Drugst.One - a plug-and-play solution for online systems medicine and network-based drug repurposing.

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.

Drugst.One - A plug-and-play solution for online systems medicine and network-based drug repurposing.

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.

Lipid network and moiety analysis for revealing enzymatic dysregulation and mechanistic alterations from lipidomics data.

Lipidomics is of growing importance for clinical and biomedical research due to many associations between lipid metabolism and diseases. The discovery of these associations is facilitated by improved lipid identification and quantification. Sophisticated computational methods are advantageous for interpreting such large-scale data for understanding metabolic processes and their underlying (patho)mechanisms. To generate hypothesis about these mechanisms, the combination of metabolic networks and graph algorithms is a powerful option to pinpoint molecular disease drivers and their interactions. Here we present lipid network explorer (LINEX$^2$), a lipid network analysis framework that fuels biological interpretation of alterations in lipid compositions. By integrating lipid-metabolic reactions from public databases, we generate dataset-specific lipid interaction networks. To aid interpretation of these networks, we present an enrichment graph algorithm that infers changes in enzymatic activity in the context of their multispecificity from lipidomics data. Our inference method successfully recovered the MBOAT7 enzyme from knock-out data. Furthermore, we mechanistically interpret lipidomic alterations of adipocytes in obesity by leveraging network enrichment and lipid moieties. We address the general lack of lipidomics data mining options to elucidate potential disease mechanisms and make lipidomics more clinically relevant.

Thinking about order: a review of common processing of magnitude and learned orders in animals.

Rich behavioral and neurobiological evidence suggests cognitive and neural overlap in how quantitatively comparable dimensions such as quantity, time, and space are processed in humans and animals. While magnitude domains such as physical magnitude, time, and space represent information that can be quantitatively compared (4 "is half of" 8), they also represent information that can be organized ordinally (1→2→3→4). Recent evidence suggests that the common representations seen across physical magnitude, time, and space domains in humans may be due to their common ordinal features rather than their common quantitative features, as these common representations appear to extend beyond magnitude domains to include learned orders. In this review, we bring together separate lines of research on multiple ordinal domains including magnitude-based and learned orders in animals to explore the extent to which there is support for a common cognitive process underlying ordinal processing. Animals show similarities in performance patterns across natural quantitatively comparable ordered domains (physical magnitude, time, space, dominance) and learned orders (acquired through transitive inference or simultaneous chaining). Additionally, they show transfer and interference across tasks within and between ordinal domains that support the theory of a common ordinal representation across domains. This review provides some support for the development of a unified theory of ordinality and suggests areas for future research to better characterize the extent to which there are commonalities in cognitive processing of ordinal information generally.

Mass cytometry of platelet-rich plasma: a new approach to analyze platelet surface expression and reactivity.

Mass cytometry (CyTOF) is a new technology that allows the investigation of protein expression at single cell level with high resolution. While several protocols are available to investigate leukocyte expression, platelet staining and analysis with CyTOF have been described only from whole blood. Moreover, available protocols do not allow sample storage but require fresh samples to be obtained, processed, and measured immediately. We provide a structured and reproducible method to stain platelets from platelet-rich plasma to study thrombocyte protein expression and reactivity using mass cytometry. With our method, it is possible to acquire a large number of events allowing deep bioinformatic investigation of platelet expression heterogeneity. Integrated in our protocol is also a previously established freezing protocol that allows the storage of stained samples and to delay their measurement. Finally, we provide a structured workflow using different platelet stimulators and a freely available bioinformatic pipeline to analyze platelet expression. Our protocol unlocks the potential of CyTOF analysis for studying platelet biology in health and disease.

A systematic comparison of novel and existing differential analysis methods for CyTOF data.

