PowerNovo: de novo peptide sequencing via tandem mass spectrometry using an ensemble of transformer and BERT models.

The primary objective of analyzing the data obtained in a mass spectrometry-based proteomic experiment is peptide and protein identification, or correct assignment of the tandem mass spectrum to one amino acid sequence. Comparison of empirical fragment spectra with the theoretical predicted one or matching with the collected spectra library are commonly accepted strategies of proteins identification and defining of their amino acid sequences. Although these approaches are widely used and are appreciably efficient for the well-characterized model organisms or measured proteins, they cannot detect novel peptide sequences that have not been previously annotated or are rare. This study presents PowerNovo tool for de novo sequencing of proteins using tandem mass spectra acquired in a variety of types of mass analyzers and different fragmentation techniques. PowerNovo involves an ensemble of models for peptide sequencing: model for detecting regularities in tandem mass spectra, precursors, and fragment ions and a natural language processing model, which has a function of peptide sequence quality assessment and helps with reconstruction of noisy sequences. The results of testing showed that the performance of PowerNovo is comparable and even better than widely utilized PointNovo, DeepNovo, Casanovo, and Novor packages. Also, PowerNovo provides complete cycle of processing (pipeline) of mass spectrometry data and, along with predicting the peptide sequence, involves the peptide assembly and protein inference blocks.

Interplay between photosynthetic electron flux and organic carbon sinks in sucrose-excreting Synechocystis sp. PCC 6803 revealed by omics approaches.

BACKGROUND: Advancing the engineering of photosynthesis-based prokaryotic cell factories is important for sustainable chemical production and requires a deep understanding of the interplay between bioenergetic and metabolic pathways. Rearrangements in photosynthetic electron flow to increase the efficient use of the light energy for carbon fixation must be balanced with a strong carbon sink to avoid photoinhibition. In the cyanobacterium Synechocystis sp. PCC 6803, the flavodiiron protein Flv3 functions as an alternative electron acceptor of photosystem I and represents an interesting engineering target for reorganizing electron flow in attempts to enhance photosynthetic CO2 fixation and increase production yield. RESULTS: We have shown that inactivation of Flv3 in engineered sucrose-excreting Synechocystis (S02:Δflv3) induces a transition from photoautotrophic sucrose production to mixotrophic growth sustained by sucrose re-uptake and the formation of intracellular carbon sinks such as glycogen and polyhydroxybutyrate. The growth of S02:Δflv3 exceeds that of the sucrose-producing strain (S02) and demonstrates unforeseen proteomic and metabolomic changes over the course of the nine-day cultivation. In the absence of Flv3, a down-regulation of proteins related to photosynthetic light reactions and CO2 assimilation occurred concomitantly with up-regulation of those related to glycolytic pathways, before any differences in sucrose production between S02 and S02:Δflv3 strains were observed. Over time, increased sucrose degradation in S02:Δflv3 led to the upregulation of respiratory pathway components, such as the plastoquinone reductase complexes NDH-11 and NDH-2 and the terminal respiratory oxidases Cyd and Cox, which transfer electrons to O2. While glycolytic metabolism is significantly up-regulated in S02:Δflv3 to provide energy for the cell, the accumulation of intracellular storage compounds and the increase in respiration serve as indirect sinks for photosynthetic electrons. CONCLUSIONS: Our results show that the presence of strong carbon sink in the engineered sucrose-producing Synechocystis S02 strain, operating under high light, high CO2 and salt stress, cannot compensate for the lack of Flv3 by directly balancing the light transducing source and carbon fixing sink reactions. Instead, the cells immediately sense the imbalance, leading to extensive reprogramming of cellular bioenergetic, metabolic and ion transport pathways that favor mixotrophic growth rather than enhancing photoautotrophic sucrose production.

Analysis of serum peptidome profiles of non-metastatic and metastatic feline mammary carcinoma using liquid chromatography-tandem mass spectrometry.

