RTF: An R package for modelling time course data.

SUMMARY: The Retarded Transient Function (RTF) approach serves as a complementary method to ordinary differential equations (ODEs) for modelling dynamics typically observed in cellular signalling processes. We introduce an R package that implements the RTF approach, originally implemented within the MATLAB-based Data2Dynamics modelling framework. This package facilitates the modelling of time and dose dependencies, and it includes the possibility of model reduction to minimize overfitting. It can be applied to experimental data or trajectories of ODE models to characterize their dynamics. Additionally, it can generate a low-dimensional representation based on the fitted RTF parameters of a set of time-resolved data, aiding in the identification of key targets of experimental perturbations. AVAILABILITY AND IMPLEMENTATION: The R package RTF is available at https://github.com/kreutz-lab/RTF. CONTACT: clemens.kreutz@uniklinik-freiburg.de.

TermineR: Extracting information on endogenous proteolytic processing from shotgun proteomics data.

State-of-the-art mass spectrometers combined with modern bioinformatics algorithms for peptide-to-spectrum matching (PSM) with robust statistical scoring allow for more variable features (i.e., post-translational modifications) being reliably identified from (tandem-) mass spectrometry data, often without the need for biochemical enrichment. Semi-specific proteome searches, that enforce a theoretical enzymatic digestion to solely the N- or C-terminal end, allow to identify of native protein termini or those arising from endogenous proteolytic activity (also referred to as "neo-N-termini" analysis or "N-terminomics"). Nevertheless, deriving biological meaning from these search outputs can be challenging in terms of data mining and analysis. Thus, we introduce TermineR, a data analysis approach for the (1) annotation of peptides according to their enzymatic cleavage specificity and known protein processing features, (2) differential abundance and enrichment analysis of N-terminal sequence patterns, and (3) visualization of neo-N-termini location. We illustrate the use of TermineR by applying it to tandem mass tag (TMT)-based proteomics data of a mouse model of polycystic kidney disease, and assess the semi-specific searches for biological interpretation of cleavage events and the variable contribution of proteolytic products to general protein abundance. The TermineR approach and example data are available as an R package at https://github.com/MiguelCos/TermineR.

Chlorhexidine digluconate mouthwash alters the oral microbial composition and affects the prevalence of antimicrobial resistance genes.

Introduction: Chlorhexidine (CHX) is a commonly used antiseptic in situations of limited oral hygiene ability such as after periodontal surgery. However, CHX is also considered as a possible factor in the emergence of cross-resistance to antibiotics. The aim of this study was to analyze the changes in the oral microbiota and the prevalence of antimicrobial resistance genes (ARGs) due to CHX treatment. Materials and methods: We analyzed the oral metagenome of 20 patients who applied a 0.2% CHX mouthwash twice daily for 4 weeks following periodontal surgical procedures. Saliva and supragingival plaque samples were examined before, directly after 4 weeks, and another 4 weeks after discontinuing the CHX treatment. Results: Alpha-diversity decreased significantly with CHX use. The Bray-Curtis dissimilarity increased in both sample sites and mainly streptococci showed a higher relative abundance after CHX treatment. Although no significant changes of ARGs could be detected, an increase in prevalence was found for genes that encode for tetracycline efflux pumps. Conclusion: CHX treatment appears to promote a caries-associated bacterial community and the emergence of tetracycline resistance genes. Future research should focus on CHX-related changes in the microbial community and whether the discovered tetracycline resistance genes promote resistance to CHX.

Data-Independent Acquisition: A Milestone and Prospect in Clinical Mass Spectrometry-Based Proteomics.

Data-independent acquisition (DIA) has revolutionized the field of mass spectrometry (MS)-based proteomics over the past few years. DIA stands out for its ability to systematically sample all peptides in a given m/z range, allowing an unbiased acquisition of proteomics data. This greatly mitigates the issue of missing values and significantly enhances quantitative accuracy, precision, and reproducibility compared to many traditional methods. This review focuses on the critical role of DIA analysis software tools, primarily focusing on their capabilities and the challenges they address in proteomic research. Advances in MS technology, such as trapped ion mobility spectrometry, or high field asymmetric waveform ion mobility spectrometry require sophisticated analysis software capable of handling the increased data complexity and exploiting the full potential of DIA. We identify and critically evaluate leading software tools in the DIA landscape, discussing their unique features, and the reliability of their quantitative and qualitative outputs. We present the biological and clinical relevance of DIA-MS and discuss crucial publications that paved the way for in-depth proteomic characterization in patient-derived specimens. Furthermore, we provide a perspective on emerging trends in clinical applications and present upcoming challenges including standardization and certification of MS-based acquisition strategies in molecular diagnostics. While we emphasize the need for continuous development of software tools to keep pace with evolving technologies, we advise researchers against uncritically accepting the results from DIA software tools. Each tool may have its own biases, and some may not be as sensitive or reliable as others. Our overarching recommendation for both researchers and clinicians is to employ multiple DIA analysis tools, utilizing orthogonal analysis approaches to enhance the robustness and reliability of their findings.