Cytometry techniques are widely used to discover cellular characteristics at single-cell resolution. Many data analysis methods for cytometry data focus solely on identifying subpopulations via clustering and testing for differential cell abundance. For differential expression analysis of markers between conditions, only few tools exist. These tools either reduce the data distribution to medians, discarding valuable information, or have underlying assumptions that may not hold for all expression patterns. Here, we systematically evaluated existing and novel approaches for differential expression analysis on real and simulated CyTOF data. We found that methods using median marker expressions compute fast and reliable results when the data are not strongly zero-inflated. Methods using all data detect changes in strongly zero-inflated markers, but partially suffer from overprediction or cannot handle big datasets. We present a new method, CyEMD, based on calculating the earth mover's distance between expression distributions that can handle strong zero-inflation without being too sensitive. Additionally, we developed CYANUS - CYtometry ANalysis Using Shiny - a user-friendly R Shiny App allowing the user to analyze cytometry data with state-of-the-art tools, including well-performing methods from our comparison. A public web interface is available at https://exbio.wzw.tum.de/cyanus/.

TF-Prioritizer: a Java pipeline to prioritize condition-specific transcription factors.

BACKGROUND: Eukaryotic gene expression is controlled by cis-regulatory elements (CREs), including promoters and enhancers, which are bound by transcription factors (TFs). Differential expression of TFs and their binding affinity at putative CREs determine tissue- and developmental-specific transcriptional activity. Consolidating genomic datasets can offer further insights into the accessibility of CREs, TF activity, and, thus, gene regulation. However, the integration and analysis of multimodal datasets are hampered by considerable technical challenges. While methods for highlighting differential TF activity from combined chromatin state data (e.g., chromatin immunoprecipitation [ChIP], ATAC, or DNase sequencing) and RNA sequencing data exist, they do not offer convenient usability, have limited support for large-scale data processing, and provide only minimal functionality for visually interpreting results. RESULTS: We developed TF-Prioritizer, an automated pipeline that prioritizes condition-specific TFs from multimodal data and generates an interactive web report. We demonstrated its potential by identifying known TFs along with their target genes, as well as previously unreported TFs active in lactating mouse mammary glands. Additionally, we studied a variety of ENCODE datasets for cell lines K562 and MCF-7, including 12 histone modification ChIP sequencing as well as ATAC and DNase sequencing datasets, where we observe and discuss assay-specific differences. CONCLUSION: TF-Prioritizer accepts ATAC, DNase, or ChIP sequencing and RNA sequencing data as input and identifies TFs with differential activity, thus offering an understanding of genome-wide gene regulation, potential pathogenesis, and therapeutic targets in biomedical research.

Platelet Surface Protein Expression and Reactivity upon TRAP Stimulation after BNT162b2 Vaccination.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection induces a coagulopathy characterized by platelet activation and a hypercoagulable state with an increased incidence of cardiovascular events. The viral spike protein S has been reported to enhance thrombosis formation, stimulate platelets to release procoagulant factors, and promote the formation of platelet-leukocyte aggregates even in absence of the virus. Although SARS-CoV-2 vaccines induce spike protein overexpression to trigger SARS-CoV-2-specific immune protection, thrombocyte activity has not been investigated in this context. Here, we provide the first phenotypic platelet characterization of healthy human subjects undergoing BNT162b2 vaccination. Using mass cytometry, we analyzed the expression of constitutive transmembrane receptors, adhesion proteins, and platelet activation markers in 12 healthy donors before and at five different time points within 4 weeks after the first BNT162b2 administration. We measured platelet reactivity by stimulating thrombocyte activation with thrombin receptor-activating peptide. Activation marker expression (P-selectin, LAMP-3, LAMP-1, CD40L, and PAC-1) did not change after vaccination. All investigated constitutive transmembrane proteins showed similar expressions over time. Platelet reactivity was not altered after BNT162b2 administration. Activation marker expression was significantly lower compared with an independent cohort of mild symptomatic COVID-19 patients analyzed with the same platform. This study reveals that BNT162b2 administration does not alter platelet protein expression and reactivity.

Network medicine for disease module identification and drug repurposing with the NeDRex platform.

Traditional drug discovery faces a severe efficacy crisis. Repurposing of registered drugs provides an alternative with lower costs and faster drug development timelines. However, the data necessary for the identification of disease modules, i.e. pathways and sub-networks describing the mechanisms of complex diseases which contain potential drug targets, are scattered across independent databases. Moreover, existing studies are limited to predictions for specific diseases or non-translational algorithmic approaches. There is an unmet need for adaptable tools allowing biomedical researchers to employ network-based drug repurposing approaches for their individual use cases. We close this gap with NeDRex, an integrative and interactive platform for network-based drug repurposing and disease module discovery. NeDRex integrates ten different data sources covering genes, drugs, drug targets, disease annotations, and their relationships. NeDRex allows for constructing heterogeneous biological networks, mining them for disease modules, prioritizing drugs targeting disease mechanisms, and statistical validation. We demonstrate the utility of NeDRex in five specific use-cases.