BACKGROUND: Feline mammary carcinoma (FMC) is a common aggressive and highly metastatic cancer affecting female cats. Early detection is essential for preventing local and distant metastasis, thereby improving overall survival rates. While acquiring molecular data before surgery offers significant potential benefits, the current protein biomarkers for monitoring disease progression in non-metastatic FMC (NmFMC) and metastatic FMC (mFMC) are limited. The objective of this study was to investigate the serum peptidome profiles of NmFMC and mFMC using liquid chromatography-tandem mass spectrometry. A cross-sectional study was conducted to compare serum peptidome profiles in 13 NmFMC, 23 mFMC and 18 healthy cats. The liquid chromatography-tandem mass spectrometry analysis was performed on non-trypsinized samples. RESULTS: Out of a total of 8284 expressed proteins observed, several proteins were found to be associated with human breast cancer. In NmFMC, distinctive protein expressions encompassed double-stranded RNA-binding protein Staufen homolog 2 (STAU2), associated with cell proliferation, along with bromodomain adjacent to zinc finger domain 2A (BAZ2A) and gamma-aminobutyric acid type A receptor subunit epsilon (GABRE), identified as potential treatment targets. Paradoxically, positive prognostic markers emerged, such as complement C1q like 3 (C1QL3) and erythrocyte membrane protein band 4.1 (EPB41 or 4.1R). Within the mFMC group, overexpressed proteins associated with poor prognosis were exhibited, including B-cell lymphoma 6 transcription repressor (BCL6), thioredoxin reductase 3 (TXNRD3) and ceruloplasmin (CP). Meanwhile, the presence of POU class 5 homeobox (POU5F1 or OCT4) and laminin subunit alpha 1 (LAMA1), reported as metastatic biomarkers, was noted. CONCLUSION: The presence of both pro- and anti-proliferative proteins was observed, potentially indicating a distinctive characteristic of NmFMC. Conversely, proteins associated with poor prognosis and metastasis were noted in the mFMC group.

Extracellular matrix remodelling pathway in peripheral blood mononuclear cells from severe COVID-19 patients: an explorative study.

There is a reciprocal relationship between extracellular matrix (ECM) remodelling and inflammation that could be operating in the progression of severe COVID-19. To explore the immune-driven ECM remodelling in COVID-19, we in this explorative study analysed these interactions in hospitalised COVID-19 patients. RNA sequencing and flow analysis were performed on peripheral blood mononuclear cells. Inflammatory mediators in plasma were measured by ELISA and MSD, and clinical information from hospitalised COVID-19 patients (N=15) at admission was included in the analysis. Further, we reanalysed two publicly available datasets: (1) lung tissue RNA-sequencing dataset (N=5) and (2) proteomics dataset from PBCM. ECM remodelling pathways were enriched in PBMC from COVID-19 patients compared to healthy controls. Patients treated at the intensive care unit (ICU) expressed distinct ECM remodelling gene profiles compared to patients in the hospital ward. Several markers were strongly correlated to immune cell subsets, and the dysregulation in the ICU patients was positively associated with plasma levels of inflammatory cytokines and negatively associated with B-cell activating factors. Finally, our analysis of publicly accessible datasets revealed (i) an augmented ECM remodelling signature in inflamed lung tissue compared to non-inflamed tissue and (ii) proteomics analysis of PBMC from severe COVID-19 patients demonstrated an up-regulation in an ECM remodelling pathway. Our results may suggest the presence of an interaction between ECM remodelling, inflammation, and immune cells, potentially initiating or perpetuating pulmonary pathology in severe COVID-19.

Metalloproteins and metalloproteomics in health and disease.

Metalloproteins represents more than one third of human proteome, with huge variation in physiological functions and pathological implications, depending on the metal/metals involved and tissue context. Their functions range from catalysis, bioenergetics, redox, to DNA repair, cell proliferation, signaling, transport of vital elements, and immunity. The human metalloproteomic studies revealed that many families of metalloproteins along with individual metalloproteins are dysregulated under several clinical conditions. Also, several sorts of interaction between redox- active or redox- inert metalloproteins are observed in health and disease. Metalloproteins profiling shows distinct alterations in neurodegenerative diseases, cancer, inflammation, infection, diabetes mellitus, among other diseases. This makes metalloproteins -either individually or as families- a promising target for several therapeutic approaches. Inhibitors and activators of metalloenzymes, metal chelators, along with artificial metalloproteins could be versatile in diagnosis and treatment of several diseases, in addition to other biomedical and industrial applications.

Integrated serum proteomic and N-glycoproteomic characterization of dengue patients.