A 14-Day Double-Blind, Randomized, Controlled Crossover Intervention Study with Anti-Bacterial Benzyl Isothiocyanate from Nasturtium (Tropaeolum majus) on Human Gut Microbiome and Host Defense.

Despite substantial heterogeneity of studies, there is evidence that antibiotics commonly used in primary care influence the composition of the gastrointestinal microbiota in terms of changing their composition and/or diversity. Benzyl isothiocyanate (BITC) from the food and medicinal plant nasturtium (Tropaeolum majus) is known for its antimicrobial activity and is used for the treatment of infections of the draining urinary tract and upper respiratory tract. Against this background, we raised the question of whether a 14 d nasturtium intervention (3 g daily, N = 30 healthy females) could also impact the normal gut microbiota composition. Spot urinary BITC excretion highly correlated with a weak but significant antibacterial effect against Escherichia coli. A significant increase in human beta defensin 1 as a parameter for host defense was seen in urine and exhaled breath condensate (EBC) upon verum intervention. Pre-to-post analysis revealed that mean gut microbiome composition did not significantly differ between groups, nor did the circulating serum metabolome. On an individual level, some large changes were observed between sampling points, however. Explorative Spearman rank correlation analysis in subgroups revealed associations between gut microbiota and the circulating metabolome, as well as between changes in blood markers and bacterial gut species.

Neoadjuvant chemo- or chemo-radiation-therapy of pancreatic ductal adenocarcinoma differentially shift ECM composition, complement activation, energy metabolism and ribosomal proteins of the residual tumor mass.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer, often diagnosed at stages that dis-qualify for surgical resection. Neoadjuvant therapies offer potential tumor regression and improved resectability. Although features of the tumor biology (e.g., molecular markers) may guide adjuvant therapy, biological alterations after neoadjuvant therapy remain largely unexplored. We performed mass spectrometry to characterize the proteomes of 67 PDAC resection specimens of patients who received either neoadjuvant chemo (NCT) or chemo-radiation (NCRT) therapy. We employed data-independent acquisition (DIA), yielding a proteome coverage in excess of 3500 proteins. Moreover, we successfully integrated two publicly available proteome datasets of treatment-naïve PDAC to unravel proteome alterations in response to neoadjuvant therapy, highlighting the feasibility of this approach. We found highly distinguishable proteome profiles. Treatment-naïve PDAC was characterized by enrichment of immunoglobulins, complement and extracellular matrix (ECM) proteins. Post-NCT and post-NCRT PDAC presented high abundance of ribosomal and metabolic proteins as compared to treatment-naïve PDAC. Further analyses on patient survival and protein expression identified treatment-specific prognostic candidates. We present the first proteomic characterization of the residual PDAC mass after NCT and NCRT, and potential protein candidate markers associated with overall survival. We conclude that residual PDAC exhibits fundamentally different proteome profiles as compared to treatment-naïve PDAC, influenced by the type of neoadjuvant treatment. These findings may impact adjuvant or targeted therapy options.

A DELAY OF GERMINATION 1 (DOG1)-like protein regulates spore germination in the moss Physcomitrium patens.

DELAY OF GERMINATION 1 is a key regulator of dormancy in flowering plants before seed germination. Bryophytes develop haploid spores with an analogous function to seeds. Here, we investigate whether DOG1 function during germination is conserved between bryophytes and flowering plants and analyse the underlying mechanism of DOG1 action in the moss Physcomitrium patens. Phylogenetic and in silico expression analyses were performed to identify and characterise DOG1 domain-containing genes in P. patens. Germination assays were performed to characterise a Ppdog1-like1 mutant, and replacement with AtDOG1 was carried out. Yeast two-hybrid assays were used to test the interaction of the PpDOG1-like protein with DELLA proteins from P. patens and A. thaliana. P. patens possesses nine DOG1 domain-containing genes. The DOG1-like protein PpDOG1-L1 (Pp3c3_9650) interacts with PpDELLAa and PpDELLAb and the A. thaliana DELLA protein AtRGA in yeast. Protein truncations revealed the DOG1 domain as necessary and sufficient for interaction with PpDELLA proteins. Spores of Ppdog1-l1 mutant germinate faster than wild type, but replacement with AtDOG1 reverses this effect. Our data demonstrate a role for the PpDOG1-LIKE1 protein in moss spore germination, possibly alongside PpDELLAs. This suggests a conserved DOG1 domain function in germination, albeit with differential adaptation of regulatory networks in seed and spore germination.

