine learning techniques are promising approaches for leveraging large-scale phosphoproteomics data to computationally predict substrates of kinases. However, the small number of experimentally validated kinase substrates (true positive) and the high data noise in many phosphoproteomics datasets together limit their applicability and utility. Here, we aim to develop advanced kinase-substrate prediction methods to address these challenges. Using a collection of seven large phosphoproteomics datasets, and both traditional and deep learning models, we first demonstrate that a 'pseudo-positive' learning strategy for alleviating small sample size is effective at improving model predictive performance. We next show that a data resampling-based ensemble learning strategy is useful for improving model stability while further enhancing prediction. Lastly, we introduce an ensemble deep learning model ('SnapKin') by incorporating the above two learning strategies into a 'snapshot' ensemble learning algorithm. We propose SnapKin, an ensemble deep learning method, for predicting substrates of kinases from large-scale phosphoproteomics data. We demonstrate that SnapKin consistently outperforms existing methods in kinase-substrate prediction. SnapKin is freely available at https://github.com/PYangLab/SnapKin.

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.

Phospho-Analyst: An Interactive, Easy-to-Use Web Platform To Analyze Quantitative Phosphoproteomics Data

Phosphoproteomics is nowadays the method of choice to comprehensively identify and quantify thousands of phosphorylated peptides and their associated proteins with the goal of interrogating changes in signal transduction pathways and other cellular processes. One of the most popular software suites to analyze phosphoproteomic data sets is MaxQuant, which converts mass spectrometric raw data into quantitative information on phosphopeptides and proteins. However, despite the increased utilization of phosphoproteomics in biomedical research, simple and user-friendly tools supporting downstream statistical analysis and interpretation of these highly complex outputs are still lacking. We have therefore developed Phospho-Analyst, which─similar to its sibling LFQ-Analyst─is an easy-to-use, interactive web application specifically designed to reproducibly perform differential expression analyses with "one click" and to visualize phosphoproteomic results in a meaningful and practical manner. Furthermore, if quantitative total proteomic information is available for the same samples, Phospho-Analyst automatically normalizes all phosphoproteomic results to underlying protein abundance levels, thereby ensuring that only genuine changes in phosphorylation events are considered. As such, Phospho-Analyst can not only be used by experienced proteomic veterans but also by researchers without any prior knowledge in (phospho)proteomics, statistics, or bioinformatics. Phospho-Analyst, including a detailed manual, is freely available at https://analyst-suites.org/apps/phospho-analyst/.

Homocysteine impedes neurite outgrowth recovery after intracerebral haemorrhage by downregulating pCAMK2A

Hyperhomocysteinemia (HHcy) is independently associated with poorer long-term prognosis in patients with intracerebral haemorrhage (ICH); however, the effect and mechanisms of HHcy on ICH are still unclear. Here, we evaluated neurite outgrowth and neurological functional recovery using simulated models of ICH with HHcy in vitro and in vivo. We found that the neurite outgrowth velocity and motor functional recovery in the ICH plus HHcy group were significantly slower than that in the control group, indicating that homocysteine (Hcy) significantly impedes the neurite outgrowth recovery after ICH. Furthermore, phosphoproteomic data and signalome analysis of perihematomal brain tissues suggested that calmodulin-dependent protein kinases 2 (CAMK2A) kinase substrate pairs were significantly downregulated in ICH with HHcy compared with autologous blood injection only, both western blot and immunofluorescence staining confirmed this finding. Additionally, upregulation of pCAMK2A significantly increased neurite outgrowth recovery in ICH with HHcy. Collectively, we clarify the mechanism of HHcy-hindered neurite outgrowth recovery, and pCAMK2A may serve as a therapeutic strategy for promoting neurological recovery after ICH.

WNTinib is a multi-kinase inhibitor with specificity against β-catenin mutant hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide. β-Catenin (CTNNB1)-mutated HCC represents 30% of cases of the disease with no precision therapeutics available. Using chemical libraries derived from clinical multi-kinase inhibitor (KI) scaffolds, we screened HCC organoids to identify WNTinib, a KI with exquisite selectivity in CTNNB1-mutated human and murine models, including patient samples. Multiomic and target engagement analyses, combined with rescue experiments and in vitro and in vivo efficacy studies, revealed that WNTinib is superior to clinical KIs and inhibits KIT/mitogen-activated protein kinase (MAPK) signaling at multiple nodes. Moreover, we demonstrate that reduced engagement on BRAF and p38α kinases by WNTinib relative to several multi-KIs is necessary to avoid compensatory feedback signaling-providing a durable and selective transcriptional repression of mutant β-catenin/Wnt targets through nuclear translocation of the EZH2 transcriptional repressor. Our studies uncover a previously unknown mechanism to harness the KIT/MAPK/EZH2 pathway to potently and selectively antagonize CTNNB1-mutant HCC with an unprecedented wide therapeutic index.