Dengue fever is a mosquito-borne viral disease caused by the dengue virus (DENV). It poses a public health threat globally and, while most people with dengue have mild symptoms or are asymptomatic, approximately 5% of affected individuals develop severe disease and need hospital care. However, knowledge of the molecular mechanisms underlying dengue infection and the interaction between the virus and its host remains limited. In the present study, we performed a quantitative proteomic and N-glycoproteomic analysis of serum from 19 patients with dengue and 11 healthy people. The results revealed distinct proteomic and N-glycoproteomic landscapes between the two groups. Notably, we report for the first time the changes in the serum N glycosylation pattern following dengue infection and provide abundant information on glycoproteins, glycosylation sites, and intact N-glycopeptides using recently developed site-specific glycoproteomic approaches. Furthermore, a series of key functional pathways in proteomic and N-glycoproteomic were identified. Collectively, our findings significantly improve understanding of host and DENV interactions and the general pathogenesis and pathology of DENV, laying a foundation for functional studies of glycosylation and glycan structures in dengue infection.

Changes in cerebrospinal fluid proteome of patients with tick-borne encephalitis.

Tick-borne encephalitis (TBE) is one of the main diseases transmitted by ticks, the incidence of which is increasing. Moreover, its diagnosis and therapy are often long and difficult according to nonspecific symptoms and complex etiology. This study aimed to observe changes in the proteome of cerebrospinal fluid from TBE patients. Cerebrospinal fluid (CSF) of TBE patients (n = 20) and healthy individuals (n = 10) was analyzed using a proteomic approach (QExactiveHF-Orbitrap mass spectrometer) and zymography. Obtained results show that in CSF of TBE patients, the top-upregulated proteins are involved in pro-inflammatory reaction (interleukins), as well as antioxidant/protective response (peroxiredoxins, heat shock proteins). Moreover, changes in the proteome of CSF are not only the result of this disease development, but they can also be an indicator of its course. This mainly applies to proteins involved in proteolysis including serpins and metalloproteinases, whose activity is proportional to the length of patients' convalescence. The obtained proteomic data strongly direct attention to the changes caused by the development of TBE to antioxidant, pro-inflammatory, and proteolytic proteins, knowledge about which can significantly contribute to faster and more accurate diagnosis of various clinical forms of TBE.

Mosquitocidal toxin-like islands in Bacillus thuringiensis S2160-1 revealed by complete-genome sequence and MS proteomic analysis.

Here, we present the whole genome sequence of Bt S2160-1, a potential alternative to the mosquitocidal model strain, Bti. One chromosome genome and four mega-plasmids were contained in Bt S2160-1, and 13 predicted genes encoding predicted insecticidal crystal proteins were identified clustered on one plasmid pS2160-1p2 containing two pathogenic islands (PAIs) designed as PAI-1 (Cry54Ba, Cry30Ea4, Cry69Aa-like, Cry50Ba2-like, Cry4Ca1-like, Cry30Ga2, Cry71Aa-like, Cry72Aa-like, Cry70Aa-like, Cyt1Da2-like and Vpb4C1-like) and PAI-2 (Cyt1Aa-like, and Tpp80Aa1-like). The clusters appear to represent mosquitocidal toxin islands similar to pathogenicity islands. Transcription/translation of 10 of the 13 predicted genes was confirmed by whole-proteome analysis using LTQ-Orbitrap LC-MS/MS. In summary, the present study identified the existence of a mosquitocidal toxin island in Bacillus thuringiensis, and provides important genomic information for understanding the insecticidal mechanism of B. thuringiensis.

Integrative multi-omics analysis unravels the host response landscape and reveals a serum protein panel for early prognosis prediction for ARDS.