In Vitro microRNA Expression Profile Alterations under CDK4/6 Therapy in Breast Cancer.

BACKGROUND: Breast cancer is the most common type of cancer worldwide. Cyclin-dependent kinase inhibition is one of the backbones of metastatic breast cancer therapy. However, there are a significant number of therapy failures. This study evaluates the biomarker potential of microRNAs for the prediction of a therapy response under cyclin-dependent kinase inhibition. METHODS: This study comprises the analysis of intracellular and extracellular microRNA-expression-level alterations of 56 microRNAs under palbociclib mono as well as combination therapy with letrozole. Breast cancer cell lines BT-474, MCF-7 and HS-578T were analyzed using qPCR. RESULTS: A palbociclib-induced microRNA signature could be detected intracellularly as well as extracellularly. Intracellular miR-10a, miR-15b, miR-21, miR-23a and miR-23c were constantly regulated in all three cell lines, whereas let-7b, let-7d, miR-15a, miR-17, miR-18a, miR-20a, miR-191 and miR301a_3p were regulated only in hormone-receptor-positive cells. Extracellular miR-100, miR-10b and miR-182 were constantly regulated across all cell lines, whereas miR-17 was regulated only in hormone-receptor-positive cells. CONCLUSIONS: Because they are secreted and significantly upregulated in the microenvironment of tumor cells, miRs-100, -10b and -182 are promising circulating biomarkers that can be used to predict or detect therapy responses under CDK inhibition. MiR-10a, miR-15b, miR-21, miR-23a and miR-23c are potential tissue-based biomarkers.

Gut Microbiota in Diagnosis, Therapy and Prognosis of Cholangiocarcinoma and Gallbladder Carcinoma-A Scoping Review.

Cancers of the biliary tract are more common in Asia than in Europe, but are highly lethal due to delayed diagnosis and aggressive tumor biology. Since the biliary tract is in direct contact with the gut via the enterohepatic circulation, this suggests a potential role of gut microbiota, but to date, the role of gut microbiota in biliary tract cancers has not been elucidated. This scoping review compiles recent data on the associations between the gut microbiota and diagnosis, progression and prognosis of biliary tract cancer patients. Systematic review of the literature yielded 154 results, of which 12 studies and one systematic review were eligible for evaluation. The analyses of microbiota diversity indices were inconsistent across the included studies. In-depth analyses revealed differences between gut microbiota of biliary tract cancer patients and healthy controls, but without a clear tendency towards particular species in the studies. Additionally, most of the studies showed methodological flaws, for example non-controlling of factors that affect gut microbiota. At the current stage, there is a lack of evidence to support a general utility of gut microbiota diagnostics in biliary tract cancers. Therefore, no recommendation can be made at this time to include gut microbiota analyses in the management of biliary tract cancer patients.

Likelihood-ratio test statistic for the finite-sample case in nonlinear ordinary differential equation models.

Likelihood ratios are frequently utilized as basis for statistical tests, for model selection criteria and for assessing parameter and prediction uncertainties, e.g. using the profile likelihood. However, translating these likelihood ratios into p-values or confidence intervals requires the exact form of the test statistic's distribution. The lack of knowledge about this distribution for nonlinear ordinary differential equation (ODE) models requires an approximation which assumes the so-called asymptotic setting, i.e. a sufficiently large amount of data. Since the amount of data from quantitative molecular biology is typically limited in applications, this finite-sample case regularly occurs for mechanistic models of dynamical systems, e.g. biochemical reaction networks or infectious disease models. Thus, it is unclear whether the standard approach of using statistical thresholds derived for the asymptotic large-sample setting in realistic applications results in valid conclusions. In this study, empirical likelihood ratios for parameters from 19 published nonlinear ODE benchmark models are investigated using a resampling approach for the original data designs. Their distributions are compared to the asymptotic approximation and statistical thresholds are checked for conservativeness. It turns out, that corrections of the likelihood ratios in such finite-sample applications are required in order to avoid anti-conservative results.

