A systematic evaluation of normalization methods in quantitative label-free proteomics

To date, mass spectrometry (MS) data remain inherently biased as a result of reasons ranging from sample handling to differences caused by the instrumentation. Normalization is the process that aims to account for the bias and make samples more comparable. The selection of a proper normalization method is a pivotal task for the reliability of the downstream analysis and results. Many normalization methods commonly used in proteomics have been adapted from the DNA microarray techniques. Previous studies comparing normalization methods in proteomics have focused mainly on intragroup variation. In this study, several popular and widely used normalization methods representing different strategies in normalization are evaluated using three spike-in and one experimental mouse label-free proteomic data sets. The normalization methods are evaluated in terms of their ability to reduce variation between technical replicates, their effect on differential expression analysis and their effect on the estimation of logarithmic fold changes. Additionally, we examined whether normalizing the whole data globally or in segments for the differential expression analysis has an effect on the performance of the normalization methods. We found that variance stabilization normalization (Vsn) reduced variation the most between technical replicates in all examined data sets. Vsn also performed consistently well in the differential expression analysis. Linear regression normalization and local regression normalization performed also systematically well. Finally, we discuss the choice of a normalization method and some qualities of a suitable normalization method in the light of the results of our evaluation.</p

COVID-19-specific transcriptomic signature detectable in blood across multiple cohorts

The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading across the world despite vast global vaccination efforts. Consequently, many studies have looked for potential human host factors and immune mechanisms associated with the disease. However, most studies have focused on comparing COVID-19 patients to healthy controls, while fewer have elucidated the specific host factors distinguishing COVID-19 from other infections. To discover genes specifically related to COVID-19, we reanalyzed transcriptome data from nine independent cohort studies, covering multiple infections, including COVID-19, influenza, seasonal coronaviruses, and bacterial pneumonia. The identified COVID-19-specific signature consisted of 149 genes, involving many signals previously associated with the disease, such as induction of a strong immunoglobulin response and hemostasis, as well as dysregulation of cell cycle-related processes. Additionally, potential new gene candidates related to COVID-19 were discovered. To facilitate exploration of the signature with respect to disease severity, disease progression, and different cell types, we also offer an online tool for easy visualization of the selected genes across multiple datasets at both bulk and single-cell levels

Benchmarking tools for detecting longitudinal differential expression in proteomics data allows establishing a robust reproducibility optimization regression approach

Quantitative proteomics has matured into an established tool and longitudinal proteomics experiments have begun to emerge. However, no effective, simple-to-use differential expression method for longitudinal proteomics data has been released. Typically, such data is noisy, contains missing values, and has only few time points and biological replicates. To address this need, we provide a comprehensive evaluation of several existing differential expression methods for high-throughput longitudinal omics data and introduce a Robust longitudinal Differential Expression (RolDE) approach. The methods are evaluated using over 3000 semi-simulated spike-in proteomics datasets and three large experimental datasets. In the comparisons, RolDE performs overall best; it is most tolerant to missing values, displays good reproducibility and is the top method in ranking the results in a biologically meaningful way. Furthermore, RolDE is suitable for different types of data with typically unknown patterns in longitudinal expression and can be applied by non-experienced users

CIP2A Constrains Th17 Differentiation by Modulating STAT3 Signaling

Cancerous Inhibitor of Protein Phosphatase 2A (CIP2A) is an oncogene and a potential cancer therapy target protein. Accordingly, a better understanding of the physiological function of CIP2A, especially in the context of immune cells, is a prerequisite for its exploitation in cancer therapy. Here, we report that CIP2A negatively regulates interleukin (IL)-17 production by Th17 cells in human and mouse. Interestingly, concomitant with increased IL-17 production, CIP2A-deficient Th17 cells had increased strength and duration of STAT3 phosphorylation. We analyzed the interactome of phosphorylated STAT3 in CIP2A-deficient and CIP2A-sufficient Th17 cells and indicated together with genome-wide gene expression profiling, a role of Acylglycerol Kinase (AGK) in the regulation of Th17 differentiation by CIP2A. We demonstrated that CIP2A regulates the strength of the interaction between AGK and STAT3, and thereby modulates STAT3 phosphorylation and expression of IL-17 in Th17 cells

Pancreas Whole Tissue Transcriptomics Highlights the Role of the Exocrine Pancreas in Patients With Recently Diagnosed Type 1 Diabetes