BACKGROUND: The multidimensional biological mechanisms underpinning acute respiratory distress syndrome (ARDS) continue to be elucidated, and early biomarkers for predicting ARDS prognosis are yet to be identified. METHODS: We conducted a multicenter observational study, profiling the 4D-DIA proteomics and global metabolomics of serum samples collected from patients at the initial stage of ARDS, alongside samples from both disease control and healthy control groups. We identified 28-day prognosis biomarkers of ARDS in the discovery cohort using the LASSO method, fold change analysis, and the Boruta algorithm. The candidate biomarkers were validated through parallel reaction monitoring (PRM) targeted mass spectrometry in an external validation cohort. Machine learning models were applied to explore the biomarkers of ARDS prognosis. RESULTS: In the discovery cohort, comprising 130 adult ARDS patients (mean age 72.5, 74.6% male), 33 disease controls, and 33 healthy controls, distinct proteomic and metabolic signatures were identified to differentiate ARDS from both control groups. Pathway analysis highlighted the upregulated sphingolipid signaling pathway as a key contributor to the pathological mechanisms underlying ARDS. MAP2K1 emerged as the hub protein, facilitating interactions with various biological functions within this pathway. Additionally, the metabolite sphingosine 1-phosphate (S1P) was closely associated with ARDS and its prognosis. Our research further highlights essential pathways contributing to the deceased ARDS, such as the downregulation of hematopoietic cell lineage and calcium signaling pathways, contrasted with the upregulation of the unfolded protein response and glycolysis. In particular, GAPDH and ENO1, critical enzymes in glycolysis, showed the highest interaction degree in the protein-protein interaction network of ARDS. In the discovery cohort, a panel of 36 proteins was identified as candidate biomarkers, with 8 proteins (VCAM1, LDHB, MSN, FLG2, TAGLN2, LMNA, MBL2, and LBP) demonstrating significant consistency in an independent validation cohort of 183 patients (mean age 72.6 years, 73.2% male), confirmed by PRM assay. The protein-based model exhibited superior predictive accuracy compared to the clinical model in both the discovery cohort (AUC: 0.893 vs. 0.784; Delong test, P < 0.001) and the validation cohort (AUC: 0.802 vs. 0.738; Delong test, P = 0.008). INTERPRETATION: Our multi-omics study demonstrated the potential biological mechanism and therapy targets in ARDS. This study unveiled several novel predictive biomarkers and established a validated prediction model for the poor prognosis of ARDS, offering valuable insights into the prognosis of individuals with ARDS.

Metaproteomics-informed stoichiometric modeling reveals the responses of wetland microbial communities to oxygen and sulfate exposure.

Climate changes significantly impact greenhouse gas emissions from wetland soil. Specifically, wetland soil may be exposed to oxygen (O2) during droughts, or to sulfate (SO42-) as a result of sea level rise. How these stressors - separately and together - impact microbial food webs driving carbon cycling in the wetlands is still not understood. To investigate this, we integrated geochemical analysis, proteogenomics, and stoichiometric modeling to characterize the impact of elevated SO42- and O2 levels on microbial methane (CH4) and carbon dioxide (CO2) emissions. The results uncovered the adaptive responses of this community to changes in SO42- and O2 availability and identified altered microbial guilds and metabolic processes driving CH4 and CO2 emissions. Elevated SO42- reduced CH4 emissions, with hydrogenotrophic methanogenesis more suppressed than acetoclastic. Elevated O2 shifted the greenhouse gas emissions from CH4 to CO2. The metabolic effects of combined SO42- and O2 exposures on CH4 and CO2 emissions were similar to those of O2 exposure alone. The reduction in CH4 emission by increased SO42- and O2 was much greater than the concomitant increase in CO2 emission. Thus, greater SO42- and O2 exposure in wetlands is expected to reduce the aggregate warming effect of CH4 and CO2. Metaproteomics and stoichiometric modeling revealed a unique subnetwork involving carbon metabolism that converts lactate and SO42- to produce acetate, H2S, and CO2 when SO42- is elevated under oxic conditions. This study provides greater quantitative resolution of key metabolic processes necessary for the prediction of CH4 and CO2 emissions from wetlands under future climate scenarios.

CSF Proteomics in Patients With Progressive Supranuclear Palsy.

