Biology subjects

Monaco, G.

Publications and source records attributed to Monaco, G..

9 recordsLinked to original sources

bioRxiv2025

An Antibody Cocktail-Based Immunoaffinity-LC-MS Method Enabled Ultra-Sensitive and Robust Quantification of Circulating Proinsulin Proteoforms and C-peptide

Accurately measuring circulating proinsulin proteoforms is crucial for clinical investigation of diabetes, but was previously not feasible owing to limited assay specificity/sensitivity. Here we devised a highly sensitive LC-MS-based strategy to quantify intact proinsulin, des-31,32 and des-64,65 proinsulin, and C-peptide in circulation. The method involves: i)quantitative, robust affinity capture using an optimized antibody cocktail, eliminating the severe quantitative bias across multiple proteomes typically introduced when using a single antibody; ii)Lys-C digestion producing unique signature peptides for each proteoform, and iii)trapping-nano-LC coupled with FAIMS/dCV-MS for an ultra-sensitive analysis. The selective trapping/delivery ensured sensitive/selective analysis of the targets while achieving excellent analytical robustness that is critical for clinical assays, and the FAIMS/dCV substantially reduces baseline noise/interferences, further enhancing S/N. The assay achieved exceptional sensitivity, with serum LOQs of 1.7, 2.3, and 3.6 pg/mL respectively for intact-proinsulin, des-31,32 and des-64,65, representing the first assay capable of sensitively quantifying these major circulating proinsulin proteoforms. We applied this assay to 78 subjects, including autoantibody positive(n=20) and new-onset type-1-diabetes(T1D, n=19) with respective age/sex/BMI-matched controls, enabling the first accurate profiling of proinsulin proteoforms in clinical groups. The assay results demonstrated a clear separation of control and new-onset T1D groups that a parallel total-proinsulin ELISA assay fails to capture. Furthermore, distinct expression patterns in relative abundance ratios among proteoforms were observed across clinical groups. This assay may provide valuable insights into the {beta}-cell functions and the onset/progression of diabetes and other associated conditions. Moreover, the strategy is broadly applicable to targeted measurement of other biomarker proteoforms.

biochemistry↗

bioRxiv2025

Tissue imprinting defines functional mosaic of dermal macrophages

Dermal macrophages (macs) protect the skin from invading pathogens. They are derived from embryonic as well as hematopoietic progenitors. However, the functional impact of their diverse origin and the control networks defining different subsets remain unclear. Here, using multidimensional analysis of dermal macs, we reveal that the absence of circulating monocytes in interferon regulatory factor 8 (Irf8) deficient mice delays mac renewal during the steady state. Yet, the functional mosaic of dermal macs remains largely intact, i.e., major dermal mac subsets develop independently of monocyte replenishment. Thus, the tissue microenvironment is sufficient to induce alternative differentiation pathways and functional specialization of resident cells. Mycobacterial skin infection induces a steep increase in mac density due to monocyte-derived macs which execute urgent antibacterial functions and differentiate into site-adapted mac subsets in wildtype but not Irf8-/-mice, while long-term resident macs are required to initiate a tissue repair program already in early stages of infection. In summary, we introduce a model, where an intricate network of specialized mac subsets develops to meet microanatomical needs and external cellular input is required only during immunological emergency situations. HighlightsO_LIIrf8-/--driven monocytopenia has negligible impact on homeostatic dermal macrophage diversity. C_LIO_LIResident dermal macrophages have diverse specializations but remain flexible to adapt to challenges such as lacking monocyte influx C_LIO_LIBone marrow-derived macrophages differentiate into specialized resident cells, with microenvironmental cues overriding origin-dependent programming. C_LIO_LIIn chronic bacterial infections, distinct specialized bone-marrow-derived macrophages mount the defense, while resident macrophages activate a tissue-modifying program from early on. C_LI

immunology↗

bioRxiv2024

Ubiquitin-specific protease 8 controls B cell proteostasis and cell survival in multiple myeloma

