Gene network-based and ensemble modeling-based selection of tumor-associated antigens with a predicted low risk of tissue damage for targeted immunotherapy.

BACKGROUND: Tumor-associated antigens and their derived peptides constitute an opportunity to design off-the-shelf mainline or adjuvant anti-cancer immunotherapies for a broad array of patients. A performant and rational antigen selection pipeline would lay the foundation for immunotherapy trials with the potential to enhance treatment, tremendously benefiting patients suffering from rare, understudied cancers. METHODS: We present an experimentally validated, data-driven computational pipeline that selects and ranks antigens in a multipronged approach. In addition to minimizing the risk of immune-related adverse events by selecting antigens based on their expression profile in tumor biopsies and healthy tissues, we incorporated a network analysis-derived antigen indispensability index based on computational modeling results, and candidate immunogenicity predictions from a machine learning ensemble model relying on peptide physicochemical characteristics. RESULTS: In a model study of uveal melanoma, Human Leukocyte Antigen (HLA) docking simulations and experimental quantification of the peptide-major histocompatibility complex binding affinities confirmed that our approach discriminates between high-binding and low-binding affinity peptides with a performance similar to that of established methodologies. Blinded validation experiments with autologous T-cells yielded peptide stimulation-induced interferon-γ secretion and cytotoxic activity despite high interdonor variability. Dissecting the score contribution of the tested antigens revealed that peptides with the potential to induce cytotoxicity but unsuitable due to potential tissue damage or instability of expression were properly discarded by the computational pipeline. CONCLUSIONS: In this study, we demonstrate the feasibility of the de novo computational selection of antigens with the capacity to induce an anti-tumor immune response and a predicted low risk of tissue damage. On translation to the clinic, our pipeline supports fast turn-around validation, for example, for adoptive T-cell transfer preparations, in both generalized and personalized antigen-directed immunotherapy settings.

Sunset Yellow protects against oxidative damage and exhibits chemoprevention in chemically induced skin cancer model.

Skin cancer and other skin-related inflammatory pathologies are rising due to heightened exposure to environmental pollutants and carcinogens. In this context, natural products and repurposed compounds hold promise as novel therapeutic and preventive agents. Strengthening the skin's antioxidant defense mechanisms is pivotal in neutralizing reactive oxygen species (ROS) and mitigating oxidative stress. Sunset Yellow (SY) exhibits immunomodulatory characteristics, evidenced by its capacity to partially inhibit the secretion of proinflammatory cytokines, regulate immune cell populations, and modulate the activation of lymphocytes. This study aimed to investigate the antioxidant and anti-genotoxic properties of SY using in-silico, in vitro, and physiochemical test systems, and to further explore its potential role in 7,12-dimethylbenz(a) anthracene (DMBA)/ 12-o-tetradecanoylphorbol-13-acetate (TPA)-induced two-stage skin carcinogenesis. In vitro experiments showed that pre-treatment of SY significantly enhanced the cell viability of HaCaT cells when exposed to tertiary-Butyl Hydrogen Peroxide (tBHP). This increase was accompanied by reduced ROS levels, restoration of mitochondrial membrane potential, and notable reduction in DNA damage in (SY + tBHP) treated cells. Mechanistic investigations using DPPH chemical antioxidant activity test and potentiometric titrations confirmed SY's antioxidant properties, with a standard reduction potential ( E o ) of 0.211 V. Remarkably, evaluating the effect of topical application of SY in DMBA/TPA-induced two-step skin carcinogenesis model revealed dose-dependent decreases in tumor latency, incidence, yield, and burden over 21-weeks. Furthermore, computational analysis and experimental validations identified GSK3ß, KEAP1 and EGFR as putative molecular targets of SY. Collectively, our findings reveal that SY enhances cellular antioxidant defenses, exhibits anti-genotoxic effects, and functions as a promising chemopreventive agent.

Whole blood RNA sequencing identifies transcriptional differences between primary sclerosing cholangitis and ulcerative colitis.