BACKGROUND AND OBJECTIVES: Identification of fluid biomarkers for progressive supranuclear palsy (PSP) is critical to enhance therapeutic development. We implemented unbiased DNA aptamer (SOMAmer) proteomics to identify novel CSF PSP biomarkers. METHODS: This is a cross-sectional study in original (18 clinically diagnosed PSP-Richardson syndrome [PSP-RS], 28 cognitively healthy controls]), validation (23 PSP-RS, 26 healthy controls), and neuropathology-confirmed (21 PSP, 52 non-PSP frontotemporal lobar degeneration) cohorts. Participants were recruited through the University of California, San Francisco, and the 4-Repeat Neuroimaging Initiative. The original and neuropathology cohorts were analyzed with the SomaScan platform version 3.0 (5026-plex) and the validation cohort with version 4.1 (7595-plex). Clinical severity was measured with the PSP Rating Scale (PSPRS). CSF proteomic data were analyzed to identify differentially expressed targets, implicated biological pathways using enrichment and weighted consensus gene coexpression analyses, diagnostic value of top targets with receiver-operating characteristic curves, and associations with disease severity with linear regressions. RESULTS: A total of 136 participants were included (median age 70.6 ± 8 years, 68 [50%] women). One hundred fifty-five of 5,026 (3.1%), 959 of 7,595 (12.6%), and 321 of 5,026 (6.3%) SOMAmers were differentially expressed in PSP compared with controls in original, validation, and neuropathology-confirmed cohorts, with most of the SOMAmers showing reduced signal (83.1%, 95.1%, and 73.2%, respectively). Three coexpression modules were associated with PSP across cohorts: (1) synaptic function/JAK-STAT (ß = -0.044, corrected p = 0.002), (2) vesicle cytoskeletal trafficking (ß = 0.039, p = 0.007), and (3) cytokine-cytokine receptor interaction (ß = -0.032, p = 0.035) pathways. Axon guidance was the top dysregulated pathway in PSP in original (strength = 1.71, p < 0.001), validation (strength = 0.84, p < 0.001), and neuropathology-confirmed (strength = 0.78, p < 0.001) cohorts. A panel of axon guidance pathway proteins discriminated between PSP and controls in original (area under the curve [AUC] = 0.924), validation (AUC = 0.815), and neuropathology-confirmed (AUC = 0.932) cohorts. Two inflammatory proteins, galectin-10 and cytotoxic T lymphocyte-associated protein-4, correlated with PSPRS scores across cohorts. DISCUSSION: Axon guidance pathway proteins and several other molecular pathways are downregulated in PSP, compared with controls. Proteins in these pathways may be useful targets for biomarker or therapeutic development.

Calib-RT: an open source python package for peptide retention time calibration in DIA mass spectrometry data.

MOTIVATION: The data independent acquisition (DIA) mass spectrometry (MS) method is increasingly popular in the field of proteomics. But the loss of the correspondence between peptide ions and their spectra in DIA makes the identification challenging. One effective approach to reduce false positive identification is to calculate the deviation between the peptide's estimated retention time (RT) and measured RT. During this process, scaling the spectral library RT into the estimated RT, known as the RT calibration, is a prerequisite for calculating the deviation. Currently, within the DIA algorithm ecosystem, there is a lack of engine-independent and readily usable RT calibration toolkits. RESULTS: In this work, we introduce Calib-RT, a RT calibration method tailored to the characteristics of RT data. This method can achieve the nonlinear calibration across various data scales and tolerate a certain level of noise interference. Calib-RT is expected to enrich the open source DIA algorithm toolchain and assist in the development of DIA identification algorithms. AVAILABILITY AND IMPLEMENTATION: Calib-RT is released as an open source software under the MIT license and can be installed from PyPi as a python module. The source code is available on GitHub at https://github.com/chenghui03/Calib_RT.

Proteomic analysis of Rana sylvatica reveals differentially expressed proteins in liver in response to anoxia, dehydration or freezing stress.

Ectothermic animals that live in seasonally cold regions must adapt to seasonal variation and specific environmental conditions. During the winter, some amphibians hibernate on land and encounter limited environmental water, deficient oxygen, and extremely low temperatures that can cause the whole body freezing. These stresses trigger physiological and biochemical adaptations in amphibians that allow them to survive. Rana sylvatica, commonly known as the wood frog, shows excellent freeze tolerance. They can slow their metabolic activity to a near halt and endure freezing of 65-70% of their total body water as extracellular ice during hibernation, returning to normal when the temperatures rise again. To investigate the molecular adaptations of freeze-tolerant wood frogs, a comprehensive proteomic analysis was performed on frog liver tissue after anoxia, dehydration, or freezing exposures using a label-free LC-MS/MS proteomic approach. Quantitative proteomic analysis revealed that 87, 118, and 86 proteins were significantly upregulated in dehydrated, anoxic, and frozen groups, suggesting potential protective functions. The presence of three upregulated enzymes, glutathione S-transferase (GST), aldolase (ALDOA), and sorbitol dehydrogenase (SORD), was also validated. For all enzymes, the specific enzymatic activity was significantly higher in the livers of frozen and anoxic groups than in the controls. This study reveals that GST, ALDOA, and SORD might participate in the freeze tolerance mechanism by contributing to regulating cellular detoxification and energy metabolism.