Conformational regulation and target-myristoyl switch of calcineurin B homologous protein 3.

Calcineurin B homologous protein 3 (CHP3) is an EF-hand Ca2+-binding protein involved in regulation of cancerogenesis, cardiac hypertrophy, and neuronal development through interactions with sodium/proton exchangers (NHEs) and signalling proteins. While the importance of Ca2+ binding and myristoylation for CHP3 function has been recognized, the underlying molecular mechanism remained elusive. In this study, we demonstrate that Ca2+ binding and myristoylation independently affect the conformation and functions of human CHP3. Ca2+ binding increased local flexibility and hydrophobicity of CHP3 indicative of an open conformation. The Ca2+-bound CHP3 exhibited a higher affinity for NHE1 and associated stronger with lipid membranes compared to the Mg2+-bound CHP3, which adopted a closed conformation. Myristoylation enhanced the local flexibility of CHP3 and decreased its affinity to NHE1 independently of the bound ion, but did not affect its binding to lipid membranes. The data exclude the proposed Ca2+-myristoyl switch for CHP3. Instead, a Ca2+-independent exposure of the myristoyl moiety is induced by binding of the target peptide to CHP3 enhancing its association to lipid membranes. We name this novel regulatory mechanism 'target-myristoyl switch'. Collectively, the interplay of Ca2+ binding, myristoylation, and target binding allows for a context-specific regulation of CHP3 functions.

Realistic simulation of time-course measurements in systems biology.

In systems biology, the analysis of complex nonlinear systems faces many methodological challenges. For the evaluation and comparison of the performances of novel and competing computational methods, one major bottleneck is the availability of realistic test problems. We present an approach for performing realistic simulation studies for analyses of time course data as they are typically measured in systems biology. Since the design of experiments in practice depends on the process of interest, our approach considers the size and the dynamics of the mathematical model which is intended to be used for the simulation study. To this end, we used 19 published systems biology models with experimental data and evaluated the relationship between model features (e.g., the size and the dynamics) and features of the measurements such as the number and type of observed quantities, the number and the selection of measurement times, and the magnitude of measurement errors. Based on these typical relationships, our novel approach enables suggestions of realistic simulation study designs in the systems biology context and the realistic generation of simulated data for any dynamic model. The approach is demonstrated on three models in detail and its performance is validated on nine models by comparing ODE integration, parameter optimization, and parameter identifiability. The presented approach enables more realistic and less biased benchmark studies and thereby constitutes an important tool for the development of novel methods for dynamic modeling.

Mutual repression between JNK/AP-1 and JAK/STAT stratifies senescent and proliferative cell behaviors during tissue regeneration.

Epithelial repair relies on the activation of stress signaling pathways to coordinate tissue repair. Their deregulation is implicated in chronic wound and cancer pathologies. Using TNF-α/Eiger-mediated inflammatory damage to Drosophila imaginal discs, we investigate how spatial patterns of signaling pathways and repair behaviors arise. We find that Eiger expression, which drives JNK/AP-1 signaling, transiently arrests proliferation of cells in the wound center and is associated with activation of a senescence program. This includes production of the mitogenic ligands of the Upd family, which allows JNK/AP-1-signaling cells to act as paracrine organizers of regeneration. Surprisingly, JNK/AP-1 cell-autonomously suppress activation of Upd signaling via Ptp61F and Socs36E, both negative regulators of JAK/STAT signaling. As mitogenic JAK/STAT signaling is suppressed in JNK/AP-1-signaling cells at the center of tissue damage, compensatory proliferation occurs by paracrine activation of JAK/STAT in the wound periphery. Mathematical modelling suggests that cell-autonomous mutual repression between JNK/AP-1 and JAK/STAT is at the core of a regulatory network essential to spatially separate JNK/AP-1 and JAK/STAT signaling into bistable spatial domains associated with distinct cellular tasks. Such spatial stratification is essential for proper tissue repair, as coactivation of JNK/AP-1 and JAK/STAT in the same cells creates conflicting signals for cell cycle progression, leading to excess apoptosis of senescently stalled JNK/AP-1-signaling cells that organize the spatial field. Finally, we demonstrate that bistable separation of JNK/AP-1 and JAK/STAT drives bistable separation of senescent signaling and proliferative behaviors not only upon tissue damage, but also in RasV12, scrib tumors. Revealing this previously uncharacterized regulatory network between JNK/AP-1, JAK/STAT, and associated cell behaviors has important implications for our conceptual understanding of tissue repair, chronic wound pathologies, and tumor microenvironments.