Ubiquitin-specific protease 8 (USP8) is a multifunctional deubiquitinating enzyme that plays a pivotal role in the regulation of endosomal and lysosomal trafficking. Several studies showed that USP8 is critically involved in the pathogenesis of various tumor entities. Recently, USP8 emerged as a vulnerability gene in multiple myeloma (MM), suggesting a functional role in the B- and plasma cell compartment. Here we have comprehensively analyzed mice with deletion of Usp8 at different stages of B-cell development. Furthermore, we evaluated the function of USP8 in proteasome inhibition susceptible and Bortezomib (BTZ) resistant patient derived MM cells using depletion of USP8 and treatment with DUB-IN-2, a widely used inhibitor published to target USP8. Usp8 depletion in Usp8f/fCd19-Cre mice affected B-cell survival and development favoring immature, innate-like B cells, and germinal center and plasma cells, while also elevating immune-responses and causing Roquin depletion. Cells expressing catalytically inactive USP8 accumulated proteins modified with mixed ubiquitin/NEDD8 chains, indicating proteotoxic stress. Moreover, we identified these modifications as preferred substrates of USP8. In MM cells, efficient USP8 knockdown reduced survival by inducing lysosomal dysfunction. In contrast, DUB-IN-2 induced an enhanced ER stress response to treatment with BTZ. Our results highlight the potential of targeting USP8 and the combination of DUB-IN-2 and BTZ in treating BTZ-resistant MM. However, our biochemical and cellular analyses raise fundamental concerns about the function of DUB-IN-2 as a USP8 inhibitor.

molecular biology↗

bioRxiv2024

Brain resident macrophages regulate sleep, with repopulated ones being unable to reestablish the original sleep circuits

Sleep is a complex behavior regulated by various brain cell types. However, the roles of brain-resident macrophages, including microglia and CNS-associated macrophages (CAMs), particularly those derived postnatally, in sleep regulation remain poorly understood. Here, we investigated the effects of natural (embryo-derived) and repopulated (postnatally derived) brain-resident macrophages on the regulation of vigilance states. We found that depletion in embryonically-derived brain macrophages caused increased sleep in the active period, but reduced its quality, reflected in reduced power of brain sleep oscillations. This was observed both for the Non-REM and REM sleep stages. Subsequent repopulation by postnatal brain macrophages unexpectedly failed to reestablish normal sleep-wake patterns and additionally induced sleep fragmentation. Furthermore, brain macrophage depletion caused excitatory-inhibitory synaptic imbalance, which was resistant to repopulation, and led to increased inhibitory synapses. At the metabolite level, the distinct metabolite profile induced by brain macrophage depletion largely returned to normal after repopulation. Our findings suggest a so far largely unknown interaction between brain-resident macrophages and sleep and emphasizes striking functional differences between embryonic and postnatally-derived brain macrophages, paving the way to future exploration of the role of brain macrophages of different origin in sleep disorders and synaptic connectivity.

neuroscience↗

bioRxiv2023

Drosophila macrophages control systemic cytokine levels in response to oxidative stress via a non-canonical DNA damage repair signaling cascade

Environmental factors, infection, or injury can cause oxidative stress in diverse tissues and loss of tissue homeostasis. Effective stress response cascades, conserved from invertebrates to mammals, ensure reestablishment of homeostasis and tissue repair. Hemocytes, the Drosophila blood-like cells, rapidly respond to oxidative stress by immune activation. However, the precise signals how they sense oxidative stress and integrate these signals to modulate and balance the response to oxidative stress in the adult fly are ill-defined. Furthermore, hemocyte diversification was not explored yet on oxidative stress. Here, we employed high throughput single nuclei RNA-sequencing to explore hemocytes and other cell types, such as fat body, during oxidative stress in the adult fly. We identified distinct cellular responder states in plasmatocytes, the Drosophila macrophages, associated with immune response and metabolic activation upon oxidative stress. We further define oxidative stress-induced DNA damage signaling as a key sensor and a rate-limiting step in immune-activated plasmatocytes controlling JNK-mediated release of the pro-inflammatory cytokine unpaired-3. We subsequently tested the role of this specific immune activated cell stage during oxidative stress and found that inhibition of DNA damage signaling in plasmatocytes, as well as JNK or upd3 overactivation, result in a higher susceptibility to oxidative stress. Our findings uncover that a balanced composition and response of hemocyte subclusters is essential for the survival of adult Drosophila on oxidative stress by regulating systemic cytokine levels and cross-talk to other organs, such as the fat body, to control energy mobilization.