Extracellular vesicles from apoptotic BMSCs ameliorate osteoporosis via transporting regenerative signals.

Rationale: Mesenchymal stromal cells (MSCs) are considered a promising resource for cell therapy, exhibiting efficacy in ameliorating diverse bone diseases. However, most MSCs undergo apoptosis shortly after transplantation and produce apoptotic extracellular vesicles (ApoEVs). This study aims to clarify the potential role of ApoEVs from apoptotic MSCs in ameliorating osteoporosis and molecular mechanism. Methods: In this study, Dio-labeled bone marrow mesenchymal stem cells (BMSCs) were injected into mice to track BMSCs apoptosis and ApoEVs production. ApoEVs were isolated from BMSCs after inducing apoptosis, the morphology, size distribution, marker proteins expression of ApoEVs were characterized. Protein mass spectrometry analysis revealed functional differences in proteins between ApoEVs and BMSCs. BMSCs were adopted to test the cellular response to ApoEVs. Ovariectomy mice were used to further compare the ability of ApoEVs in promoting bone formation. SiRNA and lentivirus were used for gain and loss-of-function assay. Results: The results showed that BMSCs underwent apoptosis within 2 days after being injected into mice and produce a substantial quantity of ApoEVs. Proteomic analysis revealed that ApoEVs carried a diverse functional array of proteins, and easily traversed the circulation to reach the bone. After being phagocytized by endogenous BMSCs, ApoEVs efficiently promoted the proliferation, migration, and osteogenic differentiation of BMSCs. In an osteoporosis mouse model, treatment of ApoEVs alleviated bone loss and promoted bone formation. Mechanistically, ApoEVs carried Ras protein and activated the Ras/Raf1/Mek/Erk pathway to promote osteogenesis and bone formation in vitro and in vivo. Conclusion: Given that BMSC-derived ApoEVs are high-yield and easily obtained, our data underscore the substantive role of ApoEVs from dying BMSCs to treat bone loss, presenting broad implications for cell-free therapeutic modalities.

Proteomics analysis of deep fascia in acute compartment syndrome.

Acute compartment syndrome (ACS) is a syndrome in which local circulation is affected due to increased pressure within the compartment. We previously found in patients with calf fractures, the pressure of fascial compartment could be sharply reduced upon the appearance of tension blisters. Deep fascia, as the important structure for compartment, might play key role in this process. Therefore, the aim of the present study was to examine the differences in gene profile in deep fascia tissue in fracture patients of the calf with or without tension blisters, and to explore the role of fascia in pressure improvement in ACS. Patients with lower leg fracture were enrolled and divided into control group (CON group, n = 10) without tension blister, and tension blister group (TB group, n = 10). Deep fascia tissues were collected and LC-MS/MS label-free quantitative proteomics were performed. Genes involved in fascia structure and fibroblast function were further validated by Western blot. The differentially expressed proteins were found to be mainly enriched in pathways related to protein synthesis and processing, stress fiber assembly, cell-substrate adhesion, leukocyte mediated cytotoxicity, and cellular response to stress. Compared with the CON group, the expression of Peroxidasin homolog (PXDN), which promotes the function of fibroblasts, and Leukocyte differentiation antigen 74 (CD74), which enhances the proliferation of fibroblasts, were significantly upregulated (p all <0.05), while the expression of Matrix metalloproteinase-9 (MMP9), which is involved in collagen hydrolysis, and Neutrophil elastase (ELANE), which is involved in elastin hydrolysis, were significantly reduced in the TB group (p all <0.05), indicating fascia tissue underwent microenvironment reconstruction during ACS. In summary, the ACS accompanied by blisters is associated with the enhanced function and proliferation of fibroblasts and reduced hydrolysis of collagen and elastin. The adaptive alterations in the stiffness and elasticity of the deep fascia might be crucial for pressure release of ACS.