Does cognition differ across species, and how do we know? Lessons from research in transitive inference.

Comparative psychologists study cognition by characterizing the behavior of individual species and explicitly comparing behavior across species. We use the extensive comparative literature on transitive inference (TI) as a case study to evaluate four central methodological questions that continue to be debated in the field of comparative psychology: 1) Are contextual variables sufficient to explain species differences in cognition? 2) Can cognitive performance be accounted for by associative processes alone? 3) Can we determine the cognitive mechanisms by which animals solve tasks? and 4) What is the role of ecologically driven hypotheses in comparative psychology? Although contextual variables and associative processes undeniably influence choice behavior in TI tasks, neither is sufficient to explain all performance. Instead, multiple distinct cognitive mechanisms, including associative processes, logical inference, and spatial representations, can and do result in successful TI performance. TI is not a unitary task solved using a single mechanism; multiple processes are recruited, with their degree of involvement dependent on context, species, and evolutionary pressures. This suggests that rather than asking whether animals possess a certain cognitive ability, research should focus on differences in when and how species employ tools from what is often a reasonably similar cognitive toolbox. We join others who have proposed that a main goal of comparative psychology should be to determine how animals solve cognitive tasks, through minimizing and studying the influence of contextual variables, evaluating the contributions of associative processes, clearly characterizing and testing alternative cognitive mechanisms, and using strong evolutionary hypotheses to guide predictions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

On the limits of active module identification.

In network and systems medicine, active module identification methods (AMIMs) are widely used for discovering candidate molecular disease mechanisms. To this end, AMIMs combine network analysis algorithms with molecular profiling data, most commonly, by projecting gene expression data onto generic protein-protein interaction (PPI) networks. Although active module identification has led to various novel insights into complex diseases, there is increasing awareness in the field that the combination of gene expression data and PPI network is problematic because up-to-date PPI networks have a very small diameter and are subject to both technical and literature bias. In this paper, we report the results of an extensive study where we analyzed for the first time whether widely used AMIMs really benefit from using PPI networks. Our results clearly show that, except for the recently proposed AMIM DOMINO, the tested AMIMs do not produce biologically more meaningful candidate disease modules on widely used PPI networks than on random networks with the same node degrees. AMIMs hence mainly learn from the node degrees and mostly fail to exploit the biological knowledge encoded in the edges of the PPI networks. This has far-reaching consequences for the field of active module identification. In particular, we suggest that novel algorithms are needed which overcome the degree bias of most existing AMIMs and/or work with customized, context-specific networks instead of generic PPI networks.

SARS-CoV-2 infection is associated with a pro-thrombotic platelet phenotype.

Novel coronavirus disease 2019 (COVID-19) is associated with a hypercoagulable state, characterized by abnormal coagulation parameters and by increased incidence of cardiovascular complications. With this study, we aimed to investigate the activation state and the expression of transmembrane proteins in platelets of hospitalized COVID-19 patients. We investigated transmembrane proteins expression with a customized mass cytometry panel of 21 antibodies. Platelets of 8 hospitalized COVID-19 patients not requiring intensive care support and without pre-existing conditions were compared to platelets of healthy controls (11 donors) with and without in vitro stimulation with thrombin receptor-activating peptide (TRAP). Mass cytometry of non-stimulated platelets detected an increased surface expression of activation markers P-Selectin (0.67 vs. 1.87 median signal intensity for controls vs. patients, p = 0.0015) and LAMP-3 (CD63, 0.37 vs. 0.81, p = 0.0004), the GPIIb/IIIa complex (4.58 vs. 5.03, p < 0.0001) and other adhesion molecules involved in platelet activation and platelet-leukocyte interactions. Upon TRAP stimulation, mass cytometry detected a higher expression of P-selectin in COVID-19 samples compared to controls (p < 0.0001). However, we observed a significantly reduced capacity of COVID-19 platelets to increase the expression of activation markers LAMP-3 and P-Selectin upon stimulation with TRAP. We detected a hyperactivated phenotype in platelets during SARS-CoV-2 infection, consisting of highly expressed platelet activation markers, which might contribute to the hypercoagulopathy observed in COVID-19. In addition, several transmembrane proteins were more highly expressed compared to healthy controls. These findings support research projects investigating antithrombotic and antiplatelet treatment regimes in COVID-19 patients, and provide new insights on the phenotypical platelet expression during SARS-CoV-2 infection.