Although type 1 diabetes (T1D) is primarily a disease of the pancreatic beta-cells, understanding of the disease-associated alterations in the whole pancreas could be important for the improved treatment or the prevention of the disease. We have characterized the whole-pancreas gene expression of patients with recently diagnosed T1D from the Diabetes Virus Detection (DiViD) study and non-diabetic controls. Furthermore, another parallel dataset of the whole pancreas and an additional dataset from the laser-captured pancreatic islets of the DiViD patients and non-diabetic organ donors were analyzed together with the original dataset to confirm the results and to get further insights into the potential disease-associated differences between the exocrine and the endocrine pancreas. First, higher expression of the core acinar cell genes, encoding for digestive enzymes, was detected in the whole pancreas of the DiViD patients when compared to non-diabetic controls. Second, In the pancreatic islets, upregulation of immune and inflammation related genes was observed in the DiViD patients when compared to non-diabetic controls, in line with earlier publications, while an opposite trend was observed for several immune and inflammation related genes at the whole pancreas tissue level. Third, strong downregulation of the regenerating gene family (REG) genes, linked to pancreatic islet growth and regeneration, was observed in the exocrine acinar cell dominated whole-pancreas data of the DiViD patients when compared with the non-diabetic controls. Fourth, analysis of unique features in the transcriptomes of each DiViD patient compared with the other DiViD patients, revealed elevated expression of central antiviral immune response genes in the whole-pancreas samples, but not in the pancreatic islets, of one DiViD patient. This difference in the extent of antiviral gene expression suggests different statuses of infection in the pancreas at the time of sampling between the DiViD patients, who were all enterovirus VP1+ in the islets by immunohistochemistry based on earlier studies. The observed features, indicating differences in the function, status and interplay between the exocrine and the endocrine pancreas of recent onset T1D patients, highlight the importance of studying both compartments for better understanding of the molecular mechanisms of T1D.publishedVersionPeer reviewe

Pancreas Whole Tissue Transcriptomics Highlights the Role of the Exocrine Pancreas in Patients With Recently Diagnosed Type 1 Diabetes

Although type 1 diabetes (T1D) is primarily a disease of the pancreatic beta-cells, understanding of the disease-associated alterations in the whole pancreas could be important for the improved treatment or the prevention of the disease. We have characterized the whole-pancreas gene expression of patients with recently diagnosed T1D from the Diabetes Virus Detection (DiViD) study and non-diabetic controls. Furthermore, another parallel dataset of the whole pancreas and an additional dataset from the laser-captured pancreatic islets of the DiViD patients and non-diabetic organ donors were analyzed together with the original dataset to confirm the results and to get further insights into the potential disease-associated differences between the exocrine and the endocrine pancreas. First, higher expression of the core acinar cell genes, encoding for digestive enzymes, was detected in the whole pancreas of the DiViD patients when compared to non-diabetic controls. Second, In the pancreatic islets, upregulation of immune and inflammation related genes was observed in the DiViD patients when compared to non-diabetic controls, in line with earlier publications, while an opposite trend was observed for several immune and inflammation related genes at the whole pancreas tissue level. Third, strong downregulation of the regenerating gene family (REG) genes, linked to pancreatic islet growth and regeneration, was observed in the exocrine acinar cell dominated whole-pancreas data of the DiViD patients when compared with the non-diabetic controls. Fourth, analysis of unique features in the transcriptomes of each DiViD patient compared with the other DiViD patients, revealed elevated expression of central antiviral immune response genes in the whole-pancreas samples, but not in the pancreatic islets, of one DiViD patient. This difference in the extent of antiviral gene expression suggests different statuses of infection in the pancreas at the time of sampling between the DiViD patients, who were all enterovirus VP1+ in the islets by immunohistochemistry based on earlier studies. The observed features, indicating differences in the function, status and interplay between the exocrine and the endocrine pancreas of recent onset T1D patients, highlight the importance of studying both compartments for better understanding of the molecular mechanisms of T1D.</p

ECCB2024 : The 23rd European Conference on Computational Biology

Non peer reviewe

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

Aims: Heterogeneity in the rate of beta-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 beta-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 beta-cell function. The second signature was related to translation and viral infection was inversely associated with change in beta-cell function. In addition, the immunomics data revealed a Natural Killer cell signature associated with rapid beta-cell decline. Conclusions: Features that differ between individuals with slow and rapid decline in beta-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

Planetary well-being

Tensions between the well-being of present humans, future humans, and nonhuman nature manifest in social protests and political and academic debates over the future of Earth. The increasing consumption of natural resources no longer increases, let alone equalises, human well-being, but has led to the current ecological crisis and harms both human and nonhuman well-being. While the crisis has been acknowledged, the existing conceptual frameworks are in some respects ill-equipped to address the crisis in a way that would link the resolving of the crisis with the pivotal aim of promoting equal well-being. The shortcomings of the existing concepts in this respect relate to anthropocentric normative orientation, methodological individualism that disregards process dynamics and precludes integrating the considerations of human and nonhuman well-being, and the lack of multiscalar considerations of well-being. This work derives and proposes the concept of planetary well-being to address the aforementioned conceptual issues, to recognise the moral considerability of both human and nonhuman well-being, and to promote transdisciplinary, cross-cultural discourse for addressing the crisis and for promoting societal and cultural transformation. Conceptually, planetary well-being shifts focus on well-being from individuals to processes, Earth system and ecosystem processes, that underlie all well-being. Planetary well-being is a state where the integrity of Earth system and ecosystem processes remains unimpaired to a degree that species and populations can persist to the future and organisms have the opportunity to achieve well-being. After grounding and introducing planetary well-being, this work shortly discusses how the concept can be operationalised and reflects upon its potential as a bridging concept between different worldviews.</p