Background & Aims: Genetic and microbiome studies across patients with primary sclerosing cholangitis (PSC) and ulcerative colitis (UC) have indicated that UC in PSC is a separate disease entity to primary UC, but expression studies for PSC are lacking. Methods: We conducted whole blood RNA sequencing experiments for 495 patients with UC, 220 patients with PSC (including 177 with UC), and 320 healthy controls from Germany and Norway. Differential expression analyses, gene ontology and coexpression analyses and random forest machine learning were performed to identify genes, ontologies and transcriptional features that discriminate diagnoses. Results: The blood transcriptome in UC and PSC is dominated by neutrophil activation genes (e.g. S100A12). In UC, but not in PSC (neither PSC alone nor patients with an additional diagnosis of UC [PSC/UC]), ribosomal, mitochondrial, and energy metabolism genes are upregulated in conjunction with antibody transcript expression (MZB1, IGJ). In PSC, there is an increase in modules related to apoptosis and expression of genes of interferon-I-related ontologies. Random forest analysis could poorly discriminate PSC alone from PSC/UC (AUROC 0.56), but could discriminate PSC, UC, and controls with high accuracy (AUROC UC vs. controls 0.95, PSC vs. controls 0.88, UC vs. PSC 0.986). The main coexpression modules relevant for distinguishing PSC, UC, and controls are enriched in neutrophil degranulation and antibody production genes. Conclusions: Supported by machine learning results, PSC and UC appear to be separate entities on a molecular level, while PSC/UC and PSC are indistinguishable. Impact and implications: Clinical and genetic studies suggest that the colitis-like symptoms in primary sclerosing cholangitis (PSC) represent a different disease entity from primary ulcerative colitis (UC). The present study supports this assumption with transcriptomic data from whole blood and describes notable differences in gene expression between primary UC and PSC, providing insights into the still unclear pathophysiology of both diseases. These findings are of interest to scientists seeking to decipher the molecular pathophysiology of both diseases and provide evidence that a redefinition of the PSC-UC phenotype should be considered. The study practically supports future molecular research by providing a large transcriptomic whole blood reference cohort.

Exploring Thiazolopyridine AV25R: Unraveling of Biological Activities, Selective Anti-Cancer Properties and In Silico Target and Binding Prediction in Hematological Neoplasms.

Thiazolopyridines are a highly relevant class of small molecules, which have previously shown a wide range of biological activities. Besides their anti-tubercular, anti-microbial and anti-viral activities, they also show anti-cancerogenic properties, and play a role as inhibitors of cancer-related proteins. Herein, the biological effects of the thiazolopyridine AV25R, a novel small molecule with unknown biological effects, were characterized. Screening of a set of lymphoma (SUP-T1, SU-DHL-4) and B- acute leukemia cell lines (RS4;11, SEM) revealed highly selective effects of AV25R. The selective anti-proliferative and metabolism-modulating effects were observed in vitro for the B-ALL cell line RS4;11. Further, we were able to detect severe morphological changes and the induction of apoptosis. Gene expression analysis identified a large number of differentially expressed genes after AV25R exposure and significant differentially regulated cancer-related signaling pathways, such as VEGFA-VEGFR2 signaling and the EGF/EGFR pathway. Structure-based pharmacophore screening approaches using in silico modeling identified potential biological AV25R targets. Our results indicate that AV25R binds with several proteins known to regulate cell proliferation and tumor progression, such as FECH, MAP11, EGFR, TGFBR1 and MDM2. The molecular docking analyses indicates that AV25R has a higher binding affinity compared to many of the experimentally validated small molecule inhibitors of these targets. Thus, here we present in vitro and in silico analyses which characterize, for the first time, the molecular acting mechanism of AV25R, including cellular and molecular biologic effects. Additionally, this predicted the target binding of the molecule, revealing a high affinity to cancer-related proteins and, thus, classified AVR25 for targeted intervention approaches.

Logic-based modeling and drug repurposing for the prediction of novel therapeutic targets and combination regimens against E2F1-driven melanoma progression.