BiCoN: network-constrained biclustering of patients and omics data.

MOTIVATION: Unsupervised learning approaches are frequently used to stratify patients into clinically relevant subgroups and to identify biomarkers such as disease-associated genes. However, clustering and biclustering techniques are oblivious to the functional relationship of genes and are thus not ideally suited to pinpoint molecular mechanisms along with patient subgroups. RESULTS: We developed the network-constrained biclustering approach Biclustering Constrained by Networks (BiCoN) which (i) restricts biclusters to functionally related genes connected in molecular interaction networks and (ii) maximizes the difference in gene expression between two subgroups of patients. This allows BiCoN to simultaneously pinpoint molecular mechanisms responsible for the patient grouping. Network-constrained clustering of genes makes BiCoN more robust to noise and batch effects than typical clustering and biclustering methods. BiCoN can faithfully reproduce known disease subtypes as well as novel, clinically relevant patient subgroups, as we could demonstrate using breast and lung cancer datasets. In summary, BiCoN is a novel systems medicine tool that combines several heuristic optimization strategies for robust disease mechanism extraction. BiCoN is well-documented and freely available as a python package or a web interface. AVAILABILITY AND IMPLEMENTATION: PyPI package: https://pypi.org/project/bicon. WEB INTERFACE: https://exbio.wzw.tum.de/bicon. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Machine learning for deciphering cell heterogeneity and gene regulation.

Epigenetics studies inheritable and reversible modifications of DNA that allow cells to control gene expression throughout their development and in response to environmental conditions. In computational epigenomics, machine learning is applied to study various epigenetic mechanisms genome wide. Its aim is to expand our understanding of cell differentiation, that is their specialization, in health and disease. Thus far, most efforts focus on understanding the functional encoding of the genome and on unraveling cell-type heterogeneity. Here, we provide an overview of state-of-the-art computational methods and their underlying statistical concepts, which range from matrix factorization and regularized linear regression to deep learning methods. We further show how the rise of single-cell technology leads to new computational challenges and creates opportunities to further our understanding of epigenetic regulation.

Analysis of the Mortality of Russian Patients With Chronic Myeloid Leukemia in the Multicenter EUTOS ELN Population-based Study (EUTOS-PBS).

INTRODUCTION: Russia took part in the multicenter population-based study (Europe) and included 6.8% adult patients with newly diagnosed chronic myeloid leukemia (CML). The objective of this study was to analyze the mortality in the Russian cohort of patients with newly diagnosed CML in the EUTOS PBS observational study. PATIENTS AND METHODS: The analyzed cohort consisted of 197 patients (>18 years) with Ph+/BCR-ABL1+ CML diagnosed in the period from October 1, 2009 through December 31, 2012 from 6 regions of Russia. The distribution of the phases of CML were: chronic phase (CP), 93.4% and accelerated phase (AP) + blast crisis (BC), 6% + 0.6%. The median age was 50 years (range, 18-82 years); the male/female ratio was equal. RESULTS: The overall survival (OS) at 5, 6, and 7 years was 80% (95% confidence interval [CI], 72%-86%), 78% (95% CI, 65%-80%), and 73% (95% CI, 65%-80%), respectively (P < .001). The 5-year OS in patients with AP and BC was 39%. In Russia, the study was prolonged, with a median follow-up of 77 months (range, 0.7-108 months): 141 (71.5%) patients were alive, 47 (24%) patients died, and the status of 9 (4.5%) patients is was unknown. Forty-seven (23.8%) patients died during the follow-up period. The largest number of deaths was observed in the first year after the CML diagnosis: 17 (36%) of 47 cases, 3 of 17 died refusing the CML treatment. At the seventh year of CML therapy, 1 patient died after allogenic hematopoietic stem cell transplantation. The causes of death were: (1) progression of CML to AP/BC in 20 (43%) patients; (2) death in remission in 5 (11%) patients with complete cytogenetic response (CCyR) and/or major molecular response; and (3) death without progression to AP/BC but with signs of leukemia in 22 (46%) patients. The 5-year cumulative incidence of death from all reasons was 20%; the cumulative incidence of CML-related and non-CML-related death at the fifth year was 18% and 11%, respectively. CONCLUSION: In general, the results of treatment in the Russian population sample of non-selected patients with CML were comparable with the data of the total European cohort. The CML-related deaths prevailed in the first year of CML therapy. The appropriate monitoring and therapy interventions during the first year of CML treatment are apparently important for the long-term treatment results.