Involvement of the p38/MK2 Pathway in MCLR Hepatotoxicity Revealed through MAPK Pharmacological Inhibition and Phosphoproteomics in HepaRG Cells

Microcystin-leucine arginine (MCLR) is one of the most common and toxic microcystin variants, a class of cyanotoxins produced by cyanobacteria. A major molecular mechanism for MCLR-elicited liver toxicity involves the dysregulation of protein phosphorylation through protein phosphatase (PP) inhibition and mitogen-activated protein kinase (MAPK) modulation. In this study, specific pharmacological MAPK inhibitors were used in HepaRG cells to examine the pathways associated with MCLR cytotoxicity. SB203580 (SB), a p38 inhibitor, rescued HepaRG cell viability, whereas treatment with SP600125 (JNK inhibitor), MK2206 (AKT inhibitor), or N-acetylcysteine (reactive oxygen species scavenger) did not. Phosphoproteomic analysis revealed that phosphosites-which were altered by the addition of SB compared to MCLR treatment alone-included proteins involved in RNA processing, cytoskeletal stability, DNA damage response, protein degradation, and cell death. A closer analysis of specific proteins in some of these pathways indicated that SB reversed the MCLR-mediated phosphorylation of the necroptosis-associated proteins, the mixed lineage kinase domain-like protein (MLKL), receptor-interacting serine/threonine kinase 1 (RIP1), DNA damage response proteins, ataxia telangiectasia and Rad3-related kinase (ATR), and checkpoint kinase 1 (CHK1). Overall, these data implicate p38/MK2, DNA damage, and necroptosis in MCLR-mediated hepatotoxicity, and suggest these pathways may be targets for prevention prior to, or treatment after, MCLR toxicity.

An optogenetic-phosphoproteomic study reveals dynamic Akt1 signaling profiles in endothelial cells

The serine/threonine kinase AKT is a central node in cell signaling. While aberrant AKT activation underlies the development of a variety of human diseases, how different patterns of AKT-dependent phosphorylation dictate downstream signaling and phenotypic outcomes remains largely enigmatic. Herein, we perform a systems-level analysis that integrates methodological advances in optogenetics, mass spectrometry-based phosphoproteomics, and bioinformatics to elucidate how different intensity, duration, and pattern of Akt1 stimulation lead to distinct temporal phosphorylation profiles in vascular endothelial cells. Through the analysis of ~35,000 phosphorylation sites across multiple conditions precisely controlled by light stimulation, we identify a series of signaling circuits activated downstream of Akt1 and interrogate how Akt1 signaling integrates with growth factor signaling in endothelial cells. Furthermore, our results categorize kinase substrates that are preferably activated by oscillating, transient, and sustained Akt1 signals. We validate a list of phosphorylation sites that covaried with Akt1 phosphorylation across experimental conditions as potential Akt1 substrates. Our resulting dataset provides a rich resource for future studies on AKT signaling and dynamics.

AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor

Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays and AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.

A comprehensive mechanistic model of adipocyte signaling with layers of confidence

Adipocyte signaling, normally and in type 2 diabetes, is far from fully understood. We have earlier developed detailed dynamic mathematical models for several well-studied, partially overlapping, signaling pathways in adipocytes. Still, these models only cover a fraction of the total cellular response. For a broader coverage of the response, large-scale phosphoproteomic data and systems level knowledge on protein interactions are key. However, methods to combine detailed dynamic models with large-scale data, using information about the confidence of included interactions, are lacking. We have developed a method to first establish a core model by connecting existing models of adipocyte cellular signaling for: (1) lipolysis and fatty acid release, (2) glucose uptake, and (3) the release of adiponectin. Next, we use publicly available phosphoproteome data for the insulin response in adipocytes together with prior knowledge on protein interactions, to identify phosphosites downstream of the core model. In a parallel pairwise approach with low computation time, we test whether identified phosphosites can be added to the model. We iteratively collect accepted additions into layers and continue the search for phosphosites downstream of these added layers. For the first 30 layers with the highest confidence (311 added phosphosites), the model predicts independent data well (70-90% correct), and the predictive capability gradually decreases when we add layers of decreasing confidence. In total, 57 layers (3059 phosphosites) can be added to the model with predictive ability kept. Finally, our large-scale, layered model enables dynamic simulations of systems-wide alterations in adipocytes in type 2 diabetes.

A basic phosphoproteomic-DIA workflow integrating precise quantification of phosphosites in systems biology

Phosphorylation is one of the most important post-translational modifications (PTMs) of proteins, governing critical protein functions. Most human proteins have been shown to undergo phosphorylation, and phosphoproteomic studies have been widely applied due to recent advancements in high-resolution mass spectrometry technology. Although the experimental workflow for phosphoproteomics has been well-established, it would be useful to optimize and summarize a detailed, feasible protocol that combines phosphoproteomics and data-independent acquisition (DIA), along with follow-up data analysis procedures due to the recent instrumental and bioinformatic advances in measuring and understanding tens of thousands of site-specific phosphorylation events in a single experiment. Here, we describe an optimized Phos-DIA protocol, from sample preparation to bioinformatic analysis, along with practical considerations and experimental configurations for each step. The protocol is designed to be robust and applicable for both small-scale phosphoproteomic analysis and large-scale quantification of hundreds of samples for studies in systems biology and systems medicine.