Melanoma presents increasing prevalence and poor outcomes. Progression to aggressive stages is characterized by overexpression of the transcription factor E2F1 and activation of downstream prometastatic gene regulatory networks (GRNs). Appropriate therapeutic manipulation of the E2F1-governed GRNs holds the potential to prevent metastasis however, these networks entail complex feedback and feedforward regulatory motifs among various regulatory layers, which make it difficult to identify druggable components. To this end, computational approaches such as mathematical modeling and virtual screening are important tools to unveil the dynamics of these signaling networks and identify critical components that could be further explored as therapeutic targets. Herein, we integrated a well-established E2F1-mediated epithelial-mesenchymal transition (EMT) map with transcriptomics data from E2F1-expressing melanoma cells to reconstruct a core regulatory network underlying aggressive melanoma. Using logic-based in silico perturbation experiments of a core regulatory network, we identified that simultaneous perturbation of Protein kinase B (AKT1) and oncoprotein murine double minute 2 (MDM2) drastically reduces EMT in melanoma. Using the structures of the two protein signatures, virtual screening strategies were performed with the FDA-approved drug library. Furthermore, by combining drug repurposing and computer-aided drug design techniques, followed by molecular dynamics simulation analysis, we identified two potent drugs (Tadalafil and Finasteride) that can efficiently inhibit AKT1 and MDM2 proteins. We propose that these two drugs could be considered for the development of therapeutic strategies for the management of aggressive melanoma.

CCR2 macrophage response determines the functional outcome following cardiomyocyte transplantation.

BACKGROUND: The immune response is a crucial factor for mediating the benefit of cardiac cell therapies. Our previous research showed that cardiomyocyte transplantation alters the cardiac immune response and, when combined with short-term pharmacological CCR2 inhibition, resulted in diminished functional benefit. However, the specific role of innate immune cells, especially CCR2 macrophages on the outcome of cardiomyocyte transplantation, is unclear. METHODS: We compared the cellular, molecular, and functional outcome following cardiomyocyte transplantation in wildtype and T cell- and B cell-deficient Rag2del mice. The cardiac inflammatory response was assessed using flow cytometry. Gene expression profile was assessed using single-cell and bulk RNA sequencing. Cardiac function and morphology were determined using magnetic resonance tomography and immunohistochemistry respectively. RESULTS: Compared to wildtype mice, Rag2del mice show an increased innate immune response at steady state and disparate macrophage response after MI. Subsequent single-cell analyses after MI showed differences in macrophage development and a lower prevalence of CCR2 expressing macrophages. Cardiomyocyte transplantation increased NK cells and monocytes, while reducing CCR2-MHC-IIlo macrophages. Consequently, it led to increased mRNA levels of genes involved in extracellular remodelling, poor graft survival, and no functional improvement. Using machine learning-based feature selection, Mfge8 and Ccl7 were identified as the primary targets underlying these effects in the heart. CONCLUSIONS: Our results demonstrate that the improved functional outcome following cardiomyocyte transplantation is dependent on a specific CCR2 macrophage response. This work highlights the need to study the role of the immune response for cardiomyocyte cell therapy for successful clinical translation.

Metabolic function-based normalization improves transcriptome data-driven reduction of genome-scale metabolic models.

Genome-scale metabolic models (GEMs) are extensively used to simulate cell metabolism and predict cell phenotypes. GEMs can also be tailored to generate context-specific GEMs, using omics data integration approaches. To date, many integration approaches have been developed, however, each with specific pros and cons; and none of these algorithms systematically outperforms the others. The key to successful implementation of such integration algorithms lies in the optimal selection of parameters, and thresholding is a crucial component in this process. To improve the predictive accuracy of context-specific models, we introduce a new integration framework that improves the ranking of related genes and homogenizes the expression values of those gene sets using single-sample Gene Set Enrichment Analysis (ssGSEA). In this study, we coupled ssGSEA with GIMME and validated the advantages of the proposed framework to predict the ethanol formation of yeast grown in the glucose-limited chemostats, and to simulate metabolic behaviors of yeast growth in four different carbon sources. This framework enhances the predictive accuracy of GIMME which we demonstrate for predicting the yeast physiology in nutrient-limited cultures.