Using adjusted local assortativity with Molecular Pixelation unveils colocalization of membrane proteins with immunological significance.

Advances in spatial proteomics and protein colocalization are a driving force in the understanding of cellular mechanisms and their influence on biological processes. New methods in the field of spatial proteomics call for the development of algorithms and open up new avenues of research. The newly introduced Molecular Pixelation (MPX) provides spatial information on surface proteins and their relationship with each other in single cells. This allows for in silico representation of neighborhoods of membrane proteins as graphs. In order to analyze this new data modality, we adapted local assortativity in networks of MPX single-cell graphs and created a method that is able to capture detailed information on the spatial relationships of proteins. The introduced method can evaluate the pairwise colocalization of proteins and access higher-order similarity to investigate the colocalization of multiple proteins at the same time. We evaluated the method using publicly available MPX datasets where T cells were treated with a chemokine to study uropod formation. We demonstrate that adjusted local assortativity detects the effects of the stimuli at both single- and multiple-marker levels, which enhances our understanding of the uropod formation. We also applied our method to treating cancerous B-cell lines using a therapeutic antibody. With the adjusted local assortativity, we recapitulated the effect of rituximab on the polarity of CD20. Our computational method together with MPX improves our understanding of not only the formation of cell polarity and protein colocalization under stimuli but also advancing the overall insight into immune reaction and reorganization of cell surface proteins, which in turn allows the design of novel therapies. We foresee its applicability to other types of biological spatial data when represented as undirected graphs.

Multi-omics analysis reveals drivers of loss of ß-cell function after newly diagnosed autoimmune type 1 diabetes: An INNODIA multicenter study.

AIMS: Heterogeneity in the rate of ß-cell loss in newly diagnosed type 1 diabetes patients is poorly understood and creates a barrier to designing and interpreting disease-modifying clinical trials. Integrative analyses of baseline multi-omics data obtained after the diagnosis of type 1 diabetes may provide mechanistic insight into the diverse rates of disease progression after type 1 diabetes diagnosis. METHODS: We collected samples in a pan-European consortium that enabled the concerted analysis of five different omics modalities in data from 97 newly diagnosed patients. In this study, we used Multi-Omics Factor Analysis to identify molecular signatures correlating with post-diagnosis decline in ß-cell mass measured as fasting C-peptide. RESULTS: Two molecular signatures were significantly correlated with fasting C-peptide levels. One signature showed a correlation to neutrophil degranulation, cytokine signalling, lymphoid and non-lymphoid cell interactions and G-protein coupled receptor signalling events that were inversely associated with a rapid decline in ß-cell function. The second signature was related to translation and viral infection was inversely associated with change in ß-cell function. In addition, the immunomics data revealed a Natural Killer cell signature associated with rapid ß-cell decline. CONCLUSIONS: Features that differ between individuals with slow and rapid decline in ß-cell mass could be valuable in staging and prediction of the rate of disease progression and thus enable smarter (shorter and smaller) trial designs for disease modifying therapies as well as offering biomarkers of therapeutic effect.

Cell membrane proteome analysis in HEK293T cells challenged with α-synuclein amyloids.

Amyloids interact with plasma membranes. Extracellular amyloids cross the plasma membrane barrier. Internalized extracellular amyloids are reported to trigger amyloidogenesis of endogenous proteins in recipient cells. To what extent these extracellular and intracellular amyloids perturb the plasma membrane proteome is not investigated. Using α-synuclein as a model amyloid protein, we performed membrane shaving followed by mass spectrometry experiments to identify the conformational changes in cell surface proteins after extracellular amyloid challenge. We also performed membrane proteomics after the biogenesis of intracellular α-synuclein amyloids. Our results suggest that promiscuous interactions with extracellular amyloids stochastically alter the conformation of plasma membrane proteins. This affects the biological processes through the plasma membrane and results in loss of cell viability. Cells that survive the extracellular amyloid shock can grow normally and gradually develop intracellular amyloids which do not directly impact the plasma membrane proteome and associated biological processes. Thus, our results suggest that α-synuclein amyloids can damage the plasma membrane and related processes during cell-to-cell transfer and not during their intracellular biogenesis.