Temporal Notch signaling regulates mucociliary cell fates through Hes-mediated competitive de-repression.

Tissue functions are determined by the types and ratios of cells present, but little is known about self-organizing principles establishing correct cell type compositions. Mucociliary airway clearance relies on the correct balance between secretory and ciliated cells, which is regulated by Notch signaling across mucociliary systems. Using the airway-like Xenopus epidermis, we investigate how cell fates depend on signaling, how signaling levels are controlled, and how Hes transcription factors regulate cell fates. We show that four mucociliary cell types each require different Notch levels and that their specification is initiated sequentially by a temporal Notch gradient. We describe a novel role for Foxi1 in the generation of Delta-expressing multipotent progenitors through Hes7.1. Hes7.1 is a weak repressor of mucociliary genes and overcomes maternal repression by the strong repressor Hes2 to initiate mucociliary development. Increasing Notch signaling then inhibits Hes7.1 and activates first Hes4, then Hes5.10, which selectively repress cell fates. We have uncovered a self-organizing mechanism of mucociliary cell type composition by competitive de-repression of cell fates by a set of differentially acting repressors. Furthermore, we present an in silico model of this process with predictive abilities.

Tyrosine kinase inhibitors can activate the NLRP3 inflammasome in myeloid cells through lysosomal damage and cell lysis.

Inflammasomes are intracellular protein complexes that promote an inflammatory host defense in response to pathogens and damaged or neoplastic tissues and are implicated in inflammatory disorders and therapeutic-induced toxicity. We investigated the mechanisms of activation for inflammasomes nucleated by NOD-like receptor (NLR) protiens. A screen of a small-molecule library revealed that several tyrosine kinase inhibitors (TKIs)-including those that are clinically approved (such as imatinib and crizotinib) or are in clinical trials (such as masitinib)-activated the NLRP3 inflammasome. Furthermore, imatinib and masitinib caused lysosomal swelling and damage independently of their kinase target, leading to cathepsin-mediated destabilization of myeloid cell membranes and, ultimately, cell lysis that was accompanied by potassium (K+) efflux, which activated NLRP3. This effect was specific to primary myeloid cells (such as peripheral blood mononuclear cells and mouse bone marrow-derived dendritic cells) and did not occur in other primary cell types or various cell lines. TKI-induced lytic cell death and NLRP3 activation, but not lysosomal damage, were prevented by stabilizing cell membranes. Our findings reveal a potential immunological off-target of some TKIs that may contribute to their clinical efficacy or to their adverse effects.

[Forecasting models to guide intensive care COVID-19 capacities in Germany]. / Prognosemodelle zur Steuerung von intensivmedizinischen COVID-19-Kapazitäten in Deutschland.

BACKGROUND: Time-series forecasting models play a central role in guiding intensive care coronavirus disease 2019 (COVID-19) bed capacity in a pandemic. A key predictor of future intensive care unit (ICU) COVID-19 bed occupancy is the number of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in the general population, which in turn is highly associated with week-to-week variability, reporting delays, regional differences, number of unknown cases, time-dependent infection rates, vaccinations, SARS-CoV­2 virus variants, and nonpharmaceutical containment measures. Furthermore, current and also future COVID ICU occupancy is significantly influenced by ICU discharge and mortality rates. METHODS: Both the number of new SARS-CoV­2 infections in the general population and intensive care COVID-19 bed occupancy rates are recorded in Germany. These data are statistically analyzed on a daily basis using epidemic SEIR (susceptible, exposed, infection, recovered) models using ordinary differential equations and multiple regression models. RESULTS: Forecast results of the immediate trend (20-day forecast) of ICU occupancy by COVID-19 patients are made available to decision makers at various levels throughout the country. CONCLUSION: The forecasts are compared with the development of available ICU bed capacities in order to identify capacity limitations at an early stage and to enable short-term solutions to be made, such as supraregional transfers.

Longitudinal dynamics of gut bacteriome and mycobiome interactions pre- and post-visceral surgery in Crohn's disease.

