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.

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/.

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.

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.

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.

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.

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.

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.

Hippocampal lesion and transitive inference: dissociation of inference-based and reinforcement-based strategies in pigeons.

A typical nonverbal transitive inference task (TI) consists of several overlapping discriminations (A+ B-, B+ C-, C+ D-, D+ E-, where letters indicate stimuli and pluses and minuses denote reinforcement and nonreinforcement). A choice of stimulus B in a novel pair BD is interpreted as indicative of a TI (if B > C and C > D, then B > D). Although hippocampus has been implicated in nonverbal TI, it is not clear whether it simply maintains memory of associative values or stores an ordered representation of stimuli. We investigated the effect of hippocampal lesion on TI in pigeons while controlling reinforcement history so that reliance on associative values would lead to a choice of a stimulus D in the pair BD instead of a choice of a stimulus B expected by inferential mechanisms. Prior to the lesion, some of the pigeons (relational group; n = 4) have selected B over D indicating TI, while other birds (associative group; n = 6) chose D over B suggesting reliance on associative values. Hippocampal lesion had no effect on postlesion performance in associative group. In contrast, the relational group that preferred stimulus B in a pair BD before lesion showed a near-chance performance after the lesion. Our results demonstrate that hippocampus may be involved in creating a representation of an ordered series of the stimuli instead of maintaining reinforcement history of each stimulus. In addition, we provide a behavioral procedure suitable for dissociating different behavioral strategies used to solving TI task. Finally, we show for the first time the involvement of avian hippocampus in the task that is not explicitly spatial in nature. These results further confirm the notion that avian hippocampus is functionally analogous to mammalian hippocampus despite the significant differences in their anatomy.

Bilateral lesions of nucleus subpretectalis/interstitio-pretecto-subpretectalis (SP/IPS) selectively impair figure-ground discrimination in pigeons.

Earlier, we reported that nucleus rotundus (Rt) together with its inhibitory complex, nucleus subpretectalis/interstitio-pretecto-subpretectalis (SP/IPS), had significantly higher activity in pigeons performing figure-ground discrimination than in the control group that did not perform any visual discriminations. In contrast, color discrimination produced significantly higher activity than control in the Rt but not in the SP/IPS. Finally, shape discrimination produced significantly lower activity than control in both the Rt and the SP/IPS. In this study, we trained pigeons to simultaneously perform three visual discriminations (figure-ground, color, and shape) using the same stimulus displays. When birds learned to perform all three tasks concurrently at high levels of accuracy, we conducted bilateral chemical lesions of the SP/IPS. After a period of recovery, the birds were retrained on the same tasks to evaluate the effect of lesions on maintenance of these discriminations. We found that the lesions of the SP/IPS had no effect on color or shape discrimination and that they significantly impaired figure-ground discrimination. Together with our earlier data, these results suggest that the nucleus Rt and the SP/IPS are the key structures involved in figure-ground discrimination. These results also imply that thalamic processing is critical for figure-ground segregation in avian brain.

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.

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.

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).

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.

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.

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.

Nonaccidental properties underlie shape recognition in Mammalian and nonmammalian vision.

An infinite number of 2D patterns on the retina can correspond to a single 3D object. How do visual systems resolve this ill-posed problem and recognize objects from only a few 2D retinal projections in varied exposure conditions? Theories of object recognition rely on the nonaccidental statistics of edge properties, mainly symmetry, collinearity, curvilinearity, and cotermination. These statistics are determined by the image-formation process (i.e., the 2D retinal projection of a 3D object ); their existence under a range of viewpoints enables viewpoint-invariant recognition. An important question in behavioral biology is whether the visual systems of nonmammalian animals have also evolved biases to utilize nonaccidental statistics . Here, we trained humans and pigeons to recognize four shapes. With the Bubbles technique, we determined which stimulus properties both species used to recognize the shapes. Both humans and pigeons used cotermination, the most diagnostic nonaccidental property of real-world objects, despite evidence from a model computer observer that cotermination was not the most diagnostic pictorial information in this particular task. This result reveals that a nonmammalian visual system that is different anatomically from the human visual system is also biased to recognize objects from nonaccidental statistics.