Principles of phosphoproteomics and applications in cancer research

Phosphorylation constitutes the most common and best-studied regulatory post-translational modification in biological systems and archetypal signalling pathways driven by protein and lipid kinases are disrupted in essentially all cancer types. Thus, the study of the phosphoproteome stands to provide unique biological information on signalling pathway activity and on kinase network circuitry that is not captured by genetic or transcriptomic technologies. Here, we discuss the methods and tools used in phosphoproteomics and highlight how this technique has been used, and can be used in the future, for cancer research. Challenges still exist in mass spectrometry phosphoproteomics and in the software required to provide biological information from these datasets. Nevertheless, improvements in mass spectrometers with enhanced scan rates, separation capabilities and sensitivity, in biochemical methods for sample preparation and in computational pipelines are enabling an increasingly deep analysis of the phosphoproteome, where previous bottlenecks in data acquisition, processing and interpretation are being relieved. These powerful hardware and algorithmic innovations are not only providing exciting new mechanistic insights into tumour biology, from where new drug targets may be derived, but are also leading to the discovery of phosphoproteins as mediators of drug sensitivity and resistance and as classifiers of disease subtypes. These studies are, therefore, uncovering phosphoproteins as a new generation of disruptive biomarkers to improve personalised anti-cancer therapies.

Extracellular vesicle proteomics and phosphoproteomics identify pathways for increased risk in patients hospitalized with COVID-19 and type 2 diabetes mellitus

Recent studies suggest that extracellular vesicles (EVs) play a role in the pathogenesis of SARS-CoV-2 infection and the severity of COVID-19. However, their role in the interaction between COVID-19 and type 2 diabetes (T2D) has not been addressed. Here, we characterized the circulating EV proteomic and phosphoproteomic landscape in patients with and without T2D hospitalized with COVID-19 or non-COVID-19 acute respiratory illness (RSP). We detected differentially expressed protein and phosphoprotein signatures that effectively characterized the study groups. The trio of immunomodulatory and coagulation proteins C1QA, C1QB, and C1QC appeared to be a central cluster in both the COVID-19 and T2D functional networks. PKCβ appeared to be retained in cells by being diverted from EV pathways and contribute to the COVID-19 and T2D interaction via a PKC/BTK/TEC axis. EV-shuttled CASP3 and ROCK1 appeared to be coregulated and likely contribute to disease interactions in patients with COVID-19 and T2D. Predicted activation of AMPK, MAPK, and SYK appeared to also play important roles driving disease interaction. These results suggest that activated cellular kinases (i.e., PKC, AMPK, MAPK, and SYK) and multiple EV-shuttled kinases (i.e., PKCβ, BTK, TEC, MAP2K2, and ROCK1) may play key roles in severe COVID-19, particularly in patients with comorbid diabetes.

Computational systems approach towards phosphoproteomics and their downstream regulation

Protein phosphorylation plays an essential role in modulating cell signalling and its downstream transcriptional and translational regulations. Until recently, protein phosphorylation has been studied mostly using low-throughput biochemical assays. The advancement of mass spectrometry (MS)-based phosphoproteomics transformed the field by enabling measurement of proteome-wide phosphorylation events, where tens of thousands of phosphosites are routinely identified and quantified in an experiment. This has brought a significant challenge in analysing large-scale phosphoproteomic data, making computational methods and systems approaches integral parts of phosphoproteomics. Previous works have primarily focused on reviewing the experimental techniques in MS-based phosphoproteomics, yet a systematic survey of the computational landscape in this field is still missing. Here, we review computational methods and tools, and systems approaches that have been developed for phosphoproteomics data analysis. We categorise them into four aspects including data processing, functional analysis, phosphoproteome annotation and their integration with other omics, and in each aspect, we discuss the key methods and example studies. Lastly, we highlight some of the potential research directions on which future work would make a significant contribution to this fast-growing field. We hope this review provides a useful snapshot of the field of computational systems phosphoproteomics and stimulates new research that drives future development.

BCAT1 redox function maintains mitotic fidelity

The metabolic enzyme branched-chain amino acid transaminase 1 (BCAT1) drives cell proliferation in aggressive cancers such as glioblastoma. Here, we show that BCAT1 localizes to mitotic structures and has a non-metabolic function as a mitotic regulator. Furthermore, BCAT1 is required for chromosome segregation in cancer and induced pluripotent stem cells and tumor growth in human cerebral organoid and mouse syngraft models. Applying gene knockout and rescue strategies, we show that the BCAT1 CXXC redox motif is crucial for controlling cysteine sulfenylation specifically in mitotic cells, promoting Aurora kinase B localization to centromeres, and securing accurate chromosome segregation. These findings offer an explanation for the well-established role of BCAT1 in promoting cancer cell proliferation. In summary, our data establish BCAT1 as a component of the mitotic apparatus that safeguards mitotic fidelity through a moonlighting redox functionality.