Introduction: Alterations of the gut microbiome are involved in the pathogenesis of Crohn's disease (CD). The role of fungi in this context is unclear. This study aimed to determine postoperative changes in the bacterial and fungal gut communities of CD patients undergoing intestinal resection, and to evaluate interactions between the bacteriome and mycobiome and their impact on the patients' outcome. Methods: We report a subgroup analysis of a prospective cohort study, focusing on 10 CD patients whose fecal samples were collected for bacterial 16S rRNA and fungal ITS2 genes next-generation sequencing the day before surgery and on the 5th or 6th postoperative day. Results: No significant differences in bacterial and fungal diversity were observed between preoperative and postoperative stool samples. By in-depth analysis, significant postoperative abundance changes of bacteria and fungi and 17 interkingdom correlations were detected. Network analysis identified 13 microbial clusters in the perioperative gut communities, revealing symbiotic and competitive interactions. Relevant factors were gender, age, BMI, lifestyle habits (smoking, alcohol consumption) and surgical technique. Postoperative abundance changes and identified clusters were associated with clinical outcomes (length of hospital stay, complications) and levels of inflammatory markers. Conclusions: Our findings highlight the importance of dissecting the interactions of gut bacterial and fungal communities in CD patients and their potential influence on postoperative and disease outcomes.

The performance of deep generative models for learning joint embeddings of single-cell multi-omics data.

Recent extensions of single-cell studies to multiple data modalities raise new questions regarding experimental design. For example, the challenge of sparsity in single-omics data might be partly resolved by compensating for missing information across modalities. In particular, deep learning approaches, such as deep generative models (DGMs), can potentially uncover complex patterns via a joint embedding. Yet, this also raises the question of sample size requirements for identifying such patterns from single-cell multi-omics data. Here, we empirically examine the quality of DGM-based integrations for varying sample sizes. We first review the existing literature and give a short overview of deep learning methods for multi-omics integration. Next, we consider eight popular tools in more detail and examine their robustness to different cell numbers, covering two of the most common multi-omics types currently favored. Specifically, we use data featuring simultaneous gene expression measurements at the RNA level and protein abundance measurements for cell surface proteins (CITE-seq), as well as data where chromatin accessibility and RNA expression are measured in thousands of cells (10x Multiome). We examine the ability of the methods to learn joint embeddings based on biological and technical metrics. Finally, we provide recommendations for the design of multi-omics experiments and discuss potential future developments.

Circulating multimeric immune complexes contribute to immunopathology in COVID-19.

A dysregulated immune response with high levels of SARS-CoV-2 specific IgG antibodies characterizes patients with severe or critical COVID-19. Although a robust IgG response is considered to be protective, excessive triggering of activating Fc-gamma-receptors (FcγRs) could be detrimental and cause immunopathology. Here, we document excessive FcγRIIIA/CD16A activation in patients developing severe or critical COVID-19 but not in those with mild disease. We identify two independent ligands mediating extreme FcγRIIIA/CD16A activation. Soluble circulating IgG immune complexes (sICs) are detected in about 80% of patients with severe and critical COVID-19 at levels comparable to active systemic lupus erythematosus (SLE) disease. FcγRIIIA/CD16A activation is further enhanced by afucosylation of SARS-CoV-2 specific IgG. Utilizing cell-based reporter systems we provide evidence that sICs can be formed prior to a specific humoral response against SARS-CoV-2. Our data suggest a cycle of immunopathology driven by an early formation of sICs in predisposed patients. These findings suggest a reason for the seemingly paradoxical findings of high antiviral IgG responses and systemic immune dysregulation in severe COVID-19. The involvement of circulating sICs in the promotion of immunopathology in predisposed patients opens new possibilities for intervention strategies to mitigate critical COVID-19 progression.

Benchmarking of analysis strategies for data-independent acquisition proteomics using a large-scale dataset comprising inter-patient heterogeneity.

Numerous software tools exist for data-independent acquisition (DIA) analysis of clinical samples, necessitating their comprehensive benchmarking. We present a benchmark dataset comprising real-world inter-patient heterogeneity, which we use for in-depth benchmarking of DIA data analysis workflows for clinical settings. Combining spectral libraries, DIA software, sparsity reduction, normalization, and statistical tests results in 1428 distinct data analysis workflows, which we evaluate based on their ability to correctly identify differentially abundant proteins. From our dataset, we derive bootstrap datasets of varying sample sizes and use the whole range of bootstrap datasets to robustly evaluate each workflow. We find that all DIA software suites benefit from using a gas-phase fractionated spectral library, irrespective of the library refinement used. Gas-phase fractionation-based libraries perform best against two out of three reference protein lists. Among all investigated statistical tests non-parametric permutation-based statistical tests consistently perform best.