Leptin deficiency-caused behavioral change - A comparative analysis using EthoVision and DeepLabCut.

Introduction: Obese rodents e.g., the leptin-deficient (ob/ob) mouse exhibit remarkable behavioral changes and are therefore ideal models for evaluating mental disorders resulting from obesity. In doing so, female as well as male ob/ob mice at 8, 24, and 40 weeks of age underwent two common behavioral tests, namely the Open Field test and Elevated Plus Maze, to investigate behavioral alteration in a sex- and age dependent manner. The accuracy of these tests is often dependent on the observer that can subjectively influence the data. Methods: To avoid this bias, mice were tracked with a video system. Video files were further analyzed by the compared use of two software, namely EthoVision (EV) and DeepLabCut (DLC). In DLC a Deep Learning application forms the basis for using artificial intelligence in behavioral research in the future, also with regard to the reduction of animal numbers. Results: After no sex and partly also no age-related differences were found, comparison revealed that both software lead to almost identical results and are therefore similar in their basic outcomes, especially in the determination of velocity and total distance movement. Moreover, we observed additional benefits of DLC compared to EV as it enabled the interpretation of more complex behavior, such as rearing and leaning, in an automated manner. Discussion: Based on the comparable results from both software, our study can serve as a starting point for investigating behavioral alterations in preclinical studies of obesity by using DLC to optimize and probably to predict behavioral observations in the future.

Cell-Type-Specific Gene Regulatory Networks of Pro-Inflammatory and Pro-Resolving Lipid Mediator Biosynthesis in the Immune System.

Lipid mediators are important regulators in inflammatory responses, and their biosynthetic pathways are targeted by commonly used anti-inflammatory drugs. Switching from pro-inflammatory lipid mediators (PIMs) to specialized pro-resolving (SPMs) is a critical step toward acute inflammation resolution and preventing chronic inflammation. Although the biosynthetic pathways and enzymes for PIMs and SPMs have now been largely identified, the actual transcriptional profiles underlying the immune cell type-specific transcriptional profiles of these mediators are still unknown. Using the Atlas of Inflammation Resolution, we created a large network of gene regulatory interactions linked to the biosynthesis of SPMs and PIMs. By mapping single-cell sequencing data, we identified cell type-specific gene regulatory networks of the lipid mediator biosynthesis. Using machine learning approaches combined with network features, we identified cell clusters of similar transcriptional regulation and demonstrated how specific immune cell activation affects PIM and SPM profiles. We found substantial differences in regulatory networks in related cells, accounting for network-based preprocessing in functional single-cell analyses. Our results not only provide further insight into the gene regulation of lipid mediators in the immune response but also shed light on the contribution of selected cell types in their biosynthesis.

Network analyses reveal new insights into the effect of multicomponent Tr14 compared to single-component diclofenac in an acute inflammation model.

BACKGROUND: Modifying the acute inflammatory response has wide clinical benefits. Current options include non-steroidal anti-inflammatory drugs (NSAIDs) and therapies that may resolve inflammation. Acute inflammation involves multiple cell types and various processes. We, therefore, investigated whether an immunomodulatory drug that acts simultaneously at multiple sites shows greater potential to resolve acute inflammation more effectively and with fewer side effects than a common anti-inflammatory drug developed as a small molecule for a single target. In this work, we used time-series gene expression profiles from a wound healing mouse model to compare the effects of Traumeel (Tr14), a multicomponent natural product, to diclofenac, a single component NSAID on inflammation resolution. RESULTS: We advance previous studies by mapping the data onto the "Atlas of Inflammation Resolution", followed by in silico simulations and network analysis. We found that Tr14 acts primarily on the late phase of acute inflammation (during resolution) compared to diclofenac, which suppresses acute inflammation immediately after injury. CONCLUSIONS: Our results provide new insights how network pharmacology of multicomponent drugs may support inflammation resolution in inflammatory conditions.