Associative models fail to characterize transitive inference performance in rhesus monkeys (Macaca mulatta).

It has been suggested that non-verbal transitive inference (if A > B and B > C, then A > C) can be accounted for by associative models. However, little is known about the applicability of such models to primate data. In Experiment 1, we tested the fit of two associative models to primate data from both sequential training, in which the training pairs were presented in a backward order, and simultaneous training, in which all training pairs are presented intermixed from the beginning. We found that the models provided an equally poor fit for both sequential and simultaneous training presentations, contrary to the case with data from pigeons. The models were also unable to predict the robust symbolic distance effects characteristic of primate transitive choices. In Experiment 2, we used the models to fit a list-linking design in which two seven-item transitive lists were first trained independently (A > B…. > F > G and H > I …. > M > N) then combined via a linking pair (G+ H-) into a single, 14-item list. The model produced accurate predictions for between-list pairs, but did not predict transitive responses for within-list pairs from list 2. Overall, our results support research indicating that associative strength does not adequately account for the behavior of primates in transitive inference tasks. The results also suggest that transitive choices may result from different processes, or different weighting of multiple processes, across species.

Smaller on the left? Flexible association between space and magnitude in pigeons (Columba livia) and blue jays (Cyanocitta cristata).

Humans and other apes represent magnitudes spatially, demonstrated by their responding faster and more accurately to one side of space when presented with small quantities and to the other side of space when presented with large quantities. This representation is flexible and shows substantial variability between cultural groups in humans and between and within individuals in great apes. In contrast, recent findings suggest that chicks show a spatial representation of magnitude that is highly lateralized and inflexible, implying a qualitatively different underlying representation than in primates. Using methods similar to those used with great apes and humans, we trained adult domestic pigeons (Columba livia) and blue jays (Cyanocitta cristata) to select the smaller (or larger) of two nonadjacent quantity arrays; later, this task was reversed. At test, birds were presented with novel probe pairs consisting of adjacent quantity pairs (e.g., 2 vs. 3). Both species showed robust evidence for a flexible spatial representation of magnitude with considerable individual variability in the orientation of this representation. These results are not consistent with an inflexible, lateralized, left-to-right representation of magnitude in birds, but are consistent with the flexible spatial representation of magnitude observed in apes and humans. We conclude that the tendency to organize quantities spatially may be a fundamental and evolutionarily ancient feature of cognition that is widespread among vertebrates. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

The effect of labor market shocks on health: The case of the Russian transition.

During the first years of the transition to the market economy in Russia, many people experienced the whole range of stressful labor market events, including job loss, wage cuts and nonpayments; some people had to change occupations or take on additional work. These events were caused externally by the unprecedented structural shifts in the economy. This natural experiment provides an opportunity to estimate the causal effect of various labor market shocks on individual health and health-related behaviors. Propensity score matching and difference-in-difference estimates using household survey data show that labor market shocks during the early transition had long-term negative effects on individual health. I also find an increased incidence of smoking and alcohol consumption as well as a higher risk of certain types of chronic health problems for the people affected by labor market shocks.

Red is the new orange: Nonlinguistic categorical color perception.

Caves et al. (2018) demonstrated categorical perception in zebra finches for the orange-red color category that conveys information about male fitness. This result implies that categorical color perception does not necessarily have linguistic origins, as has been previously believed.