immunology↗

bioRxiv2022

A Community Challenge to Predict Clinical Outcomes After Immune Checkpoint Blockade in Non-Small Cell Lung Cancer

PurposePredictive biomarkers of immune checkpoint inhibitors (ICIs) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. MethodsParticipants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. ResultsA total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The bestperforming models showed improved predictive power over reference variables, including TMB or PD-L1. ConclusionThis DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICIs clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICIs efficacy. Context summaryO_ST_ABSKey objectiveC_ST_ABSNot all patients with non-small cell lung cancer (NSCLC) eligible for immune checkpoint inhibitor (ICIs) respond to treatment, but accurate predictive biomarkers of ICIs clinical outcomes are currently lacking. This crowdsourced initiative enabled the robust assessment of predictive models using data from two randomized clinical trials of first-line ICI in metastatic NSCLC. Knowledge generatedModels submitted indicate that a combination of programmed death ligand 1 (PD-L1), tumor mutational burden (TMB), and immune gene signatures might be able to identify patients more likely to respond to ICIs. TMB and PD-L1 seemed important to predict progression-free survival and overall survival. Mechanisms including apoptosis, T-cell crosstalk, and adaptive immune resistance appeared essential to predict response. Relevance

cancer biology↗

bioRxiv2022

Community assessment of methods to deconvolve cellular composition from bulk gene expression

We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states.

bioinformatics↗

bioRxiv2021

Targeting cancer glycosylation repolarizes tumor-associated macrophages allowing effective immune checkpoint blockade

Immune checkpoint blockade (ICB) has significantly improved the prognosis of cancer patients, but the majority experience limited benefit, evidencing the need for new therapeutic approaches. Upregulation of sialic acid-containing glycans, termed hypersialylation, is a common feature of cancer-associated glycosylation, driving disease progression and immune escape via the engagement of Siglec-receptors on tumor-infiltrating immune cells. Here, we show that tumor sialylation correlates with distinct immune states and reduced survival in human cancers. The targeted removal of Siglec-ligands in the tumor microenvironment, using an antibody-sialidase conjugate, enhances anti-tumor immunity and halts tumor progression in several mouse tumor models. Using single-cell RNA sequencing, we reveal desialylation mechanistically to repolarize tumor-associated macrophages (TAMs) and identify Siglec-E on TAMs as the main receptor for hypersialylation. Finally, we show genetic and therapeutic desialylation, as well as loss of Siglec-E, to synergize with ICB. Thus, therapeutic desialylation represents a novel immunotherapeutic approach, shaping macrophage phenotypes and augmenting the adaptive anti-tumor immune response.

immunology↗

bioRxiv2019

Hyperglycemia enhances cancer immune evasion by inducing alternative macrophage polarization through increased O-GlcNAcylation

Diabetes mellitus (DM) significantly increases the risk for cancer and cancer progression. Hyperglycemia is the defining characteristic of DM and tightly correlates with a poor prognosis in cancer patients. The hexosamine biosynthetic pathway (HBP) is emerging as a pivotal cascade linking high glucose, tumor progression and impaired immune function. Here we show that enhanced glucose flow through the HBP drives cancer progression and immune evasion by increasing O-GlcNAcylation in tumor-associated macrophages (TAMs). Increased O-GlcNAc skewed macrophage polarization to a M2-like phenotype. HBP or O-GlcNAcylation inhibition reprogrammed TAMs to an anti-tumoral phenotype. Finally, we found an upregulation of M2 markers on TAMs in DM2 patients with colorectal cancer compared to non-diabetic normoglycemic patients. Our results provide evidence for a new and targetable mechanism of cancer immune evasion in patients with hyperglycemia, advocating for strict control of hyperglycemia in cancer patients.\n\nSignificanceHyperglycemia increases O-GlcNAc levels in TAMs, programing them to a pro-tumorigenic phenotype (M2-like), contributing to cancer progression. Inhibition of O-GlcNAcylation could therefore be used to reprogram intratumoral macrophages to an anti-tumoral phenotype.

cancer biology↗