Uncoupling protein 2 deficiency of non-cancerous tissues inhibits the progression of pancreatic cancer in mice.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a disease of the elderly mostly because its development from preneoplastic lesions depends on the accumulation of gene mutations and epigenetic alterations over time. How aging of non-cancerous tissues of the host affects tumor progression, however, remains largely unknown. METHODS: We took advantage of a model of accelerated aging, uncoupling protein 2-deficient (Ucp2 knockout, Ucp2 KO) mice, to investigate the growth of orthotopically transplanted Ucp2 wild-type (WT) PDAC cells (cell lines Panc02 and 6606PDA) in vivo and to study strain-dependent differences of the PDAC microenvironment. RESULTS: Measurements of tumor weights and quantification of proliferating cells indicated a significant growth advantage of Panc02 and 6606PDA cells in WT mice compared to Ucp2 KO mice. In tumors in the knockout strain, higher levels of interferon-γ mRNA despite similar numbers of tumor-infiltrating T cells were observed. 6606PDA cells triggered a stronger stromal reaction in Ucp2 KO mice than in WT animals. Accordingly, pancreatic stellate cells from Ucp2 KO mice proliferated at a higher rate than cells of the WT strain when they were incubated with conditioned media from PDAC cells. CONCLUSIONS: Ucp2 modulates PDAC microenvironment in a way that favors tumor progression and implicates an altered stromal response as one of the underlying mechanisms.

Perspectives of (/memorandum for) systems thinking on COVID-19 pandemic and pathology.

Is data-driven analysis sufficient for understanding the COVID-19 pandemic and for justifying public health regulations? In this paper, we argue that such analysis is insufficient. Rather what is needed is the identification and implementation of over-arching hypothesis-related and/or theory-based rationales to conduct effective SARS-CoV2/COVID-19 (Corona) research. To that end, we analyse and compare several published recommendations for conceptual and methodological frameworks in medical research (e.g., public health, preventive medicine and health promotion) to current research approaches in medical Corona research. Although there were several efforts published in the literature to develop integrative conceptual frameworks before the COVID-19 pandemic, such as social ecology for public health issues and systems thinking in health care, only a few attempts to utilize these concepts can be found in medical Corona research. For this reason, we propose nested and integrative systemic modelling approaches to understand Corona pandemic and Corona pathology. We conclude that institutional efforts for knowledge integration and systemic thinking, but also for integrated science, are urgently needed to avoid or mitigate future pandemics and to resolve infection pathology.

In silico investigation of molecular networks linking gastrointestinal diseases, malnutrition, and sarcopenia.

Malnutrition (MN) is a common primary or secondary complication in gastrointestinal diseases. The patient's nutritional status also influences muscle mass and function, which can be impaired up to the degree of sarcopenia. The molecular interactions in diseases leading to sarcopenia are complex and multifaceted, affecting muscle physiology, the intestine (nutrition), and the liver at different levels. Although extensive knowledge of individual molecular factors is available, their regulatory interplay is not yet fully understood. A comprehensive overall picture of pathological mechanisms and resulting phenotypes is lacking. In silico approaches that convert existing knowledge into computationally readable formats can help unravel mechanisms, underlying such complex molecular processes. From public literature, we manually compiled experimental evidence for molecular interactions involved in the development of sarcopenia into a knowledge base, referred to as the Sarcopenia Map. We integrated two diseases, namely liver cirrhosis (LC), and intestinal dysfunction, by considering their effects on nutrition and blood secretome. We demonstrate the performance of our model by successfully simulating the impact of changing dietary frequency, glycogen storage capacity, and disease severity on the carbohydrate and muscle systems. We present the Sarcopenia Map as a publicly available, open-source, and interactive online resource, that links gastrointestinal diseases, MN, and sarcopenia. The map provides tools that allow users to explore the information on the map and perform in silico simulations.

Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence.

We live in an unprecedented time in oncology. We have accumulated samples and cases in cohorts larger and more complex than ever before. New technologies are available for quantifying solid or liquid samples at the molecular level. At the same time, we are now equipped with the computational power necessary to handle this enormous amount of quantitative data. Computational models are widely used helping us to substantiate and interpret data. Under the label of systems and precision medicine, we are putting all these developments together to improve and personalize the therapy of cancer. In this review, we use melanoma as a paradigm to present the successful application of these technologies but also to discuss possible future developments in patient care linked to them. Melanoma is a paradigmatic case for disruptive improvements in therapies, with a considerable number of metastatic melanoma patients benefiting from novel therapies. Nevertheless, a large proportion of patients does not respond to therapy or suffers from adverse events. Melanoma is an ideal case study to deploy advanced technologies not only due to the medical need but also to some intrinsic features of melanoma as a disease and the skin as an organ. From the perspective of data acquisition, the skin is the ideal organ due to its accessibility and suitability for many kinds of advanced imaging techniques. We put special emphasis on the necessity of computational strategies to integrate multiple sources of quantitative data describing the tumour at different scales and levels.

The Effects of Hypoxic Preconditioned Murine Mesenchymal Stem Cells on Post-Infarct Arrhythmias in the Mouse Model.

Ventricular arrhythmias associated with myocardial infarction (MI) have a significant impact on mortality in patients following heart attack. Therefore, targeted reduction of arrhythmia represents a therapeutic approach for the prevention and treatment of severe events after infarction. Recent research transplanting mesenchymal stem cells (MSC) showed their potential in MI therapy. Our study aimed to investigate the effects of MSC injection on post-infarction arrhythmia. We used our murine double infarction model, which we previously established, to more closely mimic the clinical situation and intramyocardially injected hypoxic pre-conditioned murine MSC to the infarction border. Thereafter, various types of arrhythmias were recorded and analyzed. We observed a homogenous distribution of all types of arrhythmias after the first infarction, without any significant differences between the groups. Yet, MSC therapy after double infarction led to a highly significant reduction in simple and complex arrhythmias. Moreover, RNA-sequencing of samples from stem cell treated mice after re-infarction demonstrated a significant decline in most arrhythmias with reduced inflammatory pathways. Additionally, following stem-cell therapy we found numerous highly expressed genes to be either linked to lowering the risk of heart failure, cardiomyopathy or sudden cardiac death. Moreover, genes known to be associated with arrhythmogenesis and key mutations underlying arrhythmias were downregulated. In summary, our stem-cell therapy led to a reduction in cardiac arrhythmias after MI and showed a downregulation of already established inflammatory pathways. Furthermore, our study reveals gene regulation pathways that have a potentially direct influence on arrhythmogenesis after myocardial infarction.

Contribution of Synthetic Data Generation towards an Improved Patient Stratification in Palliative Care.

AI model development for synthetic data generation to improve Machine Learning (ML) methodologies is an integral part of research in Computer Science and is currently being transferred to related medical fields, such as Systems Medicine and Medical Informatics. In general, the idea of personalized decision-making support based on patient data has driven the motivation of researchers in the medical domain for more than a decade, but the overall sparsity and scarcity of data are still major limitations. This is in contrast to currently applied technology that allows us to generate and analyze patient data in diverse forms, such as tabular data on health records, medical images, genomics data, or even audio and video. One solution arising to overcome these data limitations in relation to medical records is the synthetic generation of tabular data based on real world data. Consequently, ML-assisted decision-support can be interpreted more conveniently, using more relevant patient data at hand. At a methodological level, several state-of-the-art ML algorithms generate and derive decisions from such data. However, there remain key issues that hinder a broad practical implementation in real-life clinical settings. In this review, we will give for the first time insights towards current perspectives and potential impacts of using synthetic data generation in palliative care screening because it is a challenging prime example of highly individualized, sparsely available patient information. Taken together, the reader will obtain initial starting points and suitable solutions relevant for generating and using synthetic data for ML-based screenings in palliative care and beyond.

Identification and epidemiological characterization of Type-2 diabetes sub-population using an unsupervised machine learning approach.

BACKGROUND: Studies on Type-2 Diabetes Mellitus (T2DM) have revealed heterogeneous sub-populations in terms of underlying pathologies. However, the identification of sub-populations in epidemiological datasets remains unexplored. We here focus on the detection of T2DM clusters in epidemiological data, specifically analysing the National Family Health Survey-4 (NFHS-4) dataset from India containing a wide spectrum of features, including medical history, dietary and addiction habits, socio-economic and lifestyle patterns of 10,125 T2DM patients. METHODS: Epidemiological data provide challenges for analysis due to the diverse types of features in it. In this case, applying the state-of-the-art dimension reduction tool UMAP conventionally was found to be ineffective for the NFHS-4 dataset, which contains diverse feature types. We implemented a distributed clustering workflow combining different similarity measure settings of UMAP, for clustering continuous, ordinal and nominal features separately. We integrated the reduced dimensions from each feature-type-distributed clustering to obtain interpretable and unbiased clustering of the data. RESULTS: Our analysis reveals four significant clusters, with two of them comprising mainly of non-obese T2DM patients. These non-obese clusters have lower mean age and majorly comprises of rural residents. Surprisingly, one of the obese clusters had 90% of the T2DM patients practising a non-vegetarian diet though they did not show an increased intake of plant-based protein-rich foods. CONCLUSIONS: From a methodological perspective, we show that for diverse data types, frequent in epidemiological datasets, feature-type-distributed clustering using UMAP is effective as opposed to the conventional use of the UMAP algorithm. The application of UMAP-based clustering workflow for this type of dataset is novel in itself. Our findings demonstrate the presence of heterogeneity among Indian T2DM patients with regard to socio-demography and dietary patterns. From our analysis, we conclude that the existence of significant non-obese T2DM sub-populations characterized by younger age groups and economic disadvantage raises the need for different screening criteria for T2DM among rural Indian residents.

On the role of data, statistics and decisions in a pandemic.

A pandemic poses particular challenges to decision-making because of the need to continuously adapt decisions to rapidly changing evidence and available data. For example, which countermeasures are appropriate at a particular stage of the pandemic? How can the severity of the pandemic be measured? What is the effect of vaccination in the population and which groups should be vaccinated first? The process of decision-making starts with data collection and modeling and continues to the dissemination of results and the subsequent decisions taken. The goal of this paper is to give an overview of this process and to provide recommendations for the different steps from a statistical perspective. In particular, we discuss a range of modeling techniques including mathematical, statistical and decision-analytic models along with their applications in the COVID-19 context. With this overview, we aim to foster the understanding of the goals of these modeling approaches and the specific data requirements that are essential for the interpretation of results and for successful interdisciplinary collaborations. A special focus is on the role played by data in these different models, and we incorporate into the discussion the importance of statistical literacy and of effective dissemination and communication of findings.

Network- and enrichment-based inference of phenotypes and targets from large-scale disease maps.

Complex diseases are inherently multifaceted, and the associated data are often heterogeneous, making linking interactions across genes, metabolites, RNA, proteins, cellular functions, and clinically relevant phenotypes a high-priority challenge. Disease maps have emerged as knowledge bases that capture molecular interactions, disease-related processes, and disease phenotypes with standardized representations in large-scale molecular interaction maps. Various tools are available for disease map analysis, but an intuitive solution to perform in silico experiments on the maps in a wide range of contexts and analyze high-dimensional data is currently missing. To this end, we introduce a two-dimensional enrichment analysis (2DEA) approach to infer downstream and upstream elements through the statistical association of network topology parameters and fold changes from molecular perturbations. We implemented our approach in a plugin suite for the MINERVA platform, providing an environment where experimental data can be mapped onto a disease map and predict potential regulatory interactions through an intuitive graphical user interface. We show several workflows using this approach and analyze two RNA-seq datasets in the Atlas of Inflammation Resolution (AIR) to identify enriched downstream processes and upstream transcription factors. Our work improves the usability of disease maps and increases their functionality by facilitating multi-omics data integration and exploration.